Intelligent work order management method for electricity power maintenance, system therefor, facility, and storage medium

Abstract

To provide an intelligent work order management method, a system therefor, a facility therefor, and a storage medium which automatically issues an electricity power maintenance work order to an electricity power maintenance team and timely notifies the team of inspection of risk hidden in a site facility and check of electricity power consumption work content.SOLUTION: A method according to the present invention has a step of generating a task work order based on a predetermined inspection plan and customer data, a step of acquiring a task occurrence place corresponding to the task work order, facility complexity coefficients, task importance coefficients, execution team requirement coefficients, weather event coefficients, and structure complexity coefficients, a step of, based on distance between the task occurrence place and a task execution terminal, substituting parameters of a plurality of task execution terminals satisfying preset task proximity execution requirements into preset relational expression with a task matching degree, the facility complexity coefficient, the task importance coefficients, the execution team requirement coefficients, the weather event coefficients, and the structure complexity coefficients, and a step of issuing the task work order to the task execution terminal with highest matching degree.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to the technical field of power systems, and more particularly to a method, system, facility and storage medium for intelligent work order management for power conservation. [Background technology]

[0002] In order to successfully host large-scale events, it is necessary to prepare for wide-area power outages caused by meteorological disasters, geological disasters, external damage, etc., to realize omnidirectional monitoring of various element information such as key customers, power transmission and distribution grid equipment, support resources, fault warnings, hidden risks, activity flow, and external weather, and to comprehensively manage and control the chain of command, resource procurement, inspection, maintenance, and repair work in all fields, and power conservation training at all levels, and to support the normalization management of power conservation configurations.

[0003] In the traditional power conservation mode, various information such as electrical equipment and external weather is obtained based on human experience and organizational management activities, and in many cases, a large number of staff are required to be organized to thoroughly check the hidden risks of on-site equipment. This not only results in low efficiency and difficulty in achieving process tracking and management control, but also makes it easy for problems such as missing the timing of task input or missing tasks due to negligence in personnel management to occur, which does not contribute to the smooth implementation of power conservation work. Summary of the Invention

[0004] The present invention provides an intelligent work order management method, system, equipment and storage medium for power conservation, which can automatically issue power conservation task work orders to power conservation teams, and timely notify them to inspect hidden risks of on-site equipment and check the power consumption work content.

[0005] In order to achieve the above object, an embodiment of the present invention provides an intelligent work order management method for power conservation, comprising: receiving verified and approved customer data inputted by a management terminal, generating a task work order according to a preset inspection plan and the customer data, and obtaining a task occurrence location, an equipment complexity coefficient, a task importance coefficient, an execution team requirement coefficient, a weather event coefficient, and a building complexity coefficient corresponding to the task work order; selecting a plurality of task execution terminals that satisfy a preset task proximity execution condition based on the distance between the task occurrence location and the task execution terminal, and the team legacy factor, group learning factor, and current task importance weight of each task execution terminal; Substituting team parameters of each of the plurality of task execution terminals into a preset relational equation between the task matching degree and the equipment complexity coefficient, the task importance coefficient, the execution team requirement coefficient, the weather event coefficient, and the building complexity coefficient, and issuing the task work order to the task execution terminal having the highest matching degree.

[0006] As an improvement of the above solution, the task proximity execution conditions are as follows:

[0007] a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; When the task execution terminal is in a no-task state or a previous task end state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; When the task execution terminal is in a state of executing a task, the task receiving location becomes the current task execution location, and the distance between the task receiving location and the task occurrence location becomes the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the time required to complete task execution by a distance corresponding to a preset unit time.

[0008] As an improvement of the above solution, the task proximity execution conditions are as follows:

[0009] a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance;

[0010] and if the team legacy factor of the task execution terminal is greater than the group learning factor, the task occurrence location is the location with the largest number of tasks corresponding to the team legacy factor;

[0011] wherein, when the task execution terminal is in a task-free state or a previous task-ended state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; When the task execution terminal is in a state of executing a task, the task receiving location becomes the current task execution location, and the distance between the task receiving location and the task occurrence location becomes the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the time required to complete task execution by a distance corresponding to a preset unit time.

[0012] As an improvement of the above solution, the task proximity execution conditions are as follows:

[0013] a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; And, if the team legacy factor of the task execution terminal is smaller than the group learning factor, the task occurrence location is the location with the largest number of tasks corresponding to other team legacy factors; wherein, when the task execution terminal is in a task-free state or a previous task-ended state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; When the task execution terminal is in a state of executing a task, the task receiving location becomes the current task execution location, and the distance between the task receiving location and the task occurrence location becomes the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the time required to complete task execution by a distance corresponding to a preset unit time.

[0014] As an improvement to the above solution, the task proximity execution condition further comprises: When the task executing terminal is in a task executing state and the team of the task executing terminal has not yet arrived at the task occurrence location corresponding to the current task, if the task importance coefficient corresponding to the current task is smaller than the task importance coefficient corresponding to the task work order to be issued, a task proximity execution condition is satisfied.

[0015] As an improvement of the above solution, the equipment complexity coefficient is as follows: JPEG2025070953000002.jpg15170JPEG2025070953000003.jpg22170

[0016] As an improvement of the above solution, the task importance coefficient is: JPEG2025070953000004.jpg9170JPEG2025070953000005.jpg29170

[0017] As an improvement of the above solution, the execution team requirement coefficient is as follows: JPEG2025070953000006.jpg14170JPEG2025070953000007.jpg24170

[0018] In an improvement of the above solution, the weather event coefficients are: JPEG2025070953000008.jpg10170JPEG2025070953000009.jpg54170

[0019] As an improvement of the above solution, the building complexity coefficient is: JPEG2025070953000010.jpg13170JPEG2025070953000011.jpg18170

[0020] As an improvement of the above-mentioned solving means, the relationship between the above-mentioned task matching degree and the equipment complexity coefficient, the task importance coefficient, the execution team requirement coefficient, the weather event coefficient, and the building complexity coefficient is as follows: JPEG2025070953000012.jpg13170JPEG2025070953000013.jpg75170

[0021] As an improvement to the above-mentioned solution, the management terminal is a taskmaster core distribution terminal, which employs a 701 chip as an arithmetic core, and performs real-time processing of a beacon for synchronizing access to a memory-sharing peripheral device between a CPU Cortex-A7 and a CPU Cortex-M33, and is configured on a bus within a predetermined distance for accessing the CPU Cortex-M33, wherein the CPU Cortex-A7 is arranged on the AXI bus of the 701 chip, and the CPU Cortex-M33 is arranged on the AHB bus of the 701 chip.

