Method, device and equipment for processing appeal work order, medium and product

Abstract

The invention discloses an appeal work order processing method, device and equipment, a medium and a product. The method comprises the steps of obtaining a to-be-processed appeal work order in a preset time period; according to the work order detail information of the to-be-processed appeal work orders, on the basis of a preset work order matching rule, performing work order classification on the to-be-processed appeal work orders to obtain at least one work order processing cluster; each work order processing cluster comprises at least one appeal work order to be processed; determining a risk index corresponding to each work order processing cluster according to the work order detail information of each to-be-processed appeal work order in the corresponding work order processing cluster; and determining a target appeal work order according to the risk index corresponding to each work order processing cluster, and performing work order processing on the target appeal work order. According to the technical scheme of the embodiment of the invention, the processing accuracy and efficiency of the customer appeal work order in the power grid power service scene are improved.

Description

Technical Field

[0001] This invention relates to the field of power grid service technology, and in particular to a method, apparatus, equipment, medium and product for processing work orders. Background Technology

[0002] In the power grid complaint handling scenario, the increasing number of power grid complaint work orders due to the expansion of the electricity customer base leads to a more complex range of work order types. Simply classifying power grid complaint work orders according to traditional complaint types results in inaccurate risk assessment. Furthermore, the increased number of power grid complaint work orders makes it difficult to accurately determine their processing priorities, leading to delays in handling high-risk requests. Therefore, a precise complaint work order classification method adapted to the characteristics of power grid business is urgently needed. Summary of the Invention

[0003] This invention provides a method, apparatus, equipment, medium, and product for processing customer request work orders, in order to improve the accuracy and efficiency of processing customer request work orders in power grid service scenarios.

[0004] According to one aspect of the present invention, a method for processing work orders is provided, comprising:

[0005] Retrieve pending work orders within a preset time period;

[0006] Based on the work order details of the pending work orders, and according to the preset work order matching rules, each pending work order is classified to obtain at least one work order processing cluster; each work order processing cluster includes at least one pending work order.

[0007] Based on the work order details of each pending request work order in the corresponding work order processing cluster, determine the risk index corresponding to each work order processing cluster.

[0008] Based on the risk index corresponding to each of the aforementioned work order processing clusters, the target request work order is determined, and the target request work order is processed.

[0009] According to another aspect of the present invention, a request work order processing device is provided, comprising:

[0010] The work order acquisition module retrieves work orders pending processing within a preset time period.

[0011] The work order classification module classifies each pending work order according to its work order details and based on preset work order matching rules, to obtain at least one work order processing cluster; each work order processing cluster includes at least one pending work order.

[0012] The risk index determination module determines the risk index corresponding to each work order processing cluster based on the work order details of each pending request work order in the corresponding work order processing cluster.

[0013] The work order processing module determines the target request work order based on the risk index corresponding to each of the work order processing clusters, and processes the target request work order.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory that is communicatively connected to at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the claim processing method of any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the work order processing method of any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the request work order processing method of any embodiment of the present invention.

[0020] This invention provides a technical solution that obtains pending work orders within a preset time period; classifies these pending work orders according to their details and a preset work order matching rule, resulting in at least one work order processing cluster; each work order processing cluster includes at least one pending work order; determines a risk index corresponding to each work order processing cluster based on the details of each pending work order within the cluster; and identifies a target work order based on the risk index of each cluster, then processes the target work order. This technical solution classifies work orders based on preset rules, assigns a corresponding risk coefficient to each classified work order, and processes the work orders according to their corresponding risk coefficients. This solves the problem of inaccurate processing of complex work orders using traditional classification methods, improves classification accuracy, precisely identifies high-risk work orders, and enhances the efficiency and accuracy of work order processing.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a request work order processing method provided in Embodiment 1 of the present invention;

[0024] Figure 2 This is a flowchart of a request work order processing method provided in Embodiment 2 of the present invention;

[0025] Figure 3A This is a flowchart of a method for determining the repetitiveness of power grid request work orders and the determination of work order processing clusters after the power grid request work orders are imported, according to Embodiment 3 of the present invention.

[0026] Figure 3B This is a flowchart for determining a target request work order in a work order processing cluster, according to Embodiment 3 of the present invention.

[0027] Figure 4 This is a schematic diagram of a request work order processing device according to Embodiment 4 of the present invention;

[0028] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the work order processing method of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Example 1

[0032] Figure 1 This is a flowchart of a request work order processing method provided in Embodiment 1 of the present invention. This embodiment is applicable to processing customer request work orders in the field of power grid services. The method can be executed by a request work order processing device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0033] S101. Obtain pending work orders within a preset time period.

