Electricity standard clause difference identification method and system based on knowledge representation learning

Abstract

The present invention relates to the technical field of methods for identifying differences in power standard clauses, and more specifically, to a method and system for identifying differences in power standard clauses based on knowledge representation learning, comprising the following steps: inputting a knowledge graph of power standard clauses; based on the knowledge graph, inputting a first standard clause to be identified and a second standard clause; converting entities and relationships involved in the knowledge graph into representation vectors for subsequent analysis; using a collaborative progressive optimization algorithm to analyze the sentence semantic differences between the first standard clause and the second standard clause, and outputting a difference evaluation result. The present invention adopts a collaborative progressive optimization algorithm in the difference identification process, organically combining five steps of initial semantic representation, multi-dimensional alignment, high-dimensional similarity enhancement, syntactic semantic fusion and global optimization, and through layer-by-layer progressive optimization, not only the shallow and deep semantic differences are captured, but also the problem of multi-dimensional feature alignment across clauses is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of methods for identifying differences in power standard clauses, and more specifically, to a method and system for identifying differences in power standard clauses based on knowledge representation learning. Background Art

[0002] With the rapid development of the power industry, the number and complexity of power standards continue to grow, and the content of various standard clauses is becoming increasingly rich. These standards cover everything from equipment installation and operation to safety assurance. In order to ensure the coordination between different standards, especially in the comparison and difference analysis of cross-standard clauses, it is crucial to accurately identify the similarities and differences of the content. However, existing technologies still face many technical challenges in dealing with the identification of differences in power standard clauses.

[0003] Most of the current difference identification methods rely on traditional shallow methods based on keyword matching or syntactic analysis. Although these methods can capture significant semantic differences, they are often unable to deal with the large number of implicit deep semantic relationships in power standard clauses, such as association rules and complex dependencies of context. In addition, existing methods are prone to matching errors or missing key differences when dealing with long texts or complex grammatical structures. These shortcomings limit their application in large-scale power standard management.

[0004] Furthermore, some existing technologies attempt to introduce deep learning models, such as text similarity calculation methods based on neural networks. However, such methods also expose some limitations in practical applications. First, traditional neural network models cannot fully utilize the entity relationships and domain knowledge between power standard clauses, and only calculate similarity through text surface features, resulting in semantic deviation. Secondly, deep learning models are prone to semantic ambiguity when there is a lack of clear contextual constraints, and it is difficult to accurately reflect the actual differences in standard clauses. In addition, when faced with difference analysis at different levels, existing models lack a progressive optimization mechanism, resulting in insufficient performance in capturing multi-dimensional semantic relationships.

[0005] The existing technology mainly has the following technical problems in identifying differences in power standard clauses:

[0006] 1. Limitations of shallow semantic matching: Traditional methods rely on keyword matching or simple syntactic structure analysis, which makes it difficult to capture the deep semantic differences in clauses. For example, "grounding resistance should be less than 10 ohms" and "grounding resistance should not be greater than 10 ohms" may be judged as differences in traditional matching, but their actual semantics are the same.

[0007] 2. Semantic bias of deep learning models: Although the introduction of deep learning has improved the ability to calculate similarities, the lack of effective use of domain knowledge graphs has resulted in the model being unable to identify subtle differences in semantic relationships. For example, although the "high-voltage equipment" and "ultra-high-voltage equipment" in the standard terms are semantically similar, there are significant differences in their scope of application and safety specifications, which are difficult for traditional models to capture.

[0008] 3. Lack of multi-level difference analysis: Existing technologies cannot progressively optimize semantic and syntactic differences at different levels. In semantic analysis of different granularities, it is impossible to coordinate features at each level, resulting in incomplete final difference recognition results.

[0009] 4. Single algorithm structure: Most existing methods use a single model to calculate semantic similarity, lacking collaborative optimization between algorithm steps. For example, when an error occurs in a certain step of the model, it cannot be corrected or compensated by subsequent steps, and the accuracy of the overall analysis results is limited.

