Grid configuration and positioning synchronization method for multi-dimensional power grid service packages

Abstract

The present invention discloses a method for grid configuration and positioning synchronization of a multi-dimensional power grid service package, and relates to the field of data processing technology. The method includes: obtaining multiple power grid service packages and collecting multi-modal information on site, performing dynamic parameter configuration after determining the operation and maintenance scenario, and establishing multiple grid configuration spaces; constructing a configuration evaluation module based on multi-dimensional evaluation factors, and introducing a reproduction constraint function, and optimizing the configuration space in combination with the evaluation module; performing precise parameter configuration of the power grid service package based on the optimization result, and realizing positioning synchronization. The present invention solves the technical problems of the lack of flexibility, insufficient equipment coordination, and slow response of the traditional power grid service package configuration method in the prior art when facing complex operation and maintenance scenarios, and achieves the technical effect of improving the power grid operation and maintenance efficiency, resource utilization, and emergency response capabilities through the optimization of the power grid service package configuration based on multi-dimensional evaluation factors.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method for grid configuration and positioning synchronization of a multi-dimensional power grid service package. Background Art

[0002] As the complexity of power systems increases, grid operation and maintenance faces more and more challenges, including equipment diversity, complexity of operation and maintenance scenarios, and efficient emergency response. Traditional grid operation and maintenance methods usually rely on manual scheduling and static configuration, which is difficult to cope with dynamically changing operation and maintenance needs. Especially when the grid encounters natural disasters, the existing manual configuration method cannot adapt to new environmental changes in real time, resulting in low resource utilization efficiency, task processing delays and other problems. Summary of the invention

[0003] The present application provides a multi-dimensional grid service package grid configuration and positioning synchronization method, which is used to solve the technical problems of the traditional grid service package configuration method in the prior art lacking flexibility, insufficient configuration accuracy and slow response when facing complex operation and maintenance scenarios.

[0004] The present application provides a method for grid configuration and positioning synchronization of multi-dimensional power grid service packages, the method comprising: obtaining multiple power grid service packages at a power grid operation and maintenance site, wherein each power grid service package includes multiple devices; collecting multimodal information of the power grid operation and maintenance site to determine a power grid operation and maintenance scenario; performing parameter configuration on the multiple power grid service packages according to the power grid operation and maintenance scenario to establish multiple grid configuration spaces; building a power grid service package configuration evaluation module according to a multi-dimensional evaluation factor of the service package configuration; introducing a power grid service package configuration reproduction constraint function, and performing reproduction competition optimization on the multiple grid configuration spaces in combination with the power grid service package configuration evaluation module to determine multiple grid configuration optimization results; performing parameter configuration on the multiple power grid service packages based on the multiple grid configuration optimization results, and performing positioning synchronization on the multiple power grid service packages after the parameter configuration is completed.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] The multi-dimensional grid configuration and positioning synchronization method of the power grid service package provided in the present application relates to the field of data processing technology. By collecting multi-modal information at the power grid operation and maintenance site, determining the operation and maintenance scenario and configuring parameters for multiple power grid service packages, a grid configuration space is established. By introducing multi-dimensional evaluation factors and configuration propagation constraint functions, and combining the configuration evaluation module for competitive optimization, the parameter configuration of the power grid service package is optimized, and finally the intelligent configuration and positioning synchronization of the power grid equipment is realized, which solves the technical problems that the traditional power grid service package configuration method in the prior art lacks flexibility and cannot quickly adjust the configuration according to the actual operation and maintenance scenario, resulting in low configuration efficiency, insufficient equipment coordination, and inability to quickly adapt to dynamic environmental changes. It achieves the technical effect of improving the power grid operation and maintenance efficiency, resource utilization, and emergency response capabilities through the optimization of the power grid service package configuration based on multi-dimensional evaluation factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A flow chart of a method for grid configuration and positioning synchronization of a multi-dimensional power grid service package provided in an embodiment of the present application.

[0009] Figure 2 A schematic diagram of the process of building a power grid service package configuration evaluation module in the multi-dimensional power grid service package grid configuration and positioning synchronization method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The present application provides a multi-dimensional grid service package grid configuration and positioning synchronization method, which is used to solve the technical problems of the traditional grid service package configuration method in the prior art lacking flexibility, insufficient equipment coordination and slow response when facing complex operation and maintenance scenarios.

[0011] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0012] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0013] Embodiment 1, as Figure 1 As shown, the present application provides a method for grid configuration and positioning synchronization of a multi-dimensional power grid service package, the method comprising:

[0014] P10: Obtain multiple power grid service packages at the power grid operation and maintenance site, where each power grid service package includes multiple devices.

[0015] Optionally, at the power grid operation and maintenance site, you first need to obtain multiple power grid service packages, that is, extract service packages from various operation and maintenance resources deployed on site. These service packages are the basic units used to perform various tasks during the power grid operation and maintenance process. Each power grid service package consists of multiple devices, covering communication, monitoring, dispatching, emergency response and other functional equipment.

[0016] Specifically, the first step is to conduct on-site identification and collect equipment information. Operation and maintenance personnel identify various types of equipment involved in the current power grid operation and maintenance through on-site scanning or automated equipment identification. For example, communication equipment (such as satellite stations and wireless broadband equipment) is used to ensure information transmission, safety monitoring equipment (such as gas detectors and safety helmets) is used to ensure the safety of operators, and dispatching and monitoring equipment is used to manage and direct emergency operations.