[0034] S102. Based on the work order details of the pending work orders, and based on the preset work order matching rules, classify each pending work order to obtain at least one work order processing cluster; each work order processing cluster includes at least one pending work order.

[0035] S103. Based on the work order details of each pending request work order in the corresponding work order processing cluster, determine the risk index corresponding to each work order processing cluster.

[0036] S104. Based on the risk index corresponding to each work order processing cluster, determine the target request work order and process the target request work order.

[0037] The preset time period can be a pre-set time interval, such as 6 months. The pending work orders can be customer work orders in the power grid service scenario that need to be processed.

[0038] For example, it is possible to connect to the power grid system to obtain all pending work orders from the past month. The power grid system can be a power management system that handles power grid-related business.

[0039] The details of pending work orders may include the work order number, submission time, customer's mobile phone number, electricity user number, type of service requested, power supply unit, and scope of impact. The type of service requested can be tagged to categorize specific areas, such as power outage, poor contact, and meter inspection.

[0040] The preset work order matching rules can be pre-set by relevant technical personnel according to actual needs. For example, the work order matching rules can be set as a combination of attributes in the work order details data of the work orders to be processed.

[0041] The work order processing cluster can be a set of all pending work orders that meet the preset work order matching rules.

[0042] For example, if the work order details of a pending request work order meet a preset work order matching rule, then at least one work order processing cluster is created according to the work order details in the work order matching rule, and the pending request work order is assigned to a work order processing cluster that meets the work order matching rule. For example, there are currently four pending request work orders, and the work order details they contain are as follows:

[0043] Pending request work order A contains: Electricity user number: ZZZZZ, Request type: Power outage, Power supply unit: Power supply station A, Equipment type: 10kV transformer; Pending request work order B contains: Electricity user number: ZZZZ, Request type: Power outage, Power supply unit: Power supply station A, Equipment type: 10kV transformer; Pending request work order C contains: Electricity user number: ZZZ, Request type: Poor contact, Power supply unit: Power supply station B, Equipment type: 5kV transformer; Pending request work order D contains: Electricity user number: ZZZZZ, Request type: Power outage, Power supply unit: Power supply station B, Equipment type: 10kV transformer.

[0044] If the preset work order matching rule is that the power supply unit is power supply station A, the equipment type is 10kV transformer, and the requested service type is power outage, then the pending work orders that meet the preset work order matching rule are pending work order A and pending work order B. Pending work orders A and B can be assigned to the same work order processing cluster m, so work order processing cluster m contains pending work orders A and B. If the preset work order matching rule is that the power supply unit is B, then the pending work orders that meet the preset work order matching rule are pending work orders C and pending work order D. Pending work orders C and D can be assigned to the same work order processing cluster n, so work order processing cluster n contains pending work orders C and D.

[0045] The risk index can be calculated by relevant technical personnel using a pre-set formula based on the work order details of the pending work orders in the work order processing cluster.

[0046] For example, relevant technical personnel can pre-set the risk index weights of pending work orders based on the type of the request, and then take the average of the risk index weights of the pending work orders in the work order processing cluster to obtain the risk index of the work order processing cluster. If a work order processing cluster contains the following 3 pending work orders:

[0047] Pending request work order A includes: Request type: power outage, Affected scope: more than 500 households, Power supply unit: Power supply station A; Pending request work order B includes: Request type: power outage, Affected scope: 100 to 500 households, Power supply unit: Power supply station A; Pending request work order C includes: Request type: power outage, Affected scope: single user, Power supply unit: Power supply station A.

[0048] For example, if a power outage affecting more than 500 households has a risk index weight of 5, a power outage affecting 100 to 500 households has a risk index weight of 3, and a power outage affecting a single user has a risk index weight of 1, then the risk index of this work order processing cluster can be the average of the risk index weights of all pending work orders in the work order processing cluster. For example: (risk index weight of pending work order A + risk index weight of pending work order B + risk index weight of pending work order C) / 3, that is, the risk index of this work order processing cluster can be (5 + 3 + 1) / 3 = 3.

[0049] Among them, the target request work order can be the request work order in the work order processing cluster that needs to be processed.

[0050] For example, after calculating the risk index of a work order processing cluster, the work order with the highest risk index and the earliest submission time in the work order processing cluster can be used as the target work order.

