Algorithm code explanation generation method and device, and computer readable storage medium

Abstract

The application discloses an algorithm code explanation generation method and device and a computer readable storage medium. The method comprises the following steps: obtaining a structured analysis result of an algorithm code, and constructing a structured reasoning input; splitting an explanation task into multiple reasoning subtasks, performing constrained reasoning on the multiple reasoning subtasks based on the structured reasoning input, and obtaining reasoning output; based on the reasoning output, generating multi-perspective explanations according to at least two preset perspective dimensions; and outputting the multi-perspective explanations, evidence chains associated with the multi-perspective explanations, and confidence information associated with the multi-perspective explanations. Through the combination of the structured constraint reasoning mode and the multi-perspective explanation generation framework, the application reduces the situation of generating logic without evidence, provides traceable explanations and their evidence chains, and is suitable for the understanding of industrial visual algorithm codes.

Description

Technical Field

[0001] This invention relates to the fields of industrial vision algorithm engineering and artificial intelligence-assisted code understanding technology, and in particular to algorithm code interpretation and generation methods, apparatus and computer-readable storage media. Background Technology

[0002] Industrial vision algorithm code typically features multi-stage processing, strong data dependencies, and implicit mathematical semantics, exhibiting significant structural and semantic differences from general business code. The engineering implementation of industrial vision systems involves the cross-application of various algorithms, such as template matching, geometric fitting, point cloud processing, and defect detection, with complex data flow dependencies between these modules. Engineers often need to expend considerable effort understanding the execution logic and mathematical meaning of the algorithm code during algorithm handover, code review, and system maintenance. However, in real-world projects, missing algorithm documentation or inconsistencies between documentation and implementation are quite common.

[0003] To address the aforementioned issues, existing technologies attempt to automatically interpret algorithm code using large language models. However, current methods typically submit the source code text directly to the large language model, lacking structured contextual information and constraints. This leads to the following technical flaws in the generated interpretations: the large language model may generate logic or variables that do not actually exist in the code based on statistical regularities, resulting in illusory content; the generated interpretations usually do not contain an explicit correspondence with the source code structure, making it difficult for engineers to verify the consistency between the interpretation content and the code implementation.

[0004] Furthermore, the explanations generated by existing methods are usually natural language descriptions from a single perspective, which cannot simultaneously cover multiple dimensions such as the engineering implementation of the algorithm code, algorithm semantics, mathematical principles, and risk analysis, making it difficult to meet the diverse understanding needs of engineers in industrial vision engineering scenarios.

[0005] Therefore, there is an urgent need for a method that can stably generate traceable, multi-perspective algorithm code explanations under structured constraints to overcome the above-mentioned shortcomings. Summary of the Invention

[0006] The purpose of this invention is to provide an algorithm code interpretation generation method, apparatus, and computer-readable storage medium to solve the technical problems in the prior art of algorithm code interpretation, such as the existence of illusory content, lack of evidence chain support, and inability to meet the needs of multi-perspective understanding.

[0007] To achieve this objective, the present invention adopts the following technical solution: In a first aspect, the present invention provides an algorithm code interpretation and generation method, comprising: S1. Obtain the structured analysis results of the algorithm code; S2. Based on the structured analysis results, construct the structured reasoning input; S3. Decompose the explanation generation task into multiple stages to obtain multiple reasoning sub-tasks; S4. Based on the structured reasoning input, perform constrained reasoning on the multiple reasoning subtasks to obtain the reasoning output; S5. Based on the reasoning output, generate a multi-perspective explanation according to at least two preset perspective dimensions; S6. Output the multi-perspective interpretation, the evidence chain associated with the multi-perspective interpretation, and the confidence information associated with the multi-perspective interpretation.

[0008] In some preferred embodiments, the structured analysis results include at least one of control flow graphs, data flow graphs, abstract syntax trees, function call graphs, variable semantic information, algorithm pattern information, and risk candidate information.

[0009] In some preferred embodiments, the structured reasoning input includes at least one of function signature, main path, key variables, variable roles, data flow information, control flow path evidence, algorithm pattern, risk candidate, and evidence index.

[0010] In some preferred embodiments, the constraints employed in the constrained reasoning include at least two of the following: structural constraints, evidence binding constraints, uncertainty labeling constraints, and input conflict prohibition constraints, wherein: The structural constraints are used to make the inference output reference the control flow path or data flow path in the structured analysis results; The evidence binding constraint is used to associate the conclusion in the reasoning output with the variables or control paths in the structured reasoning input; The uncertainty labeling constraint is used to label content in the inference output with a confidence level lower than a preset threshold; The input conflict prohibition constraint is used to prevent the inference output from conflicting with the structured inference input.