[0010] Therefore, there is an urgent need to provide a method and system for identifying differences in electricity standard clauses based on knowledge representation learning to address the shortcomings of the existing technology. Summary of the invention

[0011] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method and system for identifying differences in electricity standard clauses based on knowledge representation learning. By introducing a multi-level, collaboratively optimized progressive algorithm, the technical difficulties of traditional methods in semantic deviation, multi-level analysis and model collaboration are solved.

[0012] The present invention provides a method for identifying differences in power standard clauses based on knowledge representation learning, comprising the following steps:

[0013] Input the knowledge graph of power standard terms, which includes multiple triples, each of which consists of a head entity, a relationship, and a tail entity;

[0014] Based on the knowledge graph, input a first standard clause and a second standard clause to be identified;

[0015] Converting entities and relationships involved in the knowledge graph into representation vectors for subsequent analysis;

[0016] The collaborative progressive optimization algorithm is used to analyze the sentence semantic differences between the first standard clause and the second standard clause, and the difference evaluation results are output.

[0017] Preferably, the collaborative progressive optimization algorithm comprises the following steps:

[0018] An initial semantic representation generation algorithm based on geometric nesting optimization is used to obtain an initial semantic representation vector;

[0019] Based on a multi-dimensional alignment transformation algorithm of nonlinear mapping, a multi-dimensional alignment transformation is performed on the initial semantic representation vector to obtain an alignment vector;

[0020] Based on a high-dimensional similarity enhancement algorithm of dynamic weight allocation, high-dimensional similarity enhancement is performed on the alignment vector to obtain an enhanced similarity vector;

[0021] A syntactic-semantic fusion algorithm based on variational collaborative learning is used to fuse the enhanced similarity vector with the syntactic feature to obtain a syntactic-semantic fusion vector;

[0022] Based on a global difference optimization algorithm for multi-scale contradiction resolution, the syntactic-semantic fusion vector is globally optimized and a final difference evaluation result is output.

[0023] Preferably, the initial semantic representation generation algorithm includes the following formula:

[0024] ,

[0025] in, is the initial semantic representation vector, which represents the initial semantic embedding of the standard terms; and are the i-th feature vectors of the first standard clause and the second standard clause in the knowledge graph, respectively; is the feature weight, which represents the importance of different features; is a geometric transformation function used to optimize the geometric distance between features.

[0026] Preferably, the multidimensional alignment transformation algorithm includes the following formula:

[0027] ,

[0028] in, is the multi-dimensional alignment result, which represents the alignment characteristics of the initial representation vector in the multi-dimensional space; is the activation function, used for nonlinear transformation; and is the weight matrix, which represents the weight distribution of different dimensions; and is the bias vector used to adjust the multi-dimensional alignment results.

[0029] Preferably, the high-dimensional similarity enhancement algorithm includes the following formula:

[0030] ,

[0031] in, To enhance the similarity vector, it represents the high-dimensional similarity enhancement result; is the dynamic weight, which indicates the weight distribution between different features; is an enhancement function used to calculate the high-dimensional similarity between the alignment vector and the initial semantic vector.

[0032] Preferably, the syntactic-semantic fusion algorithm comprises the following formula:

[0033] ,

[0034] in, is the syntactic-semantic fusion vector, which represents the fusion result of syntactic and semantic features; is the joint distribution function, combining syntactic features and enhanced similarity vector; is a variational distribution with parameters , used to model the uncertainty between semantic features.

[0035] Preferably, the global difference optimization algorithm includes the following formula:

[0036] ,

[0037] in, It is the global difference optimization result, indicating the final difference value between terms; To optimize the weights, it is used to balance the contribution of features at different scales; For the The syntactic and semantic fusion results under different scales, For the The target feature value of the scale.

[0038] Preferably, the method further comprises classifying the difference assessment results based on a preset threshold value to determine whether there is a substantial difference between the first standard clause and the second standard clause.

[0039] The power standard clause difference identification system based on knowledge representation learning includes:

[0040] A processor, configured to execute the steps;

[0041] A memory, used to store the knowledge graph and standard terms data;

[0042] The data interface module is used to receive and transmit power standard clause data.

[0043] Preferably, the processor is further configured to generate a difference report according to the difference evaluation result, wherein the difference report includes detailed information of the difference location and the difference type.