[0022] As an improvement of the above solution, the management terminal traverses a pre-defined customer label database according to the customer management scope in the customer data, the process complexity, and the designer's vehicle size, and collates the customer label corresponding to each customer.

[0023] In an improvement to the above solution, prior to the step of generating a task work order based on a predefined inspection plan and the customer data, the method further comprises: generating a corresponding inspection plan based on the customer label and sending it to the customer side; The management terminal generates the task work order based on the customer data and the inspection plan in response to the approved information returned from the customer side.

[0024] In order to solve the same technical problem, the present invention also provides an intelligent work order management system for power conservation, A data collection and work order generation module is used to receive verified and approved customer data input by a management terminal, generate a task work order according to a preset inspection plan and the customer data, and obtain a task occurrence location, an equipment complexity factor, a task importance factor, an execution team requirement factor, a weather event factor, and a building complexity factor corresponding to the task work order; A team search and selection module is used to select a plurality of task execution terminals that satisfy a preset task proximity execution condition based on the distance between the task generation location and the task execution terminal, and the team legacy factor, group learning factor, and current task importance weight of each task execution terminal; and a work order matching and issuing module which is used to assign team parameters of each of the plurality of task execution terminals to a preset relational expression between the task matching degree and the equipment complexity coefficient, the task importance coefficient, the execution team requirement coefficient, the weather event coefficient, and the building complexity coefficient, and to issue the task work order to the task execution terminal having the highest matching degree.

[0025] As an improvement of the above solution, the task proximity execution conditions are as follows:

[0026] a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; When the task execution terminal is in a no-task state or a previous task end state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; When the task execution terminal is in a state of executing a task, the task receiving location becomes the current task execution location, and the distance between the task receiving location and the task occurrence location becomes the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the time required to complete task execution by a distance corresponding to a preset unit time.

[0027] As an improvement of the above solution, the task proximity execution conditions are as follows:

[0028] a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; and if the team legacy factor of the task execution terminal is greater than the group learning factor, the task occurrence location is the location with the largest number of tasks corresponding to the team legacy factor; wherein, when the task execution terminal is in a task-free state or a previous task-ended state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; When the task execution terminal is in a state of executing a task, the task receiving location becomes the current task execution location, and the distance between the task receiving location and the task occurrence location becomes the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the time required to complete task execution by a distance corresponding to a preset unit time.

[0029] As an improvement of the above solution, the task proximity execution conditions are as follows: a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; And, if the team legacy factor of the task execution terminal is smaller than the group learning factor, the task occurrence location is the location with the largest number of tasks corresponding to other team legacy factors; wherein, when the task execution terminal is in a task-free state or a previous task-ended state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; When the task execution terminal is in a state of executing a task, the task receiving location becomes the current task execution location, and the distance between the task receiving location and the task occurrence location becomes the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the time required to complete task execution by a distance corresponding to a preset unit time.

[0030] As an improvement to the above solution, the task proximity execution condition further comprises: When the task executing terminal is in a task executing state and the team of the task executing terminal has not yet arrived at the task occurrence location corresponding to the current task, if the task importance coefficient corresponding to the current task is smaller than the task importance coefficient corresponding to the task work order to be issued, a task proximity execution condition is satisfied.

[0031] As an improvement of the above solution, the equipment complexity coefficient is as follows: JPEG2025070953000014.jpg13170JPEG2025070953000015.jpg19170

[0032] As an improvement of the above solution, the task importance coefficient is: JPEG2025070953000016.jpg9170JPEG2025070953000017.jpg28170

[0033] As an improvement of the above solution, the execution team requirement coefficient is as follows: JPEG2025070953000018.jpg14170JPEG2025070953000019.jpg23170

[0034] In an improvement of the above solution, the weather event coefficients are: JPEG2025070953000020.jpg10170JPEG2025070953000021.jpg54170

[0035] As an improvement of the above solution, the building complexity coefficient is: JPEG2025070953000022.jpg13170JPEG2025070953000023.jpg18170

[0036] As an improvement of the above-mentioned solving means, the relationship between the above-mentioned task matching degree and the equipment complexity coefficient, the task importance coefficient, the execution team requirement coefficient, the weather event coefficient, and the building complexity coefficient is as follows: JPEG2025070953000024.jpg13170JPEG2025070953000025.jpg75170

[0037] As an improvement to the above-mentioned solution, the management terminal is a taskmaster core distribution terminal, which employs a 701 chip as an arithmetic core, and performs real-time processing of a beacon for synchronizing access to a memory-sharing peripheral device between a CPU Cortex-A7 and a CPU Cortex-M33, and is configured on a bus within a predetermined distance for accessing the CPU Cortex-M33, wherein the CPU Cortex-A7 is arranged on the AXI bus of the 701 chip, and the CPU Cortex-M33 is arranged on the AHB bus of the 701 chip.

[0038] As an improvement of the above solution, the management terminal traverses a pre-defined customer label database according to the customer management scope in the customer data, the process complexity, and the designer's vehicle size, and collates the customer label corresponding to each customer.

[0039] As an improvement to the above solution, the data collection and work order generation module further comprises: generating a corresponding inspection plan based on the customer label and sending it to the customer side; The management terminal is used for generating the task work order based on the customer data, the inspection plan, and in response to approved information returned from the customer side.

[0040] In order to achieve the above object, an embodiment of the present invention further provides an intelligent work order management facility for power conservation, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, realizes the above-mentioned intelligent work order management method for power conservation.

[0041] In order to achieve the above object, an embodiment of the present invention further provides a computer-readable recording medium, the computer-readable recording medium including a computer program stored therein, the computer program being configured to, when executed, control an equipment in which the computer-readable recording medium is disposed to perform the above-mentioned intelligent work order management method for power conservation.

