Work order allocation method and device and storage medium

Abstract

The invention provides a work order allocation method and device and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring a work order to be distributed; the work orders are classified based on first attribute information corresponding to the work orders, and the first attribute information refers to attribute features used for classification and corresponding to the work orders; performing priority ranking on the work orders in each class of classified work orders, wherein the priority ranking refers to determining the distribution priority of the work orders; and based on the work order sequence after priority sorting, the target work order is allocated to a target object, the target object is an object for processing the target work order, and the target work order is any work order in the work orders after priority sorting.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a work order allocation method, apparatus, and storage medium. Background Art

[0002] Work order allocation refers to the process of allocating customer service requests or other tasks to be processed to appropriate service teams or individuals after receiving them.

[0003] In the prior art, after receiving a work order, the work order is directly distributed; or, after simple screening and classification of the work order, the work order is distributed.

[0004] However, in the prior art, only a direct connection between the work order and the work order processor is simply established. In some cases, the work order allocation is inaccurate and unreasonable, resulting in a reduction in the processing efficiency of the work order. Summary of the Invention

[0005] This application provides a work order allocation method, apparatus, and storage medium, which can allocate work orders more accurately, thereby improving the processing efficiency of work orders.

[0006] To achieve the above object, this application adopts the following technical solutions:

[0007] In a first aspect, this application provides a work order allocation method, which includes:

[0008] Obtain the work order to be allocated. Classify the work order based on the first attribute information for classification corresponding to the work order. Sort the work orders in each category of the classified work orders by priority. Allocate the target work order to the target object based on the order of the work orders after priority sorting.

[0009] Among them, priority sorting refers to determining the allocation priority of the work order, the target object refers to the object for processing the target work order, and the target work order is any work order in the work orders after priority sorting.

[0010] The solution provided by this application preprocesses the work order after obtaining the work order to be allocated, that is, classifies the work order in advance to obtain different types of work orders. And determines the allocation priority corresponding to each work order in each category of work orders through priority sorting, so that the work order can be allocated more precisely according to the type and allocation priority of the work order, improving the matching degree between the work order and the work order processor, and thereby improving the processing efficiency of the work order and customer satisfaction.

[0011] A possible implementation method is to classify work orders based on the first attribute information corresponding to the work orders. It can be specifically implemented as follows: Obtain the classification score corresponding to the work order based on the first attribute information and the classification weight coefficient corresponding to the first attribute information. Classify the work orders according to the classification score. Among them, the classification score is used to determine the type of work order to which the work order belongs. The work orders are classified through the classification score calculated by the first attribute information and the classification weight coefficient. The classification weight coefficient can reflect the influence of different attribute information in the first attribute information on the classification of work orders. Therefore, the classification result can be flexibly adjusted by adjusting the classification weight coefficient, so as to adapt to different classification application scenarios.

[0012] Another possible implementation method is to classify work orders according to the classification score. It can be specifically implemented as follows: Classify the work orders based on the type of work order corresponding to the numerical range to which the classification score belongs. Different numerical ranges correspond to different types of work orders. Determine the type of work order based on the numerical range to which the classification score belongs, which clarifies the decision boundary for work order classification and further improves the work order classification efficiency.

[0013] Another possible implementation method is to perform priority sorting on the work orders in each category of classified work orders. It can be specifically implemented as follows: Obtain the sorting score corresponding to each work order in each category of work orders based on the second attribute information corresponding to each work order in each category of work orders and the sorting weight coefficient corresponding to the second attribute information. Sort the work orders in each category of work orders according to the sorting score. The second attribute information refers to the attribute characteristics corresponding to the work order for sorting, and the sorting score is used to determine the assignment order of the work orders. Calculate the sorting score according to the second attribute information and the sorting weight coefficient. The sorting weight coefficient reflects the influence of different attributes in the second attribute information on the sorting of work orders. Therefore, the sorting result can be flexibly adjusted by adjusting the sorting weight coefficient to adapt to different sorting application scenarios.

[0014] Another possible implementation method is to perform priority sorting on the work orders in each category of work orders according to the sorting score. It can be specifically implemented as follows: Sort the work orders in each category of work orders in descending order based on the sorting score. Descending order sorting can quickly locate the work order with the highest priority for assignment when the work orders are assigned, which helps to give priority to processing work orders with high assignment priorities, thereby improving the work order processing efficiency.

