An intelligent management method for distribution network equipment operation and maintenance work orders based on IoT perception

Abstract

The present invention discloses an intelligent management method for distribution network equipment operation and maintenance work orders based on Internet of Things perception, which belongs to the field of distribution network equipment operation and maintenance technology, and comprises the following steps: evaluating the importance of distribution network equipment based on the node position of the distribution network equipment corresponding to the operation and maintenance work order and the size of the harm after the failure; obtaining the operating status of the distribution network equipment based on the evaluation result of the importance according to the Internet of Things perception technology; prioritizing the operation and maintenance work orders based on the operating status and the evaluation result of the importance, and updating the priority of the operation and maintenance work orders by dynamically updating the operation status and the evaluation result; the present invention realizes adaptive updating of equipment operation status evaluation and importance evaluation based on the Internet of Things perception technology, thereby realizing dynamic updating of work order operation and maintenance priority sorting.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network equipment operation and maintenance, and in particular to an intelligent management method for distribution network equipment operation and maintenance work orders based on Internet of Things perception. Background Art

[0002] With the rapid development of the economy and society and the continuous improvement of living standards, the power load of the power grid is increasing, the number of distribution network equipment is increasing, and at the same time, the power customers' requirements for power supply reliability, power quality, emergency response and other power supply services are increasing. The task of power supply companies to ensure power supply and do a good job in power equipment maintenance is becoming more and more arduous. The development concepts of the State Grid Corporation of China, such as energy Internet and smart grid, have promoted the development of the power grid in the direction of intelligence. As a key link in the power grid, the distribution network urgently needs to promote its infrastructure construction and intelligent upgrade plan.

[0003] The current management mode of regular inspection of distribution equipment in the power grid is inefficient. With the continuous increase of distribution equipment, the inspection tasks are becoming more and more onerous. Some distribution network systems lack online status monitoring technology. They only rely on manual inspection to evaluate the operation and maintenance status of equipment, which is not reliable and has accidental and limited features. They cannot scientifically formulate operation and maintenance plans, which leads to problems such as "over-repair" and "under-repair". At present, the power system handles operation and maintenance work orders by directly arranging the team to repair them. The problem of operation and maintenance priority sorting based on the severity of the fault is not considered, which reduces the timeliness and effectiveness of operation and maintenance to a certain extent and increases the operation and maintenance cost. Based on this background, an intelligent management method for operation and maintenance work orders based on the Internet of Things sensing technology is designed to realize the operation and maintenance priority sorting of work orders based on the importance of equipment and real-time operation status.

[0004] In the existing technical solutions, there are the following modes for the operation and maintenance of distribution network equipment: (1) Periodic maintenance mode: This mode is a preventive operation and maintenance for equipment, and the equipment is inspected and repaired regularly based on time; (2) Status-based maintenance mode: The equipment is equipped with status monitoring sensors, and the equipment parameters obtained by the sensor monitoring are transmitted to the remote background for analysis and evaluation to determine the real-time operating status of the power equipment.

[0005] The following solutions are available for work order management: (1) Integrate relevant information based on distribution network automation, distribution transformer online monitoring, distribution network fault indicator, and power consumption information collection system to implement fault diagnosis and analysis. The fault equipment repair or emergency repair work order is transmitted to the dispatching platform, and the dispatching personnel directly dispatch operation and maintenance personnel to complete the equipment maintenance. (2) Based on the matching degree of operation and maintenance personnel's operation and maintenance work orders, minimizing the operation and maintenance work order time and minimizing the operation and maintenance work order cost, an operation and maintenance work order operation and maintenance scheduling model is established, and a multi-objective optimization processing model with the highest matching degree, the lowest time and cost is established.

