CN113222459A - System and method for dynamically constructing food uncertainty evaluation model by expression tree - Google Patents

Abstract

The invention relates to the field of food inspection and detection, in particular to a result uncertainty evaluation item aiming at food inspection and detection, which is applied to selecting a proper detection standard method or detection basis according to a food detection requirement item, solidifying a detection process and evaluating the uncertainty of a result. Specifically disclosed are a system and a method for dynamically constructing a food uncertainty evaluation model from an expression tree. According to the system and the method disclosed by the invention, the basic uncertainty models are selected and combined through the actual evaluation requirements to obtain the synthetic uncertainty model, so that the uncertainty evaluation of the detection items is realized; meanwhile, the system visualization evaluation process is added, so that the difficulty in researching and designing various uncertain evaluation models is reduced. When the basic uncertain model is selected, the model base can be called from a large number of model bases, and the synthesized uncertainty model is established by calling the mature model, so that a large amount of time cost can be saved, and the evaluation efficiency can be improved.

Description

System and method for dynamically constructing food uncertainty evaluation model by expression tree

Technical Field

The invention relates to the technical field of food inspection and detection, in particular to a system and a method for dynamically constructing a food uncertainty evaluation model by using an expression tree, which are applied to a process of selecting and detecting items according to food inspection requirement items aiming at detection item uncertainty items in a food inspection and detection process.

Background

The uncertainty evaluation of the food safety inspection result does not have a clear and unified mathematical model. According to the research of an uncertain evaluation system of food inspection results, when the experimental items are different, the sources of the uncertainty of the inspection results are different; uncertainty sources are different, calculation models of the uncertainty sources are also different, uncertain synthesis calculation models of different sources are also different, even uncertainty of some sources can be decomposed into uncertainty of different sub-sources, and therefore the uncertainty evaluation mathematical model expression can obtain scientific, comprehensive and accurate final uncertainty only by performing combined calculation on each most basic calculation model in different levels and different modes. The complexity, uncertainty, etc. of such models cannot be exhaustive to arrive at all possible combinations to make a practical test.

Most of the existing food inspection informatization systems only carry out static recording and archiving on experiment original data and inspection results, and do not provide support for designing calculation, or some systems provide limited capability for designing calculation by providing plug-in scripts, extended macros and other mechanisms, but also have high threshold requirements on user with considerable script programming capability. The application of an uncertain evaluation model in an information system is limited, and the uncertainty, which is an important inspection result accuracy index, is less popularized in the industry.

Therefore, the detection items for food are numerous, different detection requirement combinations are difficult to exhaust, the existing food detection is not automatic and intelligent enough, the difficulty of the food detection is increased, the efficiency of the food detection is difficult to promote, a more reasonable technical scheme needs to be provided, and the defects in the prior art are overcome.

Disclosure of Invention

In order to solve the defects of the prior art mentioned in the above, the invention provides a system and a method for dynamically constructing a food uncertainty evaluation model by an expression tree, wherein a plurality of calling modules for constructing the uncertainty model are integrated in the system, required modules are selected from different calling modules according to actual evaluation requirements, and corresponding parameter mapping relations are called to synthesize a final evaluation calculation model, so that the corresponding uncertainty is quickly evaluated and obtained.

In order to achieve the purpose, the invention specifically adopts the technical scheme that:

a system for dynamically constructing a food uncertainty evaluation model from an expression tree, comprising:

the design module comprises a basic uncertainty design unit, a synthetic uncertainty design unit, a data form design unit and a mapping unit, wherein the synthetic uncertainty design unit receives data of the basic uncertainty design unit, and the mapping unit receives data of the synthetic uncertainty design unit and the data form design unit;

the execution module comprises a form construction unit and a data unit, wherein the form construction unit receives the data of the form design unit, and the data unit receives the data of the form construction unit;

and the analysis module comprises an expression construction unit and a calculation unit, wherein the expression construction unit receives the data of the mapping unit, and the calculation unit receives the data of the expression construction unit and the data unit.