[0044] The present invention achieves the following beneficial effects:

[0045] 1. Multi-level semantic progressive analysis: This paper adopts a collaborative progressive optimization algorithm in the difference identification process, which organically combines the five steps of initial semantic representation, multi-dimensional alignment, high-dimensional similarity enhancement, syntactic semantic fusion and global optimization. Through layer-by-layer progressive optimization, it not only captures the shallow and deep semantic differences, but also solves the problem of multi-dimensional feature alignment across clauses.

[0046] 2. Combining knowledge graph with deep learning: Using the triple structure in the domain knowledge graph, the clause content is converted into an embedding vector containing entities and relations. Through variational collaborative learning, the syntactic structure and semantic content between clauses are deeply integrated to further refine the difference evaluation results.

[0047] 3. Algorithm synergy: In the five progressive steps, the present invention uses the input-output relationship between algorithms at each layer to achieve complementarity and contradiction resolution. For example, the high-dimensional similarity enhancement step optimizes the deficiencies of the initial representation through dynamic weight adjustment, while the syntactic-semantic fusion step uses syntactic features to reinforce the semantic analysis results of the previous step, ensuring the comprehensiveness and accuracy of the difference analysis results.

[0048] 4. Multi-scale optimization and global contradiction resolution: This invention introduces a multi-scale optimization mechanism to comprehensively analyze differences at different semantic granularities, and resolves the contradiction between the local and global characteristics of differences between clauses through multi-scale weight adjustment. Finally, through the global optimization algorithm, the results of all progressive steps are integrated to output a high-precision difference evaluation result.

[0049] The present invention achieves technical effects that are difficult to achieve with existing methods through the above-mentioned technical means. First, by combining knowledge graphs and deep learning models, the present invention can accurately capture the deep semantic differences in the clauses and significantly improve the accuracy of difference identification. Secondly, through progressive optimization and multi-level collaboration, the present invention achieves complementarity and efficiency between different steps in the process of clause difference identification, ensuring the comprehensiveness of the analysis results. Finally, based on the design of multi-scale optimization and global contradiction resolution, the present invention can achieve coordination and unification in the semantic and syntactic analysis of multi-dimensional features, providing strong technical support for the automated management and review of electricity standard clauses.

[0050] In summary, the present invention addresses the deficiencies in the prior art and proposes an innovative method and system, which is not only highly innovative in technical implementation, but also shows great practical value in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is the overall logic block diagram of the system of the present invention.

[0052] Figure 2 This is a flow chart of the collaborative progressive optimization algorithm of the present invention.

[0053] Figure 3 This is a flow chart of geometric nesting optimization of the present invention.

[0054] Figure 4 This is a flow chart of the multi-dimensional alignment transformation of the nonlinear mapping of the present invention.

[0055] Figure 5 This is a global difference optimization flow chart for multi-scale conflict resolution of the present invention. DETAILED DESCRIPTION

[0056] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects thereof are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0057] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0058] Please refer to Figure 1-5 The present invention relates to a method for identifying differences in power standard clauses based on knowledge representation learning. First, a knowledge graph of power standard clauses is input, and the knowledge graph is composed of multiple triples, each of which includes a head entity, a relationship, and a tail entity. The head entity represents a specific element in the standard clause, such as "protective grounding"; the relationship is used to describe the association between the head entity and the tail entity, such as "required implementation method"; the tail entity represents the relevant content in the specific description or clause, such as "complying with GB 12345-2023 standard".

[0059] Next, enter the first standard clause and the second standard clause to be identified. These two clauses can belong to different power standard documents, such as "Power Equipment Operation Standard" and "Transmission Line Safety Standard". The system will analyze the contents of these two clauses and identify their potential differences.

[0060] In this process, the entities and relationships involved in the knowledge graph will be converted into representation vectors for subsequent analysis. This vectorization process can use standard embedding methods, such as variational autoencoders or triple-based embedding models, to ensure that each entity and relationship can be numerated and participate in subsequent calculations.

[0061] In order to ensure the accuracy of the difference analysis, this method further uses a collaborative progressive optimization algorithm to analyze the sentence semantic differences between the two standard clauses and finally outputs the difference evaluation results.