[0011] In some preferred embodiments, for each of the constraints, the execution method employed includes at least one or a combination of prompt word injection, constrained decoding, and post-processing filtering.

[0012] In some preferred embodiments, the at least two preset perspective dimensions are selected from engineering perspective, algorithmic perspective, mathematical perspective, risk perspective, and optimization perspective, wherein: The engineering perspective explanation corresponding to the engineering perspective is used to describe the input, output and processing flow of the algorithm code; The algorithm perspective explanation corresponding to the algorithm perspective is used to describe the algorithm type and core steps adopted by the algorithm code; The mathematical perspective interpretation corresponding to the mathematical perspective is used to describe the mathematical functions involved in the algorithm code; The risk perspective interpretation corresponding to the risk perspective is used to describe the numerical or boundary problems of the algorithm code; The optimization perspective explanation corresponding to the optimization perspective is used to describe the parallelization or caching optimization direction of the algorithm code.

[0013] In some preferred embodiments, the at least two preset perspective dimensions are automatically determined based on the algorithm pattern recognition results in the structured analysis results, and at least one of the data structure perspective or model deployment perspective is expanded according to the algorithm code type.

[0014] In some preferred embodiments, the plurality of inference subtasks include, in sequence: The input location subtask involves locating function inputs, outputs, and code positions. Local logic interpretation subtasks that interpret local processing logic according to the control flow path; Algorithm semantic recognition subtask to identify algorithm types and industrial vision meanings; The mathematical semantic derivation subtask involves semantic derivation of the mathematical expressions involved in the algorithm code; The subtask of risk and boundary analysis is to analyze numerical risks and boundary conditions. And the cross-function aggregation subtask, which aggregates and summarizes the cross-function processing flow; The structured output of the previous subtask serves as supplementary context for the subsequent subtask.

[0015] In some preferred embodiments, the plurality of reasoning subtasks are dynamically determined based on the algorithm pattern information in the structured analysis results, and at least three are selected from the input localization subtask, local logic interpretation subtask, algorithm semantic recognition subtask, mathematical semantic derivation subtask, risk and boundary analysis subtask, and cross-function aggregation subtask, and executed in sequence.

[0016] In some preferred embodiments, before outputting the multi-view interpretation, a consistency check is performed on the multi-view interpretation to obtain a check result, and the multi-view interpretation is corrected based on the check result before being output.

[0017] In some preferred embodiments, the consistency check includes at least one of variable authenticity check, control flow path conformity check, data dependency direction check, internal conflict detection, and semantic consistency check, wherein: The variable authenticity check is used to verify whether the variables referenced in the multi-perspective explanation exist in the structured reasoning input. The control flow path compliance check is used to verify whether the execution path described in the multi-perspective interpretation conforms to the structured analysis results. The data dependency direction verification is used to verify whether the data source and destination described in the multi-view interpretation conform to the edge direction of the data flow graph in the structured analysis results; The internal conflict detection is used to detect whether there are contradictory descriptions within the multi-perspective interpretation, between different perspectives or within the same perspective. The semantic consistency check is used to verify the semantic consistency between the multi-perspective interpretation and the algorithm code.

[0018] In some preferred embodiments, when the consistency check fails, at least one of the following operations is performed based on the failure type: For formatting errors or missing fields, regenerate the corresponding output based on the original evidence; In cases where evidence is lacking but the conclusion may still be valid, the confidence level of the conclusion should be lowered, and the lack of evidence should be indicated in the uncertainty statement. If a variable is missing, the path is unreachable, or the data dependency is in the wrong direction, delete the conclusion or regenerate it based on correct evidence. When there are conflicts among multiple perspectives, the conclusion with more complete evidence and higher verification score is retained, and the reasons for the conflict of the replaced conclusion are recorded.

[0019] In some preferred embodiments, the chain of evidence includes the correlation between the conclusions in the multi-perspective interpretation and the variables or control paths in the structured reasoning input.

[0020] In some preferred embodiments, the confidence information is determined based on at least one of the following: language model log probability, multiple sampling self-consistency voting results, constraint satisfaction rate, and verification pass rate.

[0021] In some preferred embodiments, the confidence information is calculated by the following formula: , in, For the confidence of the language model, To constrain the satisfaction rate, For evidence coverage, To verify the pass rate, For self-consistency consistency rate; , , , , These are configurable non-negative weights.

