Transformer substation pipeline layout optimization method and system based on EIM

Abstract

The invention discloses an EIM-based transformer substation pipeline arrangement optimization method and system, and relates to the technical field of electric power engineering, and the method comprises the steps: collecting meteorological data and topographic data in real time through an environment monitoring sensor; transmitting the collected meteorological data and topographic data to an EIM in real time through a LoRa wireless network, carrying out data integration and processing, and generating a real-time environment state index; and analyzing problems in the multi-scene simulation test according to an evaluation result, collecting feedback information, optimizing a transformer substation pipeline arrangement scheme according to the feedback information, and generating a report for optimizing the transformer substation pipeline arrangement scheme. According to the invention, through scientific environmental data processing and an intelligent optimization method, the whole process optimization of transformer substation pipeline arrangement from design to implementation is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power engineering, and particularly to an optimization method and system for substation pipeline layout based on EIM. Background Art

[0002] With the continuous development of the power system, the substation, as an important hub for power transmission and distribution, the rationality of its pipeline layout directly affects the safety and efficiency of the power system. In recent years, with the development of smart grid and Internet of Things technologies, the design and management of substations have gradually transformed towards intelligence and automation. The progress of environmental monitoring technology has made it possible to collect meteorological and topographic data in real time, which provides rich basic data for the pipeline layout of substations. For example, through wireless sensor network (WSN) and LoRa technology, remote monitoring and data transmission of environmental status can be achieved, thereby improving the scientificity and accuracy of pipeline layout decisions. In addition, the introduction of machine learning algorithms has also made the analysis and optimization of complex data a reality, enhancing the adaptability to environmental changes.

[0003] However, there are still some deficiencies in the optimization of substation pipeline layout in the prior art. First of all, many existing solutions often rely on static environmental data and lack the ability to respond to dynamic environmental changes in real time. This lag makes it difficult for the pipeline layout plan to adapt to rapidly changing environmental conditions, which may lead to potential safety hazards and resource waste. Secondly, the prior art mainly focuses on a single data source in data processing and analysis and lacks comprehensive consideration of multiple environmental factors. This lack of comprehensive analysis method makes the final pipeline layout plan unable to fully reflect the complexity of the actual environment, affecting its effectiveness and operability. Therefore, in view of these deficiencies, the present invention can effectively improve the scientificity and flexibility of substation pipeline layout through real-time data collection and dynamic environmental adaptation mechanism. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an optimization method for substation pipeline layout based on EIM, which solves the problems of insufficient dynamic response and incomplete data analysis in the prior art.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides an optimization method for substation pipeline layout based on EIM, which includes: collecting meteorological data and terrain data in real time through environmental monitoring sensors; transmitting the collected meteorological data and terrain data to EIM in real time through a LoRa wireless network for data integration and processing to generate real-time environmental status indicators; based on the real-time environmental status indicators, using a dynamic environment adaptation mechanism to analyze the collected data, predict future environmental changes, and evaluate the impact on the substation pipeline layout according to the prediction results to generate an environmental status analysis report; according to the environmental status analysis report, identifying the optimal substation pipeline layout plan through a machine learning algorithm; subjecting the optimal substation pipeline layout plan to multi-scenario simulation tests through virtual simulation, and evaluating the performance of the new substation pipeline layout plan under different environmental conditions according to the test results; analyzing the problems in the multi-scenario simulation tests according to the evaluation results, collecting feedback information, and optimizing the substation pipeline layout plan according to the feedback information to generate an optimized substation pipeline layout plan report.

[0007] As a preferred embodiment of the optimization method for substation pipeline layout based on EIM according to the present invention, wherein: the meteorological data includes rainfall, wind speed, and temperature; The terrain data includes soil humidity and ground changes.

[0008] As a preferred embodiment of the optimization method for substation pipeline layout based on EIM according to the present invention, wherein: the specific steps of transmitting the collected meteorological data and terrain data to EIM in real time through a LoRa wireless network for data integration and processing to generate real-time environmental status indicators are as follows: Receiving data from the LoRa network in EIM, formatting the received data, integrating the meteorological data and terrain data to generate a comprehensive data set; Using a data mining algorithm to analyze the integrated comprehensive data set to generate real-time environmental status indicators, and the expression is: ; Wherein, represents the comprehensive environmental status indicator, represents the weight coefficient of the th feature, represents the standardized value of the th sample on the th feature, represents the number of effective features, represents the th feature in the data set, represents the th sample in the data set.