[0015] Another possible implementation method is that the sorting weight coefficient is dynamically adjusted according to one or more of the work order processing results, customer feedback data, or work order processing efficiency. Adjust the sorting weight coefficient through the work order processing results, customer feedback data, or work order processing efficiency, and then adaptively adjust the influence degree of different second attribute information on the work order assignment order, so that the work order assignment order is more adapted to the work order assignment and customer needs, and further improves the work order processing efficiency and the accuracy of work order assignment.

[0016] In another possible implementation, the work order allocation method provided by this application further includes: obtaining the work order processing efficiency and work order processing satisfaction of each object for processing the target work order. The above-mentioned allocation of the target work order to the target object based on the work order sequence after priority sorting can be specifically implemented as follows: based on the work order sequence after priority sorting, perform a weighted sum of the work order processing efficiency and work order processing satisfaction of each object for processing the target work order to obtain a comprehensive score for each object to process the target work order. Based on the comprehensive score, screen out the target object for processing the target work order, and allocate the target work order to the target object. Among them, the comprehensive score is used to screen the object for processing the target work order.

[0017] In a second aspect, a work order allocation device is provided, and the device includes: an obtaining module, a classification module, a sorting module, and an allocation module.

[0018] The above-mentioned obtaining module is used to obtain the work orders to be allocated.

[0019] The above-mentioned classification module is used to classify the work orders based on the first attribute information corresponding to the work orders.

[0020] The above-mentioned sorting module is used to perform priority sorting on the work orders in each category of classified work orders.

[0021] The above-mentioned allocation module is used to allocate the target work order to the target object based on the work order sequence after priority sorting.

[0022] Among them, the first attribute information refers to the attribute characteristics for classification corresponding to the work order, the priority sorting refers to determining the allocation priority of the work order, the target object refers to the object for processing the target work order, and the target work order is any work order in the work orders after priority sorting.

[0023] In a possible implementation, the above-mentioned classification module is further used to: obtain the classification score corresponding to the work order based on the first attribute information and the classification weight coefficient corresponding to the first attribute information, and classify the work order according to the classification score. The classification score is used to determine the work order type to which the work order belongs.

[0024] In another possible implementation, the above-mentioned classification module is further used to: classify the work orders based on the work order types corresponding to the numerical range to which the classification score belongs, and different numerical ranges correspond to different work order types.

[0025] In another possible implementation, the above-mentioned sorting module is further used to: obtain the sorting score corresponding to each work order in each category of work orders based on the second attribute information corresponding to each work order in each category of work orders and the sorting weight coefficient corresponding to the second attribute information, and perform priority sorting on the work orders in each category of work orders according to the sorting score. The second attribute information refers to the attribute characteristics for sorting corresponding to the work order, and the sorting score is used to determine the allocation order of the work order.

[0026] In another possible implementation manner, the above sorting module is further configured to: sort the work orders in each type of work order in descending order based on the sorting scores.

[0027] In another possible implementation manner, the sorting weight coefficient is dynamically adjusted according to one or more of the work order processing results, customer feedback data, or work order processing efficiency.

[0028] In another possible implementation manner, the above obtaining module is further configured to: obtain the work order processing efficiency and work order processing satisfaction degree of each object for processing the target work order. The above allocation module is further configured to: based on the order of the work orders after priority sorting, perform a weighted sum of the work order processing efficiency and work order processing satisfaction degree of each object for processing the target work order to obtain a comprehensive score of each object for processing the target work order, and based on the comprehensive score, screen out the target object for processing the target work order, and allocate the target work order to the target object. The comprehensive score is used to screen the object for processing the target work order.

[0029] For the technical effects corresponding to any implementation manner in the second aspect, reference may be made to the technical effects corresponding to any implementation manner in the first aspect above, which will not be elaborated here.

[0030] In a third aspect, a computer device is provided. The computer device includes: a processor and a memory. At least one computer program is stored in the memory, and at least one computer program is loaded and executed by the processor to implement the work order allocation method in the above aspect.

[0031] In a fourth aspect, a computer-readable storage medium is provided. At least one computer program is stored in the computer-readable storage medium, and at least one computer program is loaded and executed by the processor to implement the work order allocation method in the above aspect.

[0032] In a fifth aspect, a computer program product is provided. The computer program product includes a computer program or instruction. When the computer program or instruction is executed by the processor, the work order allocation method in the above aspect is implemented.

