A method and system for disassembling waste and old electric energy meters

Abstract

This invention discloses a method and system for dismantling used electricity meters, applied in the field of power system equipment dismantling technology. The method includes: identifying semantic results corresponding to all historical dismantling documents of used electricity meters; obtaining dismantling paths based on the semantic results, wherein each dismantling path is obtained by arranging the sub-components of the corresponding used electricity meters in dismantling order; analyzing the dependency relationships between any two adjacent sub-components in all dismantling paths to obtain dismantling association groups composed of two dependent sub-components; obtaining each sub-component to be dismantled based on the topology identification results of the used electricity meters to be dismantled; analyzing all sub-components to be dismantled based on the dismantling association groups, and determining the dismantling order corresponding to the used electricity meters to be dismantled based at least on the analysis results; and generating a dismantling scheme for the used electricity meters to be dismantled according to the dismantling order. The method of this invention can achieve efficient and reliable dismantling of used electricity meters.

Description

Technical Field

[0001] This invention relates to the field of power system equipment dismantling technology, and in particular to a method and system for dismantling used electricity meters. Background Technology

[0002] The dismantling of used electricity meters is a crucial step in the management of electricity resource recycling. Standardized dismantling allows for the categorized recycling and reuse of valuable components such as meter casings, terminals, metering chips, and transformers, while ensuring the environmentally sound disposal of hazardous materials like batteries and capacitors. Therefore, the standardized dismantling of used electricity meters has significant practical implications.

[0003] In existing technologies, the dismantling process of used electricity meters relies entirely on the individual operating habits and understanding of the meter's structure of the dismantling workers, who determine the dismantling order of each sub-component. However, different dismantling workers have different operating habits, which can easily lead to violent dismantling, damaging valuable components and reducing recycling value. Furthermore, different dismantling workers may have differing understandings of the meter's structure, leading to blind dismantling, leakage of hazardous substances, and safety hazards. Summary of the Invention

[0004] This invention provides a method and system for dismantling used electricity meters to solve the technical problems that manual dismantling of electricity meters can easily lead to damage to valuable components and leakage of hazardous substances, thereby achieving efficient and reliable dismantling of used electricity meters.

[0005] To address the aforementioned technical problems, this invention provides a method for dismantling used electricity meters, comprising: Identify the semantic results corresponding to all historical dismantling documents of discarded electricity meters; Based on the semantic results, each dismantling path is obtained, wherein each dismantling path is obtained by arranging the sub-components of the corresponding waste electricity meters in the order of dismantling. Analyze the dependencies between any two adjacent sub-components in all the disassembly paths to obtain a disassembly association group consisting of two sub-components with dependencies; Based on the topology identification results of the waste energy meters to be dismantled, each sub-component to be dismantled is obtained; Based on the dismantling association group, all the sub-components to be dismantled are analyzed, and the dismantling order corresponding to the waste electricity meter to be dismantled is determined at least based on the analysis results. A dismantling plan for the waste electricity meter to be dismantled is generated according to the dismantling sequence.

[0006] As one preferred embodiment, the semantic results corresponding to all historical dismantling document data of the identified waste electricity meters include: The language of all the historical dismantling documents of the acquired waste electricity meters is converted to obtain the historical dismantling document data to be identified. Based on a natural language processing model, entity-relation extraction is performed on the historical decomposition document data to be identified to obtain the corresponding semantic results.

[0007] As one preferred embodiment, the process of obtaining various decomposition paths based on the semantic results includes: Keyword extraction is performed on the historical dismantling document data to be identified to obtain information on all sub-components of the waste electricity meter; Based on a pre-defined component library, all the sub-component information is encoded to obtain sub-component identifiers; Based on the semantic results, all the sub-component identifiers are consolidated, and each of the disassembly paths is obtained based on the consolidation results.

[0008] As one preferred embodiment, the step of analyzing all the sub-components to be disassembled based on the disassembly association group, and determining the disassembly sequence corresponding to the waste energy meters to be disassembled based at least on the analysis results, includes: Based on the disassembly association group, all the sub-components to be disassembled are processed to obtain the association relationship of the sub-components to be disassembled; Based on an artificial intelligence model, the relationships between the sub-components to be disassembled are analyzed to determine the disassembly sequence of the waste electricity meter to be disassembled.

[0009] As one preferred embodiment, the artificial intelligence model includes a Bayesian network model. The analysis of the relationships between the sub-components to be disassembled based on the artificial intelligence model, and the determination of the disassembly sequence of the discarded energy meter, includes: Based on the relationships between the sub-components to be disassembled, determine all candidate disassembly sequences; The association relationship of the sub-components to be disassembled is input into the Bayesian network model to obtain the safe disassembly probability of each sub-component to be disassembled. Based on the aforementioned safe dismantling probability, all the candidate dismantling sequences are analyzed to obtain the dismantling sequence of the waste energy meter to be dismantled.

