Intelligent order dispatching method for operation and maintenance tasks of electric power acquisition equipment and storage medium

Abstract

The invention relates to an intelligent order dispatching method for operation and maintenance tasks of electric power acquisition equipment and a storage medium. The intelligent order dispatching method comprises the following steps: S1, generating a plurality of work orders according to different types of diagnosis results, wherein each work order is marked with the type of the diagnosis result; and S2, the work order is quantified through a preset quantification rule based on the marked type, the work order marked with the workload is obtained, and each operation and maintenance team is marked with the workload according to the completed work order. According to the invention, the work content of the work order is quantified into the workload through types, and the work content is averagely distributed in combination with the previous work records and work intensity of the operation and maintenance teams, so that the actual total workload of each operation and maintenance team tends to be the same, thereby avoiding the problem that the actual work intensity is very high although the work order distribution amount of some operation and maintenance teams is the same. And operation and maintenance personnel in a high-pressure state can be effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power equipment, and in particular to an intelligent dispatching method and storage medium for operation and maintenance tasks of electric power collection equipment. Background Art

[0002] At present, the number of metering and collection equipment (terminals, meters) connected on-site at each network point and provincial power company is increasing, the operating environment is complex, and there are many types of faults. After the power grid system receives fault information and reports fault information from subordinate units, it needs to dispatch a professional operation and maintenance team for repair. At present, the dispatch of work orders is carried out by the responsible person of the operation and maintenance site based on subjective work experience and observation of whether the operation and maintenance team is free. The operation and maintenance difficulty and intensity of the work orders are not comprehensively considered, nor is the work intensity of the operation and maintenance team accurately considered. As a result, the actual work intensity of each operation and maintenance team is different during the same working hours. Especially during the period when the operation and maintenance capacity is very tight, the unreasonable distribution of work orders may make the dispatch volume of some operation and maintenance teams similar, but their actual work intensity is very high. The operation and maintenance personnel are under high pressure for a long time, which is not conducive to the stability of the operation and maintenance team. Summary of the invention

[0003] Based on this, it is necessary to provide an intelligent dispatching method and storage medium for power collection equipment operation and maintenance tasks to address the problem of unreasonable work order allocation in the existing dispatching method.

[0004] A method for intelligent dispatching of power collection equipment operation and maintenance tasks, the method comprising the following steps:

[0005] S1. Generate multiple work orders according to different categories of diagnosis results, each of which is marked with the type of diagnosis result;

[0006] S2. The work order is quantified based on the type of mark using the preset quantification rules to obtain a work order marked with the workload. Each operation and maintenance team also marks the workload based on the completed work orders. Based on the principle of workload matching, the total workload after adding the workload of each operation and maintenance team and the workload of the work orders it handles tends to be the same, and the work order is automatically distributed to the corresponding operation and maintenance team.

[0007] As a preferred example, after the operation and maintenance team arrives at the site for maintenance, they check the accuracy of the diagnosis results in the work order and provide feedback on the work order processing results. At the same time, the completed work order is recorded as the workload of the operation and maintenance team.

[0008] As a preferred example, the types of the diagnosis results include high / low urgency, long / short travel time and long / short processing time.

[0009] As a preferred example, in S2, the work order is quantified based on the type of the mark by using a preset quantification rule, including the following steps:

[0010] S21, assigning a value of 1 to the types of high urgency, long travel time, and long processing time;

[0011] S22, assigning the types of low urgency, short travel time and short processing time to 0 respectively;

[0012] S23, adding corresponding values ​​according to the marking type of the work order to obtain the work quantity value A of the work order;

[0013] S24. If the workload value A=3, the work order is marked as high workload; if the workload value A=2 or 1, the work order is marked as medium workload; if the workload value A=0, the work order is marked as low workload.