[0042] Compared with the prior art, the method, system, equipment and storage medium for intelligent work order management for power conservation disclosed by the embodiments of the present invention have the following beneficial effects: 1) Based on the analysis and intelligent matching of customer information and task execution teams, instead of allocating work orders based on human experience, task work orders can be allocated to the appropriate team for execution in a rational, scientific and efficient manner, realizing structured, process-based and traceable management of customer management work, and enabling timely and early detection of important customer equipment and management problems, thereby improving the reliability of power equipment, realizing process tracking and management control of maintenance work, improving work efficiency, and avoiding human management errors that lead to allocation delays, task omissions and other problems, and ensuring the completion of all work in the process of power maintenance work. 2) In response to the requirements of customers for hierarchical management, we will realize labeling management for important customers, check for hidden risks in on-site equipment and thoroughly analyze the content of power consumption inspection work, break down the inspection items to the smallest unit, create a structured and ordered inspection task list online, and build a digital management system for important customers based on the plan-task management model. 3) Through structured, process-based and traceable digitalized management means, the maintenance of power conservation customers, power conservation equipment, supporting resources and power conservation teams is strengthened, and the function of customer-based personalized setting is realized, so that different command levels and different job types and departments can meet the personalized business needs of thematic pages to monitor and direct power conservation equipment, supporting capabilities and power conservation business flow lines. [Brief description of the drawings]

[0043] In order to more clearly describe the technical solutions of the present invention, the drawings used in the embodiments are briefly introduced below. It should be understood that the drawings in the following description are only some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative efforts. [Figure 1] 2 is a flow chart of an intelligent work order management system for power conservation provided by one embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram showing the positional relationship between a CPU and a beacon provided by an embodiment of the present invention. [Diagram 3] FIG. 2 is a block diagram showing the structure of an intelligent work order management facility for power conservation provided by one embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0044] The technical solutions in the embodiments of the present invention will be described below in a clear and complete manner in conjunction with the drawings in the embodiments of the present invention, but it is clear that the described embodiments are only a part of the embodiments of the present invention, and are not all of them. Based on the embodiments of the present invention, all other embodiments that can be obtained by those skilled in the art without any creative efforts shall fall within the protection scope of the present invention.

[0045] The power conservation targets of major events and daily power conservation activities are only a part of the key customers, and in order to ensure the improvement of the capacity and effectiveness of power conservation work for them, it is necessary to strengthen the understanding and tracking level of power conservation tasks related to the basic information, electrical equipment status, and operation management level of key customers, and establish a power conservation task work order management method based on process automation.According to the requirements of customer hierarchical management, realize the labeling management of key customers, check the hidden risks of on-site equipment and thoroughly analyze the content of power consumption inspection work, break down the inspection items to the smallest unit, create a structured and work ordered inspection task list online, and build a digital management system for key customers based on the plan-task management model.

[0046] Referring to FIG. 1, which is a flow chart of an intelligent work order management method for power conservation provided by an embodiment of the present invention, the method includes the following steps S1 to S3.

[0047] In S1, receive verified and approved customer data inputted by a management terminal, generate a task work order according to a preset inspection plan and the customer data, and obtain a task occurrence location, equipment complexity coefficient, task importance coefficient, execution team requirement coefficient, weather event coefficient, and building complexity coefficient corresponding to the task work order.

[0048] In this embodiment, the labeling management function of important customers is supported. Since there are various types of different customers in important power conservation work, the system adds customer labels in the form of grading by formal procedures according to the customer's management scope, process complexity, and designer's vehicle size. In order to meet the flexible management of hidden risk checks, power consumption checks, hidden risk checks in flood control and other tasks of each important customer, the system supports the structural design of power consumption checks, hidden risk checks, and flood control risk check items. When there are new order requirements, the system will perform fitting through the grading algorithm and then output the importance level of the requirements to the business personnel, so that the business personnel can customize the settings and select negative feedback parameters to adjust the requirement level.

[0049] Profile customer requirements to automatically create images, and based on the created regular monthly inspection plan or special inspection plan, after obtaining customer approval, the management personnel can formulate power consumption inspection tasks according to the plan and assign them to subordinate workers. The system automatically creates hidden risk inspection plans based on the plan requirements, formulates hidden risk inspection tasks based on the plan, and the system automatically completes task assignment and redistributes them to designated personnel from within the group's execution organization. According to the plan and other work schedules, various types of tasks, such as special inspections, regular inspections, and special inspections, are assigned and presented when they are about to expire and after they have expired.

[0050] By executing inspection tasks in combination with mobile devices, it supports the execution control of the entire process of work orders, and AI-based intelligent research judgment of the execution results, and returns the execution results to provide the basis for parameter adjustment for the next intelligent allocation control. At the same time, it automatically generates a notification form for the task execution results, corrects and closes any problems found, and conducts multifaceted and multilateral investigation and statistics on the inspection situation. The original power conservation solution required specialized departments to organize personnel and spend a lot of time and effort to create one book for each facility for power conservation equipment, but now it can be generated intelligently by accumulating business rules and training models on past documents.

[0051] In S2, multiple task execution terminals that satisfy a preset task proximity execution condition are selected based on the distance between the task occurrence location and the task execution terminal, and the team legacy factor, group learning factor, and current task importance weight of each task execution terminal.

[0052] In this embodiment, the purpose of performing work order allocation and management based on the particle swarm algorithm is to regard all the team objects related to the current tasks as one particle swarm, and move all the particles within a certain power conservation range, and all the teams do not know where the task occurs. However, when the task occurs, all the particles know the distance from their own current position to the task occurrence location, and at the same time, obtain the position of the particle closest to the task execution location through comparison in the system.

[0053] As can be seen, the core idea of ​​the work order particle algorithm is to utilize information sharing by individual particles in a team to generate a chaotic-to-order evolution process in the task execution space of the whole team's movements, thereby obtaining a more efficient and feasible solution to the problem. In this embodiment, the team is given several parameters.

[0054] C1: Team Legacy Factor. The higher this factor is, the more likely a team is to go to a place where they have visited the most number of tasks in the past.

[0055] C2: Group learning factor. The higher this factor, the more likely a team is to go to a place that has had the most number of tasks previously visited by other teams.

[0056] R1,R2: Random numbers on [0,1]. Randomly simulate the current task execution state of each team, where 0 represents no task being executed, 1 represents the task being completed, and the intermediate state represents the task being executed and the corresponding completion state.

[0057] W: Task importance weight. The higher this value, the less likely the team is to change its current destination and the more likely it is to go to the default task location.

