Multi-task scheduling optimization method and system based on power internet of things

Abstract

The invention discloses a multi-task scheduling optimization method and system based on an electric power internet of things, and relates to the technical field of the electric power internet of things, and the method comprises the steps: obtaining a distribution mode of a transformer area power grid, analyzing a frequent mode of a power grid fault, and recognizing the type of a high-frequency fault element and a fault probability identifier of the high-frequency fault element; obtaining redundant inventory data from the maintenance element library according to the element type and the deployment number, and carrying out equilibrium analysis on the inventory to obtain an inventory equilibrium coefficient; and when the inventory balance coefficient is lower than a threshold value, carrying out inventory supplement on the high-frequency fault component model. The technical problem that the inventory of high-frequency fault elements is insufficient due to the lack of real-time analysis of the inventory state in the existing power operation and maintenance management is solved, and the technical effect of real-time dynamic inventory optimization management is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of electric power Internet of Things, and in particular to a multi-task scheduling optimization method and system based on electric power Internet of Things. Background Art

[0002] In modern power systems, with the increase of power load and the increasing complexity of the power grid operating environment, traditional power operation and maintenance management faces many challenges, especially in the management of power distribution networks and emergency response to faults. Traditional methods that rely on manual monitoring and experience judgment have problems such as slow response speed, low prediction accuracy, and unreasonable resource scheduling. The status, failure rate, and inventory status of a large number of equipment and components in power systems are usually scattered, lacking unified real-time monitoring and intelligent optimization, resulting in an uneven distribution of component inventory and redundant inventory, which affects the efficiency and response speed of power operation and maintenance. Especially in the case of frequent failures of power grid equipment, the lack of accurate inventory status analysis and intelligent resource scheduling may lead to a shortage of spare parts, which affects the timely repair of equipment and may cause power supply interruptions. Therefore, there is an urgent need for an optimization method that can combine power Internet of Things technology for real-time monitoring, predictive analysis, and dynamic scheduling to improve the operating efficiency of the power system, reduce fault response time, and effectively manage inventory resources.

[0003] In the current relevant technologies, there is a technical problem of lack of real-time analysis of inventory status in power operation and maintenance management, which leads to insufficient inventory of high-frequency fault components. Summary of the invention

[0004] This application solves the technical problem of insufficient inventory of high-frequency fault components caused by the lack of real-time analysis of inventory status in existing power operation and maintenance management by providing a multi-task scheduling optimization method and system based on the power Internet of Things.

[0005] This application provides a multi-task scheduling optimization method based on the power Internet of Things, including:

[0006] Obtain a power grid distribution mode in the substation area; perform a frequent power grid failure pattern analysis based on the power grid distribution mode in the substation area to obtain a high-frequency fault component type, wherein the high-frequency fault component type has a component failure probability identifier; obtain the number of component deployments of the high-frequency fault component type in the substation area; obtain the redundant inventory quantity of the high-frequency fault component model of the high-frequency fault component type based on the substation area maintenance component library; perform a balance analysis on the redundant inventory quantity based on the component deployment quantity and the component failure probability identifier to obtain an inventory balance coefficient; when the inventory balance coefficient is less than or equal to an inventory balance coefficient threshold, replenish the inventory of the high-frequency fault component model.

[0007] This application also provides a multi-task scheduling optimization system based on the power Internet of Things, including:

[0008] A distribution mode acquisition module, the distribution mode acquisition module is used to obtain the distribution mode of the power grid in the substation; a mode analysis module, the mode analysis module is used to perform frequent mode analysis of power grid failures based on the power grid distribution mode of the substation, and obtain a high-frequency fault component type, wherein the high-frequency fault component type has a component failure probability identifier; a component deployment quantity acquisition module, the component deployment quantity acquisition module is used to obtain the component deployment quantity of the high-frequency fault component type in the substation; a redundant inventory quantity acquisition module, the redundant inventory quantity acquisition module is used to obtain the redundant inventory quantity of the high-frequency fault component model of the high-frequency fault component type based on the substation maintenance component library; a balance analysis module, the balance analysis module is used to perform a balance analysis on the redundant inventory quantity based on the component deployment quantity and the component failure probability identifier to obtain an inventory balance coefficient; an inventory replenishment module, the inventory replenishment module is used to replenish the inventory of the high-frequency fault component model when the inventory balance coefficient is less than or equal to the inventory balance coefficient threshold.

[0009] The multi-task scheduling optimization method and system based on the power Internet of Things proposed in this application first obtains the distribution mode of the substation power grid, analyzes the frequent fault mode of the power grid, and identifies the type of high-frequency fault components and their fault probability identification; according to the component type and deployment quantity, redundant inventory data is obtained from the maintenance component library, and the inventory is balanced and analyzed to obtain the inventory balance coefficient; when the inventory balance coefficient is lower than the threshold, the inventory of the high-frequency fault component model is replenished, thereby achieving the technical effect of real-time dynamic inventory optimization management. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0011] Figure 1 A flowchart of a multi-task scheduling optimization method based on the power Internet of Things provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of the structure of a multi-task scheduling optimization system based on the power Internet of Things provided in an embodiment of the present application.