[0051] In some embodiments, the target request work order is determined based on the risk index corresponding to each work order processing cluster. The target work order processing cluster is determined based on the risk index corresponding to each work order processing cluster. The target request work order is determined based on the remaining processing time of each pending request work order contained in the target work order processing cluster.

[0052] For example, the work order processing cluster with the highest risk index can be selected as the target work order processing cluster. If multiple work order processing clusters have the same risk index, the cluster with the largest number of pending work orders can be selected as the target work order processing cluster. If both the risk index and the number of pending work orders are the same, a work order processing cluster can be randomly selected as the target work order processing cluster.

[0053] The remaining processing time for a work order can be calculated using a formula pre-set by relevant technical personnel. For example, if the basic processing time for a pending work order is 15 days, and the submission date is January 1, 2001, and the current date is January 2, 2001, then the remaining processing time for the pending work order is 14 days. The basic processing time refers to the time interval during which a pending work order must be processed, and can be pre-set by relevant technical personnel based on the type of service requested. For example, if the service requested by a pending work order is "low voltage," the basic processing time could be 12 hours.

[0054] For example, the pending request work order with the lowest remaining processing time in the work order processing cluster can be selected as the target request work order.

[0055] The above technical solution determines the target work order processing cluster based on the risk index, and then determines the target request work order based on the remaining processing time of the work orders in the target work order processing cluster. This allows for precise location of the work order processing cluster with the greatest impact, and then determination of the most urgent target request work order, thus achieving precise allocation of work order processing resources.

[0056] To further improve the accuracy of determining the remaining processing time for pending work orders, in some embodiments, the remaining processing time for any pending work order within any work order processing cluster is determined as follows:

[0057] Step a1: Determine the basic time limit based on the business type of the request in the work order details.

[0058] Step a2: Determine the business category coefficient based on the business type in the work order details of the pending work order, and determine the business sub-category coefficient based on the sub-type corresponding to the business type.

[0059] Step a3: Determine the business complexity coefficient based on the business type of the request in the work order details information of the work order to be processed.

[0060] Step a4: Determine the remaining time limit for work order processing based on the basic time limit, business category coefficient, business subcategory coefficient, and business complexity coefficient.

[0061] For example, if the requested service type is power outage, the basic time limit can be 4 hours. If the requested service type is meter repair, the basic time limit can be 24 hours.

[0062] The business category coefficient can be preset by relevant technical personnel based on the business category of the request. The business subcategory coefficient can be preset by relevant technical personnel based on the business subcategory of the request.

[0063] For example, if the requested service type is power outage, the service category coefficient can be 0.9. If the requested service type is meter repair, the service category coefficient can be 1.1. If the service subcategory is high-voltage power outage, the service subcategory coefficient can be 0.8. If the requested service subcategory is meter verification, the service subcategory coefficient can be 1.3. The service complexity coefficient can be preset by relevant technical personnel based on the requested service category.

[0064] For example, if the requested service type is power outage, the service complexity coefficient can be 1.2. If the requested service type is meter repair, the service complexity coefficient can be 0.3.

[0065] The remaining processing time for work orders can be calculated by relevant technical personnel using a pre-set formula.

[0066] For example, the remaining processing time for a work order can be equal to the base time limit × business category coefficient × business subcategory coefficient × business complexity coefficient. If the requested business type of the work order to be processed is "distribution box malfunction," the base time limit is 24 hours, the corresponding business category coefficient is 1.1, the corresponding requested business subcategory is "distribution box maintenance," the business subcategory coefficient is 1.2, and the business complexity coefficient is 0.6, then the remaining processing time for the current pending work order is 24 × 1.1 × 1.2 × 0.6 = 19.01 hours. That is, the remaining processing time for the current pending work order is 19.01 hours.

[0067] The above technical solution determines the remaining processing time of work orders by using business category coefficient, business subcategory coefficient, and business complexity coefficient. This makes the calculation of the remaining processing time of pending work orders more reasonable and accurate, ensuring the rationality and efficiency of processing pending work orders.

[0068] It should be noted that this embodiment also provides a method for determining target request work orders. In some embodiments, the above method further includes:

[0069] If the remaining processing time of any pending work order in each work order processing cluster is found to meet the preset time judgment condition, then the pending work order that meets the time judgment condition will be updated as the target work order.

[0070] The time-based judgment criteria can be preset by relevant technical personnel according to actual needs.