[0017] Furthermore, equipment classification and integration are carried out. These equipment are classified and organized according to function and type to ensure that the equipment in each service package has clear functions and coordination. Each service package includes not only communication equipment, but also dispatching systems, control terminals, emergency power supplies, etc. The diversity of equipment types and functions ensures that the service package can fully support the operation and maintenance needs of the power grid in different scenarios.

[0018] Next, load and configure the equipment data. Load detailed basic information for the equipment in each power grid service package, and prepare the interaction information between the equipment, such as equipment status, location, performance indicators, etc. This step provides important data support for subsequent parameter configuration and grid configuration. The equipment in each power grid service package cooperates with each other to perform the specified operation and maintenance tasks together.

[0019] Finally, configure the operating environment of the service package. After determining the power grid operation and maintenance scenario, evaluate the resource requirements (such as bandwidth, processing power, power supply, etc.) and environmental requirements (such as network coverage, equipment reliability, etc.) required for each power grid service package, so as to provide a reference for the subsequent operation and maintenance configuration and resource allocation. The key to this step is to be able to clearly identify and integrate different devices and configure reasonable parameters for the devices in each service package to ensure that the various devices can work together in the subsequent scheduling and execution process, thereby improving the efficiency of power grid operation and maintenance and emergency response capabilities.

[0020] P20: Collect multimodal information of the power grid operation and maintenance site to determine the power grid operation and maintenance scenario.

[0021] Optionally, in order to effectively formulate subsequent configuration plans, it is necessary to first use multimodal information collection technology to fully grasp the actual situation on site, and then accurately determine the grid operation and maintenance scenario. The grid operation and maintenance scenario includes but is not limited to comprehensive data on the on-site environmental conditions, power load, equipment operating status, personnel health, etc.

[0022] Among them, multimodal information collection refers to obtaining different types of data sources through a variety of sensing technologies and equipment. These data can come from multiple dimensions, such as vision, sound, temperature, gas, location, etc. This information collection method can fully and accurately reflect the overall picture of the power grid operation and maintenance site, and help determine the specific operation and maintenance needs of the site.

[0023] By collecting this multimodal information, we can fully and three-dimensionally grasp the operation and maintenance scenarios on site. The operation and maintenance scenario refers to the overall working status formed by combining factors such as the on-site environment, equipment status, personnel distribution, and task progress. Different operation and maintenance scenarios may involve different work priorities. For example, in the case of equipment failure, the focus is on diagnosing the failure and performing emergency repairs; in the case of high load, the focus is on adjusting power distribution and optimizing loads; and in severe weather conditions, the focus is on ensuring personnel safety and equipment protection.

[0024] In addition, the determination of scenarios is the basis for subsequent service package configuration and operation and maintenance decisions. For example, if the on-site scenario indicates that there is an equipment overload, the system can automatically adjust the configuration of the load monitoring equipment in the power grid service package, or strengthen the use of meteorological monitoring equipment in scenarios with severe weather. This scenario analysis based on multimodal information can ensure that power grid operation and maintenance is more accurate and flexible, and can make the best response in different actual environments.

[0025] In summary, by collecting multimodal information at the power grid operation and maintenance site, we can obtain the on-site operating conditions in a comprehensive and real-time manner, ensure accurate judgment of the operation and maintenance scenario, and thus lay a solid foundation for the subsequent configuration, scheduling and optimization of power grid service packages.

[0026] P30: Parameter configuration is performed on the plurality of power grid service packages according to the power grid operation and maintenance scenario, and a plurality of grid configuration spaces are established.

[0027] Furthermore, step P30 of the embodiment of the present application further includes:

[0028] P31: Traverse the multiple power grid service packages to extract the first power grid service package; P32: Configure parameters of each device in the first power grid service package according to the power grid operation and maintenance scenario, and obtain multiple device configuration decision domains; P33: Combine parameter configuration decisions of each device according to the multiple device configuration decision domains, and obtain multiple grid configuration strategies corresponding to the first power grid service package; P34: Build a first grid configuration space according to the multiple grid configuration strategies, and add the first grid configuration space to the multiple grid configuration spaces.

[0029] It should be understood that in power grid operation and maintenance, different operation and maintenance scenarios and task requirements will affect the parameter configuration of the power grid service package. Therefore, according to different situations at the power grid operation and maintenance site, it is necessary to configure parameters for multiple power grid service packages and generate multiple grid configuration spaces based on these configurations to achieve optimal resource scheduling and efficient execution of tasks.

[0030] First, the system or operator needs to traverse multiple grid service packages on site and extract the first service package for processing. Each grid service package contains multiple devices, usually corresponding to specific grid tasks or application scenarios. Through traversal and extraction, specific parameters can be configured for each service package one by one, laying the foundation for subsequent configuration optimization.

[0031] Next, according to the current power grid operation and maintenance scenario (such as emergency repair, regular inspection or load regulation), the parameters of each device in the extracted first power grid service package are configured. For example, the bandwidth configuration of communication equipment, the task allocation of the dispatching system, the sensor sensitivity of the monitoring equipment, etc. need to be adjusted accordingly according to actual needs. Through parameter configuration, the configuration decision domain of each device is obtained, that is, the feasible configuration range and optimal working state of the device.

[0032] Next, after the configuration decision domain of each device is determined, a comprehensive decision is made based on the interaction between devices. The combination of device configuration decision domains will produce multiple possible configuration schemes, called grid configuration strategies. These strategies take into account the performance, resource consumption and task requirements of each device, ensuring that each grid service package can make the best resource configuration according to the needs of different scenarios.