[0006] In the existing technical solutions for the operation and maintenance mode of distribution network equipment, the regular maintenance mode is not reliable and has contingencies and limitations. It is impossible to scientifically formulate the operation and maintenance plan, which leads to the problems of "over-maintenance" and "under-maintenance". The state-based maintenance mode relies on the background server and long-distance communication capabilities. The communication cost is high during the data transmission to the background server, and there are problems of data interruption and loss. In addition, the abnormality of the main node of the background server may cause the overall paralysis of the maintenance system. In the management plan for work orders, the dispatcher directly dispatches the operation and maintenance personnel to complete the equipment maintenance process without considering the operation and maintenance priority of multiple faults. High-risk faults may not be handled in time, resulting in the expansion of the fault range and the increase of operation and maintenance costs. The dispatch model based on the highest matching degree, the minimum time and cost focuses on the economic problem. Under this dispatch model, there is also the problem that high-risk faults cannot be handled in time. Summary of the invention

[0007] In order to solve the above problems, the purpose of the present invention is to propose an intelligent work order management method, which realizes automatic detection and update of the real-time operating status of equipment based on the Internet of Things sensing technology, and at the same time sorts the work order operation and maintenance priorities by considering the node location of the equipment and the degree of harm that may be caused after a failure, and formulates a scientific and effective equipment operation and maintenance plan.

[0008] In order to achieve the above technical objectives, the present application provides a method for intelligent management of distribution network equipment operation and maintenance work orders based on IoT perception, comprising the following steps:

[0009] Evaluate the importance of distribution network equipment based on the node location of the distribution network equipment corresponding to the operation and maintenance work order and the damage caused by the failure;

[0010] Based on the evaluation results of importance, the operating status of distribution network equipment is obtained using IoT sensing technology;

[0011] Based on the operation status and the evaluation results of importance, the operation and maintenance work orders are prioritized, and the priority of the operation and maintenance work orders is updated by dynamically updating the operation status and evaluation results.

[0012] Preferably, in the process of evaluating the importance of distribution network equipment, the importance of distribution equipment is evaluated based on the voltage level of the distribution equipment corresponding to the operation and maintenance work order, the load level carried, the economic loss after equipment failure, the equipment centrality, and the social reputation impact of the power supply company after the failure.

[0013] Preferably, in the process of obtaining the voltage level of the power distribution equipment, the voltage level of the power distribution equipment is obtained according to a voltage level index evaluation model, wherein the voltage level index evaluation model is expressed as:

[0014]

[0015] in, x i Indicates the membership of the voltage level u of the distribution equipment in the distribution network voltage level interval, k 11 , k 12 , k 13 Respectively represent the voltage level u 1 、u 2 、u 3 Time A 1 Score value and satisfy k 11 <k 12 <k 13 ;

[0016] Voltage level index evaluation model is used to divide the distribution network voltage level into (u 0 ,u 1 ),[u 1 ,u 2 ),[u 2 ,u 3 ) and greater than or equal to u 3 Four intervals, use Determine the membership x of the voltage level u of the distribution equipment in the above four intervals i At the same time, normalization processing is implemented, and the obtained membership degree x i Substitute into the evaluation model to get the index score A 1 .

[0017] Preferably, in the process of obtaining the load level of the power distribution equipment, the load level of the power distribution equipment is obtained through a load level index evaluation model, wherein the load level index evaluation model is expressed as:

[0018]

[0019] Among them, A 2 Indicates the load level index score of the power distribution equipment; S indicates the load capacity of the equipment; S 1 , S 2 , S 3 Respectively represent the primary, secondary and tertiary load capacities of the equipment; k 21 , k 22 , k 23 A indicates when the equipment carries the first, second and third level loads respectively. 2 Score value, where k 23 <k 22 <k 21 .

[0020] Preferably, in the process of obtaining the economic loss after the equipment failure, the economic loss after the equipment failure is obtained through an economic loss index evaluation model, wherein the economic loss index evaluation model is expressed as:

[0021]

[0022] a=a 3i +a 32 +a 33 +a 34

[0023]

[0024] Where a represents the total economic loss caused by equipment failure; a 31 Indicates equipment repair or replacement costs; a 32 Indicates labor costs; a 33 Indicates the electricity loss caused by power outage; a 34 Indicates the financial compensation that the power supply company may face; k 31 , k 32 , k 33 The total economic loss is a 1 、a 2 、a 3 Time A 3 Score value and satisfy k 31 <k 32 <k 33 ;

[0025] The economic loss index evaluation model is used to divide the total economic loss caused by equipment failure into (0, a 1 )、(a 1 ,a 2 )(a 2 ,a 3 ) and greater than or equal to a 3 Four intervals, use Determine the membership degree x of the economic loss a in the above four intervals i At the same time, normalization processing is implemented, and the obtained membership degree x i Substitute into the evaluation model to get the index score A 3 , where k 31 , k 32 , k 33 The total economic loss is a 1 、a 2 、a 3 Time A 3 Score value and satisfy k 31 <k 32 <k 33 .