[0010] As one preferred embodiment, the step of analyzing the dependency relationships between any two adjacent sub-components in all the disassembly paths to obtain a disassembly association group consisting of two dependent sub-components includes: Based on the sliding window, any two adjacent sub-components in all the disassembly paths are traversed to obtain all pairs of adjacent sub-components. Based on the principles of probability and statistics, a co-occurrence frequency analysis is performed on all the adjacent sub-component pairs to obtain the dependency relationship between the adjacent sub-component pairs. Based on the dependencies, the disassembly association group is determined.

[0011] As one preferred embodiment, determining the disassembly association group based on the dependency relationship includes: Based on the dependencies between all the adjacent sub-component pairs, an association matrix is ​​obtained; The association matrix is ​​subjected to sparse processing to obtain the decomposed association group.

[0012] As one preferred embodiment, the dismantling scheme for generating the discarded energy meter to be dismantled according to the dismantling sequence includes: Based on the historical disassembly document data, the physical properties of all the sub-components to be disassembled in the disassembly sequence are analyzed to obtain the component state characteristics of all the sub-components to be disassembled. Based on the state characteristics of each component, the disassembly execution parameters of the corresponding sub-component to be disassembled are determined; Each dismantling execution parameter is processed to obtain the dismantling scheme for the waste electricity meter to be dismantled.

[0013] As one preferred embodiment, the step of analyzing the physical properties of all the sub-components to be disassembled in the disassembly sequence based on the historical disassembly document data to obtain the component state characteristics of all the sub-components to be disassembled, including: Based on image recognition algorithms, all the sub-components to be disassembled in the waste energy meter to be disassembled are processed to obtain images of all the sub-components to be disassembled. Based on the standard sub-component images, the deformation features of all the sub-component images to be disassembled are extracted; Based on the deformation characteristics, the component state characteristics of the sub-component to be disassembled are determined.

[0014] Another aspect of the present invention provides a system for dismantling used electricity meters, comprising: The semantic recognition module is used to identify the semantic results corresponding to all historical dismantling documents of discarded electricity meters; The semantic result analysis module is used to obtain each dismantling path based on the semantic results, wherein each dismantling path is obtained by arranging the corresponding sub-components of each waste energy meter in the order of dismantling. The disassembly path analysis module is used to analyze the dependency relationship between any two adjacent sub-components in all the disassembly paths, and obtain a disassembly association group consisting of two sub-components with a dependency relationship. The sub-component identification module is used to obtain each sub-component to be disassembled based on the topology identification results of the waste energy meter to be disassembled; The disassembly sequence analysis module is used to analyze all the sub-components to be disassembled based on the disassembly association group, and to determine the disassembly sequence corresponding to the waste electricity meter to be disassembled based at least on the analysis results. The dismantling scheme determination module is used to generate a dismantling scheme for the waste energy meter to be dismantled in the dismantling sequence.

[0015] Compared to existing technologies, the beneficial effects of this invention are at least one of the following: This invention identifies and analyzes the semantic results of massive historical dismantling document data, proposing multiple potential dismantling paths, thus achieving digital accumulation and intelligent reuse of past manual dismantling experience; This invention analyzes the dependencies between adjacent sub-components in the dismantling path and constructs dismantling association groups, achieving a deep understanding of the internal structural logic of the electricity meter, solving the problem of violent dismantling or incorrect sequence caused by ignoring the constraints between components; This invention combines the real-time topology identification results of the electricity meter to be dismantled with the pre-constructed dismantling association groups for matching analysis, achieving customized dismantling sequence generation for different models, different wear levels, or different internal structures of used electricity meters, solving the problem of hazardous substance leakage caused by blind dismantling in existing technologies; This invention automatically generates standardized dismantling schemes based on scientific dependency analysis, achieving standardization and optimization of the dismantling operation process, solving the problems of high damage rate of valuable components and improper disposal of hazardous substances caused by large differences in worker operating habits, while improving the dismantling efficiency of used electricity meters. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a method for dismantling waste electricity meters in one embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a waste electricity meter dismantling system in one embodiment of the present invention; Figure label: Among them, 11. Semantic recognition module; 12. Semantic result analysis module; 13. Disassembly path analysis module; 14. Sub-component recognition module; 15. Disassembly sequence analysis module; 16. Disassembly scheme determination module. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0019] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0020] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0021] One embodiment of the present invention provides a method for dismantling used electricity meters. For details, please refer to [link / reference]. Figure 1 , Figure 1 The diagram shown illustrates a process flow diagram of a method for dismantling a used electricity meter according to one embodiment of the present invention. The method includes steps S1 to S6: S1. Identify the semantic results corresponding to all historical dismantling documents of discarded electricity meters; S2. Based on the semantic results, each dismantling path is obtained, wherein each dismantling path is obtained by arranging the sub-components of the corresponding waste electricity meters in the order of dismantling. S3. Analyze the dependency relationship between any two adjacent sub-components in all the disassembly paths to obtain a disassembly association group consisting of two sub-components with a dependency relationship; S4. Based on the topology identification results of the waste energy meters to be dismantled, obtain each sub-component to be dismantled; S5. Based on the disassembly association group, analyze all the sub-components to be disassembled, and determine the disassembly sequence corresponding to the waste electricity meter to be disassembled based at least on the analysis results. S6. Generate a dismantling plan for the waste electricity meter to be dismantled according to the dismantling sequence.