[0033] Furthermore, the obtained multiple grid configuration strategies are mapped to specific configuration spaces to form a grid configuration space. This space represents the possible working state and performance of the power grid service package under different configuration schemes. By adding the first grid configuration space to the overall multiple configuration spaces, a multi-dimensional grid configuration network is constructed. Each configuration space represents the performance evaluation and scheduling scheme under different configurations of the service package, providing a basis for subsequent optimization.

[0034] Through these steps, the system can flexibly adjust the configuration of service packages based on different operation and maintenance scenarios during the power grid operation and maintenance process, forming multiple grid configuration spaces. These configuration spaces can fully consider resource optimization, task requirements and equipment characteristics, and improve the efficiency and reliability of power grid operation and maintenance.

[0035] Furthermore, step P32 of the embodiment of the present application also includes:

[0036] P32-1: Traverse the first power grid service package, extract the first device, and load the first device basic information and multi-dimensional configuration constraint information corresponding to the first device; P32-2: Retrieve parameter configuration records based on the power grid operation and maintenance scenario and the first device basic information to obtain a first device parameter configuration record set; P32-3: Identify the multi-dimensional configuration trigger interval of the first device parameter configuration record set, and obtain the intersection of the multi-dimensional configuration trigger interval and the multi-dimensional configuration constraint information to establish a first device configuration feature trigger domain; P32-4: Make a parameter configuration decision for the first device according to the first device configuration feature trigger domain, obtain a first device configuration decision domain, and add the first device configuration decision domain to the multiple device configuration decision domains, wherein the first device configuration decision domain includes multiple device configuration decisions corresponding to the first device.

[0037] Specifically, for each device in the power grid service package, detailed parameter configuration is performed in combination with the operation and maintenance scenario, and a device configuration decision domain is generated.

[0038] First, traverse each device in the first power grid service package and extract the first device for detailed configuration. During the extraction process, the system needs to load the basic information of the device (such as device model, function, parameter range, etc.), as well as the multi-dimensional configuration constraint information of the device (such as performance limitations, environmental requirements, power consumption, bandwidth requirements, etc.). This constraint information will help the system determine under what conditions the device can work most efficiently, and provide constraints for subsequent configuration decisions.

[0039] After obtaining the basic information and constraints of the device, the historical parameter configuration record set of the device is retrieved according to the current power grid operation and maintenance scenario (such as the task type, load requirements, network environment, etc. of the device). These record sets are the device configuration data in similar scenarios in the past, which can provide important references for the parameter adjustment of the current device. For example, the historical parameter configuration record set may contain the best configuration or performance of the device under different load conditions.

[0040] By analyzing historical configuration records, the system identifies multi-dimensional configuration trigger intervals triggered by device parameters under different configuration conditions. These trigger intervals define the feasible range of device configuration, such as the optimal operating parameter range of the device under specific temperature, bandwidth, and load conditions. Next, these trigger intervals are intersected with the multi-dimensional configuration constraint information of the device to obtain the configuration feature trigger domain of the device, that is, the optimal parameter range that the device can accept under the current power grid operation and maintenance scenario. This operation ensures that the device can meet performance requirements when configuring and avoid exceeding the operating range of the device.

[0041] Next, based on the established configuration feature trigger domain, a parameter configuration decision is made for the first device to determine the specific operating parameters of the device. This device configuration decision domain contains all possible configuration schemes, and the configuration decision domain of the first device is added to the configuration decision domain of all devices in the power grid service package. This decision-making process is not just a single device configuration, but takes into account the interaction and collaborative work requirements between multiple devices, ensuring that each device in the power grid service package can perform operation and maintenance tasks in a coordinated manner.

[0042] Through the above steps, the configuration of each device in the power grid service package can be finely adjusted according to the actual needs of the operation and maintenance scenario, ensuring that each device can operate in the best state in a changing power grid environment and providing reliable guarantee for the efficient execution of the overall service package.

[0043] P40: Build a power grid service package configuration evaluation module based on the multi-dimensional evaluation factors of the service package configuration.

[0044] Further, such as Figure 2 As shown, step P40 of the embodiment of the present application also includes:

[0045] P41: The service package configuration multidimensional evaluation factors include service package configuration fitness, service package configuration stability and service package configuration reliability; P42: According to the service package configuration multidimensional evaluation factors, build a service package configuration fitness evaluation model, a service package configuration stability evaluation model and a service package configuration reliability evaluation model; P43: According to the service package configuration multidimensional evaluation factors, importance evaluation is performed to obtain the importance evaluation coefficient of each factor; P44: According to the importance evaluation coefficient of each factor, weights are assigned to the service package configuration multidimensional evaluation factors to construct a service package configuration evaluation weighted model; P45: The output layers of the service package configuration fitness evaluation model, the service package configuration stability evaluation model and the service package configuration reliability evaluation model are connected to the input layer of the service package configuration evaluation weighted model to generate the power grid service package configuration evaluation module.

[0046] Optionally, by introducing multiple evaluation factors, a scientific service package configuration evaluation module is established to comprehensively evaluate the configuration of the power grid service package. This module can evaluate the performance of each configuration scheme in multiple dimensions such as adaptability, stability, and reliability, and provide support for subsequent optimization.