[0026] Preferably, in the process of obtaining the device centrality, the cascading failure risk is divided into four levels: no risk, low risk, medium risk, and high risk, which are represented by A, B, C, and D respectively. The cascading failure risk level is divided by experts based on historical data;

[0027] The device centrality score is recorded as A 4 The higher the risk, i.e. the higher the centrality, of the equipment, the higher the score, i.e. the higher the importance, and the higher the maintenance priority of the corresponding work order;

[0028] When the risk level is no risk, the corresponding centrality is A 4 =k 41 , when the risk level is low risk, the corresponding centrality is A 4 =k 42 , when the risk level is medium risk, the corresponding centrality is A 4 =k 43 , when the risk level is high risk, the corresponding centrality is A 4 =k 44 , where k 41 <k 42 <k 43 <k 44 .

[0029] Preferably, in the process of obtaining the social reputation impact of the power supply company after the fault, the social reputation impact of the power supply company after the fault is obtained through a social reputation impact index evaluation model, wherein the social reputation impact index evaluation model is expressed as:

[0030]

[0031] Among them, A 5 represents the score of the social reputation impact index; ρ is the load density of the fault affected area; k 51 , k 52 , k 53 , k 54 Denote the load density as ρ 1 Below, ρ 1 -ρ 2 , 2 -ρ 3 , 3 A 5 The score value, k 51 <k 52 <k 53 <k 54 .

[0032] Preferably, in the process of evaluating the importance, each indicator A is determined based on the fuzzy analytic hierarchy process. i Corresponding weight λ i, according to the expert's suggestion, the quantitative values ​​between the above indicators are determined to establish a judgment matrix, and then the importance of the operation and maintenance work order corresponding to the power distribution equipment is obtained. The importance is expressed as:

[0033]

[0034] Preferably, in the process of obtaining the operating status, the operating status of the power distribution equipment is obtained through an operating status evaluation model, wherein the operating status evaluation model is expressed as:

[0035]

[0036]

[0037] In the formula, B i Indicates the real-time operating status score of power distribution equipment i; Respectively indicate the equipment operation status as normal, caution, abnormal, and fault; k b1 , k b2 , k b3 Respectively The value is F 1 、F 2 、F 3 The score of B is k b1 <k b2 <k b3 .

[0038] Preferably, in the process of prioritizing and updating the operation and maintenance work orders, when the power distribution equipment fails, the work orders are sorted according to the emergency repair priority model, wherein the emergency repair priority model is expressed as:

[0039]

[0040] The repair priority model is used to indicate the priority of repair Exceeding the threshold F 3 The equipment with the most equipment will be dispatched first. ∞ The operation and maintenance work order corresponding to device i;

[0041] When there is no fault in the power distribution equipment, the equipment is sorted according to the operation and maintenance priority model, where the operation and maintenance priority model is expressed as:

[0042] C=μ 1 A+μ 2 B

[0043] In the formula, C represents the priority index of operation and maintenance work order processing, A represents the importance of the equipment, and B represents the operating status. The corresponding weights μ of the equipment importance A and the operating status B are determined based on the fuzzy hierarchical analysis method.1 , μ 2 ;

[0044] Dynamic updates to operational priorities include the following:

[0045] (1) After the operation and maintenance work order is dispatched to the relevant operation and maintenance personnel for processing, the system will transmit the fault information of each device back to the equipment operation status detection system to achieve dynamic self-adaptation of the status assessment threshold of the equipment at different periods;

[0046] (2) Feedback information on economic losses, cascading failures, system shutdowns, and social reputation impacts from the time the failure occurs to the completion of operation and maintenance to the equipment importance evaluation system to dynamically update the importance of each piece of equipment;

[0047] (3) Based on the IoT sensing technology, the real-time update of the equipment’s real-time operating status indicator B is realized.

