An artificial intelligence-based industry expansion installation work order analysis method

Abstract

This invention provides an artificial intelligence-based method for analyzing business expansion and installation work orders, belonging to the field of computer data processing technology. It aims to provide a highly feasible optimization solution for generating complex and contradictory work orders. This invention proactively identifies conflicts and introduces optimized mapping relationships for guided solution generation. It can provide solutions for work orders containing inherent contradictions, using the resolution of internal and external conflicts as a reference for solution generation and evaluation. This avoids global problems that may result from local optimization. Based on the target optimization direction, the morpheme unit set and at least one historical solution element retrieved from the historical solution database are evolved and recombined to generate a candidate solution set containing multiple candidate solutions. Guided by the target optimization direction, the selected optimized solution is the optimal solution that achieves comprehensive balance across multiple dimensions, significantly improving the intelligence level, decision-making quality, and resource allocation efficiency of business expansion and installation work order processing.

Description

Technical Field

[0001] This invention relates to the field of computer data processing technology, and in particular to an artificial intelligence-based method for analyzing business expansion application work orders. Background Technology

[0002] Application for new power grid connections is one of the core businesses of the power industry. It refers to a series of work processes carried out in response to users' electricity needs, including application acceptance, site survey, scheme design, and construction execution. This entire process generates a large amount of work order data, which includes a significant amount of unstructured text describing customer needs, site conditions, and review comments. How to efficiently and accurately process this work order data directly affects service quality and grid operation efficiency.

[0003] In existing technologies, the processing of work orders for business expansion applications largely relies on traditional information management systems. These systems typically employ keyword matching and pre-defined rule engines to perform preliminary classification and information extraction from the work order text. When configuring resources or conducting risk assessments, similar work orders are retrieved from historical databases based on basic fields such as work order type and voltage level. This process achieves a degree of digitalization and partial automation of the business process, replacing some of the purely manual review and assignment work.

[0004] However, text analysis capabilities based on keywords or fixed rules are very limited. They cannot deeply understand the complex semantics and potential constraints contained in work order texts, which can easily lead to misjudgment and omission of information. Simply matching similar historical work orders for recommendations is essentially a mechanical replication of experience. When faced with novel or special work order scenarios, it cannot provide effective solutions and there is room for improvement. Summary of the Invention

[0005] This invention provides an artificial intelligence-based method for analyzing business expansion and installation work orders, which provides a highly feasible optimization solution for generating complex and contradictory work orders.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, an artificial intelligence-based method for analyzing business expansion and installation work orders is provided, including the following steps: The unstructured text data of the business expansion application work order is deconstructed to generate a set of morpheme units corresponding to the work order; By identifying internal configuration errors in the morpheme unit set and external coordination conflicts between the morpheme unit set and external environment parameters, an optimized set is obtained. The optimization set is mapped to a contradiction matrix that stores the general engineering parameter pairs and the optimization mapping relationship to obtain at least one target optimization direction; Based on the target optimization direction, the set of morpheme units and at least one historical scheme element retrieved from the historical scheme library are evolved and recombined to generate a candidate scheme set containing multiple candidate schemes. Each candidate solution in the candidate solution set is subjected to optimization detection and fitness evaluation, and an optimized solution is output based on the evaluation results.

[0007] Optionally, the step of deconstructing the unstructured text data of the business expansion application work order to generate a set of morpheme units corresponding to the work order includes: Extract the constraint description text from the unstructured text data and convert it into constraint units; Extract conditional descriptive text from the unstructured text data and convert it into conditional units; Extract operational descriptive text from the unstructured text data and transform it into operational units; The resource requirements involved in the constraint unit, the condition unit, and the operation unit are mapped in the business expansion resource catalog to generate resource units; The constraint unit, the condition unit, the operation unit, and the resource unit are aggregated to form the morpheme unit set.