[0022] In some preferred embodiments, the confidence information is divided into high confidence, medium confidence, and low confidence, wherein: The high confidence level corresponds to a confidence level of not less than 0.80, and the variable authenticity verification, control flow path compliance verification, and data dependency direction verification all pass. The confidence level mentioned is between 0.50 and 0.80, or there is some missing evidence but no structural conflict; The low confidence level corresponds to a confidence level below 0.50, or there is significant uncertainty or an incomplete chain of evidence; the low confidence conclusion is output in an annotated manner and is not output as a definitive interpretation.

[0023] In some preferred embodiments, the method is used to interpret and generate industrial vision algorithm code, which has the characteristics of multi-stage processing and data dependence, and the algorithm types involved include at least one of template matching, geometric calculation, point cloud processing, and defect detection.

[0024] Secondly, the present invention provides an algorithm code interpretation and generation apparatus, comprising: The structured input module is used to obtain the structured analysis results of the algorithm code; The reasoning input construction module is used to construct a structured reasoning input, including an evidence index, based on the structured analysis results. The multi-stage task scheduling module is used to decompose the interpretation and generation task into multiple stages to obtain multiple inference subtasks; The constrained reasoning module is used to perform constrained reasoning on the multiple reasoning subtasks based on the structured reasoning input to obtain reasoning output; A multi-perspective explanation generation module is used to generate a multi-perspective explanation based on the reasoning output, organized according to at least two preset perspective dimensions. The output module is used to output the multi-perspective interpretation, the evidence chain associated with the multi-perspective interpretation, and the confidence information associated with the multi-perspective interpretation.

[0025] In some optional embodiments, the algorithm code interpretation generation device further includes a consistency verification module for performing consistency verification on the multi-perspective interpretation to obtain a verification result; the output module is further used to output the multi-perspective interpretation, the evidence chain associated with the multi-perspective interpretation, and the confidence information associated with the multi-perspective interpretation based on the verification result.

[0026] In some preferred embodiments, the constraints employed by the constrained reasoning module include at least two of the following: structural constraints, evidence binding constraints, uncertainty labeling constraints, and input conflict prohibition constraints, wherein: The structural constraints are used to make the inference output reference the control flow path or data flow path in the structured analysis results; The evidence binding constraint is used to associate the conclusion in the reasoning output with the variables or control paths in the structured reasoning input; The uncertainty labeling constraint is used to label content in the inference output with a confidence level lower than a preset threshold; The input conflict prohibition constraint is used to prevent the inference output from conflicting with the structured inference input.

[0027] In some preferred embodiments, the multi-perspective interpretation generated by the multi-perspective interpretation generation module covers at least two of the following: engineering perspective, algorithmic perspective, mathematical perspective, risk perspective, and optimization perspective; the multi-perspective interpretation generation module also automatically determines the perspective combination based on the algorithmic pattern recognition results in the structured analysis results.

[0028] Thirdly, the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, implement the algorithm code interpretation and generation method described in the first aspect.

[0029] Compared with the prior art, the present invention has the following beneficial effects: This invention addresses several key issues. Firstly, by encapsulating the structured analysis results of algorithm code into structured reasoning input and performing constrained reasoning after multi-stage decomposition of the interpretation generation task, it ensures that the reasoning output is associated with structured analysis evidence, reducing the likelihood of generating illogical results. Furthermore, the multi-stage structured output method improves the stability of the same code during multiple interpretations. Secondly, by organizing the generation of multi-perspective interpretations according to at least two preset perspectives, it presents the meaning of the algorithm code from multiple dimensions, including engineering implementation, algorithm logic, mathematical semantics, and risk distribution. Thirdly, by synchronously including evidence chains and confidence information associated with the multi-perspective interpretations in the output, it provides traceable structured support for the interpretation results. This facilitates engineers in verifying the correspondence between the interpretation content and the code structure and referencing the confidence information, thus aiding in code understanding and verification in industrial vision algorithm engineering scenarios.

[0030] The present invention has other features and advantages, which will be apparent from or will be set forth in detail in the accompanying drawings and the following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart of the algorithm code interpretation and generation method according to Embodiment 1 of the present invention.

[0033] Figure 2 This is a logical block diagram of the algorithm code interpretation and generation method according to Embodiment 1 of the present invention.

[0034] Figure 3 This is a flowchart of a preferred embodiment of the algorithm code interpretation and generation method of Embodiment 1 of the present invention.

[0035] Figure 4 yes Figure 3 A simplified flowchart.

[0036] Figure 5 This is a structural block diagram of the algorithm code interpretation and generation device of an optional embodiment of the present invention. Detailed Implementation

[0037] To illustrate the technical content, structural features, objectives, and effects of this invention in detail, the following description, in conjunction with the embodiments and accompanying drawings, provides a comprehensive explanation. The embodiments described herein are merely illustrative of the technical solutions of this invention and are therefore intended to be examples only, not to limit the scope of protection of this invention.