The system disclosed above determines the uncertainty to be evaluated according to the actual detection requirements, integrates limited, most basic, common operation and other basic data processing units, combines uncertainty processing models of various most basic sources according to the data processing units, and combines uncertainty processing models of different sources into a synthetic uncertainty processing model of the project according to the project. When the uncertainty is calculated specifically, according to a given parameter mapping relation, the original data is brought into a corresponding project uncertainty synthesis calculation model for calculation, and therefore uncertainty of the result is obtained.

Furthermore, in order to enhance the interaction efficiency of the system, the system further comprises a visualization module, wherein the visualization module is connected and communicated with the analysis module and is used for displaying the analysis process and the result.

The invention also discloses a method for establishing the uncertainty evaluation model, which is specifically explained as follows:

a method for dynamically constructing a food uncertainty evaluation model from an expression tree, comprising,

the design steps are as follows:

setting a plurality of basic uncertainty models according to evaluation requirements, wherein each model corresponds to one evaluation uncertainty;

synthesizing the selected basic uncertainty model to obtain a synthesized uncertainty model;

setting a data form which needs to be calculated by the synthesis uncertainty model according to the obtained synthesis uncertainty model;

mapping and associating the data form with the synthetic uncertainty model;

an interaction step:

providing a data demand by the synthetic uncertainty model, and generating and sending a calculation demand after data is obtained;

and (3) analyzing:

and analyzing and compiling according to the calculation requirement, and performing dynamic execution to obtain the final uncertainty.

Furthermore, a plurality of basic uncertainty models are set, wherein the basic uncertainty models are obtained by analyzing and modeling uncertainty of a specific source, and comprise model input parameters defining, expression character string calculating and storing. When the scheme is adopted, a basic uncertainty model can be established according to different evaluation purposes.

Further, the synthesizing the selected base uncertainty model to obtain the synthetic uncertainty model includes analyzing the inspection item, identifying the uncertainty of the specific source in the item, and modeling the synthesis of the uncertainty of each source. When the scheme is adopted, a plurality of basic uncertainty models can be selected to obtain a synthetic uncertainty model, so that all uncertainty required by evaluation is met.

Further, interaction is needed to be carried out before evaluation so as to input corresponding parameter data, specifically, a data form which needs to be operated by the synthetic uncertainty model is set according to the obtained synthetic uncertainty model, the method comprises the steps of collecting an original data item list, designing an original data form template of an item, and carrying out data interaction according to the form template in the checking process.

Further, the mapping association of the data form with the synthetic uncertainty model includes performing corresponding association setting on data form items and model parameters, so that the data form becomes a data source of the parameters.

Further, the analyzing and compiling according to the calculation requirement comprises initializing a function dictionary and loading a built-in function; and polling each model for parsing and compiling.

Still further, the polling each model for parsing and compiling includes the following steps:

s1: taking out the affix expression in the function body, and initializing an operator stack s1 and an intermediate result stack s 2;

s2: scanning infix expressions from right to left;

s3: when an operand is encountered, it is pushed to s 2;

s4: when an operator is encountered, comparing it with the priority of the s1 stack top operator;

s4.1: if s1 is empty, or the top of stack operator is right parenthesis ")", then directly push this operator onto the stack;

s4.2: otherwise, if the priority is higher than or equal to the top of stack operator, press the operator to s 1;

s4.3: otherwise, pop the top of stack operator of S1 and push it into S2, go to S4.1 again and compare it with the new top of stack operator in S1;

s5: when parentheses are encountered, the following two steps are followed:

s5.1: if right bracket ")", press directly into s 1;

s5.2: if it is left bracket "(", pop the operator at the top of the stack of S1 in turn and push S2 until the right bracket is encountered, at which point the pair of brackets is discarded;

s6: repeating steps S2-S5 until the leftmost of the expression is reached;

s7: popping up and pressing the rest operators in s1 into s2 in sequence;

s8: sequentially popping up elements in s2 to form a new character string sequence which is the corresponding prefix expression;