[0022] In the field of power system equipment dismantling, historical dismantling experience of used electricity meters is mostly in the form of unstructured documents. Manual methods cannot efficiently extract the dismantling logic and component relationships from these documents, and the inconsistent wording and terminology across different documents make it difficult to reuse the experience. Therefore, in step S1, semantic recognition is performed on the historical dismantling document data, transforming unstructured text information into machine-readable and analyzable structured semantic results. This provides unified, standardized, and mineable semantic data support for subsequent dismantling path extraction, component dependency analysis, and customized dismantling sequence generation. This achieves the digital accumulation and intelligent reuse of manual dismantling experience, avoiding problems such as damage to valuable components and leakage of hazardous substances caused by relying solely on manual dismantling. Among them, historical dismantling document data refers to the collection of all structured, semi-structured and unstructured document data generated by the operating entity in the field of power material recycling management during the entire process of dismantling waste electricity meters (including metering terminals such as single-phase electricity meters). It records dismantling operations, component information, operation procedures, problem handling and other dismantling-related data. It is a carrier of past dismantling operation experience, operation logic and component characteristics.

[0023] Specifically, firstly, all acquired historical dismantling document data undergoes standardized language conversion to obtain the historical dismantling document data to be identified. Preferably, industry slang, personalized expressions, and non-standard professional terms in the documents are standardized and converted into standard terminology in the field of power equipment dismantling. Document data with multilingual expressions and dialect expressions from different regions undergo unified language conversion, such as converting them into general Chinese expressions. Incomplete or ambiguous document data is supplemented and standardized to ensure the consistency, completeness, and standardization of the expressions in the historical dismantling document data to be identified, eliminating subsequent semantic recognition biases caused by differences in expression. Secondly, the language-converted historical dismantling document data to be identified is input into a preset natural language processing model. Through the model's three core stages—entity extraction, relation extraction, and semantic fusion—the corresponding semantic results are obtained. The semantic result is a set of entities and a set of relationships between entities in the field of waste electricity meter dismantling. Entities include waste electricity meter sub-components, dismantling operations, operating parameters, equipment models, etc. The relationships between entities include dismantling sequence, component connection, operation and component correspondence, model and component composition matching, etc.

[0024] Preferably, historical dismantling document data is obtained through the information management system for power material recycling and dismantling operations, the digital collection of paper documents in the work workshop, the synchronization of operation records of dismantling operation terminals, and the archiving and retrieval of industry dismantling technical data; for non-digital data such as paper documents and handwritten operation records, they are digitized through scanning, OCR recognition and other technologies and then included in the historical dismantling document data set; for structured data in the information system, they are directly extracted and summarized through data interfaces.

[0025] Preferably, the natural language processing model includes an input layer, a preprocessing layer, a feature extraction layer, an entity-relation extraction layer, and an output layer. The input layer serves as the model's data entry point, receiving the decomposed historical document data to be recognized after language conversion. It supports various input formats such as text, semi-structured tables, and lightweight mixed text and image documents, uniformly converting all types of data into serialized character vectors that the model can process. The preprocessing layer cleans and preprocesses the serialized character vectors, including word segmentation, stop word removal, part-of-speech tagging, and character vector embedding, providing a high-quality semantic feature foundation for subsequent feature extraction. The feature extraction layer uses BERT algorithms... The algorithm learns the professional semantic features and expression logic of the deconstruction domain, and finally outputs a fused feature vector containing contextual semantics and domain-specific features. The entity-relation extraction layer identifies various entities in the deconstruction domain through the named entity recognition (NER) algorithm and labels the entities by type. The relationship classification module determines the type of relationship between entities based on the identified entity pairs through a classification algorithm, labels the relationships, and removes entity relationships that have no practical deconstruction significance. The output layer integrates, verifies, and formats the results of the entity-relation extraction layer, transforming the identified entities and the relationships between entities into structured semantic results that can be analyzed by machines.