[0058] In one alternative embodiment, the task proximity execution conditions are as follows:

[0059] a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; When the task execution terminal is in a no-task state or a previous task end state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; When the task execution terminal is in a state of executing a task, the task receiving location becomes the current task execution location, and the distance between the task receiving location and the task occurrence location becomes the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the time required to complete task execution by a distance corresponding to a preset unit time.

[0060] In one alternative embodiment, the task proximity execution conditions are as follows:

[0061] a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; and if the team legacy factor C1 of the task execution terminal is greater than the group learning factor C2, the task occurrence location is the location with the largest number of tasks corresponding to the team legacy factor C1; wherein, when the task execution terminal is in a task-free state or a previous task-ended state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; When the task execution terminal is in a state of executing a task, the task receiving location becomes the current task execution location, and the distance between the task receiving location and the task occurrence location becomes the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the time required to complete task execution by a distance corresponding to a preset unit time.

[0062] In one alternative embodiment, the task proximity execution conditions are as follows:

[0063] a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; And, if the team legacy factor C1 of the task execution terminal is smaller than the group learning factor C2, the task occurrence location is the location with the largest number of tasks corresponding to other team legacy factors; wherein, when the task execution terminal is in a task-free state or a previous task-ended state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; When the task execution terminal is in a state of executing a task, the task receiving location becomes the current task execution location, and the distance between the task receiving location and the task occurrence location becomes the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the time required to complete task execution by a distance corresponding to a preset unit time.

[0064] In one alternative embodiment, the task proximity execution condition further comprises: When the task executing terminal is in a task executing state and the team of the task executing terminal has not arrived at the task occurrence location corresponding to the current task, if the task importance coefficient corresponding to the current task is smaller than the task importance coefficient corresponding to the task work order to be issued, a task proximity execution condition is satisfied.

[0065] In S3, the team parameters of the plurality of task execution terminals are substituted into a preset relational equation between the task matching degree and the equipment complexity coefficient, the task importance coefficient, the execution team requirement coefficient, the weather event coefficient, and the building complexity coefficient, and the task work order is issued to the task execution terminal with the highest matching degree.

[0066] JPEG2025070953000026.jpg17170JPEG2025070953000027.jpg19170

[0067] JPEG2025070953000028.jpg14170JPEG2025070953000029.jpg28170

[0068] JPEG2025070953000030.jpg19170JPEG2025070953000031.jpg23170

[0069] JPEG2025070953000032.jpg14170JPEG2025070953000033.jpg54170

[0070] JPEG2025070953000034.jpg18170JPEG2025070953000035.jpg18170

[0071] Positions P and Q continue to change with each execution of the position, eventually converging to the position where the most tasks are executed.

[0072] In one alternative embodiment, the relationship between the task matching degree and the equipment complexity coefficient, the task importance coefficient, the execution team requirement coefficient, the weather event coefficient, and the building complexity coefficient is as follows: JPEG2025070953000036.jpg13170JPEG2025070953000037.jpg75170

[0073] In order to provide a better understanding of the embodiments of the present invention, the embodiments of the present invention are described in detail below. (1) Work order distribution logic In the embodiment of the present invention, the purpose of performing work order allocation and management based on the particle swarm algorithm is to regard all the team objects related to the current tasks as one particle swarm, and move all the particles within a certain power conservation range, and all the teams do not know where the task occurs. However, when the task occurs, all the particles know the distance from their own current position to the task occurrence location, and at the same time, obtain the position of the particle closest to the task execution location through comparison in the system.

[0074] In this case, some factors that affect how work order allocation is calculated are: 1. Each team will arrive at the initial location according to the amount of tasks expected to occur at each location and the requirements of the conservation point, and mark it as starting point A.

[0075] 2. All the teams can synchronize or jointly perform tasks within one power conservation activity, and select a meeting point for the teams in each area, and mark G as the point G with the highest particle density.

[0076] 3. Each team keeps track of the past tasks it has performed and reviews and updates the location P where the most tasks have been performed.

[0077] 4. When each team has no tasks or the tasks are completed, it waits at point G. If the amount of tasks increases suddenly or the importance level of the power conservation period increases, each team changes the rally strategy and waits at point P.

[0078] 5. If a team participating in a rally for a task finishes the task before arriving at the task execution location, it moves to the nearest rally point Q.

[0079] 6. When a new task is issued, each team receives task work orders in the order 2-3-4-5-6, starting from G.

[0080] While a team is gathering toward P and G as planned, if the plan changes, it may add a new destination Q, and ultimately it must remain at one fixed point, but the starting point for the next task allocation will continue to be updated for both P and Q, and will eventually converge to the location with the highest task occurrence rate.

[0081] Here, in the task distribution process, the taskmaster core distribution terminal is used to complete task input, team assembly, and task allocation. The taskmaster core distribution terminal uses the high-performance geometric computing chip 701 as the computing core, and shortens the response time to access peripheral devices and ensures the reliability of the response time by changing the bus configuration of CPU access and the synchronous beacon position on the bus. Refer to Figure 2, in Figure 2, CM33 is the abbreviation for CPU Cortex-M33, CA7 is the abbreviation for CPU Cortex-A7, AXI is the AXI bus, and AHB is the AHB bus.

[0082] (2) Particle Algorithm for Work Order The core idea of ​​the work order particle algorithm is to exploit information sharing by individual particles in a team to generate an evolution process from chaos to order in the task execution space of the whole team's movements, thereby obtaining a more efficient and feasible solution to the problem.

[0083] Currently, we give the team some parameters. C1: Team Legacy Factor. The higher this factor is, the more likely a team is to go to a place where they have visited the most number of tasks in the past.

[0084] C2: Group learning factor. The higher this factor, the more likely a team is to go to a place that has had the most number of tasks previously visited by other teams.

[0085] R1,R2: Random numbers on [0,1]. Randomly simulate the current task execution state of each team, where 0 represents no task being executed, 1 represents the task being completed, and the intermediate state represents the task being executed and the corresponding completion state.

[0086] W: Task importance weight. The higher this value, the less likely the team is to change its current destination and the more likely it is to go to the default task location.

[0087] JPEG2025070953000038.jpg9170

[0088] (3) Distributing tasks to teams Current team's position at step N = position at step N-1 + position at step N-1 task execution time (the travel time t at each step is generally set to 1).

[0089] This team's task performance position in step N = task performance position in the previous step + team legacy factor + group learning factor.