[0013] Explanation of the reference numerals: power distribution mode acquisition module 10 , mode analysis module 20 , component deployment quantity acquisition module 30 , redundant inventory quantity acquisition module 40 , balance analysis module 50 , inventory replenishment module 60 . DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations 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 clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0017] The present application embodiment provides a multi-task scheduling optimization method based on the power Internet of Things, such as Figure 1 As shown, the method includes:

[0018] Step S100, obtain the distribution mode of the power grid in the substation. Specifically, through the intelligent sensors, monitoring equipment, geographic information system and equipment ledger in the substation power grid, multi-source data such as electrical parameters such as voltage, current, power, geographic location, equipment information, etc. are collected and integrated to build a data basic platform, and then the operation status of the substation power grid is analyzed based on these data, and abnormalities such as voltage fluctuations and current overloads, as well as power quality and power compensation are judged, and the topological structure and connection relationship are clarified. Then, data analysis algorithms and modeling techniques are used, and a mathematical model is constructed based on the power grid topology, electrical parameters and operating status with the help of power system analysis software, and the characteristics and connection relationships of each component and its dynamic behavior are described in detail. The operation response under different working conditions is simulated by simulation calculation, and the distribution mode characteristics are deeply understood, and finally the distribution mode of the substation power grid is generated, which provides support for subsequent work to achieve refined management and optimized operation.

[0019] Step S200, according to the power distribution mode of the substation power grid, the frequent mode analysis of power grid failure is performed to obtain the type of high-frequency fault component, wherein the type of high-frequency fault component has a component failure probability mark. Specifically, the power distribution mode of the substation power grid is analyzed, and the first substation component and its model and electrical control parameters are extracted to build an information library. Then, with the substation deployment environment monitoring data and the power distribution mode as constraints, a substation power grid fault sample set containing a variety of environmental and working condition information is collected. Then, according to the component model and electrical control parameters, the frequent mode analysis of power grid failure is performed on the component, and the number of samples that meet the conditions is calculated as the component trigger frequency, and the proportion of the number of samples is calculated as the fault probability mark. When the component trigger frequency exceeds the threshold, the component and its information are associated and stored in the high-frequency fault component type and the fault probability mark is stored, so as to facilitate subsequent targeted management and maintenance, and improve the reliability and stability of the substation power grid.

[0020] In a possible implementation, a frequent pattern analysis of power grid failures is performed according to the power distribution mode of the substation power grid to obtain a high-frequency fault component type, wherein the high-frequency fault component type has a component failure probability identifier, and step S200 further includes step S210, extracting a first substation component according to the power distribution mode of the substation power grid, wherein the first substation component has a first substation component model and a first substation component electrical control parameter. Specifically, the substation power grid distribution mode is deeply analyzed and data mined. The substation power grid distribution mode contains multiple information such as the topological structure of the power grid, the distribution of electrical parameters, the connection relationship of equipment, and the operating status. Through specific data extraction algorithms and model recognition technologies, the relevant information of the first substation component is accurately separated from this complex distribution mode. For example, when analyzing the topological structure of the power grid, the specific devices connected to each node are identified, and these devices are the first substation components. For each first substation component, its key attributes, namely the first substation component model and the first substation component electrical control parameters, are further extracted. Taking the transformer as an example, its model may indicate its capacity, winding structure, cooling method and other technical specifications, while the electrical control parameters cover the oil temperature monitoring range, winding temperature alarm threshold, tap changer adjustment range and control logic and other operation control related data. These component models and electrical control parameter information will serve as an important basis for subsequent fault analysis. They help to accurately locate and distinguish components of different types and characteristics among numerous power grid components, thus laying the foundation for targeted research on their fault modes.

[0021] Step S220, taking the substation deployment environment monitoring data and the substation power grid distribution mode as constraints, collect the substation power grid fault sample set. Specifically, after obtaining the first substation element and its related parameters, start to build the substation power grid fault sample set. This process uses the substation deployment environment monitoring data and the substation power grid distribution mode as constraints to ensure that the collected fault samples are representative and targeted. The substation deployment environment monitoring data contains a variety of environmental factor information such as ambient temperature, humidity, air pressure, electromagnetic interference intensity, salt spray content (in coastal or specific industrial environment areas). These environmental factors are closely related to the occurrence of power grid component failures. For example, a high temperature environment may accelerate the aging of the insulating material of electrical equipment, thereby increasing the risk of insulation breakdown failure; a high humidity environment may cause the surface of the equipment to become damp, causing leakage or short circuit failures; strong electromagnetic interference may interfere with the control signal of the equipment, resulting in malfunction or failure. At the same time, the substation power grid distribution mode provides information on the operating conditions of the power grid when a fault occurs, such as load size, power factor, voltage stability, and power flow distribution. By comprehensively considering these environmental monitoring data and distribution modal information, qualified fault samples are screened out from the historical fault record database of the power grid, the real-time fault monitoring system, and the simulated fault experimental data (if any). For example, transformer fault samples that occur under high temperature and high load conditions, or relay protection device malfunction samples that occur under strong electromagnetic interference environments, are screened out. These screened fault samples are integrated together to form a comprehensive and targeted set of substation power grid fault samples, providing sufficient data resources for subsequent frequent fault pattern analysis.