[0026] Furthermore, in step S2, the structured semantic results are further transformed into an ordered disassembly path centered on the order of sub-component disassembly. This integrates the scattered entity-relationship semantic information into a sequential sequence that conforms to the logic of actual disassembly operations, achieving a systematic extraction and concrete presentation of disassembly sequence patterns from historical disassembly experience. Simultaneously, it provides a standardized, traversable, and statistically valid basic data carrier for subsequent traversal analysis of dependencies between adjacent sub-components and the construction of disassembly association groups. This solves the technical problem that semantic results cannot be directly used for component dependency analysis, laying an ordered data foundation for the subsequent generation of customized and scientific disassembly sequences.

[0027] Specifically, firstly, based on the semantic results of the identified historical dismantling documents, a keyword matching algorithm is used to extract keywords from the semantic results, obtaining all information related to the sub-components of the discarded electricity meters, forming an original sub-component information set. Preferably, during the extraction process, precise matching is performed using a sub-component keyword library in the field of power equipment dismantling, eliminating redundant information unrelated to the sub-components in the semantic results, ensuring the completeness and domain adaptability of the extracted sub-component information. The keyword library includes the names, aliases, and functional descriptions of various standard sub-components of electricity meters. Secondly, based on a pre-defined standard component library in the field of power equipment dismantling, the extracted original sub-component information set is uniformly encoded to generate unique sub-component identifiers. Next, based on the core association information such as the dismantling sequence and component connection relationships between entities in the semantic results, the encoded sub-component identifiers are logically consolidated. Preferably, during the consolidation process, sub-component identifiers without an actual disassembly order in the semantic results are removed, and core sub-component identifiers that are essential for the disassembly operation are added. Simultaneously, based on the disassembly logic described in the semantic results, related sub-component identifiers are initially sorted to form a sub-component identifier group based on the disassembly logic. Finally, based on the consolidated sub-component identifier group and the semantic relationships of the disassembly sequence clearly defined in the semantic results, each sub-component identifier is arranged in an ordered manner according to the actual disassembly operation sequence, forming a single complete disassembly path. The semantic results of all historical disassembly document data are traversed, and corresponding ordered sub-component identifier sequences are generated for different models, batches, and disassembly scenarios of discarded energy meters, ultimately yielding a set of disassembly paths corresponding to historical disassembly experience.

[0028] In existing waste electricity meter dismantling operations, manual dismantling relies solely on operational habits to determine the component sequence, without exploring and following the objective dismantling dependencies between the electricity meter's sub-components. This easily leads to component damage or hazardous substance leakage due to incorrect dismantling sequence, or low operational efficiency due to illogical dismantling steps. At the same time, the generated dismantling path is merely an orderly arrangement of sub-components, without analyzing the inherent relationships between adjacent sub-components in the path, and cannot provide reusable component relationship logic for subsequent customized dismantling sequences.

[0029] Furthermore, in step S3, through systematic analysis of all historical dismantling paths, the objective dismantling dependencies between adjacent sub-components are quantified and mined, making the component dismantling logic hidden in the scattered historical dismantling paths explicit and structured. By constructing dismantling association groups, adjacent sub-components with strong dependencies are solidified, solving the technical problem of "no logically arranged sub-components" in subsequent dismantling sequence generation. Simultaneously, this provides core association evidence based on historical statistical patterns for subsequently matching dismantling logic with the topology identification results of the waste electricity meters to be dismantled and generating a scientifically reasonable customized dismantling sequence.

[0030] Specifically, firstly, for the sample set consisting of all generated historical dismantling paths, a sliding window algorithm with a step size of 1 is used to fully traverse each dismantling path, extracting all consecutive adjacent sub-component pairs in the path, and sequentially identifying each sub-component pair. During the traversal, dismantling paths for different models and batches of waste electricity meters are processed using a uniform rule to ensure that the extracted adjacent sub-component pairs cover all historical dismantling scenarios without omissions or duplications.