[0048] The present invention also provides an intelligent management system for distribution network equipment operation and maintenance work orders based on IoT perception, which is used to implement an intelligent management method for distribution network equipment operation and maintenance work orders, including:

[0049] The data acquisition module is used to obtain the operating status of the distribution network equipment, wherein the importance of the distribution network equipment is obtained based on the node location of the distribution network equipment corresponding to the operation and maintenance work order and the damage after the failure, and the operating status of the distribution network equipment is monitored based on the Internet of Things sensing technology;

[0050] The operation and maintenance control module is used to prioritize operation and maintenance work orders based on the operation status and the evaluation results of the importance, and to update the priority of the operation and maintenance work orders by dynamically updating the operation status and evaluation results.

[0051] The present invention discloses the following technical effects:

[0052] (1) Based on IoT sensing technology, the real-time operation status of the equipment can be automatically detected and updated, and the status detection accuracy is higher;

[0053] (2) The equipment importance evaluation system comprehensively considers the importance of the node where the equipment is located and the impact of a failure on the system and the power supply company. The evaluation system is scientific and comprehensive.

[0054] (3) Based on the IoT sensing technology, the adaptive update of equipment operation status assessment and importance assessment is realized, thereby realizing the dynamic update of work order operation and maintenance priority sorting. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. 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 paying creative work.

[0056] Figure 1 It is a flow chart of the intelligent management method for operation and maintenance work orders described in the present invention. DETAILED DESCRIPTION

[0057] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings 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. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0058] like Figure 1 As shown, the present invention proposes an intelligent management method for distribution network equipment operation and maintenance work orders based on the Internet of Things sensing technology, which aims to solve the problem that equipment with high importance or great harm after failure does not receive priority operation and maintenance in daily inspections and fault repair work, and at the same time, based on the Internet of Things sensing technology, it realizes real-time perception of the equipment operation status to improve the efficiency of operation and maintenance work and reduce the operation and maintenance cost. In combination with the importance of distribution equipment in the power grid and the real-time operation status, an intelligent management method for distribution network equipment operation and maintenance work orders based on the Internet of Things sensing technology is proposed. First, an equipment importance evaluation system is established based on the node location of the equipment and the degree of harm after the failure, and a real-time operation status detection system of the equipment is established based on the Internet of Things sensing technology. Then, the priority of the operation and maintenance work order is determined based on the equipment importance and real-time operation status, and the dynamic update of the work order operation and maintenance priority under the Internet of Things sensing technology is realized.

[0059] The implementation of the technical solution provided by the present invention mainly includes the following four parts:

[0060] 1. Establish an equipment importance evaluation system based on the node location of the equipment and the damage caused by failure;

[0061] 2. Establish a real-time equipment operation status detection system based on IoT sensing technology;

[0062] 3. Comprehensively sort the work order operation and maintenance priorities based on the real-time operation status and importance of the equipment;

[0063] 4. Dynamic update of work order operation and maintenance priority sorting under IoT sensing technology.

[0064] 1. Establish an equipment importance evaluation system based on the node location of the equipment and the damage caused by failure:

[0065] The importance of distribution equipment is evaluated based on the voltage level of the distribution equipment corresponding to the operation and maintenance work order, the load level, economic losses after failure, equipment centrality, and the social reputation impact of the power supply company after failure. The higher the importance of the equipment, the higher the priority of the work order operation and maintenance. The evaluation model is established as follows:

[0066] (1) Voltage level index evaluation model:

[0067] Establish a voltage level index evaluation model to obtain the index score A 1 The higher the voltage level of the equipment, the higher the score of this item, which means the higher the importance, and the higher the maintenance priority of the corresponding work order:

[0068] in

[0069] In this evaluation model, the distribution network voltage level is divided into (u 0 ,u 1 ),[u 1 ,u 2 ),[u 2 ,u 3 ) and greater than or equal to u 3 Four intervals, use Determine the membership x of the voltage level u of the distribution equipment in the above four intervals i At the same time, normalization processing is implemented, and the obtained membership degree x i Substitute into the evaluation model to get the index score A 1 , where k 11 , k 12 , k 13 Respectively represent the voltage level u 1 、u 2 、u 3 Time A 1 Score value and satisfy k 11 <k 12 <k 13 The inverse hyperbolic tangent function is used in this evaluation model. The function increases monotonically in (0,1) and the larger the x, the greater the function rise rate, which is consistent with the scores k in different voltage intervals. 1i Combining multiple weights can be achieved to improve evaluation sensitivity.

[0070] (2) Load level index evaluation model:

[0071] Establish a load level index evaluation model to obtain the index score A 2 Different power distribution equipment supplies different load levels. The higher the load level, the higher the score of the equipment, which means the higher the importance, and the higher the maintenance priority of the corresponding work order:

[0072]

[0073] Where: A 2 Indicates the load level index score of the power distribution equipment; S indicates the load capacity of the equipment; S 1 , S 2 , S 3 Respectively represent the primary, secondary and tertiary load capacities of the equipment; k 21 , k 22 , k 23 A indicates when the equipment carries the first, second and third level loads respectively. 2 Score value, where k 23 <k 22 <k 21 .

[0074] (3) Economic loss index evaluation model:

[0075] Establish an economic loss index evaluation model to obtain the index score A 3 ,After different equipment fails, the economic losses per unit time to the power supply company are different. The equipment with large economic losses per unit time has a higher score, which means it is more important and has a higher maintenance priority for the corresponding work order:

[0076] a=a 31 +a 32 +a 33 +a 34

[0077] Where: a represents the total economic loss caused by equipment failure; a 31 Indicates equipment repair or replacement costs; a 32 Indicates labor costs; a 33 Indicates the electricity loss caused by power outage; a 34 Indicates the financial compensation that the power supply company may face.

[0078] in

[0079] This evaluation model is similar to the voltage level index evaluation model, which divides the total economic loss caused by equipment failure into (0, a 1 )、(a 1 ,a2 )(a 2 ,a 3 ) and greater than or equal to a 3 Four intervals, use Determine the membership degree x of the economic loss a in the above four intervals i At the same time, normalization processing is implemented, and the obtained membership degree x i Substitute into the evaluation model to get the index score A 3 , where k 31 , k 32 , k 33 The total economic loss is a 1 、a 2 、a 3 Time A 3 Score value and satisfy k 31 <k 32 <k 33 . Similarly, using the inverse hyperbolic tangent function The scores k of different economic loss ranges 3i Combine and implement multiple weights to improve evaluation sensitivity.

[0080] (4) Equipment centrality evaluation index model:

[0081] Different distribution equipment and different types of failures may lead to cascading failures. The device centrality is used to evaluate the risk of cascading failures. Considering that the more central the equipment is, the higher the risk of cascading failures will be. The corresponding device centrality value is higher. Based on this, a device centrality evaluation index model is established. The cascading failure risk is divided into four levels: no risk, low risk, medium risk, and high risk, represented by A, B, C, and D respectively. The cascading failure risk level is divided by experts based on historical data. The device centrality score is recorded as A. 4 The higher the score of the equipment with high risk, i.e. high centrality, the higher the importance, and the higher the maintenance priority of the corresponding work order. 4 =k 41 , when the risk level is low risk, the corresponding centrality is A 4 =k 42 , when the risk level is medium risk, the corresponding centrality is A 4 =k 43 , when the risk level is high risk, the corresponding centrality is A 4 =k 44 , where k 41 <k 42 <k 43 <k 44 .

[0082] (5) Social reputation impact indicator evaluation model:

[0083] The social reputation impact index is evaluated using the load density of the affected area after equipment failure. It is believed that the greater the load density of the affected area, the greater the negative impact on the reputation of the power supply company. The larger the score of the indicator, the higher the corresponding work order operation and maintenance priority.