[0033] The solutions provided in the above third aspect to fifth aspect are used to implement the method provided in the first aspect above, and the specific implementation will not be elaborated one by one. For the technical effects corresponding to any implementation manner in the solutions provided in the above third aspect to fifth aspect, reference may be made to the technical effects corresponding to any implementation manner in the first aspect above, which will not be elaborated here.

[0034] It should be noted that, on the premise that the solutions are not contradictory, any possible implementation manners in the above aspects can be combined. Description of the Drawings

[0035] Figure 1 Schematic diagram of the structure of a computer system provided by an embodiment of the present application;

[0036] Figure 2 Schematic diagram of the process of a work order allocation method provided by an embodiment of the present application;

[0037] Figure 3 Schematic diagram of the process of another work order allocation method provided by an embodiment of the present application;

[0038] Figure 4 Schematic diagram of the structure of a work order allocation framework provided by an embodiment of the present application;

[0039] Figure 5 Schematic diagram of the process of yet another work order allocation method provided by an embodiment of the present application;

[0040] Figure 6 Schematic diagram of the structure of a work order allocation device provided by an embodiment of the present application;

[0041] Figure 7 Schematic diagram of the structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0042] In the embodiments of the present application, in order to facilitate the clear description of the technical solutions of the embodiments of the present application, words such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and roles. Those skilled in the art can understand that the words "first" and "second" do not limit the quantity and execution order, and the words "first" and "second" do not necessarily limit being different. There is no sequential or size order between the technical features described by the "first" and "second".

[0043] In the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "exemplary" or "for example" aims to present relevant concepts in a specific way for easy understanding.

[0044] In the embodiments of the present application, at least one can also be described as one or more, and multiple can be two, three, four or more, which is not limited in the present application.

[0045] In addition, the network architecture and scenarios described in the embodiments of this application are for more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those of ordinary skill in the art will know that with the evolution of the network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of this application are equally applicable to similar technical problems.

[0046] For ease of understanding, first, the terms involved in the embodiments of this application are explained.

[0047] Work order: It is a standardized process tool for recording and processing user requests, problems, or tasks, commonly found in fields such as customer service, Information Technology (IT) support, and operations management. It contains all the necessary information required to complete a certain task, such as task description, priority, deadline, person in charge, etc. A work order pool refers to a platform or system for centralized management, processing, and tracking of work orders, and can also be understood as the set of work tasks to be processed in an enterprise or organization to ensure that work orders are processed in a timely and effective manner.

[0048] Work order assignment: It refers to the process of allocating customer service requests or other types of work instructions / tasks to appropriate service teams or individuals after receiving them. A good work order assignment mechanism can ensure a quick response to customer needs, reduce processing time, and improve work efficiency and customer satisfaction.

[0049] Work order type: It refers to the categories divided according to the different natures / attributes, uses, or processing flows of work orders, usually determined based on the business needs of the enterprise, department functions, or the nature of customer problems, which helps the enterprise manage and track work orders more effectively and ensure that work orders are processed in a timely and accurate manner. Common work order types can include: service request work orders, fault report work orders, complaint and suggestion work orders, procurement and supply chain work orders, finance and accounting work orders, etc.

[0050] Work order processing: It refers to the object processing the work order taking corresponding measures to solve the problems reflected in the work order according to the specific content and requirements of the work order. For example, the customer service team conducts technical troubleshooting, repairs faults, provides solutions, etc. according to the work order content.

[0051] It should be noted that the information (including but not limited to device information, personal information of objects, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.), and signals involved in this application are all authorized by the object or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant laws, regulations, and standards. For example, the first attribute information, second attribute information, classification weight coefficients, sorting weight coefficients, etc. involved in this application are all obtained under full authorization.

[0052] The commonly used work order allocation methods in the industry often allocate work orders by establishing a direct connection between work orders and work order processors. The following is a brief description.

[0053] First, obtain the predicted processing duration and task information of the work order to be dispatched. Then, determine the objects to be dispatched that meet the preset association relationship with the work order to be dispatched as candidate objects. Finally, determine the target object from the candidate objects and dispatch the work order to be dispatched to the target object so that the target object can process the work order to be dispatched. Among them, the preset association relationship includes: the idle time of the object to be dispatched meets the predicted processing duration; the object to be dispatched and the task information meet the preset association degree; the attribute information of the object to be dispatched and the attribute information of the object to be dispatched corresponding to the task information meet the preset attribute similarity. The above process directly matches the work order to be dispatched and the object to be dispatched according to the preset conditions and establishes a direct connection between the work order and the work order processor.