[0008] Optionally, the step of identifying internal configuration errors in the morpheme unit set and external coordination conflicts between the morpheme unit set and external environment parameters to obtain an optimized set includes: In a domain knowledge graph that stores historical relationships between element units, query whether there is configuration degradation between any two element units in the morpheme unit set, and record the existing configuration degradation as internal configuration error. Obtain the current global resource status parameters and parallel work order parameters as external environment parameters; Determine whether the resource units in the set of morpheme units conflict with the global resource status parameters, and record any existing conflicts as external resource conflicts; All identified internal configuration errors and external resource conflicts are aggregated to form the optimization set.

[0009] Optionally, the step of evolutionarily recombining the set of morpheme units and at least one historical solution element retrieved from the historical solution database according to the target optimization direction to generate a candidate solution set containing multiple candidate solutions includes: Based on the set of morpheme units and the retrieved historical scheme elements, an initial scheme population is constructed; Parent individuals are selected based on the matching degree between the initial scheme population and the set of morpheme units; Guided by the target optimization direction and the logical constraints defined in the domain knowledge graph, crossover and mutation operations are performed on the parent individuals to generate child individuals; The offspring individuals are added to the candidate scheme set.

[0010] Optionally, the step of performing optimization detection and fitness evaluation on each candidate solution in the candidate solution set, and outputting an optimized solution based on the evaluation results, includes: For each candidate solution in the candidate solution set, residual conflicts between its internal element units and between it and the external environmental parameters are detected, and a conflict resolution score is calculated. By combining the conflict resolution score, the resource consumption score calculated based on the candidate solutions, and the estimated duration score, a comprehensive fitness score for the candidate solutions is generated. The candidate solution with the highest comprehensive fitness score is selected as the optimized solution.

[0011] Optionally, the step of performing optimization detection and fitness evaluation on each candidate solution in the candidate solution set, and outputting an optimized solution based on the evaluation results, includes: For each candidate solution in the candidate solution set, residual conflicts between its internal element units and between it and the external environmental parameters are detected, and a conflict resolution score is calculated. By combining the conflict resolution score, the resource consumption score calculated based on the candidate solutions, and the estimated duration score, a comprehensive fitness score for the candidate solutions is generated. The candidate solution with the highest comprehensive fitness score is selected as the optimized solution.

[0012] Optionally, after confirming the optimized solution as the final solution, the method further includes: The final solution, its corresponding set of morpheme units, the optimization set, and the simulation execution data are stored as a new knowledge record in the historical solution database. Based on the new associations or conflicts between element units revealed in the simulated execution data, the relationships and weights between element units in the domain knowledge graph are updated.

[0013] Optionally, mapping the optimization set to a contradiction matrix storing the mapping relationship between general engineering parameter pairs and optimizations to obtain at least one target optimization direction includes: Each conflict in the optimization set is resolved into two opposing engineering requirements; Match the opposing engineering requirements to the general engineering parameter pairs in the contradiction matrix; Based on the matched general engineering parameter pairs, the corresponding optimization mapping relationship is found and obtained in the contradiction matrix as the target optimization direction.

[0014] Optionally, the method further includes the step of establishing the domain knowledge graph: Extract historical element units from historical work order data and execution records; Analyze the co-occurrence relationships, temporal relationships, and causal relationships of the historical element units in historical schemes to generate relational data; The domain knowledge graph is constructed based on the historical element units as nodes and the relational data as edges.

[0015] Secondly, an artificial intelligence-based business expansion application work order analysis system is provided, including: The morpheme deconstruction module is used to deconstruct the unstructured text data of the business expansion application work order and generate a set of morpheme units corresponding to the work order. The conflict identification module is used to identify internal configuration errors in the morpheme unit set and external coordination conflicts between the morpheme unit set and external environment parameters, so as to obtain an optimized set. The principle mapping module is used to map the optimization set to a contradiction matrix that stores the general engineering parameter pairs and the optimization mapping relationship, so as to obtain at least one target optimization direction; The scheme evolution module is used to evolve and reorganize the set of morpheme units and at least one historical scheme element retrieved from the historical scheme library according to the target optimization direction, so as to generate a candidate scheme set containing multiple candidate schemes. The scheme evaluation and output module is used to perform optimization detection and fitness evaluation on each candidate scheme in the candidate scheme set, and output the optimized scheme based on the evaluation results.