[0031] Secondly, based on the probabilistic statistical principle of frequency estimation probability, a full statistical analysis of the co-occurrence frequency of all adjacent sub-component pairs obtained through traversal is performed. Preferably, the statistical process involves first initializing a sub-component pair co-occurrence frequency statistics table, which includes three core fields: the identifier of the preceding component, the identifier of the succeeding component, and the co-occurrence frequency; then, each adjacent sub-component pair is enumerated one by one, and the co-occurrence frequency of the corresponding sub-component pair in the statistics table is incremented by one; finally, the cumulative co-occurrence frequency of all adjacent sub-component pairs is obtained. The probabilistic statistical principle refers to the frequency estimation probability principle based on the law of large numbers, combined with the operational characteristics of the power equipment dismantling field, using the co-occurrence frequency of adjacent sub-component pairs in the historical dismantling path as a probabilistic measure of their objective dismantling dependency relationship.

[0032] Next, the dismantling dependency relationship of each sub-component pair is quantitatively determined: the relative proportion of co-occurrence frequency of each sub-component pair is calculated, that is, the ratio of the co-occurrence frequency of the sub-component pair to the total co-occurrence frequency of all adjacent sub-component pairs. This proportion is a quantitative indicator of the dismantling dependency relationship between sub-components. Subsequently, a dependency relationship determination threshold is preset. When the relative proportion of co-occurrence frequency of a certain sub-component pair is greater than or equal to the threshold, it is determined that there is a valid dismantling dependency relationship between the two. Then, based on the determination results of the valid dismantling dependency relationship, a dismantling association matrix of waste electricity meter sub-components is constructed. Here, co-occurrence frequency refers to the cumulative number of times any two sub-components appear in the dismantling path in an adjacent order of "dismantling component A first, dismantling component B later" in the sample set composed of all historical dismantling paths. Dependency relationship refers to the objectively existing dismantling order association logic between waste electricity meter sub-components mined from historical dismantling paths based on the probabilistic statistical analysis of co-occurrence frequency.

[0033] Finally, the association matrix is ​​sparsified: invalid associations with zero values ​​are removed, and only valid associations with values ​​greater than or equal to the decision threshold are retained. Based on the sparsified association matrix, all adjacent sub-component pairs with valid decomposition dependencies are extracted, and each sub-component pair with a valid dependency is independently encapsulated into a decomposition association group.

[0034] In existing waste electricity meter dismantling operations, traditional dismantling schemes are mostly based on standardized electricity meter structures. They do not take into account the actual changes in the composition and structural connection relationships of sub-components caused by factors such as service life, equipment wear and tear, model differences, and local damage to the actual electricity meters to be dismantled. Directly applying standardized schemes can easily lead to problems such as the dismantling sequence not matching the actual components, ineffective dismantling, or even secondary damage to components. At the same time, the dismantling association groups mined in the preceding steps are based on general logic based on historical dismantling experience. If the actual sub-components to be dismantled are not accurately identified, it is impossible to achieve an effective match between the general dismantling logic and the specific dismantling object.

[0035] Furthermore, in step S4, based on the topology identification results of the waste energy meter to be dismantled, the set of sub-components to be dismantled that match the actual dismantling object is accurately and comprehensively extracted. Component information irrelevant to the current energy meter to be dismantled from historical experience is eliminated, clarifying the core operation object of the actual dismantling operation. At the same time, precise control is achieved over the composition, physical state, and structural correlation characteristics of the sub-components of the actual energy meter to be dismantled. This provides a realistic and effective basis for subsequent customized dismantling sequence matching based on dismantling correlation groups, ensuring that the final generated dismantling sequence is highly compatible with the actual structure of the energy meter to be dismantled, guaranteeing the pertinence and feasibility of the dismantling plan from the source.

[0036] Specifically, firstly, the topology identification results of the waste energy meters to be dismantled are structurally analyzed to extract all core information related to sub-components, including the actual names, physical states, spatial locations, and connection relationships of the sub-components, forming an original sub-component analysis list. The topology identification results refer to the structured and visualized analysis results of the energy meter's physical structure, sub-component composition, spatial distribution, physical connections, and actual state of existence, obtained through comprehensive identification and analysis using technologies such as machine vision, structural scanning, and hardware detection. Secondly, based on the original sub-component analysis list, effective sub-components to be dismantled are selected according to the needs and specifications of the actual dismantling operation, forming a valid list of sub-components to be dismantled. The selection criteria are: firstly, removing sub-components that are missing from the topology identification results, completely damaged and without recycling value, or not part of the dismantling operation target; secondly, retaining sub-components that are valuable and recyclable, require environmentally friendly disposal, and are necessary for the dismantling operation; and thirdly, marking sub-components with structural anomalies or dismantling risks. Next, the selected sub-components to be disassembled are precisely matched with a pre-defined standard component library for power equipment disassembly, and a unique sub-component identifier is assigned to each sub-component according to a unified coding rule. Finally, the valid sub-components to be disassembled that have been identified are structurally integrated, and information such as the identifier, actual physical state, spatial location, and structural anomaly markers of each sub-component are associated and stored to form a set of sub-components to be disassembled.