[0014] As a preferred example, in S2, based on the principle of workload matching, automatically dispatching the work order to the corresponding operation and maintenance team includes the following steps:

[0015] S31. Obtain the current day work record and the historical work record of the previous seven working days of the operation and maintenance team;

[0016] S32, adding the workload value A*B of the processed work orders in the current day's work record of a single operation and maintenance team to the workload value A of the processed work orders in the historical work record to obtain the total workload value C of the operation and maintenance team, where B is the fatigue coefficient and B>0;

[0017] S33, sorting each operation and maintenance team from large to small according to the total workload value C, allocating work orders with high workload to the operation and maintenance teams in the bottom one-third of the sorting, and work orders with low workload to the operation and maintenance teams in the top one-third of the sorting, and work orders with medium workload to the remaining teams;

[0018] The methods of allocating work orders include manual allocation and random allocation. The manual allocation is performed by the team leader of the operation and maintenance team according to the total workload value C, and the random allocation is performed automatically by a program running an intelligent dispatching method.

[0019] As a preferred example, after the work order is issued, one or more preferred maintenance routes are automatically generated on the map for the area with complete geographic information data in the work order, and the operation and maintenance team moves to the site according to the preferred maintenance route; the area without complete geographic information data in the work order depends on the responsible person of the operation and maintenance team to move to the site on his own based on his understanding of the area under his jurisdiction.

[0020] As a preferred example, the operation and maintenance teams are tracked visually to display the current working status, current work content and real-time location of each operation and maintenance team.

[0021] As a preferred example, after abnormal diagnosis is performed on the power collection equipment, dispatch is performed according to the diagnosis result, the abnormal diagnosis includes an equipment fault diagnosis method, and the equipment fault diagnosis method includes the following steps:

[0022] S41, obtaining multi-dimensional data and a daily failure list in the power grid system, and calculating multiple impact vector sets according to the multi-dimensional data and the daily failure list, each of which corresponds to an abnormal cause, and the multiple impact vector sets include:

[0023] The influence vector set corresponding to the abnormal cause is the temperature environment change;

[0024] The impact vector set corresponding to the abnormal causes is clock display abnormality and clock chip damage;

[0025] The influence vector set corresponding to the abnormal cause of excessive current;

[0026] The influence vector set corresponding to the abnormal causes is voltage fluctuation and internal metering circuit failure;

[0027] S42, performing dimensionless processing on the failure list of the day to obtain a vector set of a parent sequence;

[0028] S43, all the influence vector sets in S41 are calculated one by one by the grey correlation method, and the correlation coefficient between the subsequence in each influence vector set and the parent sequence is obtained. The correlation coefficients of the subsequences in the same influence vector set are averaged to obtain the grey correlation, and the grey correlation of all the influence vector sets is calculated in sequence, wherein the correlation coefficient formula is:

[0029]

[0030] In the formula, i is the number of the subsequence in the influence vector set, j is the number of the dimension; ρ is the resolution coefficient, ρ∈(0,1); Y t is the vector set of the parent sequence, expressed as Y t =[Y t (1), Y t (2) Y t (3), ..., Y t (n)]; X ri is the influence vector set, expressed as X ri =[X r1 (1) X r 2(2),X r 3(3), ..., X r n (n)];

[0031] S44. Arrange the plurality of grey correlation degrees from large to small, and determine the abnormal cause corresponding to the influence vector set with the largest grey correlation degree as the abnormal cause of the power collection device collection failure.

[0032] As a preferred example, after abnormal diagnosis is performed on the power collection equipment, dispatch is performed according to the diagnosis result, and the abnormal diagnosis includes a data collection failure diagnosis method, and the data collection failure diagnosis method includes the following steps:

[0033] S51, obtaining a daily failure list in the power grid system, and obtaining an electric meter to be diagnosed according to the daily failure list;

[0034] S55, diagnose whether the electric meter is online through the power grid system. If the electric meter is online, enter S53; if the electric meter is not online, check whether there is a power outage in the local area. If there is no power outage in the local area but the electric meter is not online, or the electric meter is still not online after the power outage, it is necessary to dispatch operation and maintenance personnel to conduct on-site inspection;

[0035] S53, remotely synchronize the clocks of the electric meter and the concentrator through the power grid system; if the remote synchronization of the clock is successful, the first data supplement is performed; if the first data supplement is successful, it is determined that the clock is abnormal; if the first data supplement fails or the remote synchronization of the clock fails, enter S54;

[0036] S54, resetting the meter reading parameters of the electric meter and the concentrator through the power grid system; if the resetting is successful, performing a second data supplement; if the second data supplement is successful, determining that the meter reading parameters are abnormal; if the second data supplement fails or the resetting fails, entering S55;

[0037] S55. Perform a push process on the in-transit process through the power grid system; if the push process is normal, perform a third data supplement; if the third data supplement is successful, determine that the push process is abnormal; if the third data supplement fails or the push process fails, enter the subsequent process.