[0016] Thirdly, an electronic device is provided, comprising: a processor and a memory; the memory is used to store a computer program, which, when executed by the processor, causes the electronic device to perform the artificial intelligence-based business expansion application work order analysis method described in the first aspect.

[0017] In one possible design, the electronic device described in the third aspect may further include a transceiver. This transceiver may be a transceiver circuit or an interface circuit. The transceiver can be used for communication between the electronic device described in the third aspect and other electronic devices.

[0018] In the embodiments of the present invention, the electronic device described in the third aspect may be a terminal, or a chip (system) or other component or assembly disposed in the terminal, or a system containing the terminal.

[0019] Fourthly, a computer-readable storage medium is provided, comprising: a computer program or instructions; when the computer program or instructions are executed on a computer, the computer causes the computer to perform the artificial intelligence-based business expansion application work order analysis method described in the first aspect.

[0020] In summary, the above methods and systems have the following technical effects: This invention proactively identifies conflicts and introduces optimized mapping relationships for guided solution generation, providing solutions for work orders containing inherent contradictions. It uses the resolution of internal and external conflicts as a reference for solution generation and evaluation, avoiding global problems that may result from local optimization. Based on the target optimization direction, it evolves and reorganizes the set of morpheme units and at least one historical solution element retrieved from the historical solution database, generating a candidate solution set containing multiple candidate solutions. Guided by the target optimization direction, the selected optimized solution is the optimal solution that achieves comprehensive balance across multiple dimensions, significantly improving the intelligence level, decision-making quality, and resource allocation efficiency of business expansion and installation work order processing. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the AI-based business expansion application work order analysis method provided in this embodiment of the invention. Detailed Implementation

[0022] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0023] In this embodiment of the invention, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In specific implementation, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a correlation between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. Simultaneously, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0024] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be elaborated upon here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In specific implementation, the required indication method can be selected according to specific needs. This embodiment of the invention does not limit the selected indication method; therefore, the indication methods involved in this embodiment of the invention should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0025] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this embodiment of the invention. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0026] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This embodiment of the invention does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or electronic device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or electronic device. The type of memory can be any form of storage medium, and this embodiment of the invention does not limit this.

[0027] In the embodiments of this invention, "protocol" may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol applied to a future AI-based business expansion application work order analysis method system. The embodiments of this invention do not specifically limit this.

[0028] In this embodiment of the invention, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0029] In the description of the embodiments of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this invention, words such as "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0030] The network architecture and business scenarios described in the embodiments of this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0031] Figure 1 This is a flowchart illustrating the method provided in an embodiment of the present invention. The specific process of this AI-based business expansion application work order analysis method is as follows: The unstructured text data of the business expansion application work order is deconstructed to generate a set of morpheme units corresponding to the work order; By identifying internal configuration errors in the morpheme unit set and external coordination conflicts between the morpheme unit set and external environment parameters, an optimized set is obtained. The optimization set is mapped to a contradiction matrix that stores the general engineering parameter pairs and the optimization mapping relationship to obtain at least one target optimization direction; Based on the target optimization direction, the set of morpheme units and at least one historical scheme element retrieved from the historical scheme library are evolved and recombined to generate a candidate scheme set containing multiple candidate schemes. Each candidate solution in the candidate solution set is subjected to optimization detection and fitness evaluation, and an optimized solution is output based on the evaluation results.