[0009] As a preferred embodiment of the substation pipeline layout optimization method based on EIM of the present invention, wherein: based on real-time environmental status indicators, a dynamic environmental adaptation mechanism is used to analyze the collected data, predict future environmental changes, and evaluate the impact on the substation pipeline layout according to the prediction results to generate an environmental status analysis report. The specific steps are as follows: Based on real-time environmental status indicators, use a dynamic environmental adaptation mechanism to analyze the collected data, check the consistency of the data, and match the timestamps of meteorological data and terrain data; According to the analyzed data, use the random forest algorithm to predict environmental changes and generate the predicted environmental change results. The expression is: ; Wherein, represents the average value of the predicted environmental changes, represents the number of trees in the random forest, The performance index of the th sample, represents the comprehensive data set; Based on the average value of the predicted environmental changes, use the Monte Carlo simulation method to simulate the performance of the substation pipeline layout under different environmental conditions and generate the average value of the simulated pipeline layout performance. The expression is: ; Wherein, represents the average value of the simulated pipeline layout performance, represents the number of simulated samples, represents the th predicted value of the pipeline performance for the simulated environmental change, represents the th average value of the simulated predicted environmental changes; Analyze the impact of different environmental changes on the substation pipeline layout through the average value of the simulated pipeline layout performance and generate an environmental status analysis report.

[0010] As a preferred embodiment of the substation pipeline layout optimization method based on EIM of the present invention, wherein: according to the environmental status analysis report, use a machine learning algorithm to identify the optimal substation pipeline layout scheme. The specific steps are as follows: According to the environmental status analysis report, use a machine learning algorithm to identify the optimal substation pipeline layout scheme. The expression is: ; Wherein, represents the optimal substation pipeline layout scheme, represents the set of candidate schemes, represents the candidate scheme Prediction performance indicators in the environmental status analysis report underneath.

[0011] As a preferred solution of the substation pipeline layout optimization method based on EIM according to the present invention, wherein: the best substation pipeline layout scheme is subjected to multi-scenario simulation tests through virtual simulation, and according to the test results, the performance of the new substation pipeline layout scheme under different environmental conditions is evaluated. The specific steps are as follows, By inputting the data of the environmental status analysis report, the best substation pipeline layout scheme is imported into virtual simulation, and multiple simulation scenarios are designed according to different environmental conditions to evaluate the performance score of the new substation pipeline layout scheme under different environmental conditions. The expression is: ; Wherein, represents the overall performance score of the best substation pipeline layout scheme ; represents the number of designed virtual simulation scenarios, represents the th environmental status underneath the best pipeline layout scheme performance score, represents the th environmental complexity under the environmental status, represents the th importance weight of the scenario; The value range of is which represents the extremely poor performance of the pipeline layout under all environmental conditions, which represents the excellent performance of the pipeline layout under all environmental conditions.

[0012] As a preferred solution of the substation pipeline layout optimization method based on EIM according to the present invention, wherein: according to the evaluation results, the problems in the multi-scenario simulation tests are analyzed, feedback information is collected, and according to the feedback information, the substation pipeline layout scheme is optimized and an optimized substation pipeline layout scheme report is generated. The specific steps are as follows, According to the evaluation results, the problems in the multi-scenario simulation tests are analyzed, the failure rate, operation efficiency and temperature change recorded in the multi-scenario simulation tests are collected, and the performance under different scenarios is compared to identify the substation pipeline layout scheme with problems; According to the substation pipeline layout scheme with problems, feedback information is collected, and the substation pipeline layout is optimized through the feedback information, and finally an optimized substation pipeline layout scheme report is generated.