[0047] In the process of configuring the power grid service package, it is first necessary to determine multiple evaluation factors, which are used to comprehensively measure the effect and performance of the configuration. Mainly including: Service package configuration adaptability: measure the adaptability of the power grid service package configuration in the current operation and maintenance scenario. Configuration schemes with higher adaptability can work stably under different environmental conditions and meet real-time task requirements. Service package configuration stability: evaluate whether the configuration scheme can remain stable during operation to prevent overload, frequent failures or performance fluctuations. Service package configuration reliability: measure the reliability of the configuration scheme in long-term operation, that is, whether it can maintain the predetermined service quality over a long period of time to prevent equipment failures or system crashes.

[0048] Next, according to the above three evaluation factors, the corresponding evaluation models are constructed, including the fitness evaluation model: based on historical configuration data and on-site operation and maintenance scenarios, the fitness evaluation model is constructed to evaluate the adaptability of different configurations in different scenarios. Stability evaluation model: by analyzing the operation data of the service package under various workloads and environmental conditions, the stability of the configuration scheme under different pressure conditions is evaluated. Reliability evaluation model: considering factors such as equipment failure rate, mean repair time, and long-term stability, a model is established to predict the reliability of the service package configuration in long-term use.

[0049] After establishing multiple evaluation models, it is necessary to quantify the influence of each factor and determine their relative importance in the overall evaluation. By analyzing historical operation and maintenance data, the contribution of each factor to the grid service package configuration results is evaluated, and the importance coefficient of each factor is obtained. In different operation and maintenance scenarios, the weights of different factors may be different, so they need to be adjusted flexibly.

[0050] Based on the determined importance evaluation coefficients, multiple evaluation factors of the service package configuration are weighted. Each factor is assigned a weight according to its importance, and finally a weighted model is formed. The purpose of this weighted model is to comprehensively consider the impact of all evaluation factors to ensure that the role of a key factor is not ignored when comprehensively evaluating the configuration plan.

[0051] Finally, the output layer of each evaluation model (i.e., the evaluation results of fitness, stability, and reliability) is connected to the input layer of the weighted model. The output of each model is integrated through the weighted model to obtain the final grid service package configuration evaluation result. This process ensures that multi-dimensional information can be comprehensively considered to avoid one-sidedness, thereby generating a comprehensive and accurate grid service package configuration evaluation module.

[0052] Furthermore, step P42 of the embodiment of the present application also includes:

[0053] P42-1: According to the multi-dimensional evaluation factors of the service package configuration, the evaluation records of the power grid service package configuration are traced back to obtain the power grid operation and maintenance scenario sample set, the service package grid configuration strategy sample set, the service package configuration fitness sample set, the service package configuration stability sample set and the service package configuration reliability sample set; P42-2: With the power grid operation and maintenance scenario sample set and the service package grid configuration strategy sample set as input information and the service package configuration fitness sample set as output information, the configuration evaluation learning model is supervised and trained, and the fitness evaluation loss coefficient is obtained after each predetermined number of trainings; P42-3: If the fitness evaluation loss coefficient is less than the fitness evaluation loss threshold, Generate the service package configuration fitness evaluation model; P42-4: Based on the power grid operation and maintenance scenario sample set, the service package grid configuration strategy sample set and the service package configuration stability sample set, the configuration evaluation learning model is supervised and trained to obtain the service package configuration stability evaluation model with a stability evaluation loss coefficient less than a stability evaluation loss threshold; P42-5: Based on the power grid operation and maintenance scenario sample set, the service package grid configuration strategy sample set and the service package configuration reliability sample set, the configuration evaluation learning model is supervised and trained to obtain the service package configuration reliability evaluation model with a reliability evaluation loss coefficient less than a reliability evaluation loss threshold.

[0054] In a possible embodiment of the present application, in order to achieve scientific evaluation and optimization of power grid service package configuration, this step proposes a training process based on a configuration evaluation learning model, which evaluates and models the fitness, stability and reliability of the configuration by tracing back historical data and samples and using machine learning technology.

[0055] First, by looking back at historical data, multiple evaluation records are collected and sorted out. These records come from different operation and maintenance scenarios in the power grid operation and maintenance process. According to the multi-dimensional evaluation factors of the service package configuration, the following sample sets are obtained, including: the power grid operation and maintenance scenario sample set, which includes the power grid operation data and environmental information under different operation and maintenance scenarios, helping to build an understanding of scenario changes. The service package grid configuration strategy sample set records the performance of the service package under different grid configuration strategies, including the matching of each device configuration combination with the operation and maintenance task. The service package configuration fitness sample set includes the fitness performance data configured in each operation and maintenance scenario to evaluate the adaptability of the configuration in a specific scenario. The service package configuration stability sample set contains the equipment stability data under different environmental and load conditions. The service package configuration reliability sample set involves the reliability performance of the configuration in extreme environments, helping to understand the long-term operation capability of the configuration. These historical samples provide real and rich data support for subsequent machine learning training and model building.

[0056] Next, the supervised learning method in machine learning is used to train the configuration evaluation learning model by using historical sample sets. Specifically, the sample set of power grid operation and maintenance scenarios and the sample set of service package grid configuration strategy are used as input data, and the sample set of service package configuration fitness is used as output data, and trained through neural networks, decision trees or other machine learning models. In each training iteration, the model predicts the output of fitness based on the input data and compares it with the actual fitness data. The prediction error of the model is measured by the fitness evaluation loss coefficient. After each training, the model parameters are adjusted according to the error to gradually improve the prediction accuracy.