[0038] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the intelligent dispatching method for operation and maintenance tasks of power collection equipment as described above.

[0039] The beneficial effects of the present invention are:

[0040] 1. The present invention quantifies the work content of the work order into workload by type, and evenly distributes it in combination with the previous work records and work intensity of the operation and maintenance team. Dispatching is carried out based on the principle of workload matching, so that the actual overall workload of each operation and maintenance team and operation and maintenance personnel tends to be the same, that is, the work intensity of each operation and maintenance team tends to be consistent, thereby avoiding the problem that some operation and maintenance teams have the same dispatch volume but very high actual work intensity, and can effectively reduce the high pressure state of operation and maintenance personnel.

[0041] 2. Quantify work orders according to work intensity to facilitate monitoring of the performance of operation and maintenance teams and personnel, facilitate performance appraisal, and better coordinate leave and vacation adjustments for operation and maintenance personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flow chart of the intelligent dispatching method;

[0043] Figure 2 A flowchart for performing quantization processing using preset quantization rules;

[0044] Figure 3 A flowchart for automatically assigning work orders to corresponding operation and maintenance teams based on the principle of workload matching;

[0045] Figure 4 is a flow chart of a method for diagnosing equipment failure;

[0046] Figure 5 Flowchart of the method for diagnosing data collection failure. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "or / and" used herein includes any and all combinations of one or more of the related listed items.

[0049] See also Figure 1This embodiment provides a method for intelligent dispatching of power collection equipment operation and maintenance tasks. After abnormal diagnosis of the power collection equipment is performed, the intelligent dispatching method dispatches tasks according to the diagnosis results. The intelligent dispatching method specifically includes the following steps:

[0050] S1. Enter the diagnosis results into the quick query table for classification, and generate multiple work orders based on the diagnosis results of different classifications. At the same time, it can merge related work orders to enhance the connectivity of work orders and reflect the layered sense of business. Each work order is marked with a type.

[0051] In this step, the quick query table is a reference manual or reference data table compiled in advance based on experience. The quick query table records the abnormal causes contained in the diagnosis results and the corresponding processing methods of the abnormal causes. Therefore, by knowing the diagnosis results, the processing method of the fault can be quickly determined. After knowing the processing method, the corresponding work order can be generated. The information on the work order includes the abnormal cause, processing method and type. The types include high / low urgency, long / short journey time and long / short processing time. Among them, the types such as urgency and processing time are determined by comprehensive consideration of the abnormal cause, processing method, previous experience, etc. The journey time is estimated with the help of tools such as network maps (such as Siji maps). The values ​​of high / low, long / short are distinguished based on the work experience and common knowledge of the technicians in the power field. Generally, the journey time is divided by 40 minutes, and it is marked as long journey time if it is higher than 40 minutes. For example, if the cause of the abnormality is a fault in the meter itself, the estimated travel time is 80 minutes, and the solution is to replace the entire meter, the type of the work order is marked as high urgency, long travel time, and long processing time. If the cause of the abnormality is too large an error, the estimated travel time is 65 minutes, and the solution is to replace the internal measuring component, the type of the work order is marked as low urgency, long travel time, and long processing time. If the cause of the abnormality is too large an error, the estimated travel time is 25 minutes, and the solution is to check whether it is turned off or recalibrated, the type is marked as low urgency, short travel time, and short processing time.

[0052] S2. Work orders are quantified based on the type of mark using preset quantification rules, and work orders are divided into different workloads. At the same time, based on the principle of workload matching, the total workload of each operation and maintenance team and the workload of the work orders it handles are made to be the same, and work orders with different workloads are automatically assigned to different operation and maintenance teams.