[0022] Step S230, according to the set of grid fault samples in the substation, based on the first substation component model and the first substation component electrical control parameters, the first substation component is subjected to grid fault frequent mode analysis, and the first substation component trigger frequency and the first substation component fault probability identifier are obtained. Specifically, based on the constructed set of grid fault samples in the substation, the first substation component is subjected to in-depth grid fault frequent mode analysis according to the first substation component model and the first substation component electrical control parameters. This analysis process is mainly realized by data statistics and pattern recognition technology. First, for each combination of the first substation component model and its corresponding electrical control parameter, a comprehensive search and statistics are performed in the substation grid fault sample set. The number of samples that simultaneously meet the specific first substation component electrical control parameters and the first substation component model is calculated, and this number is determined as the first substation component trigger frequency. For example, for a circuit breaker of a certain specific model, if 30 samples are found in the fault sample set, and the electrical control parameters (such as abnormal opening and closing time, contact wear degree, etc.) when the fault occurs match the standard parameters of the circuit breaker of this model, then the trigger frequency of the circuit breaker of this model is 30. Furthermore, the proportion of samples that simultaneously meet the electrical control parameters of the first substation components and the first substation component model is calculated, and this proportion is set as the first substation component failure probability identification. For example, if there are 40 failure samples of a certain type of insulator out of a total of 500 failure samples and all meet their specific electrical control parameters, then its failure probability identification is 40 / 500=0.08. Through such analysis, the frequency and probability of failure of each first substation component in power grid operation can be quantitatively evaluated, thereby identifying potential high-frequency fault component types.

[0023] Step S240, when the trigger frequency of the first substation element is greater than or equal to the trigger frequency threshold, the first substation element, the first substation element model and the first substation element electrical control parameters are associated and stored in the high-frequency fault element type, and the first substation element fault probability identifier is stored in the element fault probability identifier. Specifically, when the trigger frequency of a first substation element is greater than or equal to the trigger frequency threshold, it indicates that the element belongs to a high-incidence type of faults in the operation of the power grid. At this time, the first substation element, its first substation element model and the first substation element electrical control parameters are associated and integrated, and stored in the high-frequency fault element type database. For example, if the trigger frequency of a certain model of fuse reaches or exceeds the set threshold, then the fuse and its model, electrical control parameters (such as rated current, fuse characteristic curve, etc.) are stored as a whole information in the high-frequency fault element type. At the same time, the corresponding first substation component failure probability identification is stored in a special component failure probability identification database, so that in the subsequent inventory management, equipment maintenance plan formulation, fault prediction and other tasks, the key information of these high-frequency fault components can be quickly and accurately obtained, providing strong data support and decision-making basis for improving the reliability and stability of the substation power grid.

[0024] In a possible implementation, according to the substation power grid fault sample set, the first substation component is subjected to a power grid fault frequent pattern analysis based on the first substation component model and the first substation component electrical control parameters, and the first substation component trigger frequency and the first substation component failure probability identifier are obtained. Step S230 further includes step S231, according to the substation power grid fault sample set, the number of samples that simultaneously meet the first substation component electrical control parameters and the first substation component model are counted to obtain the first substation component trigger frequency. Specifically, the substation power grid fault sample set is sorted and analyzed, and the fault sample set contains detailed records of various fault events that occurred in the substation power grid in the past period of time, such as the time and location of the fault, the equipment involved, the fault phenomenon, and the grid operating conditions at the time. For each fault sample, the component information involved therein is compared one by one with the electrical control parameters and model of the first substation component. For example, if the first substation component is a certain type of circuit breaker, its electrical control parameters include rated voltage, rated current, opening and closing time range, etc., when analyzing the fault sample, check whether the faulty circuit breaker is of this specific model, and whether its electrical parameters at the time of the fault are within the set electrical control parameter range. By performing such traversal and comparison operations on the entire substation power grid fault sample set, the number of samples that simultaneously meet the electrical control parameters of the first substation component and the first substation component model are counted. This statistical number is the trigger frequency of the first substation component, which intuitively reflects the frequency of occurrence of components of this model and with specific electrical control parameters in substation power grid fault events. For example, if it is counted that a certain type of insulator has 50 samples in the fault sample set that simultaneously meet its electrical control parameters and model requirements, then the trigger frequency of the insulator is 50, which means that in past fault situations, this type of insulator has relatively frequent problems and may be a potential high-incidence component type.

[0025] Step S232, calculate the proportion of the number of samples that simultaneously meet the electrical control parameters of the first substation component and the first substation component model, and set it as the first substation component failure probability identifier. Specifically, after determining the trigger frequency of the first substation component, further calculate the first substation component failure probability identifier. The proportion of the number of samples that simultaneously meet the electrical control parameters of the first substation component and the first substation component model to the total number of samples in the entire substation power grid fault sample set is used as the first substation component failure probability identifier. For example, if the substation power grid fault sample set has a total of 1000 samples, and the number of fault samples of a certain model of transformer that simultaneously meets its electrical control parameters and model is 80, then the fault probability identifier of the transformer of this model is 80 / 1000=0.08. This fault probability identifier quantitatively represents the probability of failure of the component of this model and with specific electrical control parameters in the substation power grid in the form of a percentage. By calculating the fault probability identification, the risk level of different first-area components in the power grid operation can be assessed more accurately, so that in the subsequent operation and maintenance management work, high-frequency fault component types can be focused on according to these probability values, spare parts can be reserved in advance, and targeted maintenance strategies can be formulated, thereby effectively improving the reliability and stability of the substation power grid and reducing the probability of faults and the losses caused by faults.