[0057] After several trainings, if the fitness evaluation loss coefficient is lower than the predetermined fitness evaluation loss threshold, it means that the prediction error of the model is small enough to accurately evaluate the fitness of the service package configuration. At this time, the training process will stop and generate the final service package configuration fitness evaluation model, which can accurately evaluate the adaptability of the service package configuration according to the power grid operation and maintenance scenario and configuration strategy.

[0058] Next, continue to use machine learning technology to train the service package configuration stability evaluation model. At this time, use the service package configuration stability sample set containing stability performance, as well as the aforementioned scenario and configuration strategy sample set as input data. After supervised learning, the model will calculate the prediction error of stability (stability evaluation loss coefficient). When the error is less than the stability evaluation loss threshold, the training process ends and the stability evaluation model is generated.

[0059] Finally, the service package configuration reliability sample set is used to train the service package configuration reliability evaluation model. Similarly, the model predicts the reliability performance of the configuration by analyzing the input operation and maintenance scenario and configuration strategy data, and calculates the reliability evaluation loss coefficient. When the error is less than the predetermined threshold, the reliability evaluation model is finally generated.

[0060] Through the above training steps, three evaluation models are finally obtained: fitness, stability and reliability models, which are used to evaluate the adaptability, stability and reliability of the power grid service package configuration respectively, and provide data support for subsequent configuration optimization and decision-making.

[0061] P50: Introducing a power grid service package configuration reproduction constraint function, combining the power grid service package configuration evaluation module to perform reproduction competition optimization on the multiple grid configuration spaces, and determining multiple grid configuration optimization results.

[0062] Furthermore, step P50 of the embodiment of the present application also includes:

[0063] P51: Evaluate each grid configuration strategy in the first grid configuration space according to the grid service package configuration evaluation module to obtain a first grid configuration evaluation distribution; P52: Perform competitive optimization on the first grid configuration space according to the first grid configuration evaluation distribution to determine a first winning grid configuration strategy; P53: Based on the first grid configuration evaluation distribution and according to the grid service package configuration reproduction constraint function, calculate the first service package configuration reproduction quantity distribution corresponding to the first grid configuration space; P54: Perform constrained reproduction on the first grid configuration space based on the first service package configuration reproduction quantity distribution to obtain a grid configuration reproduction first space; P55: Based on the grid configuration reproduction first space, perform reproduction competitive optimization on the first winning grid configuration strategy according to the grid service package configuration evaluation module and the grid service package configuration reproduction constraint function to obtain a first grid configuration optimization result; P56: Add the first grid configuration optimization result to the multiple grid configuration optimization results.

[0064] The power grid service package configuration reproduction constraint function is:

[0065] ;

[0066] Among them, Q g represents the number of configuration strategy reproductions corresponding to the g-th grid configuration strategy in the current grid configuration space, where g is a positive integer and f g Characterize the service package configuration evaluation coefficient corresponding to the g-th grid configuration strategy, f min Characterizes the minimum service package configuration evaluation coefficient corresponding to the current grid configuration space, f max Characterizes the maximum service package configuration evaluation coefficient corresponding to the current grid configuration space, Q max Characterizes the upper limit of the number of configuration strategy reproduction, Q min Represents the lower limit of the number of configuration strategy reproduction, and Floor means rounding down.

[0067] It should be understood that the introduction of the grid service package configuration reproduction constraint function, combined with the grid service package configuration evaluation module, optimizes multiple grid configuration spaces through multi-dimensional evaluation and constraint conditions, and finally obtains the optimal configuration solution.

[0068] Among them, the power grid service package configuration reproduction constraint function is mainly used to constrain the reproduction number of the configuration strategy. Through this function, the reproduction number of each configuration strategy can be controlled to ensure that the strategy with a lower evaluation coefficient will not reproduce indefinitely, while the strategy with a higher evaluation coefficient can get more "reproduction" opportunities. The specific function form is:

[0069] ;

[0070] Among them, Q g represents the number of configuration strategy reproductions corresponding to the g-th grid configuration strategy in the current grid configuration space, where g is a positive integer and f g Characterize the service package configuration evaluation coefficient corresponding to the g-th grid configuration strategy, f min Characterizes the minimum service package configuration evaluation coefficient corresponding to the current grid configuration space, f max Characterizes the maximum service package configuration evaluation coefficient corresponding to the current grid configuration space, Q max Characterizes the upper limit of the number of configuration strategy reproduction, Q min Represents the lower limit of the number of reproductions of the configuration strategy. Floor means rounding down to ensure that the final number of reproductions is an integer.

[0071] First, each grid configuration strategy in the first grid configuration space is evaluated according to the grid service package configuration evaluation module to obtain a first grid configuration evaluation distribution. The first grid configuration space contains multiple grid configuration strategies, each strategy representing a different grid service package configuration scheme. By using the grid service package configuration evaluation module, each configuration strategy is evaluated to obtain a service package configuration evaluation coefficient corresponding to each strategy. These evaluation coefficients reflect the applicability, stability and reliability of each configuration strategy in actual operation and maintenance scenarios. For example, a configuration strategy with a higher evaluation coefficient indicates that the configuration scheme can better adapt to the grid operation and maintenance needs under the current environment.

[0072] Next, based on the first grid configuration evaluation distribution, all configuration strategies are competed for optimization. The core of the competition process is to select the optimal configuration strategy by comparing the service package configuration evaluation coefficients of each strategy. That is, the configuration strategy corresponding to the maximum evaluation coefficient is selected and recorded as the first winning grid configuration strategy. This competition method simulates the survival of the fittest process of natural selection, ensuring that the system prioritizes the most appropriate power grid configuration solution.