[0071] For example, the time-based judgment condition can be set to determine whether the remaining processing time for a pending work order is 0. If the remaining processing time is 0, the pending work order is considered to have timed out. If the pending work order has timed out, the timed-out pending work order will be used as the target work order. For instance, if there is a pending work order A in the work order processing cluster with a remaining processing time of 5 hours, and a pending work order B with a remaining processing time of 0 hours, then pending work order B will be used as the target work order.

[0072] The above technical solution determines the priority of pending work orders by judging the remaining processing time, thereby improving processing efficiency.

[0073] It should be noted that this embodiment also provides a real-time processing method for power grid request work orders. In some embodiments, the above method further includes:

[0074] Step b1: Respond to the power grid request work order processing request and obtain the power grid request work order.

[0075] Step b2: Based on the work order details of the power grid request work order, and based on the work order details of at least one pending request work order contained in each work order processing cluster, determine whether the power grid request work order meets the work order repetition judgment condition.

[0076] Step b3: If not, then determine the work order processing cluster corresponding to the power grid request work order based on the work order matching information in the work order details information and the classification matching rules for generating each work order processing cluster.

[0077] Step b4: Add the power grid request work order to the work order processing cluster corresponding to the power grid request work order.

[0078] Among them, the power grid complaint work order can be a request for processing electricity-related issues reported by users to the power grid system.

[0079] Among them, the criteria for determining the repetition of work orders can be preset by relevant technical personnel according to actual needs.

[0080] For example, the work order details of the power grid request work order are matched with the work order details of any pending request work order in all work order processing clusters according to the preset work order duplication judgment conditions. If the conditions are met, it means that the two are duplicates.

[0081] If the criteria for determining duplicate work orders are set to consider a power grid request work order and a pending request work order as duplicates during the exact matching process of the power supply unit, customer mobile phone number, request service type, and account number in the work order details, then an exact matching process can be used, where the matched data is completely identical.

[0082] If the work order details for the power grid request are as follows: Customer mobile number: XXXXX, Request type: power outage, Affected scope: more than 200 households, Power supply unit: Power supply station A, Account number: ZZ.

[0083] There are two pending work orders in the work order processing cluster: Pending work order A: Customer mobile number: XXXX, Request type: power outage, Affected scope: more than 500 households, Power supply unit: Power supply station, Account number: ZZ; Pending work order B: Customer mobile number: XXXX, Request type: power outage, Affected scope: more than 100 households, Power supply unit: Power supply station A, Account number: XX.

[0084] The work order details of the power grid complaint work order match the customer's mobile phone number, complaint service type, and account number of pending complaint work order A precisely, thus meeting the criteria for work order duplication. However, the power grid complaint work order and pending complaint work order B do not meet the criteria for work order duplication.

[0085] Among them, the work order matching information can determine whether the power grid request work order meets the preset work order matching rules.

[0086] For example, the work order details of the power grid request work order are obtained and matched with preset work order matching rules. If a correct work order match is found, the power grid request work order is assigned to the corresponding work order processing cluster. If no match is found, the key work order information in the work order details of the power grid request work order is grouped into a separate work order processing cluster. The key work order information can be the request service type and power supply unit in the work order details.

[0087] The above technical solution determines the processing cluster of power grid request work orders by judging whether they meet the work order duplication judgment conditions, thereby avoiding duplication of pending request work orders and improving the processing efficiency and accuracy of pending request work orders.

[0088] This invention provides a technical solution that obtains pending work orders within a preset time period; classifies these work orders according to their details and a preset work order matching rule, resulting in at least one work order processing cluster; each processing cluster includes at least one pending work order; determines a risk index corresponding to each processing cluster based on the details of the pending work orders within that cluster; and identifies and processes target work orders based on the risk indices corresponding to each processing cluster. This technical solution classifies work orders based on preset rules, assigns corresponding risk coefficients to the classified work orders, and processes the work orders according to their risk coefficients. This solves the problem of inaccurate processing of complex work orders using traditional classification methods, improves classification accuracy, precisely identifies high-risk work orders, and enhances the efficiency and accuracy of work order processing.

[0089] Example 2

[0090] Figure 2 This is a flowchart of a request work order processing method provided in Embodiment 2 of the present invention. This embodiment is an optimization and improvement based on the above technical solutions.