[0038] Example 1 Please see Figures 1-4 The algorithm code interpretation generation method in this embodiment can stably generate traceable, multi-view algorithm code interpretations under structured constraints. The template matching algorithm code in industrial vision is used as the main example below, supplemented by geometric fitting algorithm code and point cloud processing algorithm code, to explain in detail the specific implementation process of this embodiment and its adaptability to different algorithm types.

[0039] S1. Obtain the structured analysis results of the algorithm code.

[0040] Understandably, this step involves static analysis of the template matching algorithm code to be explained to obtain structured analysis results, which include control flow graphs, data flow graphs, abstract syntax trees, function call relationship graphs, variable semantic information, and algorithm pattern information.

[0041] The control flow graph describes the execution jump relationships between the basic blocks within a function.

[0042] The data flow graph describes the read / write dependencies between variables image, template, and score. Image and template both affect score, and score is written to resultList after being determined by threshold.

[0043] The semantic information of variables indicates the type, scope, and semantic role of each variable.

[0044] The current function for algorithm pattern information recognition implements a template matching algorithm.

[0045] S2. Based on the structured analysis results, construct the structured reasoning input.

[0046] This step extracts and normalizes the structured analysis results obtained in step S1 to generate structured inference input, which includes fields such as function signature, main path, key variables, control flow path evidence, algorithm pattern, risk candidates, and evidence index.

[0047] The function signature records the function name, parameter list, and return type.

[0048] The main path records the main control flow path of the function execution and the sequence of basic blocks it passes through.

[0049] Key variables record the names, types, and semantic roles of core variables such as image, template, mask, threshold, and maxCount.

[0050] The control flow path evidence record includes the basic block numbers and branch condition expressions on the main path.

[0051] The algorithm pattern is labeled as template matching.

[0052] Risk candidate records with extreme values ​​for threshold and boundary cases when maxCount is 0.

[0053] The evidence index associates the variable names, control flow node numbers, and code line number ranges involved in the above fields, providing a reference for tracing in subsequent steps. It is the core indexing method that runs through the entire process from S2 to S6.

[0054] Furthermore, the sources of each field in the structured reasoning input are as follows: The function signature is taken directly from the structured analysis results; The main path is obtained by traversing the control flow graph from the inlet to the outlet, and the path that covers the main output variables and frequently called nodes is selected. Key variables and variable roles are derived from the semantic information of the variables; Data flow information is obtained by back-slicing the data flow graph starting from the return values ​​and core variables; Evidence of the control flow path includes the basic block numbers, branch conditions, and loop ranges on the main path; The algorithm pattern is taken from the algorithm pattern recognition result; Risk candidate records may be triggered by out-of-bounds values ​​or empty inputs.

[0055] S3. The explanation generation task is broken down into multiple stages to obtain multiple reasoning subtasks.

[0056] See Figure 3 This step, based on the algorithm pattern information and evidence index of step S2, interprets the generation task as being broken down into multiple reasoning subtasks and executed sequentially.

[0057] Furthermore, in a preferred embodiment, the plurality of inference subtasks are executed in the following order: The input positioning subtask confirms the data types and code location evidence of the input parameters image, template, mask, threshold, and maxCount of the function, and determines the data structure of the function output resultList; The local logic interpretation subtask interprets the local processing logic segments such as image preprocessing, template sliding matching, and score filtering according to the main path order of the control flow graph. The algorithm semantic recognition subtask identifies that the current function implements the template matching algorithm and marks its typical applications in industrial vision scenarios; The semantic meaning of the mathematical expressions involved in the similarity calculation process is derived from the subtask of mathematical semantic derivation. The risk and boundary analysis subtask analyzes the numerical risks that may be caused by threshold being a boundary value and maxCount being 0, such as empty results or an excessive number of abnormal results. The cross-function aggregation subtask aggregates the structured outputs of the aforementioned subtasks to generate a global summary of the overall processing logic of the function.

[0058] It is understandable that the above reasoning subtasks are executed sequentially, with the structured output of the previous subtask serving as supplementary context for each subsequent subtask.

[0059] In other preferred embodiments, at least three of the above subtasks can be dynamically selected and executed sequentially based on the algorithm pattern information to adapt to the interpretation requirements of different types of algorithm code.

[0060] S4. Based on the structured reasoning input, perform constrained reasoning on the multiple reasoning subtasks to obtain the reasoning output.