[0032] Furthermore, the step of deconstructing the unstructured text data of the business expansion application work order to generate a set of morpheme units corresponding to the work order includes: Extract the constraint description text from the unstructured text data and convert it into constraint units; Extract conditional descriptive text from the unstructured text data and convert it into conditional units; Extract operational descriptive text from the unstructured text data and transform it into operational units; The resource requirements involved in the constraint unit, the condition unit, and the operation unit are mapped in the business expansion resource catalog to generate resource units; The constraint unit, the condition unit, the operation unit, and the resource unit are aggregated to form the morpheme unit set.

[0033] Specifically: receiving unstructured text data for business expansion and installation work orders, such as customer applications and survey reports; First, preprocessing such as stop word removal is performed. Then, a pre-trained sequence labeling model is called to process the text. This model is trained based on historical work order data, and it identifies semantic segments in the text and converts them into structured units in the form of key-value pairs. Constraint Unit: Extracts hard requirements regarding time, budget, and standards (e.g., {Type: "Duration", Value: "<15 days"}). Conditional unit: Extracts descriptions of the objective environment (e.g., {type: "environment", value: "old residential area"}). Operation unit: Extracts the task to be executed (e.g., {type: "task", value: "new transformer installation"}); For resource descriptions in the text, resource units are generated through semantic matching. Specifically, resource feature vectors extracted from the text are calculated. Feature vector of standard entries in the business expansion resource catalog Cosine similarity S: ; When S is greater than a preset threshold (e.g., 0.85), the resource description is mapped to a standardized resource unit (including encoding, specifications, and quantity). The above four types of units are aggregated to form a set of morpheme units corresponding to the work order.

[0034] 3. The AI-based business expansion application work order analysis method according to claim 2, characterized in that, the step of identifying internal configuration errors in the morpheme unit set and external coordination conflicts between the morpheme unit set and external environmental parameters to obtain an optimized set includes: In a domain knowledge graph that stores historical relationships between element units, query whether there is configuration degradation between any two element units in the morpheme unit set, and record the existing configuration degradation as internal configuration error. Obtain the current global resource status parameters and parallel work order parameters as external environment parameters; Determine whether the resource units in the set of morpheme units conflict with the global resource status parameters, and record any existing conflicts as external resource conflicts; All identified internal configuration errors and external resource conflicts are aggregated to form the optimization set.

[0035] Specifically: Conflict identification and optimization set generation: An optimized set is generated by performing dual detection on the morpheme unit set. Internal configuration error identification is performed by querying the historical relationship between any two element units in the morpheme unit set in the domain knowledge graph. If the relationship type defined in the graph is "conflict" and the weight exceeds a preset threshold (such as 0.7), it is recorded as an internal configuration error. External cooperation conflict identification: The system obtains the current global resource status parameters (inventory, scheduling) and parallel work order parameters.

[0036] Calculate resource conflict levels: Cr=max(0,Dr Ar); Where Dr is the demand and Ar is the availability. If Cr>0, it is recorded as an external resource conflict. The spatiotemporal constraints of the work order are compared with the parameters of the parallel work orders to identify mutual exclusion situations (such as occupying the same device at the same time), which are recorded as external configuration errors. All identified errors and conflicts are aggregated into an optimization set.

[0037] Further, the step of evolutionarily recombining the set of morpheme units and at least one historical scheme element retrieved from the historical scheme database according to the target optimization direction to generate a candidate scheme set containing multiple candidate schemes includes: Based on the set of morpheme units and the retrieved historical scheme elements, an initial scheme population is constructed; Parent individuals are selected based on the matching degree between the initial scheme population and the set of morpheme units; Guided by the target optimization direction and the logical constraints defined in the domain knowledge graph, crossover and mutation operations are performed on the parent individuals to generate child individuals; The offspring individuals are added to the candidate scheme set.

[0038] Specifically: The system constructs an initial scheme population based on a set of morpheme units and similar historical scheme elements retrieved from the history database, and selects and calculates the fitness of individuals.