[0091] Furthermore, the step "determine the risk index corresponding to each work order processing cluster based on the work order details of each pending work order in the corresponding work order processing cluster" is refined into "determine the number of pending work orders contained in the corresponding work order processing cluster; determine the priority weight coefficient of the corresponding work order processing cluster based on the request business type in the work order details of each pending work order in the corresponding work order processing cluster; determine the work order processing time of the corresponding work order processing cluster based on the work order generation time in the work order details of each pending work order in the corresponding work order processing cluster; and determine the risk index corresponding to each work order processing cluster based on the number of work orders, priority weight coefficient, and work order processing time of the corresponding work order processing cluster." This improves the work order processing method.

[0092] It should be noted that for parts not described in detail in the embodiments of the present invention, please refer to the descriptions in other embodiments. For example... Figure 2 As shown, the method includes the following specific steps:

[0093] S201. Obtain pending work orders within a preset time period.

[0094] S202. Based on the work order details of the pending work orders, and based on the preset work order matching rules, classify each pending work order to obtain at least one work order processing cluster; each work order processing cluster includes at least one pending work order.

[0095] S203. Determine the number of pending request work orders contained in the corresponding work order processing cluster.

[0096] S204. Determine the priority weight coefficient of the corresponding work order processing cluster based on the request business type in the work order details information of each pending request work order in the corresponding work order processing cluster.

[0097] S205. Determine the workflow duration of the corresponding work order processing cluster based on the work order generation time in the work order details of each pending request work order in the corresponding work order processing cluster.

[0098] S206. Based on the number of work orders, priority weight coefficient, and work order processing time of the corresponding work order processing cluster, determine the risk index corresponding to each work order processing cluster.

[0099] S207. Based on the risk index corresponding to each work order processing cluster, determine the target request work order and process the target request work order.

[0100] For example, the number of pending request work orders can be determined based on the number of work order numbers in the pending request work orders in the work order processing cluster.

[0101] The priority weight coefficient can be preset by relevant technical personnel according to actual needs.

[0102] For example, if the service type of the pending work order is "Power outage affecting 500 households", the priority weight coefficient can be set to 1.5. If the service type of the pending work order is "Power outage for a single user", the priority weight coefficient can be set to 0.7.

[0103] The work order processing time can be defined as the time interval between the submission time of the pending work order and the current time. The work order processing time of a work order processing cluster can be defined as the average processing time of all pending work orders in the work order processing cluster.

[0104] For example, if there are the following two pending work orders in work order processing cluster x:

[0105] Pending Request Work Order A includes: Work Order Number: 001, Submission Time: January 4, 2001, Customer Mobile Number: XXXXX, Power Supply Unit: Power Supply Station A; Pending Request Work Order B includes: Work Order Number: 002, Submission Time: January 5, 2001, Customer Mobile Number: XXXX, Power Supply Unit: Power Supply Station A.

[0106] If the current time is January 6, 2001, then the processing time for pending request work order A is 2 days, and the processing time for pending request work order B is 1 day. The processing time for work order cluster x is (1+2) / 2=1.5 days.

[0107] The risk index can be calculated by relevant technical personnel using a pre-defined formula based on actual needs. For example, the risk index calculation formula could be: Risk Index = Number of Work Orders × Priority Weight Coefficient × Work Order Processing Time. The number of pending work orders can be the total number of pending work orders in the work order processing cluster. The priority weight coefficient can also be pre-defined by relevant technical personnel based on actual needs. For example, if the pending work order's service type is power outage and its impact range is more than 500 households, the priority weight coefficient can be set to 1.5. If the pending work order's service type is power outage and its impact range is a single user, the priority weight coefficient can be set to 1. If the priority weight coefficients of pending work orders in a work order processing cluster are different, the average priority weight coefficient of all pending work orders in the cluster can be calculated as the priority weight coefficient of that work order processing cluster. For example, in a work order processing cluster, the priority weight coefficient of the work order A to be processed is 3, and the priority weight coefficient of the work order B to be processed is 7. Then the priority weight coefficient of the work order processing cluster is (3+7) / 2=5.

[0108] For example, if a work order processing cluster has a corresponding work order matching rule that matches all pending request work orders where the power supply unit is power supply station A, and there are the following 3 pending request work orders:

[0109] Pending request work order A includes: Work order number: 001, submission time: January 4, 2001, customer mobile phone number: XXXXX, request type: power outage, affected scope: more than 500 households; Pending request work order B includes: Work order number: 002, submission time: January 5, 2001, customer mobile phone number: XXXX, request type: power outage, affected scope: more than 500 households; Pending request work order C includes: Work order number: 003, submission time: January 6, 2001, customer mobile phone number: XXX, request type: power outage, affected scope: single user.