[0054] However, the above method directly matches work orders and work order processors without preprocessing work orders or work order processors. The calculation involved in the work order allocation process is large, and work orders cannot be accurately allocated to work order processors. In application scenarios where work order types or requirements are relatively complex, the allocation deviation of work orders is more obvious, and the professional lines of work orders and work order processors do not match, resulting in a decrease in work order processing efficiency.

[0055] Based on this, the present application provides a work order allocation method. By preprocessing the work orders to be allocated, that is, classifying the work orders in advance to obtain different types of work orders, and then determining the allocation priorities of each type of work order through priority sorting, more accurate allocation can be achieved according to the types and allocation priorities of work orders during work order allocation, improving the matching degree between work orders and work order processors, and thus improving the work order processing efficiency and customer satisfaction.

[0056] The following specifically describes the solution provided by the embodiments of the present application with reference to the accompanying drawings.

[0057] The solution provided by the present application can be applied to Figure 1 the computer system shown in Figure 1 the schematic architecture diagram of the computer system shown.

[0058] Exemplarily, Figure 1The computer system shown includes a computer device 100, a work order 101 to be assigned, and an object 102 for processing the work order. The computer device 100 can be a high-performance server, which serves as the core of the work order assignment process and is responsible for assigning the work order 101 to be assigned to the object 102 for processing, or for outputting the work order 101 to be assigned and the assignment result of the processing object. The computer device 100 can directly obtain the work order 101 to be assigned created by the customer, or obtain the work order 101 to be assigned from a work order management platform, a work order management system, etc., and classify, sort, and assign the work order 101 to be assigned. The computer device 100 can also accept instructions from external staff and flexibly adjust the work order classification, sorting, and assignment process based on the instructions. The computer device 100 can also deploy a neural network model, etc., and train the model based on historical or existing work order assignment data to flexibly adapt to work order assignment scenarios with different requirements. The computer device 100 can also update the work order classification, sorting, and assignment algorithms in real time according to newly added work order assignment data, or update the model parameters deployed in the computer device 100. The "acquisition" of the computer device 100 in this application includes any term with an acquisition function such as query, discovery, extraction, etc., and this application does not limit this.

[0059] Figure 1 Exemplarily, one computer device 100, eight work orders 101 to be assigned, and five objects 102 for processing work orders are shown. The embodiments of this application do not limit the quantities of the computer device 100, the work order 101 to be assigned, and the object 102 for processing the work order. Optionally, the computer device 100 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server, cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, content delivery network (CDN), and cloud servers for basic cloud computing services such as big data that provide cloud computing services. The embodiments of this application do not limit the implementation manner and application scenario of the computer device 100.

[0060] Figure 2 It is a schematic flowchart of a work order assignment method provided for an exemplary embodiment. This method can be executed by a computer device. The computer device can be Figure 1 the server in

[0061] As Figure 2 shown, the work order assignment method provided by the embodiments of this application may include:

[0062] S201: The computer device acquires the work order to be assigned.

[0063] Among them, the work order to be assigned refers to the work order that has been created or received but has not been assigned to a specific processing object for processing.

[0064] In some embodiments, the work orders to be assigned are collected into a centralized work order management system or a work order pool for unified management and assignment. The computer device can directly obtain the work orders to be assigned from the work order management system or the work order pool; or, when the computer device itself serves as the work order management system, the computer device directly obtains the work orders to be assigned.

[0065] Exemplarily, the computer device obtains the work orders to be assigned in the following ways:

[0066] Method 1: The customer initiates a work order request to create a work order, and the created work order is stored in the work order management system or the work order pool. The computer device obtains the work orders to be assigned from the work order management system or the work order pool.

[0067] Method 2: The computer device directly generates the work orders to be assigned based on the work order request initiated by the customer.

[0068] Method 3: The computer device obtains the work orders within a certain period of time from the work order management system or the work order pool as the work orders to be assigned. For example: The computer device obtains the work orders of the current day or the past three days from the work order management system or the work order pool as the work orders to be assigned.