[0073] Next, according to the first grid configuration evaluation distribution, combined with the grid service package configuration reproduction constraint function, the reproduction number of each grid configuration strategy is calculated. This process is similar to the reproduction operation in the genetic algorithm, and the purpose is to expand the application scope of the winning strategy. The calculation of the reproduction number is weighted according to the evaluation coefficient. The configuration strategy with a larger evaluation coefficient has a larger reproduction number, which reflects a higher priority.

[0074] Furthermore, the first gridded configuration space is optimized by constraining the propagation operation and using the calculated service package configuration propagation quantity distribution. The propagation quantity of each gridded configuration strategy determines its proportion in the new round of configuration, thereby propagating a new configuration space in the first gridded configuration space to obtain the first gridded configuration propagation space. The purpose of constraining propagation is to ensure diversity in the propagation process, while keeping high-quality configuration strategies with higher evaluation coefficients to occupy a larger proportion in the propagation process.

[0075] In the first grid configuration breeding space, the first winning grid configuration strategy is further bred and optimized through the grid service package configuration evaluation module and the grid service package configuration breeding constraint function. This step further optimizes the configuration strategy to ensure that a better configuration strategy can be generated based on the previous round of breeding, and finally obtains the first grid configuration optimization result, that is, the optimal configuration solution.

[0076] Finally, the first grid configuration optimization result is added to the overall configuration optimization result to form a complete multiple grid configuration optimization results. These optimization results represent the optimal solutions for multiple power grid service package configurations. After multiple rounds of reproduction and optimization, it is ultimately ensured that each configuration solution has the best effect and meets the power grid operation and maintenance needs.

[0077] Furthermore, step P55 of the embodiment of the present application also includes:

[0078] P55-1: Evaluate the first grid configuration reproduction space according to the power grid service package configuration evaluation module to obtain a second grid configuration evaluation distribution; P55-2: Compete and optimize the first grid configuration reproduction space and the first winning grid configuration strategy according to the second grid configuration evaluation distribution to determine the second winning grid configuration strategy; P55-3: Based on the second grid configuration evaluation distribution and according to the power grid service package configuration reproduction constraint function, calculate the second service package configuration reproduction quantity distribution corresponding to the first grid configuration reproduction space; P55-4: Based on the second service package configuration reproduction quantity distribution, constrain reproduction is performed on the first grid configuration reproduction space to obtain a second grid configuration reproduction space; P55-5: Based on the grid configuration reproduction second space, continue to perform reproduction competition and optimization on the second winning grid configuration strategy according to the power grid service package configuration evaluation module and the power grid service package configuration reproduction constraint function to obtain the first grid configuration optimization result that meets the reproduction number constraint.

[0079] Optionally, the optimization strategy for the grid service package configuration can be further refined and improved through multiple rounds of competition and reproduction processes to ensure that the best configuration solution is obtained.

[0080] First, the grid service package configuration evaluation module is used to re-evaluate the first grid configuration propagation space (i.e., the configuration space obtained after the first propagation operation) to obtain a new second grid configuration evaluation distribution. This distribution reflects the evaluation results of each grid configuration strategy during the second round of propagation, i.e., the fitness, stability, and reliability of each configuration strategy. This evaluation distribution helps evaluate which configuration strategies are adapted after propagation, ensuring that the best performing strategies are selected for further optimization.

[0081] Next, according to the second grid configuration evaluation distribution, the strategies in the first grid configuration breeding space compete with the first winning grid configuration strategy for optimization. Similar to the first round of breeding, the system compares the evaluation coefficients of each configuration strategy and selects the grid configuration strategy with the largest service package configuration evaluation coefficient as the second winning grid configuration strategy. Through this competitive method, the selection of configuration strategies is further optimized to ensure that the winning strategies are continuously selected and bred.

[0082] Next, according to the second grid configuration evaluation distribution, combined with the grid service package configuration reproduction constraint function, the reproduction number of each configuration strategy in the second round of reproduction is calculated. This step is similar to P53, except that the reproduction number is determined based on the evaluation results of the second round. During the reproduction process, configuration strategies with higher evaluation coefficients will get more reproduction opportunities, further increasing their influence in the optimization space.

[0083] Furthermore, by calculating the distribution of the number of configurations of the second service package, the system constrains the first space of grid configuration reproduction, and finally forms the second space of grid configuration reproduction. In this process, the distribution of the number of reproductions will strictly limit the number of reproductions of each configuration strategy to ensure that the predetermined number of reproduction constraints are met. In this way, it is possible to effectively avoid some configuration strategies from occupying too much optimization space due to excessive reproduction.

[0084] Finally, based on the second space of grid configuration reproduction, the grid service package configuration evaluation module and the grid service package configuration reproduction constraint function are used to further reproduce and compete for the second winning grid configuration strategy, and finally the first grid configuration optimization result that meets the reproduction number constraint is obtained. This step ensures that the final configuration strategy of the entire optimization process not only meets the performance requirements, but also obtains the best adjustment and optimization under the reproduction number and constraint conditions. It can achieve all-round optimization configuration of the grid service package, ensure that the configuration scheme is optimal in adaptability, stability and reliability, and finally form an efficient configuration scheme that meets the actual grid operation and maintenance needs.