[0053] In this embodiment, different workloads include high workload, medium workload and low workload. The work orders are quantified and divided into different workloads. Figure 2 As shown, the implementation of the preset quantization rule includes the following steps:

[0054] S21. Assign a value of 1 to the types of high urgency, long journey time, and long processing time, respectively.

[0055] S22. Assign values ​​of 0 to the types of low urgency, short travel time and short processing time respectively.

[0056] S23. Add the corresponding values ​​according to the marking type of the work order to obtain the work quantity value A of the work order.

[0057] S24. If the workload value A=3, the work order is marked as high workload; if the workload value A=2 or 1, the work order is marked as medium workload; if the workload value A=0, the work order is marked as low workload.

[0058] After determining the workload of the work order, based on the principle of workload matching, the work orders with different workloads, or the related work orders, are packaged together to calculate the workload value A, and automatically distributed to different operation and maintenance teams. The purpose is to make the overall workload of different operation and maintenance teams tend to be the same, that is, to make the work intensity of each operation and maintenance team tend to be consistent. In this embodiment, Figure 3 As shown in the figure, based on the principle of workload matching, automatically dispatching work orders to corresponding operation and maintenance teams includes the following steps:

[0059] S31. Obtain the current day's work records and the historical work records of the previous seven working days of the operation and maintenance team.

[0060] S32, add the workload value A*B of the processed work orders in the current day's work record of a single operation and maintenance team to the workload value A of the processed work orders in the historical work record to obtain the total workload value C of the operation and maintenance team. Wherein, B is the fatigue coefficient, and B>0, and the relevant value is taken according to the size of the total operation and maintenance capacity in the time period.

[0061] S33. Sort each operation and maintenance team from large to small according to the total workload value C. Work orders with high workload are assigned to the operation and maintenance teams in the bottom one-third of the sort, work orders with low workload are assigned to the operation and maintenance teams in the top one-third of the sort, and work orders with medium workload are assigned to the remaining teams. There are two ways to allocate work orders: manual allocation and automatic allocation. Manual allocation is performed by the team leader or responsible person of the operation and maintenance team, who allocates the pending work orders according to the total workload value C. In special circumstances, the team leader or responsible person can also decide the allocation priority and allocation time node by themselves, and decide the allocation result by themselves. Automatic allocation is automatic allocation by a program that runs an intelligent dispatching method, and the allocation is performed according to the above-designed allocation logic. At the same time, automatic allocation can also take into account rules such as address location and customer preferences, and these rules are combined to allow the program or system to automatically dispatch orders.

[0062] After the work order is assigned, if the generated work order contains complete geographic information data of the area, the specific address is queried using the Siji map, and one or more preferred maintenance routes are automatically generated on the map. The operation and maintenance team moves to the site according to the preferred maintenance routes; if the generated work order does not contain complete geographic information data of the area, the responsible person will move to the site on his own based on his understanding of the area under his jurisdiction.

[0063] On the other hand, the operation and maintenance team will perform repairs after arriving at the site, and check the accuracy of the diagnostic results in the work order, and provide feedback on the work order processing results. The completed work order is used as the work record for the day, and is fed back into the calculation of the total workload value C to update the data for the next dispatch of the operation and maintenance team, thereby improving the accuracy of work order allocation.

[0064] The above intelligent dispatching method can reasonably distribute the work order tasks of each operation and maintenance team according to the work intensity. At the same time, the maintenance process of the operation and maintenance team is visually tracked to display the current work status, current work content and real-time location of each operation and maintenance team, so that the dispatch of work orders can be adjusted at any time according to the displayed information to deal with emergencies. At the same time, the performance of the operation and maintenance team and operation and maintenance personnel can be monitored to facilitate performance appraisal, and at the same time, it can better coordinate the leave and adjustment of operation and maintenance personnel.

[0065] The implementation of the intelligent dispatching method in this embodiment depends on the confirmation of the diagnosis result. To this end, in another embodiment, a method for abnormal diagnosis of power collection equipment is also provided. Figure 4 As shown, the abnormal diagnosis of the power collection equipment includes a device fault diagnosis method, and the device fault diagnosis method includes the following steps:

[0066] S41. Collect multi-dimensional data and a daily failure list in the power grid system, and calculate multiple impact vector sets based on the multi-dimensional data and the daily failure list. Each impact vector set corresponds to an abnormal cause. The multiple impact vector sets include:

[0067] The corresponding abnormal cause is the impact vector set of temperature environment changes.