[0013] Second aspect, the present invention provides a substation pipeline layout optimization system based on EIM, including a data acquisition module, a status index generation module, an analysis report generation module, an optimal pipeline layout plan module, a plan evaluation module, and a report generation module; the data acquisition module is used to collect meteorological data and terrain data in real time through environmental monitoring sensors; the status index generation module is used to transmit the collected meteorological data and terrain data to EIM in real time through the LoRa wireless network for data integration and processing to generate real-time environmental status indexes; the analysis report generation module is used to analyze the collected data based on the real-time environmental status indexes by using a dynamic environment adaptation mechanism, predict future environmental changes, and evaluate the impact on the substation pipeline layout according to the prediction results to generate an environmental status analysis report; the optimal pipeline layout plan module is used to identify the optimal substation pipeline layout plan through a machine learning algorithm according to the environmental status analysis report; the plan evaluation module is used to perform multi-scenario simulation tests on the optimal substation pipeline layout plan through virtual simulation, and evaluate the performance of the new substation pipeline layout plan under different environmental conditions according to the test results; the plan report generation module is used to analyze the problems in the multi-scenario simulation tests according to the evaluation results, collect feedback information, optimize the substation pipeline layout plan according to the feedback information, and generate an optimized substation pipeline layout plan report.

[0014] Third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the substation pipeline layout optimization method based on EIM described in the first aspect of the present invention is implemented.

[0015] Fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the substation pipeline layout optimization method based on EIM described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: through scientific environmental data processing and intelligent optimization methods, the full-process optimization of the substation pipeline layout from design to implementation is realized. Among them, "prediction of environmental changes based on the dynamic environment adaptation mechanism" and "multi-scenario simulation test based on virtual simulation" are two of the most creative steps, starting from two key links of environmental data analysis and plan verification respectively, ensuring the scientificity, adaptability, and robustness of the plan. These technical means effectively solve the problems of insufficient environmental adaptability and lack of design verification in traditional pipeline layout methods, and provide reliable technical support for the construction and operation of substations. Description of the Drawings

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of the substation pipeline layout optimization method based on EIM in Embodiment 1.

[0019] Figure 2 It is a schematic diagram of the substation pipeline layout optimization system based on EIM in Embodiment 1. Specific Embodiments

[0020] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification.

[0021] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0022] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments.

[0023] Embodiment 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides a substation pipeline layout optimization method based on EIM, including the following steps: S1. Real-time collect meteorological data and terrain data through environmental monitoring sensors; The meteorological data includes rainfall, wind speed, and temperature; The terrain data includes soil humidity and ground changes.

[0024] S2. Transmit the collected meteorological data and terrain data to EIM in real time through the LoRa wireless network for data integration and processing to generate real-time environmental status indicators; Receive data from the LoRa network in EIM, format the received data, integrate the meteorological data and terrain data to generate a comprehensive data set; It should be noted that through formatting, EIM can unify data into a standard format, including timestamps, numerical ranges, units, etc. The comprehensive dataset includes rainfall, wind speed, temperature, soil humidity, and ground changes.

[0025] Use data mining algorithms to analyze the integrated comprehensive dataset to generate real-time environmental status indicators, with the expression: ; where represents the comprehensive environmental status indicator, represents the weight coefficient of the th feature, represents the standardized value of the th sample on the th feature, represents the number of effective features, represents the th feature in the dataset, represents the th sample in the dataset; It should be noted that is a quantified indicator used to reflect the comprehensive situation of the current environment. The influence degrees of different environmental features on the overall environmental status may vary, so different weights are assigned to them to reflect the importance of the features. In the calculation, standardized data is used to eliminate the influence of dimensions between different features, enabling them to be compared on the same basis. refers to the total number of features selected in the comprehensive dataset for calculating the comprehensive environmental status indicator.

[0026] S3. Based on the real-time environmental status indicators, use the dynamic environment adaptation mechanism to analyze the collected data, predict future environmental changes, and evaluate the impact on the substation pipeline layout according to the prediction results to generate an environmental status analysis report; Based on the real-time environmental status indicators, use the dynamic environment adaptation mechanism to analyze the collected data, check the data consistency, and match the timestamps of meteorological data and terrain data; It should be noted that through the dynamic environment adaptation mechanism to check data consistency and match timestamps, EIM can more accurately evaluate the impact of the environment on the substation pipeline layout. This process not only improves the reliability of data analysis but also provides a solid foundation for subsequent environmental change prediction and pipeline optimization, ultimately enhancing the safety and operating efficiency of the substation.