[0069] S202: The computer device classifies the work orders based on the first attribute information corresponding to the work orders.

[0070] Exemplarily, the work orders to be assigned correspond to the attribute information of the work orders, such as: information such as the source of the work order, the content description of the work order, the urgency of the work order, and the customer level of the work order.

[0071] Among them, the attribute information corresponding to the work order includes: first attribute information and second attribute information.

[0072] The first attribute information refers to the attribute characteristics for classification corresponding to the work order, and the second attribute information refers to the attribute characteristics for sorting corresponding to the work order.

[0073] It should be noted that the first attribute information and the second attribute information may have overlapping attribute information.

[0074] Optionally, the first attribute information includes the work order number, the urgency of the work order, the customer level of the work order, and the initial type of the work order, etc.

[0075] Among them, the work order number is a combination of numbers or letters that uniquely identifies a work order, which helps to improve work efficiency and ensure that the work order can be accurately and timely queried and processed. For example: For 3 different work orders, their work order numbers can be P1, P2, and P3 respectively.

[0076] The urgency level of a work order is used to evaluate the processing priority of the work order, the work order type, etc., and depends on factors such as the time requirement, resource demand, and risk level of the work order.

[0077] Exemplarily, the urgency level of a work order is reflected by different levels. For example: The urgency level of a work order is divided into three levels, and the three levels are respectively represented by numbers. Level "1" indicates that the problem described in the work order is extremely urgent and needs to be processed immediately, such as a data loss problem; Level "2" indicates that the problem described in the work order is relatively important, but the urgency level is slightly lower and can be processed within the specified time, such as a service performance degradation problem; Level "3" indicates that the problem described in the work order is a general failure, and the urgency level and the affected range are relatively small, such as the problem that a certain function of the system occasionally lags.

[0078] The customer level of a work order is a classification of customers based on factors such as the value and needs of the customers, which is convenient for more effectively managing work orders, optimizing work order processing resources, and improving customer satisfaction.

[0079] For example: The customer level of a work order can be divided into Class A customers, Class B customers, and Class C customers. Class A customers have high purchasing power, high loyalty, and high growth rate, and their needs need to be prioritized, and personalized services, exclusive customer service, etc. should be provided; Class B customers have relatively low purchase frequency and consumption amount, but a large number, and have certain development potential, and standardized products and services need to be provided; Class C customers have very low purchase frequency and consumption amount, and low loyalty, or there are certain risks (such as credit problems, default risks, etc.), and it is necessary to reduce the management cost of customers at this level and adopt low-cost service methods.

[0080] The initial type of a work order refers to the type obtained by initially classifying the work order after creating or receiving the work order. For example: The initial type of a work order can include reporting a problem, consulting, complaining, etc.

[0081] Exemplarily, the initial type of a work order is the type filled in by the customer when creating the work order, or the initial type of a work order is the type obtained by the work order processing personnel or the work order management system through preliminary analysis of the work order.

[0082] Exemplarily, the work order is classified to obtain the corresponding work order type.

[0083] Among them, the work order type is the same as / partially the same as the initial type of the work order, or the work order type is a refinement of the initial type of the work order. For example: When the initial type of a work order includes reporting a problem, the work order type can include equipment problem reporting and software problem reporting.

[0084] Optionally, the second attribute information includes the work order number, the customer level of the work order, the importance of the work order, the urgency level of the work order, the remaining processing time of the work order, and the tendency of the situation development, etc.

[0085] Among them, the importance of a work order refers to the degree of long-term impact of the problem or request represented by the work order on customer satisfaction or business goals, and is used to describe the potential impact of the work order on the overall business or customer experience.

[0086] Exemplarily, the importance of a work order can be divided from minor to severe, or can be represented by numbers or letters. For example: the importance of a work order is divided into three levels: minor, medium, and severe. Minor means that the problem described in the work order has a relatively small impact on the business or customers, such as a work order for product feedback suggestions; medium means that the problem described in the work order has a certain impact on the business or customers, but is not a critical problem, such as a work order for consultation questions; severe means that the problem described in the work order has a major impact on the business or customers and may cause greater losses, such as a work order for an emergency failure.

[0087] The remaining processing time of a work order refers to the length of time remaining from the current moment until the work order is completely processed. The shorter the remaining processing time of a work order, the higher the priority for it to be processed.