[0037] Furthermore, in step S5, the historical disassembly logic contained in the disassembly association group is deeply integrated and analyzed with the actual characteristics of the sub-components to be disassembled. The disassembly dependency relationship is matched and adapted to the actual object to be disassembled through standardized analysis methods. At the same time, an artificial intelligence model, namely a Bayesian network model, is introduced to consider the safety and rationality of the disassembly operation and quantify the safe disassembly probability of each candidate disassembly sequence. Finally, a unique, scientific and executable disassembly sequence is determined from the multi-dimensional analysis results.

[0038] Preferably, the Bayesian network model uses probabilistic reasoning as its core to quantify and determine the safety risks of sub-component disassembly operations under different disassembly sequences. Its basic architecture consists of a three-layer structure: a topology layer, a parameter layer, and an inference layer.

[0039] The safe disassembly probability refers to the probability that, under a specific disassembly sequence, no safety risks such as damage to valuable components, leakage of hazardous substances, or operational errors will occur when performing disassembly operations on a certain sub-component, and the disassembly process specifications are met. It is divided into two categories: single-component safe disassembly probability and overall disassembly operation safe disassembly probability. The safe disassembly probability is derived from a Bayesian network model combined with two core criteria: first, the historical disassembly dependencies inherent in the disassembly association group, ensuring that the probability determination conforms to industry-standard disassembly logic; second, the actual characteristics of the sub-component to be disassembled, ensuring that the probability determination is adapted to the actual situation of a single object to be disassembled.

[0040] Specifically, firstly, based on the set of disassembly association groups, and combined with the actual spatial topology distribution and physical connection relationships of the sub-components to be disassembled, a unique association relationship for each sub-component is constructed. Secondly, taking each sub-component to be disassembled as a potential starting point, and following the principle of "prioritizing strong dependencies and complying with process logic," subsequent sub-components are selected sequentially to form a complete disassembly sequence. The generated candidate disassembly sequences are initially screened, eliminating invalid sequences that violate the physical structure logic and process specifications of the electricity meter, ultimately obtaining a set of candidate disassembly sequences that conform to the basic logic. Next, the candidate disassembly sequence set and the actual characteristics of the sub-components to be disassembled are input into a pre-trained Bayesian network model for the disassembly of waste electricity meters. Through Bayesian probabilistic inference in the model's inference layer, the single-component safe disassembly probability and the overall safe disassembly operation probability under each set of candidate disassembly sequences are calculated. Finally, combining the three core dimensions of disassembly logic adaptability, overall safe disassembly probability, and disassembly operation efficiency, the candidate disassembly sequence set is comprehensively analyzed and optimized to determine the final disassembly sequence. The specific optimization rules are as follows: (1) First priority: Select candidate disassembly sequences with an overall safe disassembly probability ≥ preset safety threshold, and eliminate high-risk sequences with a probability lower than the threshold to ensure the safety of disassembly operations; (2) Second priority: Among the candidate order that meets the safety threshold, select the order with the highest matching degree of the correlation between the sub-component to be disassembled, to ensure that the disassembly order conforms to the structure of the electricity meter and the disassembly process logic; (3) Third priority: If multiple sequences satisfy the first two priorities at the same time (i.e., parallel cases), then the index rules of the effective index of the sub-component to be dismantled from small to large and the historical average time of the sub-component dismantling operation from short to long are compared in turn to determine the unique, scientific and executable dismantling order of the waste electricity meter to be dismantled.

[0041] Furthermore, in step S6, using the disassembly sequence as the core framework, the actual state of the sub-components to be disassembled, disassembly industry process specifications, and on-site operational requirements are integrated. Through component state analysis and execution parameter determination, the abstract disassembly sequence is transformed into a complete disassembly plan that includes operation procedures, execution parameters, safety requirements, and efficiency indicators. This provides standardized operational guidelines for on-site disassembly operations that are step-by-step, directly executable, and highly targeted, ensuring that the disassembly sequence, when implemented on-site, not only meets logical requirements but also considers operational safety, operability, and disassembly efficiency. Ultimately, this achieves the standardization, refinement, and intelligent implementation of waste electricity meter disassembly operations.