[0027] According to the analyzed data, use the random forest algorithm to conduct environmental change prediction and generate the predicted environmental change results, with the expression: ; Among them, represents the predicted average value of environmental changes, represents the number of trees in the random forest, the performance index of the nth sample, It should be noted that is the quantitative prediction result of the future environmental state. The random forest is an ensemble learning method that improves the accuracy and robustness of prediction by constructing multiple decision trees. Each sample is predicted independently, reflecting its judgment of environmental changes. contains processed and integrated meteorological data and terrain data as the input of the model.

[0028] Based on the predicted average value of environmental changes, using the Monte Carlo simulation method, simulate the performance of the substation pipeline layout under different environmental conditions, and generate the average value of the simulated pipeline layout performance. The expression is: ; Among them, represents the average value of the simulated pipeline layout performance, represents the number of simulated samples, represents the pipeline performance prediction value of the nth simulated environmental change, represents the average value of the nth simulated predicted environmental change; It should be noted that reflects the overall performance of the pipeline under different environmental conditions. represents the number of different environmental conditions generated during the Monte Carlo simulation. reflects the performance of the pipeline layout under specific environmental conditions.

[0029] Analyze the impact of different environmental changes on the substation pipeline layout through the average value of the pipeline layout performance obtained by simulation, and generate an environmental state analysis report; It should be noted that analyzing the impact of different environmental changes on the substation pipeline layout through the average value of the pipeline layout performance obtained by simulation and generating an environmental state analysis report are important steps to achieve scientific decision-making and optimize pipeline design.

[0030] S4. According to the environmental state analysis report, identify the optimal substation pipeline layout scheme through machine learning algorithms; According to the environmental status analysis report, the best substation pipeline layout plan is identified through machine learning algorithms, and the expression is: ; where, represents the best substation pipeline layout plan, represents the set of candidate plans, represents the candidate plan under the environmental status analysis report for the predicted performance metrics; It should be noted that is the plan with the best performance among all candidate plans. contains all possible pipeline layout plans, and these plans may be selected based on different design principles, materials, or layouts. This metric can be calculated based on a machine learning model and reflects the performance of the plan in terms of safety, stability, and efficiency under specific environmental conditions.

[0031] S5. Conduct multi-scenario simulation tests on the best substation pipeline layout plan through virtual simulation. According to the test results, evaluate the performance of the new substation pipeline layout plan under different environmental conditions; By inputting the data of the environmental status analysis report for the best substation pipeline layout plan, import it into virtual simulation. Design multiple simulation scenarios according to different environmental conditions, and evaluate the performance score of the new substation pipeline layout plan under different environmental conditions. The expression is: ; where, represents the overall performance score of the best substation pipeline layout plan , represents the number of designed virtual simulation scenarios, represents the th environmental status for the performance score of the best pipeline layout plan represents the th environmental complexity under the th scenario, represents the importance weight of the th scenario; It should be noted that is a comprehensive evaluation of the performance of this plan under different environmental conditions.

[0032] The value range is , indicating that the pipeline layout performs extremely poorly under all environmental conditions, indicating that the pipeline layout performs excellently under all environmental conditions; It should be noted that reflects the insufficient effectiveness and adaptability of the solution under the most adverse environment. Indicates that the solution can maintain good performance under various environmental conditions.

[0033] S6. According to the evaluation results, analyze the problems in the multi-scenario simulation test, collect feedback information, optimize the substation pipeline layout plan based on the feedback information, and generate an optimized substation pipeline layout plan report; According to the evaluation results, analyze the problems in the multi-scenario simulation test, collect the failure rate, operation efficiency, and temperature changes recorded in the multi-scenario simulation test, compare the performances under different scenarios, and identify the substation pipeline layout plans with problems; It should be noted that calculate the failure rate under each scenario and identify the pipeline layout plans with a significantly higher failure rate than other scenarios. Compare the operation efficiencies under each scenario and identify the pipeline layout plans with poor performances. Monitor the temperature changes of the pipelines under different scenarios and identify the plans with abnormally high or low temperatures.