[0026] Step S300, obtain the number of components of the high-frequency fault component type deployed in the substation. Specifically, clarify the data source for obtaining the number of components of the high-frequency fault component type deployed in the substation, integrate the substation power grid equipment management system database, asset ledger records and real-time monitoring system equipment operation status data to build a substation equipment information data set, then use database query statements or data mining algorithms to filter matching component records in the data set based on the model and electrical control parameters of the high-frequency fault component type, and finally count the number of filtered matching component records, exclude components that cannot be operated in the short term due to scrapping, dismantling and maintenance, and verify the results by comparing with on-site operation and maintenance inspection records or confirming with remote monitoring equipment, so as to provide a reliable data basis for subsequent work.

[0027] In one possible implementation, the number of components deployed in the substation of the high-frequency fault component type is obtained, and step S300 further includes step S310, obtaining the number of components deployed in the substation that simultaneously meet the electrical control parameters of the first substation component and the first substation component model. Specifically, the equipment information data is obtained from the substation equipment management system and integrated with the operation and maintenance record data to form a substation equipment information library, and then the component records that meet the requirements are accurately screened out based on the model and electrical control parameters of the first substation component using database query technology. During the process, the electrical control parameters are strictly matched to eliminate differences, and then the number of screened component records is counted. When counting, the component status is judged based on the operation and maintenance records and the actual situation to decide whether to include it in the deployment quantity. Finally, the operation and maintenance personnel are arranged to conduct on-site inspections and verify the statistical results to ensure the accuracy of the component deployment quantity, providing reliable data support for subsequent work.

[0028] Step S400, according to the substation maintenance component library, obtain the redundant inventory quantity of the high-frequency fault component model of the high-frequency fault component type. Specifically, log in to the substation maintenance component library through the database access interface and identity authentication, then use the database query function to use the high-frequency fault component model as the key search condition and combine the inventory status field to filter out the component records in the standby, idle or deployable state, then directly read the value of the inventory quantity field in the filtered component record to determine the redundant inventory quantity, and finally verify the integrity and accuracy of the data by comparing with the inventory change history record, so as to provide a reliable basis for subsequent inventory management decisions.

[0029] Step S500, according to the component deployment quantity and the component failure probability identifier, the redundant inventory quantity is balanced and analyzed to obtain the inventory balance coefficient. Specifically, the predicted number of component failures is obtained by multiplying the component deployment quantity and the component failure probability identifier. This number estimates the number of possible faulty components in the future based on the current component distribution and the past failure probability. Then, the ratio of the redundant inventory quantity to the predicted number of component failures is calculated as the inventory balance coefficient. This coefficient reflects the sufficiency of the redundant inventory relative to the failure risk. If it is close to 1, it means that the inventory is more reasonable. If it is far less than 1, there is a risk of operation and maintenance interruption. If it is far greater than 1, there may be an inventory backlog. It can intuitively present the balance relationship between inventory and failure risk, and provide a scientific basis for inventory replenishment or adjustment to ensure the stable operation of the power system and optimize inventory management.

[0030] In a possible implementation, according to the number of component deployments and the component failure probability identifier, the redundant inventory quantity is balanced and analyzed to obtain the inventory balance coefficient. Step S500 further includes step S510, which multiplies the number of component deployments and the component failure probability identifier to obtain the predicted number of component failures. Specifically, two key data, the number of component deployments and the component failure probability identifier, are obtained. The number of component deployments reflects the actual distribution scale of a specific type of component in the substation power grid. For example, a total of 100 switch components of a certain model are deployed in a certain substation. The component failure probability identifier is obtained through an in-depth analysis of the substation power grid fault sample set, which quantifies the possibility of failure of the model component in the past operation process. For example, the failure probability identifier of the model switch component is 0.1. The product of these two data is calculated, that is, 100×0.1=10, and the result 10 is the predicted number of component failures. This means that based on the current deployment situation and the historical failure probability, it is expected that 10 of the model switch components may fail in future operation. This calculation takes into account the current number of components and their propensity to fail, providing a basic forecast for assessing whether inventory can cope with possible failures.

[0031] Step S520, calculate the ratio of the redundant inventory quantity to the predicted number of component failures, and set it as the inventory balance coefficient. Specifically, after obtaining the predicted number of component failures, then calculate the inventory balance coefficient. Obtain the redundant inventory quantity. The redundant inventory quantity refers to the number of components of this model that can be deployed to deal with failures in the maintenance component library of the substation, in addition to those currently in use and allocated for use. Assume that the redundant inventory quantity of this model of switching components is 5. Calculate the ratio of the redundant inventory quantity to the predicted number of component failures, that is, 5÷10=0.5, and this 0.5 is the inventory balance coefficient. The inventory balance coefficient is an important indicator to measure the balance relationship between inventory and possible component failures. If the inventory balance coefficient is equal to 1, it means that the redundant inventory quantity can just meet the expected number of faulty components, and the inventory is in a relatively balanced state; if the inventory balance coefficient is less than 1, such as the current 0.5, it means that the redundant inventory quantity is insufficient to cope with possible failure situations, and it may be necessary to consider replenishing inventory to reduce the risk of power supply interruption or operation and maintenance difficulties due to component failure; if the inventory balance coefficient is greater than 1, it may mean that there is a certain backlog of inventory, and further assessment is needed to determine whether there is a problem of poor inventory management or excessive component reserves, so as to reasonably optimize the inventory structure, improve the utilization efficiency of inventory resources and reduce costs.