[0088] The tendency of the situation development refers to the possible development direction or trend of a work order during the processing.

[0089] Exemplarily, the types of the tendency of the situation development can be divided into an escalation tendency (tendency to tighten), a smooth resolution tendency (tendency to be stable), a tendency to ease, and a long-term tracking tendency, etc.

[0090] Among them, the escalation tendency means that a work order may be escalated due to the complexity of the problem, the urgency, or the dissatisfaction of the customer. After escalation, the work order requires the intervention of a higher-level support team or management personnel to seek a more effective solution. For example: a customer makes a complaint due to product quality problems. The customer communicates with the customer service multiple times but no satisfactory solution is obtained. A work order that may require a higher-level customer service or technical personnel to handle.

[0091] Smooth resolution means that after a work order is processed in a timely manner and effective communication is carried out, it will develop in the direction of smooth resolution, which helps to improve customer satisfaction and trust. Or, it means that the scope of influence, urgency, or difficulty of solution of the problem or request described in the work order is in a relatively stable state. For example: a work order for a customer to consult the specific content of a certain package. Generally, the customer service can handle it in a timely manner and carry out effective communication.

[0092] The tendency to ease means that the scope of influence, urgency, or difficulty of solution of the problem or request described in the work order is gradually decreasing. For example: for a user feedback problem caused by a software defect, after a patch is released, the number of affected users decreases significantly, and the number of work orders receiving new relevant feedback also gradually decreases.

[0093] Long-term tracking means that due to the particularity or complexity of the issue, a work order may require long-term tracking and attention, which may involve collaboration among multiple departments or teams, as well as multiple communications and coordinations. For example: For a work order where a customer indicates that a certain product may have potential faults, customer service or technical personnel need to communicate with the customer and track the handling for a long time.

[0094] S203: The computer device sorts the work orders in each category of the classified work orders by priority.

[0095] Among them, sorting by priority means determining the assignment priority of the work order.

[0096] Optionally, sorting by priority includes: sorting by acquisition time, sorting by urgency, sorting by the importance of the work order to be assigned, sorting by the customer level corresponding to the work order to be assigned, etc., but not limited to this.

[0097] Exemplarily, the assignment priority of the work orders in each type of work order is determined respectively, and the processes of determining the assignment priority for different types of work orders do not interfere with each other.

[0098] Optionally, the assignment priority is expressed in forms such as scores, letters, serial numbers, etc. For example: There are 5 work orders of a certain type. These 5 work orders are sorted by priority, and the order of the work orders after sorting by priority is: work order b, work order e, work order a, work order c, and work order d. The assignment priority of work order a is expressed as 3, that is, the third assignment; the assignment priority of work order b is expressed as 1, that is, the first assignment; the assignment priority of work order c is expressed as 4, that is, the fourth assignment; the assignment priority of work order d is expressed as 5, that is, the fifth assignment; the assignment priority of work order e is expressed as 2, that is, the second assignment.

[0099] S204: The computer device assigns the target work order to the target object based on the order of the work orders after sorting by priority.

[0100] Among them, the order of work orders refers to the order of assigning the work orders to be assigned.

[0101] The target object refers to the object that processes the target work order. For example: The target object is a work order processor such as a customer service, a technical engineer, or a work order administrator who processes the target work order.

[0102] The target work order is any one of the work orders after sorting by priority.

[0103] For example, the computer device assigns work order a, work order c, and work order d in sequence based on the assignment priority. During the process of assigning work order a, work order a is used as the target work order and is assigned to the corresponding target object; during the process of assigning work order c, work order c is used as the target work order and is assigned to the corresponding target object.

[0104] Exemplarily, the manner in which the computer device assigns the target work order to the target object based on the assigned priority is as follows:

[0105] Method 1: The work orders to be assigned are sequentially assigned to different objects for processing work orders according to the assigned priority. For example, when the work order sequence is Work Order 1, Work Order 2, and Work Order 3, Work Order 1 is assigned to Object a, Work Order 2 is assigned to Object b, and Work Order 3 is assigned to Object c. After each object is assigned a work order, a second work order is assigned to each object.

[0106] Method 2: The work orders to be assigned are assigned a certain quantity to each object for processing work orders in sequence according to the assigned priority. For example, when the work order sequence is Work Order 1, Work Order 2, Work Order 3, Work Order 4, Work Order 5, and Work Order 6, Work Orders 1 and 2 are assigned to Object a, Work Orders 3 and 4 are assigned to Object b, and Work Orders 5 and 6 are assigned to Object c.