[0061] Specifically, this step applies the following constraints when reasoning for each reasoning subtask: Structural constraints require that the description of function logic in the inference output must reference the control flow path nodes or variable nodes in the data flow graph from the structured analysis results, and prohibit the description of paths that do not exist in the code. Evidence binding constraints require that each conclusion in the reasoning output be associated with and bound to the variable names or control path evidence in the structured reasoning input; Uncertainty labeling constraints automatically label inference content with confidence levels below a preset threshold with uncertainty descriptions; Input conflict prohibition constraint detection prevents the inference output from conflicting with known facts in the structured inference input.

[0062] It is understandable that the execution of each constraint can employ at least one or a combination of prompt word injection, constraint decoding, and post-processing filtering.

[0063] In a preferred embodiment, the evidence binding constraint is implemented through structured output fields. Each interpretation conclusion includes at least the conclusion text, a list of associated variables, a sequence of control flow node numbers, a sequence of data flow edge numbers, a range of code lines, and an evidence type. Evidence binding verification is completed through field whitelists, variable table matching, and control flow and data flow index matching.

[0064] For example, the conclusion of the interpretation of the meaning of the scoring variable is associated with the dependency edge between the scoring variable and the image and template in the data flow graph, and the number of the scoring calculation node and the corresponding code line range in the control flow graph are bound to form a verifiable evidence record.

[0065] S5. Based on the reasoning output, generate a multi-perspective explanation according to at least two preset perspective dimensions.

[0066] This step, based on the algorithm pattern information identified in step S2, automatically selects engineering perspective, algorithm perspective, mathematical perspective, risk perspective, and optimization perspective, and generates corresponding explanatory content for each. The engineering perspective explains the input parameter specifications of the description function, the data structure requirements for the image and template, and the output format of the matching results.

[0067] The algorithmic approach explains the processing steps and core computational logic of the template matching algorithm.

[0068] A mathematical perspective explains the semantic meaning of the mathematical expressions upon which similarity calculations are based.

[0069] From a risk perspective, it is pointed out that when threshold is set to an extreme value and maxCount is set to 0, boundary problems such as an empty result set or an abnormally large number of results may occur.

[0070] The optimization perspective suggests potential optimization directions such as caching template features or parallel traversal of candidate regions.

[0071] S6. Output the multi-perspective interpretation, the evidence chain associated with the multi-perspective interpretation, and the confidence information associated with the multi-perspective interpretation.

[0072] The final output of this step consists of three parts. The first part is the multi-perspective explanatory text, which is presented in segments according to the five perspectives mentioned above. The second part is the chain of evidence, which records the relationship between each conclusion in the explanation of each perspective and the variable names or control flow path nodes in the structured reasoning input.

[0073] The third part is the confidence level information, calculated by the following formula: , in, For the confidence of the language model, To constrain the satisfaction rate, For evidence coverage, To verify the pass rate, For self-consistency consistency rate; , , , , These are configurable non-negative weights.

[0074] The confidence level information is output along with the multi-perspective interpretation for engineers' reference.

[0075] In other preferred embodiments, a consistency check can be performed on the multi-view interpretation before output, and the check includes the following: First, verify whether the variable names referenced in the explanations from each perspective exist in the variable list of the structured reasoning input; Secondly, the graph traversal algorithm is used to determine whether the node sequence described in each perspective is reachable in the control flow graph, and to verify whether the execution path matches the main path or alternative path. Third, verify whether the data dependencies described in each perspective are consistent with the edge directions of the data flow graph; Fourth, it examines whether there are contradictory descriptions between different perspectives.

[0076] Specifically, when the consistency check fails, the corresponding confidence reduction, deletion, or regeneration operation is performed based on the failure type.

[0077] In other implementations, the algorithm code to be explained is geometric fitting algorithm code. Specifically, taking circle fitting algorithm code as an example, the function takes a point set as input and outputs geometric parameters and fitting error.

[0078] In step S1, the structured analysis results show that the code sequentially performs invalid point filtering, least squares equation construction, geometric parameter solving, and residual calculation.

[0079] In step S2, points are labeled as the input point set, center, radius, lineParam, etc. are labeled as geometric parameters, residual is labeled as the fitting error, and boundary risks of insufficient points and matrix singularity are recorded in the risk candidate.

[0080] In step S3, the mathematical semantic derivation subtask attempts to identify the mathematical form of the error function from the expression abstract syntax tree and data flow graph. If the sum of squares or distance function expression does not explicitly appear in the code, the mathematical semantic derivation subtask only outputs a verifiable geometric flow explanation and labels the mathematical formula with low confidence, reflecting the actual effect of uncertainty labeling constraints.

[0081] In step S5, explanations from the engineering perspective, algorithm perspective, mathematical perspective, and risk perspective are automatically output. The mathematical perspective only outputs verifiable content and provides a low-confidence explanation for parts whose specific formula form cannot be determined from the code.