[0090] When processing a task, it is first necessary to establish a task management pool, and sort all current task work orders from 1 to M based on the task production time T, then match all current teams that can perform the task with the task work orders one by one, and for each task, rank the team matching degree in descending order, and select the team with the highest matching degree to perform the current task. After that, remove the team and task work order that have completed the assignment from the queue, and continue to match the next task. An example of single task matching is shown in Table 1 below. JPEG2025070953000040.jpg34170

[0091] The task matching process for each team corresponding to the current task consists of the following six steps: (1) Reading data and calculating matching factors: (2) According to the particle algorithm of the work order, extract each environmental parameter and generate equipment complexity coefficient, task importance coefficient, execution team requirement coefficient, weather event coefficient, and building complexity coefficient.

[0092] (2) Constructing a matching model: Based on the calculation method of task matching degree, a factor is added to the calculation to determine the matching degree of each team corresponding to the current matching target task.

[0093] JPEG2025070953000041.jpg17170JPEG2025070953000042.jpg34170

[0094] JPEG2025070953000043.jpg13170JPEG2025070953000044.jpg31170

[0095] JPEG2025070953000045.jpg14170

[0096] JPEG2025070953000046.jpg18170JPEG2025070953000047.jpg36170

[0097] JPEG2025070953000048.jpg14170JPEG2025070953000049.jpg81170

[0098] JPEG2025070953000050.jpg17170JPEG2025070953000051.jpg18170

[0099] (3) Matching model training: instantiate the model, select the model training resource (CPU), and specify the calculation method of the model iteration. Here, we specifically use the training model CPU integrated with the 701 chip, which is responsible for the early factor calculation of the model, the mid-term training, the late matching degree sorting, and the calibration and output of the final result.

[0100] (4) Saving the training model: Calculate the model multiple times to obtain and save the matching degree of each team corresponding to the current task.

[0101] (5) Model verification and evaluation: All current matching degrees are screened by machine, and the teams are sorted in descending order based on the matching degree. Here, the team with the highest matching degree is placed at the top, and the teams with different matching degrees are sorted in descending order.

[0102] JPEG2025070953000052.jpg13170

[0103] JPEG2025070953000053.jpg13170JPEG2025070953000054.jpg18170

[0104] The ability of each power conservation team to execute a task at a certain position can be represented by an adaptive value, and each team uses its own past task occurrence position to find its optimal position (local optimum, equivalent to an extreme point), and the optimal position of all teams in the group can be regarded as the optimal task waiting point of the whole team (global optimum, equivalent to a maximum point). As can be foreseen, the power conservation tasks of the entire power conservation activity generally move toward the global optimum task waiting area, and the continuous movement of the team's execution task position, i.e., continuous iteration, keeps updating the task progress, and all teams are approaching the optimal position step by step.

[0105] Particles: Candidate solutions to optimization problems, Location: The location where the candidate task is located. Velocity: The speed at which candidate tasks evolve; Fitness: A value that evaluates the degree of matching between particles and tasks. It is generally set as the objective function value. Team Optimal Position: The best position found so far by one particle, Group Optimal Position: The best position found by all particles so far.

[0106] In one optional embodiment, the management terminal is a task master core distribution terminal, which employs a 701 chip as an arithmetic core, and configures a beacon for synchronizing access to a memory sharing peripheral device between a CPU Cortex-A7 and a CPU Cortex-M33 on a bus within a predetermined distance for real-time processing and access to the CPU Cortex-M33, wherein the CPU Cortex-A7 is arranged on the AXI bus of the 701 chip, and the CPU Cortex-M33 is arranged on the AHB bus of the 701 chip.

[0107] The AXI bus of the 701 computing chip is equipped with a DDR SDRAM controller, a Giga-bit Ethernet controller, a USB OTG controller, an LCD controller, and other devices, so a large bus bandwidth is required for high-speed interconnection. When the CPU Cortex-A7 located on this AXI bus accesses low-speed peripheral devices, it is necessary to improve the response time via the AXI, AHB, and APB buses. Here, access to bus-shared resources leads to uncertainty in the access time.

[0108] Using the 701 computing chip as the Taskmaster core distribution terminal has the following technical advantages: Advantage 1: (1) In the embodiment of the present invention, the CPU Cortex-M33 is placed on the AHB bus, which allows it to be properly isolated from resource sharing on the AXI bus, allowing devices on the bus to respond quickly and immediately to system requests, improving the overall response speed and therefore the performance of the chip.

[0109] (2) In isochronous applications, deterministic interrupt response ensures the quality of immediate response, further enhancing the processing power and responsiveness of the processor, and to a certain extent improving the performance and quality of the chip.

[0110] (3) The beacon for synchronizing access to memory-sharing peripherals between the CPU Cortex-A7 and the CPU Cortex-M33 is configured on a bus close to the access to the CPU Cortex-M33, which is responsible for real-time processing and can satisfy low response latency. This shortens the response time of access and optimizes the quality of immediate response.

[0111] Innovation point 2: By establishing a dedicated bus channel and isolating the sharing of bus resources, the bandwidth when accessing DDR DRAM is guaranteed and the reliability of the CPU and LCD is guaranteed.

[0112] The AXI bus has a dedicated channel, which allows the CPU Cortex-A7 to access DDR DRAM without sharing bus resources with other bus master controllers on the AXI bus. This guarantees the bandwidth and certainty when the CPU accesses memory. Another dedicated AXI bus channel guarantees the access bandwidth and certainty when the LCD controller accesses DDR DRAM.

[0113] Advantage 2: (1) The AXI bus has a dedicated channel, which isolates bus resource sharing and ensures that the bandwidth is guaranteed when the CPU is used, thus avoiding the bandwidth limitation problem and improving memory access performance.

[0114] The AXI bus also has a dedicated channel for the LCD controller, guaranteeing bandwidth to the controller when devices on the bus are interconnected at high speeds and optimizing overall memory performance.

[0115] In one alternative embodiment, the management terminal traverses a pre-defined customer label database according to the customer's scope of control in the customer data, the complexity of the process, and the designer's vehicle size, and collates customer labels corresponding to each customer.

[0116] In one optional embodiment, prior to the step of generating a task work order based on a pre-defined inspection plan and the customer data, the method further comprises: generating a corresponding inspection plan based on the customer label and sending it to the customer side; The management terminal generates the task work order based on the customer data and the inspection plan in response to the approved information returned from the customer side.