s9: initializing an expression stack s3, scanning prefix expressions from right to left, and sequentially pushing dynamic expressions corresponding to operands into a stack s 3;

s9.1: if the operand is a constant, constructing a decimal constant lambda expression, wherein the value is a constant value;

s9.2: if the operand is a variable, constructing a real parameter lambda expression with a data type of decimal, wherein the name is a variable name;

s9.3: if the operand is a basic mathematical function call, function names and parameter lists are analyzed, and dictionary lookup is carried out through the function names to obtain corresponding lambda expressions;

s9.4: if the operand is called by a model formula, performing dictionary lookup by calling a function name;

s9.4.1: if the dictionary does not have the keyword, searching the model definition, creating a dynamic lambda expression by the model recursion step 1, wherein the name is the model name, and loading the dynamic lambda expression to a function dictionary;

s9.4.2: if the keyword exists in the dictionary, directly returning to the corresponding lambda expression;

s10: until an operator is met, the stack s3 pops up the lambda expressions of the data with the same number of the operator, constructs parameter lambda expressions, searches a function dictionary with the operator to obtain the operator processing lambda expressions, creates calling lambda expressions according to the operator processing lambda expressions, and pushes the calling lambda expressions into the stack s 3;

s11: popping up the lambda expression in the stack s3 until the last prefix expression element is scanned, namely the final expression tree;

s12: the model definition name Key loads the final expression into the function dictionary.

Further, the performing dynamic execution to obtain the final uncertainty includes the following steps:

s1: searching the model definition of the item, and searching a function dictionary by taking the name of the model definition as Key to obtain a corresponding dynamic expression;

s2: searching form data in the steps of mapping configuration and interaction of model parameters and form items, and constructing real parameter expressions called by expression trees

S3: and transmitting the Invoke of the dynamic expression by the parameter expression, and converting a returned result into decimal, namely an evaluation result.

Compared with the prior art, the invention has the beneficial effects that:

according to the system and the method disclosed by the invention, the basic uncertainty models are selected and combined through actual evaluation requirements to obtain a synthetic uncertainty model, so that the uncertainty flatness of the detection project is realized; meanwhile, the system visualization evaluation process is added, so that the difficulty in researching and designing various uncertain evaluation models is reduced. When the basic uncertain model is selected, the model base can be called from a large number of model bases, and the synthesized uncertainty model is established by calling the mature model, so that a large amount of time cost can be saved, and the evaluation efficiency can be improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

FIG. 1 is a block diagram of the system.

Fig. 2 is a schematic diagram of the internal components and logic of each module.

Detailed Description

The invention is further explained below with reference to the drawings and the specific embodiments.

It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. Specific structural and functional details disclosed herein are merely illustrative of example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.

Example 1

As shown in fig. 1 and 2, the system for dynamically constructing a food uncertainty evaluation model from an expression tree includes:

the design module comprises a basic uncertainty design unit, a synthetic uncertainty design unit, a data form design unit and a mapping unit, wherein the synthetic uncertainty design unit receives data of the basic uncertainty design unit, and the mapping unit receives data of the synthetic uncertainty design unit and the data form design unit;

the execution module comprises a form construction unit and a data unit, wherein the form construction unit receives the data of the form design unit, and the data unit receives the data of the form construction unit;

and the analysis module comprises an expression construction unit and a calculation unit, wherein the expression construction unit receives the data of the mapping unit, and the calculation unit receives the data of the expression construction unit and the data unit.

The system disclosed above determines the uncertainty to be evaluated according to the actual detection requirements, integrates limited, most basic, common operation and other basic data processing units, combines uncertainty processing models of various most basic sources according to the data processing units, and combines uncertainty processing models of different sources into a synthetic uncertainty processing model of the project according to the project. When the uncertainty is calculated specifically, according to a given parameter mapping relation, the original data is brought into a corresponding project uncertainty synthesis calculation model for calculation, and therefore uncertainty of the result is obtained.

In this embodiment, in order to enhance the interaction efficiency of the system, the system further includes a visualization module, and the visualization module is connected to the parsing module for communication and is used for displaying the parsing process and the result.