[0084]

[0085] Where: A 5 represents the score of the social reputation impact index; ρ is the load density of the fault affected area; k 51 , k 52 , k 53 , k 54 Denote the load density as ρ 1 Below, ρ 1 -ρ 2 , 2 -ρ 3 , 3 A 5 The score value, k 51 <k 52 <k 53 <k 54 .

[0086] Based on the fuzzy analytic hierarchy process, the A i Corresponding weight λ i According to the expert's suggestion, the quantitative values ​​between the above indicators are determined to establish a judgment matrix, and then the comprehensive importance degree A of the power distribution equipment corresponding to the operation and maintenance work order is obtained:

[0087]

[0088] 2. Establish a real-time equipment operation status detection system based on IoT sensing technology:

[0089] During the operation of the equipment, partial discharge, temperature, humidity, etc. will affect the normal operation of the equipment. According to the application characteristics of the distribution network equipment, the primary and secondary device fusion technology is used to configure ultrasonic sensors, TEV sensors, humidity sensors, thermal imaging sensors, infrared visible light sensors and other IoT sensing sensors to realize the real-time status perception of the equipment, and configure intelligent IoT sensing terminals to realize the collection and preprocessing of various sensor sensing data. Considering the dependency relationship between the collected multi-dimensional IoT sensing data, the operating status of the equipment is detected by combining historical fault data and multi-dimensional IoT sensing data to obtain the real-time operating status of the equipment.

[0090] Definition i,t is the vector of multi-dimensional IoT sensing data of device i at time t, converting the multi-dimensional data vector into numerical value β i,t =f(γ i,t ), based on the multi-dimensional IoT sensing data at time t and before, the corresponding time βi,t , and then predict the time t+δ

[0091]

[0092] Where: μ i,t =g(β i,1 ,β i,2 ,…,β i,t ); models f, g, φ are all obtained by neural network modeling.

[0093] Defining predicted values The distance from the actual value β

[0094]

[0095] Defining predicted values The distance from the actual value β The threshold F 1 、F 2 、F 3 , the equipment operation status is divided into four types: normal, warning, abnormal, and fault:

[0096]

[0097] Where: B i Indicates the real-time operating status score of power distribution equipment i. The larger the value, the worse the real-time operating status of the equipment. Respectively indicate the equipment operation status as normal, caution, abnormal, and fault; k b1 , k b2 , k b3 Respectively The value is F 1 、F 2 、F 3 The score of B is k b1 <k b2 <k b3 .

[0098] 3. Comprehensive equipment real-time operation status and importance to realize work order operation and maintenance priority sorting:

[0099] Based on the real-time operating status and importance of the power distribution equipment determined in 1 and 2, the worse the real-time operating status and the higher the importance of the power distribution equipment, the higher the priority of the corresponding work order operation and maintenance processing should be.

[0100] (1) When the equipment is in a faulty state, work orders are dispatched first. If there are n equipment Determine the repair priority of these devices based on the infinite norm:

[0101]

[0102] Priority repair Exceeding the threshold F 3 The equipment with the most equipment will be dispatched first. ∞ The operation and maintenance work order corresponding to device i.

[0103] (2) When the equipment is in a non-faulty operating state, the operation and maintenance work orders are dispatched in sequence based on the determined operation and maintenance priority index C. The method for determining the operation and maintenance priority index C is as follows:

[0104] Determine the weight μ corresponding to the equipment importance A and operating status B based on fuzzy hierarchical analysis method 1 , μ 2 According to the expert's suggestion, the quantitative values ​​between the indicators are determined to establish a judgment matrix. It is believed that the impact of the real-time operation status of the equipment is greater than the impact of the equipment's importance, and then the operation and maintenance work order processing priority indicator C is obtained:

[0105] C=μ 1 A+μ 2 B

[0106] 4. Dynamic update of work order operation and maintenance priority sorting under IoT sensing technology:

[0107] The dynamic update of equipment operation and maintenance priority under IoT sensing technology includes the following parts:

[0108] (1) The intelligent work order management system dispatches the operation and maintenance work order to the relevant operation and maintenance personnel. After the work order is processed, the system transmits the fault information of each device back to the equipment operation status detection system to achieve dynamic self-adaptation of the status assessment threshold of the equipment at different periods;

[0109] (2) Feedback information such as economic losses, cascading failures, system shutdowns, and social reputation impacts from the time the failure occurs to the completion of operation and maintenance to the equipment importance evaluation system to achieve dynamic updates of the importance of each piece of equipment;

[0110] (3) Based on the multi-dimensional data and operation status detection system of the Internet of Things sensing sensors configured in Content 2, the real-time update of the equipment real-time operation status indicator B is realized.