[0117] In order to perform the power conservation work of major events well, the power conservation command system makes a power conservation work plan and task list in a directional manner, so as to easily manage the content and progress of the power conservation work. Through digitalization means and artificial intelligence means, an online coordination mechanism for power conservation work is established, a power conservation work task list is structured, and a power conservation work task online control module is established with the active power conservation plan as the main line and the maintenance work task list as the driving force, so as to realize the functions of online maintenance, dynamic tracking and online control of power conservation work tasks, work problems and work plans at various levels of command systems and various stages.

[0118] The system executes the task and then feeds back to the system the real-time data collected by the current customer, such as task importance (the task importance level is determined according to the scale of power conservation of the current task and the number of power conservation objects), equipment complexity (the complexity of the maintenance equipment related to the task is determined according to the type of equipment to be maintained this time, the number of equipment, and the number of monitoring nodes), team (the degree of matching between this team and the current work order is determined taking into consideration the human factors related to the execution of the current task, such as the specialty, number of people, the distance from the location to the task occurrence location, and arrival events), environment (the complexity of the current task is determined according to parameters such as the surrounding environmental factors at the time of and where the task is executed, such as weather, topography, and altitude), and emergency situation (the task complexity parameters are adjusted by real-time negative feedback depending on whether emergency factors are overlapping during the execution of the current task, and support capacity that meets the execution conditions is added).

[0119] The present invention realizes structured, process-based and traceable management of key customer management work, thereby enabling timely and early detection of problems in key customer equipment and management, dynamically tracking the problem correction process, and guiding the customer to improve the operation and maintenance management of the electrical equipment, thereby improving the reliability of the power equipment, realizing the process tracking and management control of the maintenance work, improving work efficiency, and avoiding human management errors that lead to allocation delays, task omissions and other problems, and ensuring the completion of all work in the power maintenance work process.

[0120] Through structured, process-based and traceable digital management measures, the maintenance of power conservation customers, power conservation equipment, supporting resources and power conservation teams is strengthened, and the function of customer-based personalized setting is realized to meet the personalized business needs of different command levels and different job types and departments to monitor and direct power conservation equipment, supporting capabilities and power conservation business flow lines.

[0121] In order to solve the same technical problem, the present invention also provides an intelligent work order management system for power conservation, A data collection and work order generation module is used to receive verified and approved customer data input by a management terminal, generate a task work order according to a preset inspection plan and the customer data, and obtain a task occurrence location, an equipment complexity factor, a task importance factor, an execution team requirement factor, a weather event factor, and a building complexity factor corresponding to the task work order; A team search and selection module is used to select a plurality of task execution terminals that satisfy a preset task proximity execution condition based on the distance between the task generation location and the task execution terminal, and the team legacy factor, group learning factor, and current task importance weight of each task execution terminal; and a work order matching and issuing module which is used to assign team parameters of each of the plurality of task execution terminals to a preset relational expression between the task matching degree and the equipment complexity coefficient, the task importance coefficient, the execution team requirement coefficient, the weather event coefficient, and the building complexity coefficient, and to issue the task work order to the task execution terminal having the highest matching degree.

[0122] Optionally, the task proximity execution conditions are as follows: a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; When the task execution terminal is in a no-task state or a previous task end state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; When the task execution terminal is in a state of executing a task, the task receiving location becomes the current task execution location, and the distance between the task receiving location and the task occurrence location becomes the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the time required to complete task execution by a distance corresponding to a preset unit time.

[0123] Optionally, the task proximity execution conditions are as follows: a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; and if the team legacy factor of the task execution terminal is greater than the group learning factor, the task occurrence location is the location with the largest number of tasks corresponding to the team legacy factor; wherein, when the task execution terminal is in a task-free state or a previous task-ended state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; When the task execution terminal is in a state of executing a task, the task receiving location becomes the current task execution location, and the distance between the task receiving location and the task occurrence location becomes the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the time required to complete task execution by a distance corresponding to a preset unit time.

[0124] Optionally, the task proximity execution conditions are as follows: a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; And, if the team legacy factor of the task execution terminal is smaller than the group learning factor, the task occurrence location is the location with the largest number of tasks corresponding to other team legacy factors; wherein, when the task execution terminal is in a task-free state or a previous task-ended state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; When the task execution terminal is in a state of executing a task, the task receiving location becomes the current task execution location, and the distance between the task receiving location and the task occurrence location becomes the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the time required to complete task execution by a distance corresponding to a preset unit time.

[0125] Optionally, the task proximity execution condition further comprises: When the task executing terminal is in a task executing state and the team of the task executing terminal has not arrived at the task occurrence location corresponding to the current task, if the task importance coefficient corresponding to the current task is smaller than the task importance coefficient corresponding to the task work order to be issued, a task proximity execution condition is satisfied.

[0126] Optionally, the equipment complexity factor is: JPEG2025070953000055.jpg13170JPEG2025070953000056.jpg19170

[0127] Optionally, the task importance factor is: JPEG2025070953000057.jpg9170JPEG2025070953000058.jpg29170

[0128] Optionally, the execution team requirement coefficients are: JPEG2025070953000059.jpg14170JPEG2025070953000060.jpg23170

[0129] Optionally, the weather event coefficients are: JPEG2025070953000061.jpg10170JPEG2025070953000062.jpg48170

[0130] Optionally, the building complexity factor is: JPEG2025070953000063.jpg13170JPEG2025070953000064.jpg19170

[0131] Optionally, the relationship between the above-mentioned task matching degree and the equipment complexity coefficient, the task importance coefficient, the execution team requirement coefficient, the weather event coefficient, and the building complexity coefficient is as follows: JPEG2025070953000065.jpg13170JPEG2025070953000066.jpg76170

[0132] Optionally, the management terminal is a taskmaster core distribution terminal, which employs a 701 chip as an arithmetic core, and configures a beacon for synchronizing access to a memory-sharing peripheral device between a CPU Cortex-A7 and a CPU Cortex-M33 on a bus within a predetermined distance for real-time processing and access to the CPU Cortex-M33, wherein the CPU Cortex-A7 is arranged on the AXI bus of the 701 chip, and the CPU Cortex-M33 is arranged on the AHB bus of the 701 chip.