[0039] in, Based on the degree to which constraints and conditions are met, individuals with high fitness are selected as parents. Crossover and mutation: operations are performed on the parents according to the target optimization direction obtained in step S3.

[0040] For example, when the direction is "combination", a cross operation of merging elements is performed; when the direction is "splitting", a mutation operation of decomposing the operation unit is performed. The generated offspring need to pass the logical constraints of the domain knowledge graph, such as resource-operation dependency. Qualified ones are added to the candidate solution set.

[0041] Further, the step of performing optimization detection and fitness evaluation on each candidate solution in the candidate solution set, and outputting an optimized solution based on the evaluation results, includes: For each candidate solution in the candidate solution set, residual conflicts between its internal element units and between it and the external environmental parameters are detected, and a conflict resolution score is calculated. By combining the conflict resolution score, the resource consumption score calculated based on the candidate solutions, and the estimated duration score, a comprehensive fitness score for the candidate solutions is generated. The candidate solution with the highest comprehensive fitness score is selected as the optimized solution.

[0042] Specifically: In the evaluation and output of candidate solutions, a comprehensive fitness score is calculated for each solution in the candidate solution set. : ; in, The score is given for the degree of conflict resolution (an inverse indicator; the fewer the conflicts, the higher the score). Rate resource consumption. For the estimated project duration score; W is the corresponding weight, and Set to the highest level (e.g., 0.6), then select... The best solution is the optimal solution.

[0043] Furthermore, before outputting the optimized solution, the following steps are also included: The optimization scheme is input into a digital twin simulation environment for simulation and verification. The digital twin simulation environment has built-in physical rules and random event models for business expansion projects. In the digital twin simulation environment, the execution process of the optimization scheme is simulated, and it is monitored whether new conflicts arise during the execution process; If the number of new conflicts does not exceed a preset risk threshold, then the optimized solution is confirmed as the final solution.

[0044] Furthermore, after confirming the optimized solution as the final solution, the process also includes: The final solution, its corresponding set of morpheme units, the optimization set, and the simulation execution data are stored as a new knowledge record in the historical solution database. Based on the new associations or conflicts between element units revealed in the simulated execution data, the relationships and weights between element units in the domain knowledge graph are updated.

[0045] Specifically, before outputting, the optimization scheme is input into a digital twin environment with built-in physical rules (such as standard working hours) and random event models (such as weather and failure probability).

[0046] Run multiple rounds of Monte Carlo simulations to monitor for the emergence of new conflicts. Calculate the simulation risk score. ( These are the number of newly occurring delays and resource conflicts, respectively.

[0047] like If the value is below the threshold, it is confirmed as the final solution and archived in the historical solution database. The system updates the relationship weights between elements in the domain knowledge graph based on simulated data. ; Where E is the target weight (1 for a successful solution and -1 for a conflict). To achieve dynamic evolution of the knowledge base, the learning rate is used.

[0048] For example, physical rules also include verifying the maximum actual load current flowing through each cable and transformer planned in the scheme. Is it less than the equipment's rated current carrying capacity? Multiply by the derating factor k (taking into account temperature and installation method): ; If the conditions are not met, the system determines that an "overload" conflict has occurred; Further, mapping the optimization set to a contradiction matrix storing the mapping relationship between general engineering parameter pairs and optimizations to obtain at least one target optimization direction includes: Each conflict in the optimization set is resolved into two opposing engineering requirements; Match the opposing engineering requirements to the general engineering parameter pairs in the contradiction matrix; Based on the matched general engineering parameter pairs, the corresponding optimization mapping relationship is found and obtained in the contradiction matrix as the target optimization direction.