[0068] The corresponding abnormal causes are the impact vector set of clock display abnormality and clock chip damage.

[0069] The influence vector set corresponding to the abnormal cause of excessive current.

[0070] The influence vector set corresponding to the abnormal causes is voltage fluctuation and internal metering circuit failure.

[0071] S42, non-dimensionalize the failure list of the day to obtain a vector set of the parent sequence. Similarly, non-dimensionalize the impact vector set to reduce the difference in absolute values ​​of the data.

[0072] S43, all the influence vector sets in S41 are calculated one by one by the grey correlation method, and the correlation coefficient between the subsequence and the parent sequence in each influence vector set is obtained. The correlation coefficients of the subsequences in the same influence vector set are averaged to obtain the grey correlation, and the grey correlation of all influence vector sets is calculated in turn. The correlation coefficient formula is:

[0073]

[0074] In the formula, i is the number of the subsequence in the influence vector set, j is the number of the dimension; ρ is the resolution coefficient, ρ∈(0,1); Y t is the vector set of the parent sequence, expressed as Y t =[Y t (1), Y t (2) Y t (3), ..., Y t (n)]; X ri is the influence vector set, expressed as X ri =[X r1 (1) X r 2(2),X r 3(3), ..., X r n (n)].

[0075] S44. Arrange the multiple grey correlations from large to small, and determine the abnormal cause corresponding to the influence vector set with the largest grey correlation as the abnormal cause of the power collection device collection failure.

[0076] The above multi-dimensional data includes environmental data, verification and detection data, automatic data collection of power collection equipment, operation and maintenance closed-loop work orders, equipment failure record data, historical processing data and other data related to power collection equipment that can be collected. Extract the data that is not collected or shows collection failure, collect common and regular abnormal problems, and count and form a list of failures for the day. This equipment fault diagnosis method can be used to determine the fault of the meter itself or its internal components, that is, the hardware fault.

[0077] In another embodiment, the abnormality diagnosis of the power collection device also includes a data collection failure diagnosis method, such as Figure 5 As shown, the data acquisition failure diagnosis method includes the following steps:

[0078] S51. Obtain the daily failure list in the power grid system, and obtain the electric meter to be diagnosed based on the daily failure list. The power grid system will call for meter data at dawn every day to collect the meter reading information of the day, accompanied by a large amount of collection failure and abnormal information. Combined with the principle of meter collection, we determine the target electric meter to be diagnosed by querying the daily failure list.

[0079] S52. Diagnose whether the meter is online through the power grid system. If the meter is online, go to S53. If the meter is not online, check whether there is a power outage in the local area. If there is no power outage in the local area but the meter is not online, it is necessary to dispatch operation and maintenance personnel to conduct on-site investigation. If there is a power outage in the local area, perform system verification steps after power is supplied to ensure that the abnormal diagnosis method is performed in the local area when there is power. If the meter is still not online after power is supplied, it is also necessary to dispatch operation and maintenance personnel to conduct on-site investigation.

[0080] S53, remotely synchronize the clocks of the meter and the concentrator through the power grid system. If the remote synchronization of the clock is successful, the first data supplement is performed. If the first data supplement is successful, the cause of the abnormality is determined, that is, the abnormality is caused by the abnormality of the clocks of the meter and the concentrator. If both the first data supplement and the remote synchronization fail, enter S54. At this time, it is unclear whether the collection abnormality is caused by the clock or other reasons, and the cause can only be further investigated by the subsequent process.

[0081] S54, the meter reading parameters of the electric meter and the concentrator are sent to be reset through the power grid system. If the reset is successful, the second data supplement is performed. If the second data supplement is successful, the abnormal cause is determined, that is, the abnormal cause is that the meter reading parameters of the electric meter and the concentrator are abnormal. Similarly, if the second data supplement and reset fail, and it is unclear what caused it, enter S55.