[0110] If the current time is January 7, 2001, then the workflow time for the current work order processing cluster is (3+2+1) / 3 = 2 days. There are 3 work orders. If the priority weighting coefficient is set as follows: if the requested work order's service type is power outage and its impact range is more than 500 households, then the priority weighting coefficient is set to 4. If the requested work order's service type is power outage and its impact range is a single user, then the priority weighting coefficient is set to 1. Therefore, the priority weighting coefficient for the current work order processing cluster is (4+4+1) / 3 = 3. The risk coefficient corresponding to the current work order processing cluster is: number of work orders × priority weighting coefficient × work order workflow time, i.e., 3 × 3 × 2 = 18.

[0111] The above embodiments acquire pending work orders within a preset time period, classify work orders according to their details based on preset work order matching rules, obtain at least one work order processing cluster, determine the number of pending work orders in each processing cluster, determine the priority weight coefficient of the corresponding processing cluster based on the request business type in the details of each pending work order, determine the work order processing time of the corresponding processing cluster based on the work order generation time in the details of each pending work order, and then calculate the risk index corresponding to each processing cluster based on the number of work orders, priority weight coefficient, and work order processing time. Finally, the target work order is determined and processed based on the risk index, which not only ensures timely response to high-risk and high-priority work orders, but also improves the orderliness and efficiency of overall work order processing.

[0112] Example 3

[0113] This embodiment provides a preferred example based on the above embodiments.

[0114] like Figure 3A As shown, the method for determining the repetitiveness of power grid request work orders and the determination of the work order processing cluster after the power grid request work orders are imported includes the following specific steps:

[0115] S301. In response to the power grid demand work order processing request, obtain the power grid demand work order.

[0116] S302. Based on the work order details of the power grid request work order, and based on the work order details of at least one pending request work order contained in each work order processing cluster, determine whether the power grid request work order meets the work order repetition judgment condition; if yes, then do not import; if no, then execute S303.

[0117] S303. Based on the work order matching information in the work order details of the power grid request work order and the corresponding classification matching rules for generating each work order processing cluster, determine the work order processing cluster corresponding to the power grid request work order.

[0118] S304. Add the power grid request work order to the work order processing cluster corresponding to the power grid request work order.

[0119] S305. Obtain pending work orders within a preset time period.

[0120] S306. Based on the work order details of the pending work orders, and based on the preset work order matching rules, classify each pending work order to obtain at least one work order processing cluster; each work order processing cluster includes at least one pending work order.

[0121] Regarding the determination of the target request work order in the work order processing cluster, as follows: Figure 3B As shown:

[0122] S401. Determine the number of pending request work orders contained in the corresponding work order processing cluster.

[0123] S402. Determine the priority weight coefficient of the corresponding work order processing cluster based on the request business type in the work order details information of each pending request work order in the corresponding work order processing cluster.

[0124] S403. Determine the workflow duration of the corresponding work order processing cluster based on the work order generation time in the work order details of each pending request work order in the corresponding work order processing cluster.

[0125] S404. Based on the number of work orders, priority weight coefficient, and work order processing time of the corresponding work order processing cluster, determine the risk index corresponding to each work order processing cluster.

[0126] S405. Determine the target work order processing cluster based on the risk index corresponding to each work order processing cluster. Determine the basic time limit based on the request business type in the work order details information of the pending request work orders in the target processing cluster.

[0127] S406. Determine the business category coefficient based on the business type in the work order details of the pending work order, and determine the business sub-category coefficient based on the sub-type corresponding to the business type.

[0128] S407. Determine the business complexity coefficient based on the business type of the request in the work order details information of the pending request work order.

[0129] S408. Determine the remaining time limit for work order processing based on the basic time limit, business category coefficient, business subcategory coefficient, and business complexity coefficient.

[0130] S409. Determine whether the preset time judgment condition is met. If the condition is met, execute step S410; otherwise, execute step S411.

[0131] S410. Update pending request work orders that meet the time judgment conditions to target request work orders.

[0132] S411. Determine the target request work order based on the remaining time limit for work order processing, and process the target request work order.

[0133] Example 4

[0134] Figure 4This is a schematic diagram of a work order processing device according to Embodiment 4 of the present invention. The work order processing device provided in this embodiment of the present invention is applicable to processing power grid work orders. This work order processing device can be implemented in hardware and / or software, such as... Figure 4 As shown, the device includes: a request work order processing module 420, a work order classification module 421, a risk index determination module 422, and a work order processing module 423. Wherein:

[0135] The Request Work Order Acquisition Module 420 is used to acquire pending request work orders within a preset time period.