[0085] P60: Parameter configuration is performed on the multiple power grid service packages based on the multiple grid configuration optimization results, and positioning and synchronizing the multiple power grid service packages after the parameter configuration is completed.

[0086] In the above steps, multiple propagated and optimized grid configuration optimization results are obtained. These results represent the optimal configuration strategies for different power grid service packages. Next, according to these optimization results, actual parameter configuration is performed for each power grid service package, and it is ensured that the configured power grid service package can be correctly synchronized and positioned.

[0087] The parameter configuration process is based on the grid configuration strategy. By introducing optimized configuration decisions, the equipment and configuration parameters in each power grid service package are adjusted to the optimal state. This step ensures that the equipment in the service package can achieve the best performance in the operation and maintenance scenario, meeting key requirements such as real-time monitoring, load balancing, and fault recovery.

[0088] After the parameters of the power grid service package are configured, positioning synchronization is performed to ensure that all power grid service packages can be correctly connected and work together according to the physical location and network topology in actual applications. Positioning synchronization refers to ensuring the consistency of the configuration of each device in the service package in space and time. Especially in large-scale power grid operation and maintenance, the physical location and parameter configuration of the equipment must be accurately synchronized to ensure the stable operation of the system.

[0089] The process of positioning synchronization involves network configuration, geographic location calibration, status monitoring, etc. of power grid equipment. Through precise synchronization, the response speed and fault tolerance of the power grid can be improved, ensuring the coordination and efficiency of the power grid service package when handling complex operation and maintenance tasks.

[0090] Through the above steps, multiple power grid service packages can achieve efficient collaboration and provide accurate and reliable services in different power grid operation and maintenance scenarios, thereby improving the overall power grid operation and maintenance capabilities and emergency response speed.

[0091] In summary, the embodiments of the present application have at least the following technical effects:

[0092] This application collects multimodal information from the power grid operation and maintenance site, determines the operation and maintenance scenario, configures parameters for multiple power grid service packages, and establishes a grid configuration space. By introducing multidimensional evaluation factors and configuration propagation constraint functions, combined with configuration evaluation modules for competitive optimization, the parameter configuration of the power grid service package is optimized, and ultimately the intelligent configuration and positioning synchronization of power grid equipment is realized, thereby improving the efficiency and reliability of power grid operation.

[0093] The technical effect of improving grid operation and maintenance efficiency, resource utilization and emergency response capabilities by optimizing grid service package configuration based on multi-dimensional evaluation factors has been achieved.

[0094] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0096] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for synchronizing grid configuration and positioning of a multi-dimensional power grid service package, characterized in that: The method comprises: Obtaining multiple power grid service packages at a power grid operation and maintenance site, wherein each power grid service package includes multiple devices; Collecting multimodal information of the power grid operation and maintenance site to determine the power grid operation and maintenance scenario; Performing parameter configuration on the plurality of power grid service packages according to the power grid operation and maintenance scenario to establish a plurality of grid configuration spaces; According to the multi-dimensional evaluation factors of the service package configuration, a power grid service package configuration evaluation module is built; Introducing a power grid service package configuration reproduction constraint function, combining the power grid service package configuration evaluation module to perform reproduction competition optimization on the multiple grid configuration spaces, and determining multiple grid configuration optimization results; The plurality of grid service packages are parameterized based on the plurality of grid configuration optimization results, and positioning synchronization is performed on the plurality of grid service packages after the parameter configuration is completed.

2. The method for grid configuration and positioning synchronization of a multi-dimensional power grid service package according to claim 1, characterized in that: Parameter configuration is performed on the plurality of power grid service packages according to the power grid operation and maintenance scenario to establish a plurality of grid configuration spaces, including: Traversing the plurality of power grid service packages to extract a first power grid service package; According to the power grid operation and maintenance scenario, parameter configuration is performed on each device in the first power grid service package to obtain multiple device configuration decision domains; Combining parameter configuration decisions of each device according to the multiple device configuration decision domains to obtain multiple grid configuration strategies corresponding to the first power grid service package; According to the multiple grid configuration strategies, a first grid configuration space is constructed, and the first grid configuration space is added to the multiple grid configuration spaces.

3. The method for grid configuration and positioning synchronization of multi-dimensional power grid service packages according to claim 2, characterized in that: According to the power grid operation and maintenance scenario, parameters of each device in the first power grid service package are configured to obtain multiple device configuration decision domains, including: Traversing the first power grid service package, extracting the first device, and loading the first device basic information and multi-dimensional configuration constraint information corresponding to the first device; Perform parameter configuration record retrieval based on the power grid operation and maintenance scenario and the first device basic information to obtain a first device parameter configuration record set; Identify the multi-dimensional configuration triggering interval of the first device parameter configuration record set, and obtain the intersection of the multi-dimensional configuration triggering interval and the multi-dimensional configuration constraint information to establish a first device configuration feature triggering domain; A parameter configuration decision is made for the first device according to the first device configuration feature trigger domain, a first device configuration decision domain is obtained, and the first device configuration decision domain is added to the multiple device configuration decision domains, wherein the first device configuration decision domain includes multiple device configuration decisions corresponding to the first device.