[0034] Based on the substation pipeline layout plans with problems, collect feedback information, optimize the design of the substation pipeline layout through the feedback information, and finally generate an optimized substation pipeline layout plan report; It should be noted that combine the feedback information with the previous engineering data to identify the specific problems leading to poor performance, such as unreasonable design, improper material selection, or insufficient environmental adaptability. Analyze the root causes of the problems, which may involve multiple aspects such as design defects, construction quality, and material properties.

[0035] This embodiment also provides an EIM-based optimization system for substation pipeline layout, including: a data acquisition module, a status indicator generation module, an analysis report generation module, an optimal pipeline layout plan module, a plan evaluation module, and a report generation module; the data acquisition module is used to collect meteorological data and terrain data in real time through environmental monitoring sensors; the status indicator generation module is used to transmit the collected meteorological data and terrain data to the EIM in real time through the LoRa wireless network for data integration and processing to generate real-time environmental status indicators; the analysis report generation module is used to analyze the collected data based on the real-time environmental status indicators by using a dynamic environment adaptation mechanism, predict future environmental changes, and evaluate the impact on the substation pipeline layout according to the prediction results to generate an environmental status analysis report; the optimal pipeline layout plan module is used to identify the optimal substation pipeline layout plan through a machine learning algorithm according to the environmental status analysis report; the plan evaluation module is used to perform multi-scenario simulation tests on the optimal substation pipeline layout plan through virtual simulation, and evaluate the performance of the new substation pipeline layout plan under different environmental conditions according to the test results; the plan report generation module is used to analyze the problems in the multi-scenario simulation tests according to the evaluation results, collect feedback information, optimize the substation pipeline layout plan according to the feedback information, and generate an optimized substation pipeline layout plan report.

[0036] This embodiment also provides a computer device applicable to the case of the EIM-based optimization method for substation pipeline layout, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the EIM-based optimization method for substation pipeline layout proposed in the above embodiment.

[0037] This computer device can be a terminal, and this computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of this computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, a touchpad, or a mouse, etc.

[0038] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for optimizing the pipeline layout of a substation based on EIM as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0039] In summary, through scientific environmental data processing and intelligent optimization methods, the present invention realizes the full-process optimization of the substation pipeline layout from design to implementation. Among them, "environmental change prediction based on a dynamic environment adaptation mechanism" and "multi-scenario simulation test based on virtual simulation" are two of the most creative steps, starting from the two key links of environmental data analysis and solution verification respectively, ensuring the scientific nature, adaptability and robustness of the solution. These technical means effectively solve the problems of insufficient environmental adaptability and lack of design verification in traditional pipeline layout methods, and provide reliable technical support for the construction and operation of substations.

[0040] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A substation pipeline layout optimization method based on EIM, characterized in that: include, Collect meteorological and terrain data in real time through environmental monitoring sensors; The collected meteorological and terrain data are transmitted to EIM in real time via the LoRa wireless network for data integration and processing to generate real-time environmental status indicators; Based on real-time environmental status indicators, the collected data is analyzed using a dynamic environmental adaptation mechanism to predict future environmental changes, and the impact on substation pipeline layout is evaluated based on the prediction results to generate an environmental status analysis report; Based on the environmental status analysis report, the optimal substation pipeline layout plan is identified through machine learning algorithms; The optimal substation pipeline layout scheme is tested in multiple scenarios through virtual simulation. Based on the test results, the performance of the new substation pipeline layout scheme under different environmental conditions is evaluated; According to the evaluation results, the problems in the multi-scenario simulation test are analyzed, feedback information is collected, and based on the feedback information, the substation pipeline layout plan is optimized, and an optimized substation pipeline layout plan report is generated.

2. The EIM-based substation pipeline layout optimization method according to claim 1, characterized in that: The meteorological data include rainfall, wind speed and temperature; The terrain data includes soil moisture and ground changes.

3. The EIM-based substation pipeline layout optimization method according to claim 2, characterized in that: The collected meteorological data and terrain data are transmitted to EIM in real time through the LoRa wireless network for data integration and processing to generate real-time environmental status indicators. The specific steps are as follows: Receive data from the LoRa network in the EIM, format the received data, integrate meteorological data and terrain data to generate a comprehensive data set; The integrated comprehensive data set is analyzed using data mining algorithms to generate real-time environmental status indicators.