[0032] Step S600, when the inventory balance coefficient is less than or equal to the inventory balance coefficient threshold, the high-frequency fault component model is replenished. Specifically, first, the inventory balance coefficient is continuously monitored and compared with the preset threshold, which is determined based on the reliability requirements of the substation power grid, the procurement cycle and other factors. When the inventory balance coefficient is less than or equal to the threshold, the inventory replenishment decision process is triggered, and then the gap between the predicted number of component failures and the redundant inventory number of each high-frequency fault component model is traversed and calculated to determine the replenishment demand. At the same time, the replenishment quantity is adjusted based on the recent component replacement plan, replaceable components and inventory cost budget, and then an inventory replenishment plan is formulated, covering the determination of supplier selection criteria such as reputation, quality, price, delivery cycle, etc., considering the transportation method and logistics arrangement and formulating transportation guarantee measures for special components, and finally executing according to the plan, including signing contracts, tracking progress, arrival inspection and warehousing registration, etc., to improve the reliability and stability of substation power grid operation and maintenance, and ensure continuous and stable power supply.

[0033] In a possible implementation, when the inventory balancing coefficient is less than or equal to the inventory balancing coefficient threshold, the high-frequency fault component model is replenished in stock, and step S600 further includes step S610, traversing the predicted fault quantity of the first component model until the predicted fault quantity of the Mth component model, and performing balanced inventory analysis in combination with the inventory balancing coefficient threshold to obtain the balanced inventory of the first component model until the balanced inventory of the Mth component model. Specifically, the system will traverse the predicted fault quantity of the first component model to the predicted fault quantity of the Mth component model in sequence. For each component model, its predicted fault quantity and the inventory balancing coefficient threshold are used as key data to carry out balanced inventory analysis. For example, assuming that the inventory balancing coefficient threshold is set to 0.8, for a specific first component model, if its predicted fault quantity is 50, then its balanced inventory is calculated according to the formula, assuming that the calculated balanced inventory of this model should be 40. This calculation process is based on an accurate analysis of the estimated component failure risk and the overall inventory balance requirements. By combining the predicted number of failures for each component model with the threshold, and comprehensively considering various factors such as the importance of the component, the procurement cycle, and the storage cost, the balanced inventory volume that can ensure the stable operation of the power system while achieving a reasonable allocation of inventory resources is determined. After traversing all component models from the first to the Mth, a complete data set of the balanced inventory volume of the first component model to the balanced inventory volume of the Mth component model can be obtained, which will serve as the key basis for subsequent inventory replenishment.

[0034] Step S620, replenish the inventory of the high-frequency fault component model according to the balanced inventory of the first component model until the balanced inventory of the Mth component model. Specifically, after obtaining the balanced inventory of the first component model until the balanced inventory of the Mth component model, the inventory replenishment work of the high-frequency fault component model is carried out based on these data. For each high-frequency fault component model, its current actual inventory quantity is compared with the corresponding balanced inventory quantity. If the actual inventory quantity of a certain high-frequency fault component model is lower than its balanced inventory quantity, such as a certain model of transformer, its balanced inventory quantity is 10 units, and the actual inventory is only 6 units, then the inventory needs to be replenished. The replenished quantity is determined according to the difference between the two, that is, 4 units are replenished. In the replenishment process, factors such as the supplier's supply capacity, supply cycle, and transportation links also need to be considered. For example, select a supplier with a good reputation, who can supply on time and in quantity and whose transportation is safe and reliable, to ensure that the replenished components can arrive at the substation warehouse in a timely and accurate manner, thereby effectively improving the inventory level, enhancing the ability to deal with component failures, ensuring the stable operation of the substation power grid, and reducing the occurrence of problems such as power supply interruption or operation and maintenance difficulties caused by component shortages.

[0035] In a possible implementation, the high-frequency fault component model is replenished according to the balanced inventory of the first component model until the balanced inventory of the Mth component model, and step S620 further includes step S621 to obtain the idle inventory. Specifically, the inventory management system of the substation is comprehensively checked and data statistics are performed to determine the idle inventory. This process involves a detailed inventory of various components in the warehouse, including information such as the model, quantity, storage location, and inventory status of the components. Through specific inventory management software or data recording system, the number of components that have not been assigned to use tasks, are in an idle state, and can be deployed for replenishing inventory are screened out, and the idle inventory data is summarized. For example, in the substation warehouse, after careful inventory, it is found that 30 insulators of a certain model, 20 fuses of a certain model, etc. are idle. These quantities will be used as one of the important basic data for the subsequent optimization of the inventory replenishment plan, because the amount of idle inventory directly affects the scale of resources that can be used to replenish the inventory of high-frequency fault component models.