Example 2

The embodiment also discloses a method for establishing an uncertainty evaluation model, which is specifically described as follows:

the method for dynamically constructing the food uncertainty evaluation model by the expression tree specifically comprises the following steps:

s1: design step

S101: setting a plurality of basic uncertainty models according to evaluation requirements, wherein each model corresponds to one evaluation uncertainty;

s102: synthesizing the selected basic uncertainty model to obtain a synthesized uncertainty model;

s103: setting a data form which needs to be calculated by the synthesis uncertainty model according to the obtained synthesis uncertainty model;

s104: mapping and associating the data form with the synthetic uncertainty model;

s2: interaction step

Providing a data demand by the synthetic uncertainty model, and generating and sending a calculation demand after data is obtained;

s3: step of analysis

S301: and analyzing and compiling according to the calculation requirement, and performing dynamic execution to obtain the final uncertainty.

The method comprises the steps of setting a plurality of basic uncertainty models, wherein the basic uncertainty models are obtained by analyzing and modeling uncertainty of a specific source, and comprise the steps of defining model input parameters, calculating expression character strings and storing the expression character strings. When the scheme is adopted, a basic uncertainty model can be established according to different evaluation purposes.

For example, a model of uncertainty (typically calculated using standard deviation) due to repeated measurements, and a mathematical function as follows, f (x) stdev (x), the model may be defined as follows:

the model name: func

Inputting parameters: sequential parameter name data type

1x decimal[]

Function body: stdev (x)

Complete model function func (x) stdev (x)

The definition of the basic uncertainty model is the encapsulation of general calculation steps in the uncertainty calculation process, and is convenient for repeated calling in different evaluation requirements without paying attention to the internal process.

In this embodiment, the synthesizing the selected basic uncertainty model to obtain the synthetic uncertainty model includes analyzing the inspection item, identifying the uncertainty of a specific source in the item, and modeling the synthesis of the uncertainty of each source. When the scheme is adopted, a plurality of basic uncertainty models can be selected to obtain a synthetic uncertainty model, so that all uncertainty required by evaluation is met.

For example, in a food inspection project, there are 3 uncertain sources introduced by the test process, which are respectively func1(x, y), func2(a, b, c) and func3(k), and assuming that all are already defined in the basic model, the contract model of the project is defined as follows:

the model name: func _ com

Function body: round (pow (func1(x, y),2) + pow (func2(x + y + z),2) + pow (func3(z),2),1/2),2)

The complete model function: func _ com (x, y, z) ═ f

round(pow(pow(func1(x,y),2)+pow(func2(x+y+z)，2)+pow(func3(z),2),1/2),2)

Before evaluation, interaction is needed to be carried out to input corresponding parameter data, specifically, a data form which needs to be synthesized with the uncertainty model for operation is set according to the obtained synthesis uncertainty model, an original data item list is collected, an original data form template of an item is designed, and data interaction is carried out according to the form template in the checking process.

The form template definition comprises a form name and a data item list (data item name, data type, control type and presentation sequence number).

For example, a polymerase chain reaction analyzer calibrates the raw record form template (part):

list name: food _ form1

Form templates guide the laboratory operator in collecting data ranges, criteria, which enable them to provide normative data input for project model calculations.

In this embodiment, the mapping and associating the data form with the synthetic uncertainty model includes performing corresponding association setting on data form items and model parameters, so that the data form becomes a data source of the parameters.

When the mapping data items and the parameters are associated, the data types of the data items and the parameter data types are required to be consistent, and the data items of the document type control cannot be mapped to the project model parameters; the associated form template data items must be specified for each model parameter; the same form template data item may be associated to different parameters of the project model.

Model: fun (x, y, z) ═ round (Sqrt (x,2) + pow (func1(y),2) + pow (z)), (4)

Form: form _ xxx

The analyzing and compiling according to the calculation requirement comprises initializing a function dictionary and loading a built-in function; and polling each model for parsing and compiling.