[0136] The work order classification module 421 is used to classify each of the pending work orders according to the work order details information and based on the preset work order matching rules, to obtain at least one work order processing cluster; each work order processing cluster includes at least one pending work order.

[0137] The risk index determination module 422 is used to determine the risk index corresponding to each work order processing cluster based on the work order details information of each pending request work order in the corresponding work order processing cluster.

[0138] The work order processing module 423 is used to determine the target request work order based on the risk index corresponding to each of the work order processing clusters, and to process the target request work order.

[0139] This invention provides a technical solution that obtains pending work orders within a preset time period; classifies these pending work orders according to their details and a preset work order matching rule, resulting in at least one work order processing cluster; each work order processing cluster includes at least one pending work order; determines a risk index corresponding to each work order processing cluster based on the details of each pending work order within the cluster; and identifies a target work order based on the risk index of each cluster, then processes the target work order. This technical solution classifies work orders based on preset rules, assigns a corresponding risk coefficient to each classified work order, and processes the work orders according to their corresponding risk coefficients. This solves the problem of inaccurate processing of complex work orders using traditional classification methods, improves classification accuracy, precisely identifies high-risk work orders, and enhances the efficiency and accuracy of work order processing.

[0140] Optional, the risk index determination module 422 is specifically used for:

[0141] Determine the number of pending request work orders contained in the corresponding work order processing cluster.

[0142] Based on the request business type in the work order details of each pending request work order in the corresponding work order processing cluster, determine the priority weight coefficient of the corresponding work order processing cluster.

[0143] The workflow duration of the corresponding work order processing cluster is determined based on the work order generation time in the work order details of each pending request work order in the corresponding work order processing cluster.

[0144] Based on the number of work orders, priority weight coefficient, and work order processing time of the corresponding work order processing cluster, the risk index corresponding to each work order processing cluster is determined.

[0145] Optional, the work order processing module 423 is specifically used for:

[0146] The target work order processing cluster is determined based on the risk index corresponding to each of the aforementioned work order processing clusters.

[0147] The target request work order is determined based on the remaining processing time of each pending request work order contained in the target work order processing cluster. The target request work order is then processed.

[0148] Optionally, the device further includes:

[0149] The Request Work Order Acquisition Module is used to respond to the processing request of power grid request work orders and acquire power grid request work orders.

[0150] The work order duplication judgment module determines whether the power grid request work order meets the work order duplication judgment condition based on the work order details information of the power grid request work order and the work order details information of at least one pending request work order included in each work order processing cluster.

[0151] If the work order processing cluster determination module does not meet the work order repetition judgment condition, it determines the work order processing cluster corresponding to the power grid request work order based on the work order matching information in the work order details information of the power grid request work order and the classification matching rules for generating each work order processing cluster.

[0152] The work order addition module adds the power grid request work order to the work order processing cluster corresponding to the power grid request work order.

[0153] Optionally, the remaining processing time for any pending request work order within any work order processing cluster can be determined as follows:

[0154] Determine the basic time limit based on the business type of the pending work order details.

[0155] Based on the request business type in the work order details, determine the business category coefficient, and based on the sub-type corresponding to the request business type, determine the business sub-category coefficient.

[0156] The business complexity coefficient is determined based on the business type of the pending work order details.

[0157] The remaining time limit for work order processing is determined based on the basic time limit, the business category coefficient, the business subcategory coefficient, and the business complexity coefficient.

[0158] Optionally, the device further includes:

[0159] The target request work order update module is used to determine the remaining time limit for work order processing. If it is detected that the remaining time limit for processing any pending request work order in each of the work order processing clusters meets the preset time judgment condition, the pending request work order that meets the time judgment condition will be updated as the target request work order.

[0160] The work order processing device provided in this embodiment of the invention can execute the work order processing method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0161] Example 5

[0162] Figure 5 A schematic diagram of an electronic device 50 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0163] like Figure 5 As shown, the electronic device 50 includes at least one processor 51 and a memory, such as a read-only memory (ROM) 52 and a random access memory (RAM) 53, communicatively connected to the at least one processor 51. The memory stores computer programs executable by the at least one processor. The processor 51 can perform various appropriate actions and processes based on the computer program stored in the ROM 52 or loaded from storage unit 58 into the RAM 53. The RAM 53 can also store various programs and data required for the operation of the electronic device 50. The processor 51, ROM 52, and RAM 53 are interconnected via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0164] Multiple components in electronic device 50 are connected to I / O interface 55, including: input unit 56, such as keyboard, mouse, etc.; output unit 57, such as various types of monitors, speakers, etc.; storage unit 58, such as disk, optical disk, etc.; and communication unit 59, such as network card, modem, wireless transceiver, etc. Communication unit 59 allows electronic device 50 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0165] Processor 51 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 51 performs the various methods and processes described above, such as the appeal work order processing method.