[0082] In some other implementations, the algorithm code to be explained is point cloud processing algorithm code. Specifically, the point cloud processing algorithm code involves filtering, segmentation, and feature extraction operations of three-dimensional coordinate data, which differs from the template matching algorithm in terms of data structure and processing logic.

[0083] In step S1, the structured analysis results also include type labeling and variable semantic recognition of the dimensions of the three-dimensional coordinate array, the normal vector field, and the intensity information in the point cloud data.

[0084] In step S2, the evidence index additionally records the transformation nodes of each field of the point cloud data in the processing flow.

[0085] In step S5, based on the algorithm pattern recognition results, and on the basis of engineering perspective, algorithm perspective, mathematical perspective, risk perspective, and optimization perspective, the data structure perspective is automatically expanded. The dimensional structure of the point cloud coordinate array, the generation method of the normal vector field, and the role of intensity information in the filtering process are explained in detail to meet the specific understanding needs of point cloud algorithms in the engineering scenario of 3D perception system.

[0086] Therefore, the method can adaptively expand the combination of perspectives for multi-view interpretation based on the algorithm pattern recognition results in the structured analysis results, so as to cover engineering understanding scenarios of different industrial vision algorithm types.

[0087] Example 2 See Figure 5The algorithm code interpretation and generation apparatus of this embodiment corresponds to and is used to execute the algorithm code interpretation and generation method described in Embodiment 1. The algorithm code interpretation and generation apparatus includes: The structured input module 10 is used to obtain the structured analysis results of the algorithm code.

[0088] The reasoning input construction module 20 is used to construct a structured reasoning input including an evidence index based on the structured analysis results.

[0089] The multi-stage task scheduling module 30 is used to decompose the interpretation and generation task into multiple stages to obtain multiple inference subtasks.

[0090] The constrained reasoning module 40 is used to perform constrained reasoning on the multiple reasoning subtasks based on the structured reasoning input to obtain reasoning output.

[0091] The multi-perspective explanation generation module 50 is used to generate a multi-perspective explanation based on the inference output according to at least two preset perspective dimensions.

[0092] The output module 70 is used to output the multi-perspective interpretation, the evidence chain associated with the multi-perspective interpretation, and the confidence information associated with the multi-perspective interpretation.

[0093] In an optional configuration, the algorithm code interpretation and generation device further includes a consistency verification module 60, which is used to perform consistency verification on the multi-perspective interpretation and obtain the verification result; The output module 70 is also used to output the multi-perspective interpretation, the evidence chain associated with the multi-perspective interpretation, and the confidence information associated with the multi-perspective interpretation based on the verification result.

[0094] The specific functions of the above basic modules correspond one-to-one with the method steps S1 to S6 described in Embodiment 1; in the optional configuration including the consistency verification module 60, the consistency verification module 60 is used to perform consistency verification before output.

[0095] Example 3 This embodiment provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the algorithm code interpretation and generation method described in Embodiment 1. The computer-readable storage medium may include non-volatile storage media such as hard disks, solid-state drives, flash memory, and optical discs.

[0096] Combination Figures 1-5This invention addresses several key issues. Firstly, by encapsulating the structured analysis results of algorithm code into structured reasoning input and performing constrained reasoning after multi-stage decomposition of the interpretation generation task, it ensures that the reasoning output is associated with structured analysis evidence, reducing the likelihood of generating illogical results. Furthermore, the multi-stage structured output method improves the stability of the same code in multiple interpretations. Secondly, by organizing the generation of multi-perspective interpretations according to at least two preset perspectives, it presents the meaning of the algorithm code from multiple dimensions, including engineering implementation, algorithm logic, mathematical semantics, and risk distribution. Thirdly, by synchronously attaching evidence chains and confidence information associated with the multi-perspective interpretations to the output, it provides traceable structured support for the interpretation results. This facilitates engineers in verifying the correspondence between the interpretation content and the code structure and referencing the confidence information, thus aiding in code understanding and verification in industrial vision algorithm engineering scenarios.

[0097] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating algorithm code interpretation, characterized in that, include: Obtain the structured analysis results of the algorithm code; Based on the structured analysis results, a structured reasoning input is constructed; The explanation generation task is broken down into multiple stages to obtain multiple reasoning subtasks; Based on the structured reasoning input, constrained reasoning is performed on the multiple reasoning subtasks to obtain the reasoning output; Based on the inference output, a multi-perspective explanation is generated by organizing it according to at least two preset perspective dimensions. Output the multi-perspective interpretation, the evidence chain associated with the multi-perspective interpretation, and the confidence information associated with the multi-perspective interpretation.