4. The EIM-based substation pipeline layout optimization method according to claim 3, characterized in that: Based on the real-time environmental status indicators, the collected data is analyzed using the dynamic environmental adaptation mechanism to predict future environmental changes, and the impact on the substation pipeline layout is evaluated based on the prediction results to generate an environmental status analysis report. The specific steps are as follows: Based on real-time environmental status indicators, the collected data is analyzed using a dynamic environmental adaptation mechanism to check data consistency and match the timestamps of meteorological data with terrain data; Based on the analyzed data, the random forest algorithm is used to predict environmental changes and generate predicted environmental change results; Based on the predicted average value of environmental changes, the Monte Carlo simulation method is used to simulate the performance of substation pipeline layout under different environmental conditions and generate the average value of the simulated pipeline layout performance; The influence of different environmental changes on the substation pipeline layout is analyzed by the average value of the pipeline layout performance obtained through simulation, and an environmental status analysis report is generated.

5. The EIM-based substation pipeline layout optimization method according to claim 4, characterized in that: According to the environmental status analysis report, the optimal substation pipeline layout plan is identified through a machine learning algorithm. The specific steps are as follows: Based on the environmental status analysis report, the optimal substation pipeline layout plan is identified through machine learning algorithms.

6. The EIM-based substation pipeline layout optimization method according to claim 5, characterized in that: The optimal substation pipeline layout scheme is tested through multi-scenario simulation through virtual simulation. According to the test results, the performance of the new substation pipeline layout scheme under different environmental conditions is evaluated. The specific steps are as follows: The optimal substation pipeline layout plan is imported into virtual simulation by inputting data from the environmental status analysis report, and multiple simulation scenarios are designed according to different environmental conditions to evaluate the performance score of the new substation pipeline layout plan under different environmental conditions; The value range is , Indicates that the pipeline layout performs very poorly under all environmental conditions. This means that the pipeline arrangement performs well in all environmental conditions.

7. The EIM-based substation pipeline layout optimization method according to claim 6, characterized in that: According to the evaluation results, the problems in the multi-scenario simulation test are analyzed, feedback information is collected, and the substation pipeline layout plan is optimized according to the feedback information, and an optimized substation pipeline layout plan report is generated. The specific steps are as follows: According to the evaluation results, analyze the problems in the multi-scenario simulation test, collect the failure rate, operation efficiency and temperature changes recorded in the multi-scenario simulation test, and compare the performance in different scenarios to identify the substation pipeline layout plans with problems; According to the problematic substation pipeline layout plan, feedback information is collected, the substation pipeline layout is optimized and designed through the feedback information, and finally an optimized substation pipeline layout plan report is generated.

8. A substation pipeline layout optimization system based on EIM, based on the substation pipeline layout optimization method based on EIM according to any one of claims 1 to 7, characterized in that: Including data acquisition module, status indicator generation module, analysis report generation module, optimal pipeline layout solution module, solution evaluation module and report generation module; Data acquisition module, used to collect meteorological data and terrain data in real time through environmental monitoring sensors; The status indicator generation module is used to transmit the collected meteorological data and terrain data to the EIM in real time through the LoRa wireless network, integrate and process the data, and generate real-time environmental status indicators; The analysis report generation module is used to analyze the collected data based on the real-time environmental status indicators and the dynamic environmental adaptation mechanism, predict future environmental changes, and evaluate the impact on the substation pipeline layout based on the prediction results to generate an environmental status analysis report; The optimal pipeline layout module is used to identify the optimal substation pipeline layout plan based on the environmental status analysis report through machine learning algorithms; The scheme evaluation module is used to conduct multi-scenario simulation tests on the optimal substation pipeline layout scheme through virtual simulation, and evaluate the performance of the new substation pipeline layout scheme under different environmental conditions based on the test results; The solution report generation module is used to analyze the problems in the multi-scenario simulation test according to the evaluation results, collect feedback information, optimize the substation pipeline layout plan according to the feedback information, and generate an optimized substation pipeline layout plan report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the substation pipeline layout optimization method based on EIM according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the substation pipeline layout optimization method based on EIM according to any one of claims 1 to 7 are implemented.

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