[0042] Specifically, firstly, based on historical dismantling documentation data, a physical property analysis is performed on each sub-component to be dismantled in the dismantling sequence. On the one hand, the original state information of the corresponding sub-component is retrieved; on the other hand, combined with the dismantling operation records of similar sub-components in the historical dismantling documentation data, the vulnerable points, dismantling difficulties, hazardous substance encapsulation characteristics, and key points for protecting valuable components of the sub-component are analyzed. Simultaneously, the visualized image of the sub-component is refined using image recognition algorithms to extract detailed state features such as deformation, aging, and loose welding, forming the component state characteristics of each sub-component. Secondly, based on the component state characteristics of each sub-component, combined with the process specifications of the power equipment dismantling industry and the configuration of on-site operating equipment, dismantling execution parameters are determined for each step of the dismantling sequence. These parameters include dismantling adaptation tools, operating force and speed requirements, dismantling operation specifications, safety protection requirements, and reference values ​​for single-step operation time. For sub-components with structural abnormalities, additional emergency operation parameters and risk handling plans are developed. Then, the sub-component status characteristics, disassembly execution parameters, safety protection requirements, time reference values, risk handling requirements, and each step of the disassembly sequence are linked and integrated to form a disassembly plan.

[0043] Another embodiment of the present invention provides a system for dismantling used electricity meters. For details, please refer to [link / reference]. Figure 2 , Figure 2 The diagram shown illustrates the structure of a waste electricity meter dismantling system according to one embodiment of the present invention. The system includes: Semantic recognition module 11 is used to identify the semantic results corresponding to all historical dismantling document data of waste electricity meters; The semantic result analysis module 12 is used to obtain each dismantling path based on the semantic result, wherein each dismantling path is obtained by arranging the corresponding sub-components of each waste energy meter in the order of dismantling. The disassembly path analysis module 13 is used to analyze the dependency relationship between any two adjacent sub-components in all the disassembly paths, and obtain a disassembly association group composed of two sub-components with a dependency relationship. The sub-component identification module 14 is used to obtain each sub-component to be disassembled based on the topology identification results of the waste energy meter to be disassembled; The disassembly sequence analysis module 15 is used to analyze all the sub-components to be disassembled based on the disassembly association group, and at least determine the disassembly sequence corresponding to the waste energy meter to be disassembled based on the analysis results. The dismantling scheme determination module 16 is used to generate a dismantling scheme for the waste energy meter to be dismantled in the dismantling sequence.

[0044] Compared to existing technologies, the beneficial effects of this invention are at least one of the following: This invention identifies and analyzes the semantic results of massive historical dismantling document data, proposing multiple potential dismantling paths, thus achieving digital accumulation and intelligent reuse of past manual dismantling experience; This invention analyzes the dependencies between adjacent sub-components in the dismantling path and constructs dismantling association groups, achieving a deep understanding of the internal structural logic of the electricity meter, solving the problem of violent dismantling or incorrect sequence caused by ignoring the constraints between components; This invention combines the real-time topology identification results of the electricity meter to be dismantled with the pre-constructed dismantling association groups for matching analysis, achieving customized dismantling sequence generation for different models, different wear levels, or different internal structures of used electricity meters, solving the problem of hazardous substance leakage caused by blind dismantling in existing technologies; This invention automatically generates standardized dismantling schemes based on scientific dependency analysis, achieving standardization and optimization of the dismantling operation process, solving the problems of high damage rate of valuable components and improper disposal of hazardous substances caused by large differences in worker operating habits, while improving the dismantling efficiency of used electricity meters.

[0045] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for dismantling used electricity meters, characterized in that, include: Identify the semantic results corresponding to all historical dismantling documents of discarded electricity meters; Based on the semantic results, each dismantling path is obtained, wherein each dismantling path is obtained by arranging the sub-components of the corresponding waste electricity meters in the order of dismantling. Analyze the dependencies between any two adjacent sub-components in all the disassembly paths to obtain a disassembly association group consisting of two sub-components with dependencies; Based on the topology identification results of the waste energy meters to be dismantled, each sub-component to be dismantled is obtained; Based on the dismantling association group, all the sub-components to be dismantled are analyzed, and the dismantling order corresponding to the waste electricity meter to be dismantled is determined at least based on the analysis results. A dismantling plan for the waste electricity meter to be dismantled is generated according to the dismantling sequence.

2. The method for dismantling waste electricity meters as described in claim 1, characterized in that, The semantic results corresponding to all historical dismantling document data of the identified waste electricity meters include: Language conversion is performed on all the historical dismantling document data of the acquired waste electricity meters to obtain the historical dismantling document data to be identified; Based on a natural language processing model, entity-relation extraction is performed on the historical decomposition document data to be identified to obtain the corresponding semantic results.