[0082] S55. Perform an empty push process on the in-transit process through the power grid system. If the empty push process is normal, perform the third data supplement. If the third data supplement is successful, determine the cause of the abnormality, that is, the abnormal cause is that the in-transit process of the meter and the concentrator is abnormal. At this time, the data acquisition failure diagnosis method has been completed, and the required abnormal cause can be obtained. In another embodiment, if both the third data supplement and the empty push process fail, the subsequent process can be continued, and the subsequent process includes checking the items specified by the next business rule. According to the above steps, the items are continuously checked and the data is called until the abnormal cause is found or all subsequent processes fail. The above-mentioned data acquisition failure diagnosis method checks the common and most fault types on the meter by whether the remote data is collected or not, and is mainly used to determine the faults in the communication or parameters of the meter, that is, the software faults. Through the troubleshooting of hardware and software faults, the cause of the meter fault can be accurately located, thereby ensuring the accuracy of the diagnosis results, which is more conducive to the effective implementation of the intelligent dispatching method.

[0083] The present invention also provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the intelligent dispatching method disclosed in the above embodiment is implemented. The computer-readable storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc., which is used to store and install application software and an executable computer program for implementing the intelligent dispatching method for the operation and maintenance tasks of the power collection equipment.

[0084] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An intelligent dispatching method for power collection equipment operation and maintenance tasks, characterized in that: The intelligent dispatching method comprises the following steps: S1. Generate multiple work orders according to different categories of diagnosis results, each of which is marked with the type of diagnosis result; S2. The work order is quantified based on the type of mark using the preset quantification rules to obtain a work order marked with the workload. Each operation and maintenance team also marks the workload based on the completed work orders. Based on the principle of workload matching, the total workload after adding the workload of each operation and maintenance team and the workload of the work orders it handles tends to be the same, and the work order is automatically distributed to the corresponding operation and maintenance team.

2. The intelligent dispatching method for power collection equipment operation and maintenance tasks according to claim 1 is characterized in that: After the operation and maintenance team arrives at the on-site maintenance site, they will check the accuracy of the diagnostic results in the work order and provide feedback on the work order processing results. At the same time, the completed work order is recorded as the workload of the operation and maintenance team.

3. The intelligent dispatching method for power collection equipment operation and maintenance tasks according to claim 2 is characterized in that: The types of the diagnosis results include high / low urgency, long / short travel time, and long / short processing time.

4. The intelligent dispatching method for power collection equipment operation and maintenance tasks according to claim 3 is characterized in that: In S2, the work order is quantified based on the type of the tag using the preset quantification rules, including the following steps: S21, assigning a value of 1 to the types of high urgency, long travel time, and long processing time; S22, assigning the types of low urgency, short travel time and short processing time to 0 respectively; S23, adding corresponding values ​​according to the marking type of the work order to obtain the work quantity value A of the work order; S24. If the workload value A=3, the work order is marked as high workload; if the workload value A=2 or 1, the work order is marked as medium workload; if the workload value A=0, the work order is marked as low workload.

5. The intelligent dispatching method for power collection equipment operation and maintenance tasks according to claim 4 is characterized in that: In S2, based on the principle of workload matching, the work order is automatically assigned to the corresponding operation and maintenance team, including the following steps: S31. Obtain the current day work record and the historical work record of the previous seven working days of the operation and maintenance team; S32, adding the workload value A*B of the processed work orders in the current day's work record of a single operation and maintenance team to the workload value A of the processed work orders in the historical work record to obtain the total workload value C of the operation and maintenance team, where B is the fatigue coefficient and B>0; S33, sorting each operation and maintenance team from large to small according to the total workload value C, allocating work orders with high workload to the operation and maintenance teams in the bottom one-third of the sorting, and work orders with low workload to the operation and maintenance teams in the top one-third of the sorting, and work orders with medium workload to the remaining teams; The methods of allocating work orders include manual allocation and random allocation. The manual allocation is performed by the team leader of the operation and maintenance team according to the total workload value C, and the random allocation is performed automatically by a program running an intelligent dispatching method.