[0036] Step S622, constructing an inventory replenishment fitness function: Q3=w1Q1+w2Q2, where Q1 represents the first fitness function, Q2 represents the second fitness function, Q3 represents the inventory replenishment fitness function, and p i Characterizes the failure probability of the i-th component model, x i Characterizes the inventory of the i-th component model configuration, x i0Characterizes the equilibrium inventory of the i-th component model, w1 represents the first weight, and w2 represents the second weight. Specifically, constructing the inventory replenishment fitness function is a key step in optimizing the inventory replenishment plan. The function consists of multiple parts. The first is the first fitness function where p i Characterizes the failure probability of the i-th component model. It should be noted that there may be multiple failure probability identifiers for each component model. In this case, the maximum value shall prevail. For example, a certain type of transformer may have multiple failure probability values ​​under different operating conditions or historical fault records, such as 0.05, 0.08, etc., and 0.08 is taken as p i The value of x i Characterizes the inventory of the i-th component model configuration, x i0 Characterizes the equilibrium inventory of the i-th component model. This function quantifies the relationship between the failure probability of different component models and the configured inventory and equilibrium inventory, reflecting the correlation between component failure risk and inventory level. Second fitness function It mainly starts from the difference between inventory and equilibrium inventory, and measures the impact of the deviation of the current configuration inventory from the equilibrium inventory on the inventory replenishment plan. i With x i0 The greater the difference, The smaller the value, the less ideal the inventory status is and the more adjustments are needed. Finally, the inventory replenishment fitness function Q3 = w1Q1 + w2Q2, where w1 represents the first weight and w2 represents the second weight. The determination of these two weights requires comprehensive consideration of factors such as the tolerance of the substation power grid to fault risks and the requirements for inventory costs and resource utilization efficiency. For example, if the substation power grid is more sensitive to fault risks, then the value of w1 may be relatively large, and more emphasis may be placed on optimizing the inventory replenishment plan based on the probability of faults; conversely, if more emphasis is placed on inventory cost control, the value of w2 may be appropriately increased.

[0037] Step S623, according to the idle inventory, combined with the inventory replenishment fitness function, the inventory replenishment plan for the high-frequency fault component model is optimized according to the balanced inventory of the first component model until the balanced inventory of the Mth component model, and the target inventory replenishment plan corresponding to the maximum value of the inventory replenishment fitness function is obtained for inventory replenishment. Specifically, according to the obtained idle inventory, combined with the constructed inventory replenishment fitness function, the inventory replenishment plan for the high-frequency fault component model is optimized with the balanced inventory of the first component model until the balanced inventory of the Mth component model as the target reference. During the optimization process, different inventory replenishment strategies are substituted into the inventory replenishment fitness function for calculation and evaluation. For example, for a certain high-frequency fault component model, different combinations of replenishment quantities are considered, and the Q3 value under each combination is calculated. Through continuous trials and calculations, the inventory replenishment plan that makes the inventory replenishment fitness function Q3 obtain the maximum value is found, and this plan is the target inventory replenishment plan. Once the target inventory replenishment plan is determined, the actual inventory replenishment operation is performed according to the plan. This includes placing orders with suppliers to purchase the required components, arranging the transportation and receipt of components, and updating the inventory management system in a timely manner after the components are put into storage, etc., to ensure the smooth completion of inventory replenishment work, thereby improving the reliability of the substation power grid operation and maintenance and the scientific nature of inventory management.

[0038] The embodiment of the present application adopts the method of acquiring the power distribution mode of the substation power grid, analyzing the frequent fault mode of the power grid, identifying the type of high-frequency fault components and their fault probability identification; obtaining redundant inventory data from the maintenance component library according to the component type and deployment quantity, and performing a balance analysis on the inventory to obtain the inventory balance coefficient; when the inventory balance coefficient is lower than the threshold, the inventory of the high-frequency fault component model is replenished, thereby achieving the technical effect of real-time dynamic inventory optimization management.

[0039] In the above, refer to Figure 1 The multi-task scheduling optimization method based on the power Internet of Things according to an embodiment of the present invention is described in detail. Figure 2 A multi-task scheduling optimization system based on the power Internet of Things according to an embodiment of the present invention is described.

[0040] The multi-task scheduling optimization system based on the power Internet of Things according to the embodiment of the present invention is used to solve the technical problem of insufficient inventory of high-frequency fault components due to the lack of real-time analysis of inventory status in the existing power operation and maintenance management, and achieves the technical effect of real-time dynamic inventory optimization management. The multi-task scheduling optimization system based on the power Internet of Things includes: a distribution mode acquisition module 10, a mode analysis module 20, a component deployment quantity acquisition module 30, a redundant inventory quantity acquisition module 40, a balance analysis module 50, and an inventory replenishment module 60.

[0041] The power distribution mode acquisition module 10 is used to obtain the power distribution mode of the substation power grid.

[0042] The mode analysis module 20 is used to perform frequent mode analysis of power grid faults according to the power distribution mode of the substation power grid to obtain a high-frequency fault component type, wherein the high-frequency fault component type has a component fault probability identifier.

[0043] The component deployment quantity acquisition module 30 is used to obtain the number of components of the high-frequency fault component type deployed in the substation.

[0044] The redundant inventory quantity acquisition module 40 is used to obtain the redundant inventory quantity of the high-frequency fault component model of the high-frequency fault component type according to the substation maintenance component library.

[0045] The balance analysis module 50 is used to perform a balance analysis on the redundant inventory quantity according to the component deployment quantity and the component failure probability identifier to obtain an inventory balance coefficient.

[0046] The inventory replenishment module 60 is used to replenish the inventory of the high-frequency fault component model when the inventory balance coefficient is less than or equal to the inventory balance coefficient threshold.