3. The method for dismantling a waste electricity meter as described in claim 2, characterized in that, Based on the semantic results, the following decomposition paths are obtained: Keyword extraction is performed on the historical dismantling document data to be identified to obtain information on all sub-components of the waste electricity meter; Based on a pre-defined component library, all the sub-component information is encoded to obtain sub-component identifiers; Based on the semantic results, all the sub-component identifiers are consolidated, and each of the disassembly paths is obtained based on the consolidation results.

4. The method for dismantling a waste electricity meter as described in claim 1, characterized in that, The step of analyzing all the sub-components to be disassembled based on the disassembly association group, and determining the disassembly sequence corresponding to the waste electricity meter to be disassembled based at least on the analysis results, includes: Based on the disassembly association group, all the sub-components to be disassembled are processed to obtain the association relationship of the sub-components to be disassembled; Based on an artificial intelligence model, the relationships between the sub-components to be disassembled are analyzed to determine the disassembly sequence of the waste electricity meter to be disassembled.

5. The method for dismantling a waste electricity meter as described in claim 4, characterized in that, The artificial intelligence model includes a Bayesian network model. Based on this model, the relationships between the sub-components to be disassembled are analyzed to determine the disassembly sequence of the discarded energy meters, including: Based on the relationships between the sub-components to be disassembled, determine all candidate disassembly sequences; The association relationship of the sub-components to be disassembled is input into the Bayesian network model to obtain the safe disassembly probability of each sub-component to be disassembled. Based on the aforementioned safe dismantling probability, all the candidate dismantling sequences are analyzed to obtain the dismantling sequence of the waste energy meter to be dismantled.

6. The method for dismantling a waste electricity meter as described in claim 1, characterized in that, The analysis of the dependencies between any two adjacent sub-components in all the disassembly paths yields a disassembly association group consisting of two dependent sub-components, including: Based on the sliding window, any two adjacent sub-components in all the disassembly paths are traversed to obtain all pairs of adjacent sub-components. Based on the principles of probability and statistics, a co-occurrence frequency analysis is performed on all the adjacent sub-component pairs to obtain the dependency relationship between the adjacent sub-component pairs. Based on the dependencies, the disassembly association group is determined.

7. The method for dismantling a waste electricity meter as described in claim 6, characterized in that, Determining the disassembly association group based on the dependency relationship includes: Based on the dependencies between all the adjacent sub-component pairs, an association matrix is ​​obtained; The association matrix is ​​subjected to sparse processing to obtain the decomposed association group.

8. The method for dismantling a waste electricity meter as described in claim 1, characterized in that, The dismantling scheme for generating the waste electricity meter to be dismantled according to the dismantling sequence includes: Based on the historical disassembly document data, the physical properties of all the sub-components to be disassembled in the disassembly sequence are analyzed to obtain the component state characteristics of all the sub-components to be disassembled. Based on the state characteristics of each component, the disassembly execution parameters of the corresponding sub-component to be disassembled are determined; Each dismantling execution parameter is processed to obtain the dismantling scheme for the waste electricity meter to be dismantled.

9. A method for dismantling waste electricity meters as described in claim 8, characterized in that, Based on the historical disassembly document data, the physical properties of all the sub-components to be disassembled in the disassembly sequence are analyzed to obtain the component state characteristics of all the sub-components to be disassembled, including: Based on image recognition algorithms, all the sub-components to be disassembled in the waste energy meter to be disassembled are processed to obtain images of all the sub-components to be disassembled. Based on the standard sub-component images, the deformation features of all the sub-component images to be disassembled are extracted; Based on the deformation characteristics, the component state characteristics of the sub-component to be disassembled are determined.

10. A system for dismantling used electricity meters, characterized in that, include: The semantic recognition module is used to identify the semantic results corresponding to all historical dismantling documents of discarded electricity meters; The semantic result analysis module is used to obtain each dismantling path based on the semantic results, wherein each dismantling path is obtained by arranging the corresponding sub-components of each waste energy meter in the order of dismantling. The disassembly path analysis module is used to analyze the dependency relationship between any two adjacent sub-components in all the disassembly paths, and obtain a disassembly association group consisting of two sub-components with a dependency relationship. The sub-component identification module is used to obtain each sub-component to be disassembled based on the topology identification results of the waste energy meter to be disassembled; The disassembly sequence analysis module is used to analyze all the sub-components to be disassembled based on the disassembly association group, and to determine the disassembly sequence corresponding to the waste electricity meter to be disassembled based at least on the analysis results. The dismantling scheme determination module is used to generate a dismantling scheme for the waste energy meter to be dismantled in the dismantling sequence.

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