[0133] Optionally, the management terminal traverses a pre-defined customer label database according to the customer's management scope in the customer data, the process complexity, and the designer's vehicle size, and collates customer labels corresponding to each customer.

[0134] Optionally, the data collection and work order generation module further comprises: generating a corresponding inspection plan based on the customer label and sending it to the customer side; The management terminal is used for generating the task work order based on the customer data, the inspection plan, and in response to approved information returned from the customer side.

[0135] An embodiment of the present invention further provides a computer-readable recording medium, the computer-readable recording medium including a computer program stored therein, the computer program being configured to, when executed, control an equipment in which the computer-readable recording medium is disposed to perform the intelligent work order management method for power conservation described in any one of the above embodiments.

[0136] 3, which is a block diagram showing the structure of an intelligent work order management facility for power conservation provided by an embodiment of the present invention, the intelligent work order management facility for power conservation 20 includes a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, it realizes the steps in the embodiment of the intelligent work order management method for power conservation described above. Or, when the processor 21 executes the computer program, it realizes the functions of each module / unit in each device embodiment described above.

[0137] Exemplarily, the computer program can be divided into one or more modules / units, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, and the instruction segments are used to describe the execution process of the computer program in the power conservation intelligent work order management facility 20.

[0138] The intelligent power conservation work order management facility 20 may be a computing device such as a desktop computer, a laptop, a personal digital assistant, and a cloud server. The intelligent power conservation work order management facility 20 may include, but is not limited to, a processor 21 and a memory 22. As will be understood by those skilled in the art, the schematic diagram is only an example of the intelligent power conservation work order management facility 20 and is not intended to limit the intelligent power conservation work order management facility 20, which may include more or less components than those shown, or a combination of some components, or different components, for example, the intelligent power conservation work order management facility 20 may include input / output equipment, network access equipment, buses, etc.

[0139] The so-called processor 21 may be a Central Processing Unit (CPU), or may be other general-purpose processors, Digital Signal Processors (DSP), Application Specific Integrated Circuits (ASIC), Field-Programmable Gate Arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any general processor, etc., and the processor 21 is the control center of the intelligent work order management facility 20 for power conservation, and connects each part of the whole intelligent work order management facility 20 for power conservation through various interfaces and lines.

[0140] The memory 22 can be used to store the computer programs and / or modules, and the processor 21 operates or executes the computer programs and / or modules stored in the memory 22 and calls up the data stored in the memory 22 to realize various functions of the intelligent work order management facility for power conservation 20. The memory 22 mainly includes a program storage area and a data storage area, where the program storage area may store an operating system, applications required for at least one function (e.g., audio playback function, image playback function), etc., and the data storage area may store data created according to the usage of the mobile phone (e.g., audio data, phone book, etc.). In addition, the memory 22 may include a high-speed random access memory, and may include a non-volatile memory such as a hard disk, an internal memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device.

[0141] Here, the modules / units integrated in the intelligent work order management facility for power conservation 20 may be implemented in the form of software functional units and stored in a computer-readable recording medium when sold or used as a standalone product. Based on this understanding, the present invention may instruct related hardware by a computer program to complete all or part of the processes in the method of the above embodiments, and the computer program may be stored in a computer-readable recording medium, and the computer program may realize the steps of each of the above method embodiments when executed by the processor 21. Here, the computer program includes a computer program code, and the computer program code may be in the form of source code, object code, executable file, or any intermediate form, etc. The computer-readable recording medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, an electric communication signal, and a software distribution medium, etc.

[0142] It should be noted that the above-mentioned device embodiment is merely illustrative, and the units described therein as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be in one place or distributed among multiple network units. Depending on the actual needs, some or all of these modules may be selected to achieve the objective of the solution of the embodiment. Furthermore, in the drawings of the device embodiment provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and specifically, can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without any creative effort.

[0143] It should be noted that the above is a preferred embodiment of the present invention, and those skilled in the art can make many improvements and modifications without departing from the principle of the present invention, and these improvements and modifications also fall within the protection scope of the present invention.

Claims

1. receiving verified and approved customer data input by a management terminal, generating a task work order according to a preset inspection plan and the customer data, and obtaining a task occurrence location, equipment complexity coefficient, task importance coefficient, execution team requirement coefficient, weather event coefficient, and building complexity coefficient corresponding to the task work order, wherein the management terminal traverses a preset customer label database according to the customer's management scope, process complexity, and designer's vehicle size in the customer data, and matches the customer label corresponding to each customer; a step of selecting a plurality of task execution terminals that satisfy a preset task proximity execution condition based on the distance between the task occurrence location and the task execution terminal, and the team legacy factor, group learning factor, and weight of current task importance of each task execution terminal, the team legacy factor and the group learning factor being given in advance, the larger the team legacy factor, the stronger the tendency of the execution team to go to a location where the execution team has visited the largest number of tasks in the past, and the larger the group learning factor, the stronger the tendency of the execution team to go to a location where another execution team has visited the largest number of tasks in the past; Substituting team parameters of each of the plurality of task execution terminals into a preset relational expression between the task matching degree and the equipment complexity coefficient, the task importance coefficient, the execution team requirement coefficient, the weather event coefficient, and the building complexity coefficient, and issuing the task work order to the task execution terminal having the highest matching degree; The equipment complexity coefficient is as follows:

2. The task proximity execution conditions are as follows: a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; When the task execution terminal is in a no-task state or a previous task end state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; The intelligent work order management method for power conservation according to claim 1, characterized in that, when the task execution terminal is in a task execution state, the task receiving location becomes a current task execution location, and the distance between the task receiving location and the task occurrence location is the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the task execution completion time by the distance corresponding to a preset unit time.

3. The task proximity execution conditions are as follows: a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; and if the team legacy factor of the task execution terminal is greater than the group learning factor, the task occurrence location is the location with the largest number of tasks corresponding to the team legacy factor; When the task execution terminal is in a no-task state or a previous task end state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; The intelligent work order management method for power conservation according to claim 1, characterized in that, when the task execution terminal is in a task execution state, the task receiving location becomes a current task execution location, and the distance between the task receiving location and the task occurrence location is the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the task execution completion time by the distance corresponding to a preset unit time.