[0111] Combining the above three parts, the dynamic update of the operation and maintenance work order processing priority index C is realized. . .

[0112] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0114] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent management method for distribution network equipment operation and maintenance work orders based on IoT perception, characterized in that: The following steps are involved: Based on the node location of the distribution network equipment corresponding to the operation and maintenance work order and the magnitude of the harm after the failure, the importance of the distribution network equipment is evaluated. In the process of evaluating the importance of the distribution network equipment, the importance of the distribution network equipment is evaluated based on the voltage level, load level, economic loss after the equipment failure, equipment centrality, and social reputation impact of the power supply company after the failure of the distribution network equipment corresponding to the operation and maintenance work order; Based on the evaluation result of the importance, the operating status of the distribution network equipment is obtained according to the IoT sensing technology; Based on the operation status and the evaluation result of the importance, the operation and maintenance work order is prioritized, and the priority of the operation and maintenance work order is updated by dynamically updating the operation status and the evaluation result. In the process of prioritizing and updating the operation and maintenance work order, when the power distribution equipment fails, the operation and maintenance work order is prioritized according to the emergency repair priority model, wherein the emergency repair priority model is expressed as: The repair priority model is used to indicate the priority of repair The equipment that exceeds the threshold F3 the most will be dispatched first ||D|| ∞ The operation and maintenance work order corresponding to device i; When there is no fault in the power distribution equipment, the equipment is sorted according to the operation and maintenance priority model, where the operation and maintenance priority model is expressed as: C=μ1A+μ2B In the formula, C represents the priority index of operation and maintenance work order processing, A represents the importance of equipment, and B represents the operating status. The corresponding weights μ1 and μ2 of the equipment importance A and the operating status B are determined based on the fuzzy hierarchical analysis method. Dynamic updates to operational priorities include the following: (1) After the operation and maintenance work order is dispatched to the relevant operation and maintenance personnel for processing, the system will transmit the fault information of each device back to the equipment operation status detection system to achieve dynamic self-adaptation of the status assessment threshold of the equipment at different periods; (2) Feedback information on economic losses, cascading failures, system shutdowns, and social reputation impacts from the time the failure occurs to the completion of operation and maintenance to the equipment importance evaluation system to dynamically update the importance of each piece of equipment; (3) Based on the IoT sensing technology, the real-time update of the device's real-time operating status indicator B is achieved.

2. According to claim 1, a method for intelligent management of distribution network equipment operation and maintenance work orders based on IoT perception, characterized in that: In the process of obtaining the voltage level of the power distribution equipment, the voltage level of the power distribution equipment is obtained according to the voltage level index evaluation model, wherein the voltage level index evaluation model is expressed as: in, x i Indicates the membership of the voltage level u of the distribution equipment in the distribution network voltage level interval, k 11 , k 12 , k 13 Respectively represent the A1 score values ​​when the voltage levels are u1, u2, and u3 and meet k 11 <k 12 <k 13 ; The voltage level index evaluation model is used to divide the distribution network voltage level into four intervals: (u0, u1), [u1, u2), [u2, u3] and greater than or equal to u3. Determine the membership x of the voltage level u of the distribution equipment in the above four intervals i At the same time, normalization processing is implemented, and the obtained membership degree x i Substitute into the evaluation model to obtain the indicator score A1.