4. The method for grid configuration and positioning synchronization of a multi-dimensional power grid service package according to claim 1, characterized in that: According to the multi-dimensional evaluation factors of the service package configuration, a power grid service package configuration evaluation module is built, including: The service package configuration multidimensional evaluation factors include service package configuration adaptability, service package configuration stability and service package configuration reliability; According to the multi-dimensional evaluation factors of the service package configuration, a service package configuration fitness evaluation model, a service package configuration stability evaluation model and a service package configuration reliability evaluation model are constructed; Performing importance evaluation on the multi-dimensional evaluation factors configured according to the service package, and obtaining importance evaluation coefficients of the factors; According to the importance evaluation coefficients of the factors, weights are assigned to the multidimensional evaluation factors of the service package configuration to construct a service package configuration evaluation weighted model; The output layers of the service package configuration fitness evaluation model, the service package configuration stability evaluation model and the service package configuration reliability evaluation model are connected to the input layer of the service package configuration evaluation weighted model to generate the power grid service package configuration evaluation module.

5. The method for grid configuration and positioning synchronization of multi-dimensional power grid service packages according to claim 4, characterized in that: According to the multi-dimensional evaluation factors of the service package configuration, a service package configuration fitness evaluation model, a service package configuration stability evaluation model and a service package configuration reliability evaluation model are constructed, including: According to the multi-dimensional evaluation factors of the service package configuration, the evaluation records of the power grid service package configuration are traced back to obtain a sample set of power grid operation and maintenance scenarios, a sample set of service package grid configuration strategies, a sample set of service package configuration fitness, a sample set of service package configuration stability, and a sample set of service package configuration reliability; Taking the power grid operation and maintenance scenario sample set and the service package grid configuration strategy sample set as input information and the service package configuration fitness sample set as output information, supervised training is performed on the configuration evaluation learning model, and a fitness evaluation loss coefficient is obtained after each predetermined number of trainings; If the fitness evaluation loss coefficient is less than the fitness evaluation loss threshold, generating the service package configuration fitness evaluation model; Based on the power grid operation and maintenance scenario sample set, the service package grid configuration strategy sample set and the service package configuration stability sample set, the configuration evaluation learning model is supervised and trained to obtain the service package configuration stability evaluation model whose stability evaluation loss coefficient is less than the stability evaluation loss threshold; The configuration evaluation learning model is supervised and trained based on the power grid operation and maintenance scenario sample set, the service package grid configuration strategy sample set and the service package configuration reliability sample set to obtain the service package configuration reliability evaluation model with a reliability evaluation loss coefficient less than a reliability evaluation loss threshold.

6. The method for grid configuration and positioning synchronization of multi-dimensional power grid service packages according to claim 1, characterized in that: Introducing a power grid service package configuration reproduction constraint function, combining the power grid service package configuration evaluation module to perform reproduction competition optimization on the multiple grid configuration spaces, and determining multiple grid configuration optimization results, including: Evaluate each grid configuration strategy in the first grid configuration space according to the grid service package configuration evaluation module to obtain a first grid configuration evaluation distribution; Competitively searching for the first grid configuration space according to the first grid configuration evaluation distribution to determine a first winning grid configuration strategy; Based on the first gridded configuration evaluation distribution and according to the power grid service package configuration reproduction constraint function, calculating the first service package configuration reproduction quantity distribution corresponding to the first gridded configuration space; Based on the first service package configuration reproduction quantity distribution, constrained reproduction is performed on the first grid configuration space to obtain a grid configuration reproduction first space; Based on the grid configuration breeding first space, the first winning grid configuration strategy is subjected to breeding competition optimization according to the grid service package configuration evaluation module and the grid service package configuration breeding constraint function to obtain a first grid configuration optimization result; The first gridded configuration optimization result is added to the plurality of gridded configuration optimization results.

7. The method for grid configuration and positioning synchronization of multi-dimensional power grid service packages according to claim 6, characterized in that: Based on the grid configuration breeding first space, the first winning grid configuration strategy is subjected to breeding competition optimization according to the grid service package configuration evaluation module and the grid service package configuration breeding constraint function to obtain a first grid configuration optimization result, including: Evaluate the grid configuration propagation first space according to the power grid service package configuration evaluation module to obtain a second grid configuration evaluation distribution; Competitively searching for the best between the first grid configuration breeding space and the first winning grid configuration strategy according to the second grid configuration evaluation distribution to determine a second winning grid configuration strategy; Based on the second grid configuration evaluation distribution and according to the power grid service package configuration reproduction constraint function, calculating the second service package configuration reproduction quantity distribution corresponding to the first grid configuration reproduction space; Based on the second service package configuration reproduction quantity distribution, constraining reproduction is performed on the grid configuration reproduction first space to obtain a grid configuration reproduction second space; Based on the grid configuration breeding second space, the second winning grid configuration strategy is continued to be bred competitively and optimized according to the grid service package configuration evaluation module and the grid service package configuration breeding constraint function to obtain the first grid configuration optimization result that meets the breeding number constraint.

8. The method for grid configuration and positioning synchronization of multi-dimensional power grid service packages according to claim 1, characterized in that: The grid service package configuration reproduction constraint function is: ； Among them, Q g represents the number of configuration strategy reproductions corresponding to the g-th grid configuration strategy in the current grid configuration space, where g is a positive integer and f g Characterize the service package configuration evaluation coefficient corresponding to the g-th grid configuration strategy, f min Characterizes the minimum service package configuration evaluation coefficient corresponding to the current grid configuration space, f max Characterizes the maximum service package configuration evaluation coefficient corresponding to the current grid configuration space, Q max Characterizes the upper limit of the number of configuration strategy reproduction, Q min Represents the lower limit of the number of configuration strategy reproduction, and Floor means rounding down.

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