Preferably, the polling each model for parsing and compiling includes the following steps:

s1: taking out the affix expression in the function body, and initializing an operator stack s1 and an intermediate result stack s 2;

s2: scanning infix expressions from right to left;

s3: when an operand is encountered, it is pushed to s 2;

s4: when an operator is encountered, comparing it with the priority of the s1 stack top operator;

s4.1: if s1 is empty, or the top of stack operator is right parenthesis ")", then directly push this operator onto the stack;

s4.2: otherwise, if the priority is higher than or equal to the top of stack operator, press the operator to s 1;

s4.3: otherwise, pop the top of stack operator of S1 and push it into S2, go to S4.1 again and compare it with the new top of stack operator in S1;

s5: when parentheses are encountered, the following two steps are followed:

s5.1: if right bracket ")", press directly into s 1;

s5.2: if it is left bracket "(", pop the operator at the top of the stack of S1 in turn and push S2 until the right bracket is encountered, at which point the pair of brackets is discarded;

s6: repeating steps S2-S5 until the leftmost of the expression is reached;

s7: popping up and pressing the rest operators in s1 into s2 in sequence;

s8: sequentially popping up elements in s2 to form a new character string sequence which is the corresponding prefix expression;

s9: initializing an expression stack s3, scanning prefix expressions from right to left, and sequentially pushing dynamic expressions corresponding to operands into a stack s 3;

s9.1: if the operand is a constant, constructing a decimal constant lambda expression, wherein the value is a constant value;

s9.2: if the operand is a variable, constructing a real parameter lambda expression with a data type of decimal, wherein the name is a variable name;

s9.3: if the operand is a basic mathematical function call, function names and parameter lists are analyzed, and dictionary lookup is carried out through the function names to obtain corresponding lambda expressions;

s9.4: if the operand is called by a model formula, performing dictionary lookup by calling a function name;

s9.4.1: if the dictionary does not have the keyword, searching the model definition, creating a dynamic lambda expression by the model recursion step 1, wherein the name is the model name, and loading the dynamic lambda expression to a function dictionary;

s9.4.2: if the keyword exists in the dictionary, directly returning to the corresponding lambda expression;

s10: until an operator is met, the stack s3 pops up the lambda expressions of the data with the same number of the operator, constructs parameter lambda expressions, searches a function dictionary with the operator to obtain the operator processing lambda expressions, creates calling lambda expressions according to the operator processing lambda expressions, and pushes the calling lambda expressions into the stack s 3;

s11: popping up the lambda expression in the stack s3 until the last prefix expression element is scanned, namely the final expression tree;

s12: the model definition name Key loads the final expression into the function dictionary.

Preferably, the performing dynamically to obtain the final uncertainty includes the following steps:

s1: searching the model definition of the item, and searching a function dictionary by taking the name of the model definition as Key to obtain a corresponding dynamic expression;

s2: searching form data in the steps of mapping configuration and interaction of model parameters and form items, and constructing real parameter expressions called by expression trees

S3: and transmitting the Invoke of the dynamic expression by the parameter expression, and converting a returned result into decimal, namely an evaluation result.

When the method in the present embodiment is applied specifically, one example is given here.

Content inspection item of YY substance in certain food (for convenience of description, only the repeatability measurement and the uncertainty brought by a balance are considered, and a user inputs and displays information and only focuses on data related to uncertain model calculation)

And (3) a project design stage:

1. design of repeatability measurement uncertainty model

The uncertainty of repeatability measurement, i.e., the calculation of standard deviation, relates to the standard deviation of the basic mathematical formula (STDEV), approximate evaluation (ROUND)

Defining variables: array-representing 1 to a plurality of sets of measurement values; the variable d represents the decimal place that the result is to retain.

Repeatability measurement uncertainty formula: ROUND (stdev (array), d).

2. Design of synthetic uncertainty model for YY substance content verification project in food

The basic mathematical formula includes POWER Operation (POWER) and approximate evaluation (ROUND)

Definitions variable m-uncertainty value representing a specific weighing balance (origin certificate of origin)

Synthesizing an uncertainty formula: ROUND (POWER (fun1(array, d),2) + POWER (m, N),1/2), d).