3. According to claim 2, a method for intelligent management of distribution network equipment operation and maintenance work orders based on IoT perception is characterized in that: In the process of obtaining the load level of the power distribution equipment, the load level of the power distribution equipment is obtained through a load level index evaluation model, wherein the load level index evaluation model is expressed as: Among them, A2 represents the load level index score of the power distribution equipment; S represents the load capacity of the equipment; S1, S2, and S3 represent the first-level, second-level, and third-level load capacities of the equipment respectively; k 21 , k 22 , k 23 Respectively represent the A2 score value when the equipment carries the first, second and third level load, where k 23 <k 22 <k 21 .

4. According to claim 3, a method for intelligent management of distribution network equipment operation and maintenance work orders based on IoT perception is characterized in that: In the process of obtaining the economic loss after the equipment failure, the economic loss after the equipment failure is obtained through the economic loss index evaluation model, wherein the economic loss index evaluation model is expressed as: a=a 31 +a 32 +a 33 +a 34 Where a represents the total economic loss caused by equipment failure; a 31 Indicates equipment repair or replacement costs; a 32 Indicates labor costs; a 33 Indicates the electricity loss caused by power outage; a 34 Indicates the financial compensation that the power supply company may face; k 31 , k 32 , k 33 They represent the score of A3 when the total economic losses are a1, a2, and a3 and satisfy k 31 <k 32 <k 33 ; The economic loss index evaluation model is used to divide the total economic loss caused by equipment failure into four intervals: (0, a1), (a1, a2), (a2, a3) and greater than or equal to a3. Determine the membership degree x of the economic loss a in the above four intervals i At the same time, normalization processing is implemented, and the obtained membership degree x i Substitute into the evaluation model to obtain the index score A3, where k 31 , k 32 , k 33 They represent the score of A3 when the total economic losses are a1, a2, and a3 and satisfy k 31 <k 32 <k 33 .

5. According to claim 4, a method for intelligent management of distribution network equipment operation and maintenance work orders based on IoT perception is characterized in that: In the process of obtaining the equipment centrality, the cascading failure risk is divided into four levels: no risk, low risk, medium risk, and high risk, which are represented by A, B, C, and D respectively. The cascading failure risk level is divided by experts based on historical data; The equipment centrality score is recorded as A4. The higher the risk, i.e., the higher the centrality, the higher the score, i.e., the higher the importance, and the higher the maintenance priority of the corresponding work order. When the risk level is no risk, the corresponding centrality is A4=k 41 , when the risk level is low risk, the corresponding centrality is A4=k 42 , when the risk level is medium risk, the corresponding centrality is A4=k 43 , when the risk level is high risk, the corresponding centrality is A4=k 44 , where k 41 <k 42 <k 43 <k 44 .

6. According to claim 5, a method for intelligent management of distribution network equipment operation and maintenance work orders based on IoT perception is characterized in that: In the process of obtaining the social reputation impact of the power supply company after the fault, the social reputation impact of the power supply company after the fault is obtained through the social reputation impact index evaluation model, wherein the social reputation impact index evaluation model is expressed as: Among them, A5 represents the score of the social reputation impact index; ρ is the load density of the fault affected area; k 51 , k 52 , k 53 , k 54 They represent the score values ​​of A5 when the load density is below ρ1, ρ1-ρ2, ρ2-ρ3, ρ3 and above, respectively. 51 <k 52 <k 53 <k 54 .

7. According to claim 6, a method for intelligent management of distribution network equipment operation and maintenance work orders based on IoT perception, characterized in that: In the process of evaluating the importance, the fuzzy analytic hierarchy process is used to determine the index A i Corresponding weight λ i According to the expert's suggestion, the quantitative values ​​between the above indicators are determined to establish a judgment matrix, and then the importance of the operation and maintenance work order corresponding to the power distribution equipment is obtained. The importance is expressed as:

8. According to claim 7, a method for intelligent management of distribution network equipment operation and maintenance work orders based on IoT perception is characterized in that: In the process of obtaining the operating status, the operating status of the power distribution equipment is obtained through an operating status evaluation model, wherein the operating status evaluation model is expressed as: In the formula, B i Indicates the real-time operating status score of power distribution equipment i; Respectively indicate the equipment operation status as normal, caution, abnormal, and fault; k b1 , k b2 , k b3 Respectively The B score when the value is F1, F2, F3, where k b1 <k b2 <k b3 .

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