[0047] The specific configuration of the pattern analysis module 20 will be described in detail below. As described above, a frequent pattern analysis of power grid faults is performed according to the power grid distribution mode of the substation to obtain a high-frequency fault component type, wherein the high-frequency fault component type has a component failure probability identifier, and the pattern analysis module 20 further includes: a first substation component extraction unit, the first substation component extraction unit is used to extract a first substation component according to the power grid distribution mode of the substation, wherein the first substation component has a first substation component model and a first substation component electrical control parameter; a sample set acquisition unit, the sample set acquisition unit is used to collect a substation power grid fault sample set based on the substation deployment environment monitoring data and the power grid distribution mode of the substation as constraints; frequency identifier acquisition The frequency identification acquisition unit is used to analyze the frequent power grid fault patterns of the first substation element based on the first substation element model and the first substation element electrical control parameters according to the substation power grid fault sample set, and obtain the first substation element trigger frequency and the first substation element failure probability identification; the probability identification storage unit is used to associate the first substation element, the first substation element model and the first substation element electrical control parameters into the high-frequency fault element type when the first substation element trigger frequency is greater than or equal to the trigger frequency threshold, and store the first substation element failure probability identification into the element failure probability identification.

[0048] Among them, according to the substation area power grid fault sample set, the first substation area component is subjected to a power grid fault frequent pattern analysis based on the first substation area component model and the first substation area component electrical control parameters, and the first substation area component trigger frequency and the first substation area component failure probability identifier are obtained, and the frequency identifier acquisition unit further includes: a first substation area component trigger frequency acquisition subunit, the first substation area component trigger frequency acquisition subunit is used to count the number of samples that simultaneously meet the first substation area component electrical control parameters and the first substation area component model according to the substation area power grid fault sample set, and obtain the first substation area component trigger frequency; a sample number proportion calculation subunit, the sample number proportion calculation subunit is used to calculate the proportion of the number of samples that simultaneously meet the first substation area component electrical control parameters and the first substation area component model, and set it as the first substation area component failure probability identifier.

[0049] The specific configuration of the component deployment quantity acquisition module 30 will be described in detail below. As described above, the component deployment quantity acquisition module 30 further includes: a component deployment quantity acquisition unit, which is used to obtain the component deployment quantity of the components that simultaneously meet the first station component electrical control parameters and the first station component model in the station.

[0050] The specific configuration of the balance analysis module 50 will be described in detail below. As described above, according to the component deployment quantity and the component failure probability identifier, the redundant inventory quantity is balanced analyzed to obtain the inventory balance coefficient. The balance analysis module 50 further includes: a product calculation unit, the product calculation unit is used to perform product calculation on the component deployment quantity and the component failure probability identifier to obtain the component failure prediction quantity; and an inventory balance coefficient setting unit, the inventory balance coefficient setting unit is used to calculate the ratio of the redundant inventory quantity to the component failure prediction quantity, and set it as the inventory balance coefficient.

[0051] The specific configuration of the inventory replenishment module 60 will be described in detail below. As described above, when the inventory balancing coefficient is less than or equal to the inventory balancing coefficient threshold, the high-frequency fault component model is replenished in stock, and the inventory replenishment module 60 further includes: a balanced inventory quantity analysis unit, the balanced inventory quantity analysis unit is used to traverse the first component model fault prediction quantity until the Mth component model fault prediction quantity, and perform balanced inventory quantity analysis in combination with the inventory balancing coefficient threshold to obtain the first component model balanced inventory quantity until the Mth component model balanced inventory quantity; an inventory replenishment unit, the inventory replenishment unit is used to replenish the high-frequency fault component model according to the first component model balanced inventory quantity until the Mth component model balanced inventory quantity.

[0052] According to the balanced inventory of the first component model until the balanced inventory of the Mth component model, the high-frequency fault component model is replenished in stock, and the inventory replenishment unit further includes: an idle inventory acquisition subunit, the idle inventory acquisition subunit is used to obtain the idle inventory; and an inventory replenishment fitness function construction subunit, the inventory replenishment fitness function construction subunit is used to construct an inventory replenishment fitness function: Q3=w1Q1+w2Q2, where Q1 represents the first fitness function, Q2 represents the second fitness function, Q3 represents the inventory replenishment fitness function, and p i Characterizes the failure probability of the i-th component model, x i Characterizes the inventory of the i-th component model configuration, x i0 Characterize the balanced inventory of the i-th component model, w1 characterizes the first weight, and w2 characterizes the second weight; a replenishment plan optimization subunit, the replenishment plan optimization subunit is used to optimize the inventory replenishment plan of the high-frequency fault component model according to the free inventory and the inventory replenishment fitness function, according to the balanced inventory of the first component model until the balanced inventory of the M-th component model, and obtain the target inventory replenishment plan corresponding to the maximum value of the inventory replenishment fitness function for inventory replenishment.

[0053] The multi-task scheduling optimization system based on the power Internet of Things provided in the embodiment of the present invention can execute the multi-task scheduling optimization method based on the power Internet of Things provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0054] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0055] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does 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.