3. Data form template definition

item 1: sample repetition measure (array input control):

item 2: decimal accurate digit (integer input control)

item 3: scale uncertainty value (numerical input control)

4. Model parameter and data table entry mapping

Parameter array mapping item1

Parameter d mapping item2

Parameter m mapping item3

5. Experiment execution phase

During the testing experiment, the system displays the original data entry interface (the system dynamically generates according to the form design)

6. Data entry

The data entered by the experimenter when testing the samples were as follows:

repeatability measurements: 5.02,5.01,5.03,5.01,5.02

Decimal accurate digit: 4

Weighing balance uncertainty value: 0.0052

7. System calculated results

According to the project, the system automatically calls the corresponding formula

Automatically bringing the data input by experimenters into calculation according to the mapping relation of the parameters/data items

1) Repeatability measurement uncertainty

The formula: fun1 (ROUND, d)

And (3) calculating the result: 0.0084

2) Synthetic uncertainty:

the formula: fun (array, d, m) ═ ROUND (POWER (fun1(array, d),2) + POWER (m, N),1/2), d)

And (3) calculating the result: 0.0099

The system displays the results (the system dynamically displays user input, source uncertainty, synthesis uncertainty based on model design and form design)

The above embodiments are just exemplified in the present embodiment, but the present embodiment is not limited to the above alternative embodiments, and those skilled in the art can obtain other various embodiments by arbitrarily combining with each other according to the above embodiments, and any other various embodiments can be obtained by anyone in light of the present embodiment. The above detailed description should not be construed as limiting the scope of the present embodiments, which should be defined in the claims, and the description should be used for interpreting the claims.

Claims (10)

1. A system for dynamically constructing a food uncertainty evaluation model from an expression tree, comprising:

the design module comprises a basic uncertainty design unit, a synthetic uncertainty design unit, a data form design unit and a mapping unit, wherein the synthetic uncertainty design unit receives data of the basic uncertainty design unit, and the mapping unit receives data of the synthetic uncertainty design unit and the data form design unit;

the execution module comprises a form construction unit and a data unit, wherein the form construction unit receives the data of the form design unit, and the data unit receives the data of the form construction unit;

and the analysis module comprises an expression construction unit and a calculation unit, wherein the expression construction unit receives the data of the mapping unit, and the calculation unit receives the data of the expression construction unit and the data unit.

2. The system for dynamically building a food uncertainty evaluation model from an expression tree as claimed in claim 1, wherein: the analysis module is connected with the visualization module for communication and is used for displaying the analysis process and the analysis result.

3. A method for dynamically constructing a food uncertainty evaluation model from an expression tree, comprising,

the design steps are as follows:

setting a plurality of basic uncertainty models according to evaluation requirements, wherein each model corresponds to one evaluation uncertainty;

synthesizing the selected basic uncertainty model to obtain a synthesized uncertainty model;

setting a data form which needs to be calculated by the synthesis uncertainty model according to the obtained synthesis uncertainty model;

mapping and associating the data form with the synthetic uncertainty model;

an interaction step:

providing a data demand by the synthetic uncertainty model, and generating and sending a calculation demand after data is obtained;

and (3) analyzing:

and analyzing and compiling according to the calculation requirement, and performing dynamic execution to obtain the final uncertainty.

4. The method of dynamically constructing a food uncertainty evaluation model from an expression tree as claimed in claim 3, wherein:

the method comprises the steps of setting a plurality of basic uncertainty models, wherein the basic uncertainty models are obtained by analyzing and modeling uncertainty of a specific source, and comprise the steps of defining model input parameters, calculating expression character strings and storing the expression character strings.

5. The method of dynamically constructing a food uncertainty evaluation model from an expression tree as claimed in claim 4, wherein:

the synthesizing of the selected basic uncertainty model to obtain the synthetic uncertainty model comprises analyzing the inspection project, identifying the uncertainty of specific sources in the project, and modeling the synthesis of the uncertainty of each source.