[0049] Specifically: Based on the contradiction matrix, the optimization direction is determined by parsing each conflict in the optimization set into two opposing engineering requirements (e.g., "improving operation speed" and "ensuring reliability"). Using a semantic matching model, these two requirements are mapped to a pair of general engineering parameters in the preset contradiction matrix (e.g., "performance" and "reliability"). According to the mapped parameter pair, the corresponding cross cell is found in the contradiction matrix to obtain the optimization mapping relationship stored therein (e.g., "segmentation principle", "pre-action principle", etc.), which is used as the target optimization direction.

[0050] The method also includes the step of establishing the domain knowledge graph: Extract historical element units from historical work order data and execution records; Analyze the co-occurrence relationships, temporal relationships, and causal relationships of the historical element units in historical schemes to generate relational data; The domain knowledge graph is constructed based on the historical element units as nodes and the relational data as edges.

[0051] This embodiment illustrates the process of establishing the domain knowledge graph required by the above method, extracting historical element units from historical work orders and execution records, and establishing edges in the following manner: Co-occurrence relation, calculating lift: ; like Establish strong correlation edges, temporal relationships, statistical operation unit sequences, and identify high-frequency sequential dependencies (such as 95% X before Y) to establish directed edges.

[0052] Causal logical relationship: Analyze the significant impact of conditional units (independent variables) on the execution result (dependent variable) using a multiple regression model, and establish logical relationship edges.

[0053] In one embodiment, an artificial intelligence-based business expansion application work order analysis system includes: The morpheme deconstruction module is used to deconstruct the unstructured text data of the business expansion application work order and generate a set of morpheme units corresponding to the work order. The conflict identification module is used to identify internal configuration errors in the morpheme unit set and external coordination conflicts between the morpheme unit set and external environment parameters, so as to obtain an optimized set. The principle mapping module is used to map the optimization set to a contradiction matrix that stores the general engineering parameter pairs and the optimization mapping relationship, so as to obtain at least one target optimization direction; The scheme evolution module is used to evolve and reorganize the set of morpheme units and at least one historical scheme element retrieved from the historical scheme library according to the target optimization direction, so as to generate a candidate scheme set containing multiple candidate schemes. The scheme evaluation and output module is used to perform optimization detection and fitness evaluation on each candidate scheme in the candidate scheme set, and output the optimized scheme based on the evaluation results.

[0054] In a preferred embodiment of the present invention, the "contradiction matrix" does not refer to a general TRIZ matrix, but rather to a special mapping table adapted for the power industry: Although the matrix uses 39 general engineering parameters (such as speed, force, temperature, etc.), in the scenario of business expansion and application, the system pre-defines a clear semantic mapping dictionary to convert power business indicators into general parameters. Specific mapping examples are shown in the table below:

[0055] The structure and search example of the contradiction matrix: The contradiction matrix is ​​a... Two-dimensional array , where i represents the improved parameter, j represents the worsened parameter, and the cell content is the invention principle number (1-40).

[0056] Specific application scenario: Suppose the conflict identification module identifies that "in order to shorten the construction period (improvement), the risk of construction quality increases (deteriorates)."

[0057] Mapping process: "Shortening the construction period" is mapped to parameter No. 9 speed, and "construction quality risk" is mapped to parameter No. 27 reliability.

[0058] Search matrix element The recommendation principles are: Principle 1 (segmentation), Principle 10 (pre-processing), and Principle 35 (parameter variation).

[0059] Principle implementation: Based on "Principle 10 Pre-processing", a solution was developed that "the substation foundation is prefabricated in the factory (pre-fabrication), and only assembly is carried out on site", thereby resolving the conflict.

[0060] The electronic device provided in this embodiment of the invention, exemplarily, can be a network device, or a chip (system) or other component or assembly that can be disposed in a network device. The electronic device may include a processor. Optionally, the electronic device may also include a memory and / or a transceiver. The processor is coupled to the memory and transceiver, for example, by means of a communication bus connection.