Claims

1. A multi-task scheduling optimization method based on the power Internet of Things, characterized in that: include: Obtain the power distribution mode of the power grid in the substation area; Performing a frequent fault pattern analysis on the power grid according to the power distribution mode of the substation area power grid to obtain a high-frequency fault component type, wherein the high-frequency fault component type has a component fault probability identifier; Obtain the number of components deployed in the substation of the high-frequency fault component type; According to the maintenance component library of the substation, the redundant inventory quantity of the high-frequency fault component model of the high-frequency fault component type is obtained; According to the component deployment quantity and the component failure probability identifier, a balance analysis is performed on the redundant inventory quantity to obtain an inventory balance coefficient; When the inventory balance coefficient is less than or equal to the inventory balance coefficient threshold, the inventory of the high-frequency fault component model is replenished.

2. The method according to claim 1, characterized in that Performing a frequent fault mode analysis on the power grid according to the power distribution mode of the substation area to obtain a high-frequency fault component type, wherein the high-frequency fault component type has a component fault probability identifier, including: Extracting a first substation component according to the substation power grid distribution mode, wherein the first substation component has a first substation component model and a first substation component electrical control parameter; Collecting a set of substation power grid fault samples based on the substation deployment environment monitoring data and the substation power grid distribution mode as constraints; According to the grid fault sample set of the substation area, based on the first substation area component model and the first substation area component electrical control parameter, a grid fault frequent mode analysis is performed on the first substation area component to obtain the first substation area component trigger frequency and the first substation area component failure probability identifier; When the trigger frequency of the first area element is greater than or equal to the trigger frequency threshold, the first area element, the first area element model and the first area element electrical control parameters are associated and stored in the high-frequency fault element type, and the first area element failure probability identifier is stored in the element failure probability identifier.

3. The method according to claim 2, characterized in that According to the grid fault sample set of the substation area, based on the first substation area component model and the first substation area component electrical control parameter, a grid fault frequent mode analysis is performed on the first substation area component to obtain the first substation area component trigger frequency and the first substation area component failure probability identifier, including: According to the grid fault sample set of the substation, the number of samples that simultaneously meet the electrical control parameters of the first substation component and the first substation component model are counted to obtain the triggering frequency of the first substation component; The proportion of the number of samples that simultaneously meet the electrical control parameters of the first area component and the first area component model is calculated and set as the first area component failure probability identifier.

4. The method according to claim 2, characterized in that Obtaining the number of components deployed in the substation of the high-frequency fault component type, including: The component deployment quantity of the components in the stage area that simultaneously meet the first stage area component electrical control parameters and the first stage area component model is obtained.

5. The method according to claim 1, characterized in that According to the component deployment quantity and the component failure probability identifier, a balance analysis is performed on the redundant inventory quantity to obtain an inventory balance coefficient, including: Calculating the product of the component deployment quantity and the component failure probability identifier to obtain the component failure prediction quantity; The ratio of the redundant inventory quantity to the predicted component failure quantity is calculated and set as the inventory balance coefficient.

6. The method according to claim 5, characterized in that When the inventory balance coefficient is less than or equal to the inventory balance coefficient threshold, the high-frequency fault component model is replenished with inventory, including: Traversing the predicted number of faults of the first component model until the predicted number of faults of the Mth component model, performing balanced inventory analysis in combination with the inventory balance coefficient threshold, and obtaining the balanced inventory of the first component model until the balanced inventory of the Mth component model; The high-frequency fault component model is replenished in stock according to the balanced inventory of the first component model until the balanced inventory of the Mth component model.

7. The method according to claim 6, characterized in that According to the balanced inventory of the first component model until the balanced inventory of the Mth component model, the inventory of the high-frequency fault component model is replenished, including: Get the free inventory amount; Construct the inventory replenishment fitness function: Q3=w1Q1+w2Q2, Among them, Q1 represents the first fitness function, Q2 represents the second fitness function, Q3 represents the inventory replenishment fitness function, and p i Characterizes the failure probability of the i-th component model, x i Characterizes the inventory of the i-th component model configuration, x i0 represents the balanced inventory of the i-th component model, w1 represents the first weight, and w2 represents the second weight; According to the free inventory and in combination with the inventory replenishment fitness function, the inventory replenishment plan for the high-frequency fault component model is optimized according to the balanced inventory of the first component model until the balanced inventory of the Mth component model, and the target inventory replenishment plan corresponding to the maximum value of the inventory replenishment fitness function is obtained for inventory replenishment.

8. The multi-task scheduling optimization system based on the power Internet of Things is characterized by: The system is used to implement the multi-task scheduling optimization method based on the power Internet of Things according to any one of claims 1 to 7, and the system includes: A power distribution mode acquisition module, which is used to obtain the power distribution mode of the power grid in the substation area; A mode analysis module, the mode analysis module is used to perform frequent mode analysis of power grid faults according to the power distribution mode of the substation power grid to obtain a high-frequency fault component type, wherein the high-frequency fault component type has a component fault probability identifier; A component deployment quantity acquisition module, the component deployment quantity acquisition module is used to obtain the number of components deployed in the substation of the high-frequency fault component type; A redundant inventory quantity acquisition module, the redundant inventory quantity acquisition module is used to obtain the redundant inventory quantity of the high-frequency fault component model of the high-frequency fault component type according to the maintenance component library of the substation area; A balance analysis module, the balance analysis module is used to perform a balance analysis on the redundant inventory quantity according to the component deployment quantity and the component failure probability identifier to obtain an inventory balance coefficient; An inventory replenishment module is used to replenish the inventory of the high-frequency fault component model when the inventory balance coefficient is less than or equal to the inventory balance coefficient threshold.

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