[0062] Preferably, in one embodiment of the present invention, the collaborative progressive optimization algorithm includes five hierarchical progressive algorithm steps:

[0063] First, the initial semantic representation vector is obtained through the initial semantic representation generation algorithm based on geometric nesting optimization. In this step, the initial semantic representation vector not only includes the basic content of the clause, but also considers the geometric features related to the context.

[0064] Secondly, a multi-dimensional alignment transformation algorithm based on nonlinear mapping is used to perform a multi-dimensional alignment transformation on the initial semantic representation vector. This transformation can solve the problem of semantic deviation caused by differences in grammatical structure of different clauses.

[0065] Furthermore, a high-dimensional similarity enhancement algorithm based on dynamic weight assignment is used to align the vectors for enhancement, so that the important information in the feature dimension is further amplified. This enhancement method is combined with an adaptive weight assignment technique, which enables the algorithm to dynamically adjust according to the specific characteristics of the clause content.

[0066] Next, the syntactic semantic fusion algorithm based on variational collaborative learning is used to fuse the enhanced similarity vector with the syntactic features. This fusion process can effectively combine the syntactic structure and semantic information of the clause content and improve the accuracy of clause difference recognition.

[0067] Finally, the global difference optimization algorithm based on multi-scale contradiction resolution performs global optimization on the syntactic semantic fusion vector, thereby outputting the final difference evaluation result. By comprehensively analyzing the feature performance at different scales, the comprehensiveness and accuracy of the clause difference evaluation is ensured.

[0068] Preferably, in the initial semantic representation generation algorithm based on geometric nesting optimization, the following formula is used to calculate the initial semantic representation vector:

[0069] ,

[0070] in, is the initial semantic representation vector, which represents the initial embedding of the standard terms; is the i-th eigenvector of the first standard clause, for example, the specific item of “electrical insulation” involved in the clause; is the i-th eigenvector of the second standard clause, such as the specific description of “insulation thickness” involved in the clause; is the feature weight, preferably, the weight can be learned from historical data, and the typical range is 0.1 to 0.9; is a geometric transformation function, such as an exponential function or a logarithmic function, which is used to adjust the geometric distance between features.

[0071] Through this formula, we can preliminarily obtain the semantic representation of the clause content and lay the foundation for subsequent alignment and enhancement.

[0072] In the multidimensional alignment transformation algorithm based on nonlinear mapping, the following formula is used for multidimensional alignment:

[0073] ,

[0074] in, is the multidimensional alignment result, which represents the alignment characteristics of standard terms in multidimensional space;

[0075] is an activation function, such as ReLU function or Sigmoid function, used for nonlinear transformation; and is the weight matrix, with a typical value between [-0.5, 0.5], used to adjust the feature map; and is a bias vector whose value is usually a zero-mean normally distributed random variable.

[0076] The algorithm performs nonlinear mapping on the initial semantic vector, so that the differences between different standard clauses in multidimensional space can be more clearly reflected.

[0077] In the high-dimensional similarity enhancement algorithm based on dynamic weight allocation, the following formula is used for enhancement calculation:

[0078] ,

[0079] in, To enhance the similarity vector, it represents the high-dimensional similarity enhancement result;

[0080] is a dynamic weight, which can be obtained through online learning or historical data, and the preferred range is 0.05 to 0.95; is an enhancement function, such as a Gaussian kernel function or a polynomial kernel function, which is used to calculate the high-dimensional similarity between the alignment vector and the initial semantic vector.

[0081] Through the above calculations, the expressive power of the clause features can be effectively enhanced, ensuring that more subtle differences are captured in high-dimensional space.

[0082] The present invention realizes accurate identification of differences between power standard clauses through the above algorithm steps. The input and output of each step are closely connected, forming a whole from initial representation generation to final difference optimization. The parameter selection in each algorithm is based on practical experience and theoretical verification, ensuring the reliability and applicability of the method.