2. The algorithm code interpretation and generation method as described in claim 1, characterized in that, The structured analysis results include at least one of the following: control flow graph, data flow graph, abstract syntax tree, function call relationship graph, variable semantic information, algorithm pattern information, and risk candidate information.

3. The algorithm code interpretation and generation method as described in claim 1, characterized in that, The structured reasoning input includes at least one of the following: function signature, main path, key variables, variable roles, data flow information, control flow path evidence, algorithm pattern, risk candidates, and evidence index.

4. The algorithm code interpretation and generation method as described in claim 1, characterized in that, The constraints employed in the constrained reasoning include at least two of the following: structural constraints, evidence binding constraints, uncertainty labeling constraints, and input conflict prohibition constraints, wherein: The structural constraints are used to make the inference output reference the control flow path or data flow path in the structured analysis results; The evidence binding constraint is used to associate the conclusion in the reasoning output with the variables or control paths in the structured reasoning input; The uncertainty labeling constraint is used to label content in the inference output with a confidence level lower than a preset threshold; The input conflict prohibition constraint is used to prevent the inference output from conflicting with the structured inference input.

5. The algorithm code interpretation and generation method as described in claim 4, characterized in that, For each of the constraints, the execution method employed includes at least one or a combination of prompt word injection, constrained decoding, and post-processing filtering.

6. The algorithm code interpretation and generation method as described in claim 1, characterized in that, The at least two preset perspective dimensions are selected from engineering perspective, algorithmic perspective, mathematical perspective, risk perspective, and optimization perspective, wherein: The engineering perspective explanation corresponding to the engineering perspective is used to describe the input, output and processing flow of the algorithm code; The algorithm perspective explanation corresponding to the algorithm perspective is used to describe the algorithm type and core steps adopted by the algorithm code; The mathematical perspective interpretation corresponding to the mathematical perspective is used to describe the mathematical functions involved in the algorithm code; The risk perspective interpretation corresponding to the risk perspective is used to describe the numerical or boundary problems of the algorithm code; The optimization perspective explanation corresponding to the optimization perspective is used to describe the parallelization or caching optimization direction of the algorithm code.

7. The algorithm code interpretation and generation method as described in claim 1, characterized in that, The at least two preset perspective dimensions are automatically determined based on the algorithm pattern recognition results in the structured analysis results, and at least one of the data structure perspective or model deployment perspective is extended according to the algorithm code type.

8. The algorithm code interpretation and generation method as described in claim 1, characterized in that, The multiple reasoning subtasks include, in order: The input location subtask involves locating function inputs, outputs, and code positions. Local logic interpretation subtasks that interpret local processing logic according to the control flow path; Algorithm semantic recognition subtask to identify algorithm types and industrial vision meanings; The mathematical semantic derivation subtask involves semantic derivation of the mathematical expressions involved in the algorithm code; The subtask of risk and boundary analysis is to analyze numerical risks and boundary conditions. And the cross-function aggregation subtask, which aggregates and summarizes the cross-function processing flow; The structured output of the previous subtask serves as supplementary context for the subsequent subtask.

9. The algorithm code interpretation and generation method as described in claim 1, characterized in that, The multiple reasoning subtasks are dynamically determined based on the algorithm pattern information in the structured analysis results, and at least three are selected from the input localization subtask, local logic interpretation subtask, algorithm semantic recognition subtask, mathematical semantic derivation subtask, risk and boundary analysis subtask, and cross-function aggregation subtask, and executed in sequence.

10. The algorithm code interpretation and generation method as described in claim 1, characterized in that, Before outputting the multi-perspective interpretation, a consistency check is performed on the multi-perspective interpretation to obtain the check result. Based on the check result, the multi-perspective interpretation is corrected and then output.

11. The algorithm code interpretation and generation method as described in claim 10, characterized in that, The consistency verification includes at least one of the following: variable authenticity verification, control flow path compliance verification, data dependency direction verification, internal conflict detection, and semantic consistency verification, wherein: The variable authenticity check is used to verify whether the variables referenced in the multi-perspective explanation exist in the structured reasoning input; The control flow path compliance check is used to verify whether the execution path described in the multi-perspective interpretation conforms to the structured analysis results. The data dependency direction verification is used to verify whether the data source and destination described in the multi-view interpretation conform to the edge direction of the data flow graph in the structured analysis results; The internal conflict detection is used to detect whether there are contradictory descriptions within the multi-perspective interpretation, between different perspectives or within the same perspective. The semantic consistency check is used to verify the semantic consistency between the multi-perspective interpretation and the algorithm code.