[0166] In some embodiments, the work order processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 58. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 50 via ROM 52 and / or communication unit 59. When the computer program is loaded into RAM 53 and executed by processor 51, one or more steps of the work order processing method described above may be performed. Alternatively, in other embodiments, processor 51 may be configured as the work order processing method by any other suitable means (e.g., by means of firmware).

[0167] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0168] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0169] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0170] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0171] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0172] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0173] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0174] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for processing work orders, characterized in that, include: Retrieve pending work orders within a preset time period; Based on the work order details of the pending work orders, and based on the preset work order matching rules, each pending work order is classified to obtain at least one work order processing cluster. Each of the aforementioned work order processing clusters includes at least one pending request work order; Based on the work order details of each pending request work order in the corresponding work order processing cluster, determine the risk index corresponding to each work order processing cluster. Based on the risk index corresponding to each of the aforementioned work order processing clusters, the target request work order is determined, and the target request work order is processed.

2. The method according to claim 1, characterized in that, The step of determining the risk index corresponding to each work order processing cluster based on the work order details of each pending request work order in the corresponding work order processing cluster includes: Determine the number of pending request work orders contained in the corresponding work order processing cluster. Based on the request business type in the work order details of each pending request work order in the corresponding work order processing cluster, determine the priority weight coefficient of the corresponding work order processing cluster. Based on the work order generation time in the work order details of each pending request work order in the corresponding work order processing cluster, determine the work order processing time of the corresponding work order processing cluster. Based on the number of work orders, priority weight coefficient, and work order processing time of the corresponding work order processing cluster, the risk index corresponding to each work order processing cluster is determined.

3. The method according to claim 1, characterized in that, The step of determining the target request work order based on the risk index corresponding to each of the work order processing clusters includes: The target work order processing cluster is determined based on the risk index corresponding to each of the aforementioned work order processing clusters. The target request work order is determined based on the remaining processing time of each pending request work order contained in the target work order processing cluster.

4. The method according to claim 1, characterized in that, The method further includes: In response to the power grid request processing request, obtain the power grid request work order; Based on the work order details of the power grid request work order, and based on the work order details of at least one pending request work order included in each work order processing cluster, it is determined whether the power grid request work order meets the work order repetition judgment condition. If not, then the work order processing cluster corresponding to the power grid request work order is determined based on the work order matching information in the work order details information of the power grid request work order and the work order matching rules that generate each work order processing cluster. Add the power grid request work order to the work order processing cluster corresponding to the power grid request work order.

5. The method according to claim 3, characterized in that, The remaining processing time for any pending request work order within any work order processing cluster is determined as follows: Determine the basic time limit based on the business type of the pending work order in the work order details information; Based on the request business type in the work order details information of the pending request work order, determine the business category coefficient, and based on the sub-type corresponding to the request business type, determine the business sub-category coefficient; Determine the business complexity coefficient based on the business type of the pending work order details. The remaining time limit for work order processing is determined based on the basic time limit, the business category coefficient, the business subcategory coefficient, and the business complexity coefficient.

6. The method according to claim 5, characterized in that, The method further includes: If it is detected that the remaining processing time of any pending work order in each of the work order processing clusters meets the preset time judgment condition, then the pending work order that meets the time judgment condition will be updated as the target work order.

7. A work order processing device, characterized in that, include The work order acquisition module retrieves work orders pending processing within a preset time period. The work order classification module classifies each of the pending work orders based on the work order details and a preset work order matching rule, thereby obtaining at least one work order processing cluster. Each of the aforementioned work order processing clusters includes at least one pending request work order; The risk index determination module determines the risk index corresponding to each work order processing cluster based on the work order details of each pending request work order in the corresponding work order processing cluster. The work order processing module determines the target request work order based on the risk index corresponding to each of the work order processing clusters, and processes the target request work order.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the request work order processing method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the work order processing method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the request work order processing method according to any one of claims 1-6.

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