6. The intelligent dispatching method for power collection equipment operation and maintenance tasks according to claim 1, characterized in that: After the work order is issued, one or more preferred maintenance routes are automatically generated on the map for the area with complete geographic information data in the work order, and the operation and maintenance team moves to the site according to the preferred maintenance route. For areas without complete geographic information data in the work order, the responsible person of the operation and maintenance team depends on his or her understanding of the area under his or her jurisdiction and moves to the site on his or her own.

7. The intelligent dispatching method for power collection equipment operation and maintenance tasks according to claim 1, characterized in that: Visually track the operation and maintenance teams to display the current work status, current work content and real-time location of each operation and maintenance team.

8. The intelligent dispatching method for power collection equipment operation and maintenance tasks according to claim 1, characterized in that: After abnormal diagnosis of the power collection equipment is performed, dispatch is performed according to the diagnosis result, wherein the abnormal diagnosis includes an equipment fault diagnosis method, and the equipment fault diagnosis method includes the following steps: S41, obtaining multi-dimensional data and a daily failure list in the power grid system, and calculating multiple impact vector sets according to the multi-dimensional data and the daily failure list, each of which corresponds to an abnormal cause, and the multiple impact vector sets include: The influence vector set corresponding to the abnormal cause is the temperature environment change; The impact vector set corresponding to the abnormal causes is clock display abnormality and clock chip damage; The influence vector set corresponding to the abnormal cause of excessive current; The influence vector set corresponding to the abnormal causes is voltage fluctuation and internal metering circuit failure; S42, performing dimensionless processing on the failure list of the day to obtain a vector set of a parent sequence; S43, all the influence vector sets in S41 are calculated one by one by the grey correlation method, and the correlation coefficient between the subsequence in each influence vector set and the parent sequence is obtained. The correlation coefficients of the subsequences in the same influence vector set are averaged to obtain the grey correlation, and the grey correlation of all the influence vector sets is calculated in sequence, wherein the correlation coefficient formula is: In the formula, i is the number of the subsequence in the influence vector set, j is the number of the dimension; ρ is the resolution coefficient, ρ∈(0,1); Y t is the vector set of the parent sequence, expressed as Y t =[Y t (1), Y t (2) Y t (3), ..., Y t (n)]; X ri is the influence vector set, expressed as X ri =[X r1 (1) X r2 (2) X r3 (3), ..., X rn (n)]; S44. Arrange the plurality of grey correlation degrees from large to small, and determine the abnormal cause corresponding to the influence vector set with the largest grey correlation degree as the abnormal cause of the power collection device collection failure.

9. The intelligent dispatching method for power collection equipment operation and maintenance tasks according to claim 1 or 8, characterized in that: After abnormal diagnosis of the power collection equipment is performed, dispatch is performed according to the diagnosis result, wherein the abnormal diagnosis includes a data collection failure diagnosis method, and the data collection failure diagnosis method includes the following steps: S51, obtaining a daily failure list in the power grid system, and obtaining an electric meter to be diagnosed according to the daily failure list; S55, diagnose whether the electric meter is online through the power grid system. If the electric meter is online, enter S53; if the electric meter is not online, check whether there is a power outage in the local area. If there is no power outage in the local area but the electric meter is not online, or the electric meter is still not online after the power outage, it is necessary to dispatch operation and maintenance personnel to conduct on-site inspection; S53, remotely synchronize the clocks of the electric meter and the concentrator through the power grid system; if the remote synchronization of the clock is successful, the first data supplement is performed; if the first data supplement is successful, it is determined that the clock is abnormal; if the first data supplement fails or the remote synchronization of the clock fails, enter S54; S54, resetting the meter reading parameters of the electric meter and the concentrator through the power grid system; if the resetting is successful, performing a second data supplement; if the second data supplement is successful, determining that the meter reading parameters are abnormal; if the second data supplement fails or the resetting fails, entering S55; S55. Perform a push process on the in-transit process through the power grid system; if the push process is normal, perform a third data supplement; if the third data supplement is successful, determine that the push process is abnormal; if the third data supplement fails or the push process fails, enter the subsequent process.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for intelligent dispatching of operation and maintenance tasks of power collection equipment as described in any one of claims 1 to 9 is implemented.

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