[0061] The following is a detailed introduction to the various components of the electronic device: In this context, the processor is the control center of the electronic device. It can be a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0062] Alternatively, the processor can perform various functions of the electronic device, such as the methods described above, by running or executing software programs stored in memory and by calling data stored in memory.

[0063] In a specific implementation, as one example, the processor may include one or more CPUs, such as CPU0 and CPU1.

[0064] In a specific implementation, as one example, the electronic device may also include multiple processors. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0065] The memory is used to store the software program that executes the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0066] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processor through the interface circuit of the electronic device; the embodiments of the present invention do not specifically limit this.

[0067] A transceiver is used for communication with other electronic devices. For example, if the electronic device is a terminal, the transceiver can be used to communicate with a network device or with another terminal device. Similarly, if the electronic device is a network device, the transceiver can be used to communicate with a terminal or with another network device.

[0068] Optionally, the transceiver may include a receiver and a transmitter. The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0069] Optionally, the transceiver can be integrated with the processor or exist independently and coupled to the processor through the interface circuit of the electronic device. This embodiment of the invention does not specifically limit this.

[0070] It is understood that the structure of the electronic device in this embodiment does not constitute a limitation on the electronic device. The actual electronic device may include more or fewer components, or combine certain components, or have different component arrangements.

[0071] Furthermore, the technical effects of the electronic device can be referred to the technical effects of the method described in the above method embodiments, and will not be repeated here.

[0072] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0073] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0074] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0075] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0076] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0077] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0078] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0079] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0080] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0081] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0082] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0083] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0084] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for analyzing installation work orders based on artificial intelligence, characterized in that, Includes the following steps: The unstructured text data of the business expansion application work order is deconstructed to generate a set of morpheme units corresponding to the work order; By identifying internal configuration errors in the morpheme unit set and external coordination conflicts between the morpheme unit set and external environment parameters, an optimized set is obtained. The optimization set is mapped to a contradiction matrix that stores the general engineering parameter pairs and the optimization mapping relationship to obtain at least one target optimization direction; Based on the target optimization direction, the set of morpheme units and at least one historical scheme element retrieved from the historical scheme library are evolved and recombined to generate a candidate scheme set containing multiple candidate schemes. Each candidate solution in the candidate solution set is subjected to optimization detection and fitness evaluation, and an optimized solution is output based on the evaluation results.

2. The artificial intelligence-based work order analysis method for business expansion and installation as described in claim 1, characterized in that, The process of deconstructing the unstructured text data of the business expansion application work order to generate a set of morpheme units corresponding to the work order includes: Extract the constraint description text from the unstructured text data and convert it into constraint units; Extract conditional descriptive text from the unstructured text data and convert it into conditional units; Extract operational descriptive text from the unstructured text data and transform it into operational units; The resource requirements involved in the constraint unit, the condition unit, and the operation unit are mapped in the business expansion resource catalog to generate resource units; The constraint unit, the condition unit, the operation unit, and the resource unit are aggregated to form the morpheme unit set.

3. The artificial intelligence-based work order analysis method for business expansion and installation as described in claim 2, characterized in that, The process of identifying internal configuration errors in the morpheme unit set and external coordination conflicts between the morpheme unit set and external environment parameters to obtain an optimized set includes: In a domain knowledge graph that stores historical relationships between element units, query whether there is configuration degradation between any two element units in the morpheme unit set, and record the existing configuration degradation as internal configuration error. Obtain the current global resource status parameters and parallel work order parameters as external environment parameters; Determine whether the resource units in the set of morpheme units conflict with the global resource status parameters, and record any existing conflicts as external resource conflicts; All identified internal configuration errors and external resource conflicts are aggregated to form the optimization set.