6. The method of dynamically constructing a food uncertainty evaluation model from an expression tree as claimed in claim 3, wherein:

and setting a data form which needs to be operated by the synthesis uncertainty model according to the obtained synthesis uncertainty model, wherein the step of collecting an original data item list and designing an original data form template of the item comprises the steps of carrying out data interaction according to the form template in the inspection process.

7. The method of dynamically constructing a food uncertainty evaluation model from an expression tree as claimed in claim 3, wherein:

the mapping association of the data form and the synthetic uncertainty model comprises the corresponding association setting of data form items and model parameters, so that the data form becomes a data source of the parameters.

8. The method of dynamically constructing a food uncertainty evaluation model from an expression tree as claimed in claim 3, wherein:

the analyzing and compiling according to the calculation requirement comprises initializing a function dictionary and loading a built-in function; and polling each model for parsing and compiling.

9. The method of claim 8, wherein said polling each model for parsing and compiling comprises the steps of:

s1: taking out the affix expression in the function body, and initializing an operator stack s1 and an intermediate result stack s 2;

s2: scanning infix expressions from right to left;

s3: when an operand is encountered, it is pushed to s 2;

s4: when an operator is encountered, comparing it with the priority of the s1 stack top operator;

s4.1: if s1 is empty, or the top of stack operator is right parenthesis ")", then directly push this operator onto the stack;

s4.2: otherwise, if the priority is higher than or equal to the top of stack operator, press the operator to s 1;

s4.3: otherwise, pop the top of stack operator of S1 and push it into S2, go to S4.1 again and compare it with the new top of stack operator in S1;

s5: when parentheses are encountered, the following two steps are followed:

s5.1: if right bracket ")", press directly into s 1;

s5.2: if it is left bracket "(", pop the operator at the top of the stack of S1 in turn and push S2 until the right bracket is encountered, at which point the pair of brackets is discarded;

s6: repeating steps S2-S5 until the leftmost of the expression is reached;

s7: popping up and pressing the rest operators in s1 into s2 in sequence;

s8: sequentially popping up elements in s2 to form a new character string sequence which is the corresponding prefix expression;

s9: initializing an expression stack s3, scanning prefix expressions from right to left, and sequentially pushing dynamic expressions corresponding to operands into a stack s 3;

s9.1: if the operand is a constant, constructing a decimal constant lambda expression, wherein the value is a constant value;

s9.2: if the operand is a variable, constructing a real parameter lambda expression with a data type of decimal, wherein the name is a variable name;

s9.3: if the operand is a basic mathematical function call, function names and parameter lists are analyzed, and dictionary lookup is carried out through the function names to obtain corresponding lambda expressions;

s9.4: if the operand is called by a model formula, performing dictionary lookup by calling a function name;

s9.4.1: if the dictionary does not have the keyword, searching the model definition, creating a dynamic lambda expression by the model recursion step 1, wherein the name is the model name, and loading the dynamic lambda expression to a function dictionary;

s9.4.2: if the keyword exists in the dictionary, directly returning to the corresponding lambda expression;

s10: until an operator is met, the stack s3 pops up the lambda expressions of the data with the same number of the operator, constructs parameter lambda expressions, searches a function dictionary with the operator to obtain the operator processing lambda expressions, creates calling lambda expressions according to the operator processing lambda expressions, and pushes the calling lambda expressions into the stack s 3;

s11: popping up the lambda expression in the stack s3 until the last prefix expression element is scanned, namely the final expression tree;

s12: the model definition name Key loads the final expression into the function dictionary.

10. The method of claim 3, wherein the dynamically performing to obtain the final uncertainty comprises the steps of:

s1: searching the model definition of the item, and searching a function dictionary by taking the name of the model definition as Key to obtain a corresponding dynamic expression;

s2: searching form data in the steps of mapping configuration and interaction of model parameters and form items, and constructing real parameter expressions called by expression trees

S3: and transmitting the Invoke of the dynamic expression by the parameter expression, and converting a returned result into decimal, namely an evaluation result.

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