4. The task proximity execution conditions are as follows: a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; And, if the team legacy factor of the task execution terminal is smaller than the group learning factor, the task occurrence location is the location with the largest number of tasks corresponding to other team legacy factors; When the task execution terminal is in a no-task state or a previous task end state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; The intelligent work order management method for power conservation according to claim 1, characterized in that, when the task execution terminal is in a task execution state, the task receiving location becomes a current task execution location, and the distance between the task receiving location and the task occurrence location is the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the task execution completion time by the distance corresponding to a preset unit time.

5. The task proximity execution condition further comprises: The intelligent work order management method for power conservation according to any one of claims 2 to 4, characterized in that, when the task execution terminal is in a task execution state and the team of the task execution terminal has not arrived at the task occurrence location corresponding to the current task, if the task importance coefficient corresponding to the current task is smaller than the task importance coefficient corresponding to the task work order to be issued, the task proximity execution condition is satisfied.

6. The intelligent work order management method for power conservation according to claim 1, characterized in that: the management terminal is a task master core distribution terminal, the task master core distribution terminal adopts a 701 chip as an arithmetic core, and a beacon for synchronizing access to memory sharing peripheral devices between a CPU Cortex-A7 and a CPU Cortex-M33 is configured on a bus within a predetermined distance for real-time processing and access to the CPU Cortex-M33, the CPU Cortex-A7 is arranged on the AXI bus of the 701 chip, and the CPU Cortex-M33 is arranged on the AHB bus of the 701 chip.

7. Prior to the step of generating a task work order based on a predefined inspection plan and the customer data, the method further comprises: generating a corresponding inspection plan based on the customer label and sending it to the customer side; The intelligent work order management method for power conservation according to claim 1, further comprising: the management terminal generating the task work order based on the customer data and the inspection plan in response to the approved information returned from the customer side.

8. A data collection and work order generation module is used to receive verified and approved customer data input by a management terminal, generate a task work order according to a preset inspection plan and the customer data, and obtain a task occurrence location, equipment complexity coefficient, task importance coefficient, execution team requirement coefficient, weather event coefficient, and building complexity coefficient corresponding to the task work order, wherein the management terminal traverses a preset customer label database according to the customer management scope, process complexity, and designer vehicle size in the customer data, and matches the customer label corresponding to each customer; a team search and selection module used to select a plurality of task execution terminals that satisfy a preset task proximity execution condition based on the distance between the task occurrence location and the task execution terminal, and the team legacy factor, group learning factor, and weight of current task importance of each task execution terminal, the team legacy factor and the group learning factor being given in advance, the larger the team legacy factor, the stronger the tendency of the execution team to go to a location where the execution team has visited the most in the past, and the larger the group learning factor, the stronger the tendency of the execution team to go to a location where another execution team has visited the most in the past; a work order matching and issuing module used to assign team parameters of each of the plurality of task execution terminals to a preset relational expression between the task matching degree and the equipment complexity coefficient, the task importance coefficient, the execution team requirement coefficient, the weather event coefficient, and the building complexity coefficient, and to issue the task work order to the task execution terminal having the highest matching degree; The equipment complexity coefficient is as follows:

9. The task proximity execution conditions are as follows: a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; When the task execution terminal is in a no-task state or a previous task end state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; 9. The intelligent work order management system for power conservation according to claim 8, characterized in that when the task execution terminal is in a task execution state, the task receiving location becomes a current task execution location, and the distance between the task receiving location and the task occurrence location is the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the task execution completion time by a distance corresponding to a preset unit time.

10. The task proximity execution conditions are as follows: a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; and if the team legacy factor of the task execution terminal is greater than the group learning factor, the task occurrence location is the location with the largest number of tasks corresponding to the team legacy factor; When the task execution terminal is in a no-task state or a previous task end state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; 9. The intelligent work order management system for power conservation according to claim 8, characterized in that when the task execution terminal is in a task execution state, the task receiving location becomes a current task execution location, and the distance between the task receiving location and the task occurrence location is the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the task execution completion time by a distance corresponding to a preset unit time.

11. The task proximity execution conditions are as follows: a distance between the task receiving position of the task execution terminal and the task generation location is equal to or less than a preset maximum task proximity execution distance; And, if the team legacy factor of the task execution terminal is smaller than the group learning factor, the task occurrence location is the location with the largest number of tasks corresponding to other team legacy factors; When the task execution terminal is in a no-task state or a previous task end state, the task receiving location is a task waiting gathering location, and the distance between the task receiving location and the task occurrence location is the distance between the task waiting gathering location and the task occurrence location; 9. The intelligent work order management system for power conservation according to claim 8, characterized in that when the task execution terminal is in a task execution state, the task receiving location becomes a current task execution location, and the distance between the task receiving location and the task occurrence location is the sum of the distance between the current task execution location and the task occurrence location and the distance obtained by multiplying the task execution completion time by a distance corresponding to a preset unit time.

12. The task proximity execution condition further comprises: The intelligent work order management system for power conservation according to any one of claims 8 to 11, characterized in that, when the task execution terminal is in a task execution state and the team of the task execution terminal has not arrived at the task occurrence location corresponding to the current task, if the task importance coefficient corresponding to the current task is smaller than the task importance coefficient corresponding to the task work order to be issued, the task proximity execution condition is satisfied.

13. The intelligent work order management system for power conservation according to claim 8, characterized in that the management terminal is a task master core distribution terminal, the task master core distribution terminal adopts a 701 chip as an arithmetic core, and a beacon for synchronizing access to memory sharing peripheral devices between a CPU Cortex-A7 and a CPU Cortex-M33 is configured on a bus within a predetermined distance for real-time processing and access to the CPU Cortex-M33, the CPU Cortex-A7 is arranged on the AXI bus of the 701 chip, and the CPU Cortex-M33 is arranged on the AHB bus of the 701 chip.

14. The data collection and work order generation module further comprises: generating a corresponding inspection plan based on the customer label and sending it to the customer side; 9. The intelligent work order management system for power conservation according to claim 8, wherein the management terminal is used for generating the task work order based on the customer data and the inspection plan in response to the approved information returned from the customer side.

15. An intelligent work order management facility for power conservation comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the intelligent work order management method for power conservation according to any one of claims 1 to 4 and 6 to 7 when executing the computer program.

16. A computer readable recording medium including a computer program stored thereon, the computer program being characterized in that, when executed, the computer program controls a facility in which the computer readable recording medium is disposed to perform the method for intelligent work order management for power conservation according to any one of claims 1 to 4 and 6 to 7.

Images