[0083] Preferably, in one embodiment of the present invention, a syntactic semantic fusion algorithm based on variational collaborative learning further enhances the ability of difference recognition based on a high-dimensional similarity enhancement algorithm based on dynamic weight allocation. The algorithm adopts the following formula:

[0084] ,

[0085] in, It is the syntactic-semantic fusion vector, which is used to integrate the deep fusion results of syntactic and semantic features; is the joint distribution function, which represents the joint relationship between the enhanced similarity vector and the syntactic features; is a variational distribution, whose parameters Control the shape and range of the distribution to capture potential semantic and syntactic interactions; is a transformation function, which can be a linear transformation or a nonlinear transformation, used to adjust the weights between different features.

[0086] In this step, by jointly modeling the syntactic structure and semantic content, the potential differences between clauses can be further refined. The initial value of is set between 0.01 and 0.1, which can capture the coupling relationship between semantics and syntax more quickly in the initial stage and improve the convergence speed.

[0087] In further optimizing the identification of clause differences, the present invention preferably adopts a global difference optimization algorithm for multi-scale contradiction resolution to ensure the accuracy and comprehensiveness of identification. The algorithm is based on the following formula:

[0088] ,

[0089] in, The result is optimized for global difference, which represents the final difference between the two standard terms;

[0090] is the syntactic and semantic fusion vector in Performance under scale; For the The target value of the scale can preferably be set based on domain experience, for example, the threshold for short sentences can be set to 0.7, while for complex syntactic structures it can be set to 0.5; It is a multi-scale weight used to balance the feature contributions at different scales, and its range is preferably between 0.1 and 0.9.

[0091] Through the above formula, the system can comprehensively analyze the differences at multiple feature scales and optimize the difference results at different scales to solve the contradictions in the content of the clauses at different semantic granularities.

[0092] Preferably, the present invention further includes a classification method based on the difference evaluation result, for determining whether there is a substantial difference between the two standard clauses. , the system compares it with a preset threshold.

[0093] Preferably, the present invention further includes a classification method based on the difference evaluation result, for determining whether there is a substantial difference between the two standard clauses. ,The system compares it with the preset threshold. Empirical data shows that for most standard terms, the difference threshold and They can be set to 0.4 and 0.6 respectively. , then it is determined that there is no substantial difference between the two clauses; if , then it is considered that there is a significant difference; and when When the differences are found, they can be further confirmed through manual review or more complex analysis models.

[0094] This classification method not only improves the accuracy of automatic recognition, but also reduces misjudgments through a hierarchical judgment strategy.

[0095] The present invention also provides a system for identifying differences in power standard clauses based on knowledge representation learning. The system includes the following components:

[0096] Processor 1 is used to execute each step of the above-mentioned method for identifying differences in power standard clauses based on knowledge representation learning. Processor 1 can efficiently perform data calculation and model training to ensure the real-time performance and high performance of the system.

[0097] Memory 2 is used to store knowledge graph data, standard clause information, and model parameters. Preferably, the capacity of memory 2 should be set according to actual business needs. For example, when processing a large power standard library containing millions of clauses, a high-performance memory with a capacity of not less than 1TB can be used.

[0098] The data interface module 3 is used to interact with external systems to achieve real-time acquisition and update of knowledge graphs and standard clause data, for example, by connecting with the State Grid database or the enterprise's internal standard library.

[0099] The above components are connected through a bus to ensure the high speed and stability of data transmission, forming a complete, coordinated and efficient difference recognition system.

[0100] Preferably, the processor 1 is further configured to generate a difference report according to the difference evaluation result, wherein the difference report records in detail the difference position, difference type and corresponding context information between the first standard clause and the second standard clause.

[0101] Specifically, the difference position includes the chapter number and item number of the clause, such as "Chapter 3, Section 2". The difference type can be subdivided into semantic difference, syntactic difference and entity relationship difference. For each difference type, a confidence score is attached to the report. For example, the confidence score of semantic difference can be 0.85, indicating that the model's judgment confidence in the difference is 85%.

[0102] This difference report provides users with detailed and accurate analysis results, making it easier for users to review, revise and manage standard terms in actual business scenarios.

[0103] In combination with the above, the present invention provides a method and system for identifying differences in power standard clauses based on knowledge representation learning. It realizes the automatic identification and evaluation of differences between power standard clauses through five progressive algorithm steps and a powerful hardware system.