4. The artificial intelligence-based work order analysis method for business expansion and installation as described in claim 3, characterized in that, The step of evolving and recombining the set of morpheme units and at least one historical scheme element retrieved from the historical scheme database according to the target optimization direction to generate a candidate scheme set containing multiple candidate schemes includes: Based on the set of morpheme units and the retrieved historical scheme elements, an initial scheme population is constructed; Parent individuals are selected based on the matching degree between the initial scheme population and the set of morpheme units; Guided by the target optimization direction and the logical constraints defined in the domain knowledge graph, crossover and mutation operations are performed on the parent individuals to generate child individuals; The offspring individuals are added to the candidate scheme set.

5. The artificial intelligence-based work order analysis method for business expansion and installation as described in claim 4, characterized in that, The process of optimizing and evaluating the fitness of each candidate solution in the candidate solution set, and outputting an optimized solution based on the evaluation results, includes: For each candidate solution in the candidate solution set, residual conflicts between its internal element units and between it and the external environmental parameters are detected, and a conflict resolution score is calculated. By combining the conflict resolution score, the resource consumption score calculated based on the candidate solutions, and the estimated duration score, a comprehensive fitness score for the candidate solutions is generated. The candidate solution with the highest comprehensive fitness score is selected as the optimized solution.

6. The artificial intelligence-based work order analysis method for business expansion and installation as described in claim 5, characterized in that, Before outputting the optimized solution, the following is also included: The optimization scheme is input into a digital twin simulation environment for simulation and verification. The digital twin simulation environment has built-in physical rules and random event models for business expansion projects. In the digital twin simulation environment, the execution process of the optimization scheme is simulated, and it is monitored whether new conflicts arise during the execution process; If the number of new conflicts does not exceed a preset risk threshold, then the optimized solution is confirmed as the final solution.

7. The artificial intelligence-based work order analysis method for business expansion and installation as described in claim 6, characterized in that, After confirming the optimized solution as the final solution, the following steps are also included: The final solution, its corresponding set of morpheme units, the optimization set, and the simulation execution data are stored as a new knowledge record in the historical solution database. Based on the new associations or conflicts between element units revealed in the simulated execution data, the relationships and weights between element units in the domain knowledge graph are updated.

8. The method for analyzing business expansion and installation work orders based on artificial intelligence according to claim 1, characterized in that, The step of mapping the optimization set to a contradiction matrix storing the general engineering parameter pairs and optimization mapping relationships to obtain at least one target optimization direction includes: Each conflict in the optimization set is resolved into two opposing engineering requirements; Match the opposing engineering requirements to the general engineering parameter pairs in the contradiction matrix; Based on the matched general engineering parameter pairs, the corresponding optimization mapping relationship is found and obtained in the contradiction matrix as the target optimization direction.

9. The artificial intelligence-based work order analysis method for business expansion and installation as described in claim 3, characterized in that, The method also includes the step of establishing the domain knowledge graph: Extract historical element units from historical work order data and execution records; Analyze the co-occurrence relationships, temporal relationships, and causal relationships of the historical element units in historical schemes to generate relational data; The domain knowledge graph is constructed based on the historical element units as nodes and the relational data as edges.

10. An AI-based business expansion and installation work order analysis system, comprising the AI-based business expansion and installation work order analysis method according to any one of claims 1-9, characterized in that, include: The morpheme deconstruction module is used to deconstruct the unstructured text data of the business expansion application work order and generate a set of morpheme units corresponding to the work order. The conflict identification module is used to identify internal configuration errors in the morpheme unit set and external coordination conflicts between the morpheme unit set and external environment parameters, so as to obtain an optimized set. The principle mapping module is used to map the optimization set to a contradiction matrix that stores the general engineering parameter pairs and the optimization mapping relationship, so as to obtain at least one target optimization direction; The scheme evolution module is used to evolve and reorganize the set of morpheme units and at least one historical scheme element retrieved from the historical scheme library according to the target optimization direction, so as to generate a candidate scheme set containing multiple candidate schemes. The scheme evaluation and output module is used to perform optimization detection and fitness evaluation on each candidate scheme in the candidate scheme set, and output the optimized scheme based on the evaluation results.

Images