12. The algorithm code interpretation and generation method as described in claim 11, characterized in that, When the consistency check fails, perform at least one of the following operations based on the failure type: For formatting errors or missing fields, regenerate the corresponding output based on the original evidence; In cases where evidence is lacking but the conclusion may still be valid, the confidence level of the conclusion should be lowered, and the lack of evidence should be indicated in the uncertainty statement. If a variable is missing, the path is unreachable, or the data dependency is in the wrong direction, delete the conclusion or regenerate it based on correct evidence. When there are conflicts among multiple perspectives, the conclusion with more complete evidence and higher verification score is retained, and the reasons for the conflict of the replaced conclusion are recorded.

13. The algorithm code interpretation and generation method as described in claim 1, characterized in that, The chain of evidence includes the relationships between the conclusions in the multi-perspective interpretation and the variables or control paths in the structured reasoning input.

14. The algorithm code interpretation and generation method as described in claim 1, characterized in that, The confidence information is determined based on at least one of the following: the logarithmic probability of the language model, the self-consistency voting results of multiple sampling, the constraint satisfaction rate, and the verification pass rate.

15. The algorithm code interpretation and generation method as described in claim 14, characterized in that, The confidence level information is calculated using the following formula: ， in, For the confidence of the language model, To constrain the satisfaction rate, For evidence coverage, To verify the pass rate, For self-consistency consistency rate; , , , , These are configurable non-negative weights.

16. The algorithm code interpretation and generation method as described in claim 15, characterized in that, The confidence level information is divided into high confidence, medium confidence, and low confidence levels, where: The high confidence level corresponds to a confidence level of not less than 0.80, and the variable authenticity verification, control flow path compliance verification, and data dependency direction verification all pass. The confidence level mentioned is between 0.50 and 0.80, or there is some missing evidence but no structural conflict; The low confidence level corresponds to a confidence level below 0.50, or there is significant uncertainty or an incomplete chain of evidence; the low confidence conclusion is output in an annotated manner and is not output as a definitive interpretation.

17. The algorithm code interpretation and generation method as described in claim 1, characterized in that, The method is used to interpret and generate industrial vision algorithm code, which has the characteristics of multi-stage processing and data dependence, and the algorithm types involved include at least one of template matching, geometric calculation, point cloud processing, and defect detection.

18. An algorithm code interpretation and generation device, characterized in that, include: The structured input module is used to obtain the structured analysis results of the algorithm code; The reasoning input construction module is used to construct a structured reasoning input, including an evidence index, based on the structured analysis results. The multi-stage task scheduling module is used to decompose the interpretation and generation task into multiple stages to obtain multiple inference subtasks; The constrained reasoning module is used to perform constrained reasoning on the multiple reasoning subtasks based on the structured reasoning input to obtain reasoning output; A multi-perspective explanation generation module is used to generate a multi-perspective explanation based on the reasoning output, organized according to at least two preset perspective dimensions. The output module is used to output the multi-perspective interpretation, the evidence chain associated with the multi-perspective interpretation, and the confidence information associated with the multi-perspective interpretation.

19. The algorithm code interpretation and generation apparatus as described in claim 18, characterized in that, The constraints employed by the constrained reasoning module include at least two of the following: structural constraints, evidence binding constraints, uncertainty labeling constraints, and input conflict prohibition constraints, wherein: The structural constraints are used to make the inference output reference the control flow path or data flow path in the structured analysis results; The evidence binding constraint is used to associate the conclusion in the reasoning output with the variables or control paths in the structured reasoning input; The uncertainty labeling constraint is used to label content in the inference output with a confidence level lower than a preset threshold; The input conflict prohibition constraint is used to prevent the inference output from conflicting with the structured inference input.

20. The algorithm code interpretation and generation apparatus as described in claim 18, characterized in that, The multi-perspective interpretation generation module generates multi-perspective interpretations that cover at least two of the following: engineering perspective, algorithmic perspective, mathematical perspective, risk perspective, and optimization perspective. The multi-perspective interpretation generation module also automatically determines the perspective combination based on the algorithmic pattern recognition results in the structured analysis results.

21. The algorithm code interpretation and generation apparatus as described in claim 18, characterized in that, It also includes a consistency verification module, which is used to perform consistency verification on the multi-perspective interpretation and obtain the verification result; the output module is also used to output the multi-perspective interpretation, the evidence chain associated with the multi-perspective interpretation, and the confidence information associated with the multi-perspective interpretation based on the verification result.

22. A computer-readable storage medium having instructions stored thereon, characterized in that, When the instruction is executed by the processor, it implements the algorithm code interpretation and generation method as described in any one of claims 1 to 17.

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