[0104] For example, in a certain practical application scenario, there may be subtle semantic differences between the "Equipment Grounding Requirements" in the "Electric Power Equipment Operation Standard" and the "Grounding System Description" in the "Transmission Line Safety Standard". The method of the present invention can accurately capture the semantic and syntactic differences between the two clauses and present them in detail in the difference report.

[0105] In summary, the present invention not only improves the accuracy and efficiency of identifying differences in power standard clauses, but also provides strong technical support for the standardized management of the power industry.

[0106] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying differences in power standard clauses based on knowledge representation learning, characterized in that: The following steps are involved: Input the knowledge graph of power standard terms, which includes multiple triples, each of which consists of a head entity, a relationship, and a tail entity; Based on the knowledge graph, input a first standard clause and a second standard clause to be identified; Converting entities and relationships involved in the knowledge graph into representation vectors for subsequent analysis; Using a collaborative progressive optimization algorithm, analyzing the semantic differences between the first standard clause and the second standard clause, and outputting a difference evaluation result; The collaborative progressive optimization algorithm comprises the following steps: An initial semantic representation generation algorithm based on geometric nesting optimization is used to obtain an initial semantic representation vector; Based on a multi-dimensional alignment transformation algorithm of nonlinear mapping, a multi-dimensional alignment transformation is performed on the initial semantic representation vector to obtain an alignment vector; Based on a high-dimensional similarity enhancement algorithm of dynamic weight allocation, high-dimensional similarity enhancement is performed on the alignment vector to obtain an enhanced similarity vector; A syntactic-semantic fusion algorithm based on variational collaborative learning is used to fuse the enhanced similarity vector with the syntactic feature to obtain a syntactic-semantic fusion vector; Based on a global difference optimization algorithm for multi-scale contradiction resolution, the syntactic-semantic fusion vector is globally optimized to output a final difference evaluation result; The multi-dimensional alignment transformation algorithm includes the following formula: ， in, is the multi-dimensional alignment result, which represents the alignment characteristics of the initial representation vector in the multi-dimensional space; is the activation function, used for nonlinear transformation; and is the weight matrix, which represents the weight distribution of different dimensions; and is the bias vector used to adjust the multi-dimensional alignment result; The syntactic semantic fusion algorithm includes the following formula: ， in, is the syntactic-semantic fusion vector, which represents the fusion result of syntactic and semantic features; is the joint distribution function, combining syntactic features and enhanced similarity vector; is a variational distribution with parameters , used to model the uncertainty between semantic features.

2. The method according to claim 1, characterized in that The initial semantic representation generation algorithm includes the following formula: ， in, is the initial semantic representation vector, which represents the initial semantic embedding of the standard terms; and are the i-th feature vectors of the first standard clause and the second standard clause in the knowledge graph, respectively; is the feature weight, which represents the importance of different features; is a geometric transformation function used to optimize the geometric distance between features.

3. The method according to claim 2, characterized in that The high-dimensional similarity enhancement algorithm includes the following formula: ， in, To enhance the similarity vector, it represents the high-dimensional similarity enhancement result; is the dynamic weight, which indicates the weight distribution between different features; is an enhancement function used to calculate the high-dimensional similarity between the alignment vector and the initial semantic vector.

4. The method according to claim 3, characterized in that The global difference optimization algorithm includes the following formula: ， in, It is the global difference optimization result, indicating the final difference value between terms; To optimize the weights, it is used to balance the contribution of features at different scales; For the The syntactic and semantic fusion results under different scales, For the The target feature value of the scale.

5. The method according to claim 1, characterized in that The method further includes classifying the difference assessment results based on a preset threshold to determine whether there is a substantial difference between the first standard clause and the second standard clause.

6. The power standard clause difference identification system based on knowledge representation learning is characterized by: include: A processor, configured to execute the method according to any one of claims 1 to 5; A memory, used to store the knowledge graph and standard terms data; The data interface module is used to receive and transmit power standard clause data.

7. The system according to claim 6, characterized in that The processor is further configured to generate a difference report according to the difference evaluation result, where the difference report includes detailed information on the difference location and the difference type.

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