[0107] Method 3: The work orders to be assigned are preferentially assigned to the objects who are good at processing the work orders according to the assigned priority. For example, when the work order sequence is Work Order 1, Work Order 2, and Work Order 3, Work Order a is a complaint work order, Work Order b is a trouble reporting work order, Work Order c is a complaint work order, and Object a is good at processing complaint work orders, and Object b is good at processing trouble reporting work orders, then Work Orders 1 and 3 are assigned to Object a, and Work Order 2 is assigned to Object b.

[0108] In summary, the solution provided by this application pre-processes the work orders to be assigned, that is, classifies the work orders in advance to obtain different types of work orders, and then determines the assigned priority of each type of work order through priority sorting, so that when the work orders are assigned, more accurate assignment can be achieved according to the type and assigned priority of the work orders, improving the matching degree between the work orders and the work order processors, and thus improving the processing efficiency of the work orders and customer satisfaction.

[0109] Figure 3 It is a schematic flowchart of another work order assignment method provided for an exemplary embodiment. This method can be executed by a computer device. This method includes:

[0110] S301: The computer device obtains the work orders to be assigned.

[0111] For the introduction of this step, refer to step S201, and no more details will be elaborated here.

[0112] S302: The computer device obtains the classification score corresponding to the work order based on the first attribute information and the classification weight coefficient corresponding to the first attribute information, and classifies the work order according to the classification score.

[0113] Among them, the classification score is used to judge the work order type to which the work order belongs.

[0114] In some embodiments, the data corresponding to the first attribute information of the work order and the classification weight coefficient corresponding to the first attribute information are weighted and summed to calculate the classification score of the work order.

[0115] For example: Table 1 shows the first attribute information of a certain group of work orders and their corresponding data.

[0116] Table 1: First attribute information and its corresponding data

[0117]

[0118]

[0119] Among them, work order initial type 1 represents a fault report, work order initial type 2 represents a consultation, and work order initial type 3 represents a complaint; work order urgency level 1 represents a relatively low work order urgency level, work order urgency level 2 represents a medium work order urgency level, and work order urgency level 3 represents a relatively high work order urgency level; customer level 1 represents that the customer to whom the work order belongs is a low-level customer, customer level 2 represents that the customer to whom the work order belongs is an average-level customer, and customer level 3 represents that the customer to whom the work order belongs is a high-level customer.

[0120] Exemplarily, when the classification weight coefficients corresponding to the first attribute information in Table 1 are 0.4, 0.3, and 0.3 respectively, the classification scores of this group of work orders are:

[0121] score(p1) = 0.4 * 1 + 0.3 * 3 + 0.3 * 2 = 1.9;

[0122] score(p2) = 0.4 * 2 + 0.3 * 2 + 0.3 * 1 = 1.7;

[0123] score(p3) = 0.4 * 3 + 0.3 * 1 + 0.3 * 3 = 2.4;

[0124] score(p4) = 0.4 * 1 + 0.3 * 2 + 0.3 * 3 = 2.3;

[0125] score(p5) = 0.4 * 2 + 0.3 * 3 + 0.3 * 2 = 2.3.

[0126] Among them, score(p1) represents the classification score of work order p1, score(p2) represents the classification score of work order p2, score(p3) represents the classification score of work order p3, score(p4) represents the classification score of work order p4, and score(p5) represents the classification score of work order p5.

[0127] Optionally, the classification weight coefficient corresponding to the first attribute information is a default value, or a system / user-defined value, or an empirical value summarized from historical work order data, which can be flexibly adjusted according to specific application scenarios. For example: If the work orders processed by the current department are urgent for high-level customers, the classification weight coefficient of the customer level can be appropriately increased to achieve more accurate classification and matching of work orders for high-star customers.

[0128] In some embodiments, the work orders are classified based on the work order types corresponding to the numerical ranges of the classification scores, and different numerical ranges correspond to different work order types.

[0129] For example: When the classification score is greater than 2, the corresponding work order type is a complaint; when the classification score is less than 1.8, the corresponding work order type is a consultation; when the classification score is in the range of [1.8, 2], the corresponding work order type is a fault report. Then, the work order type of work order p1 shown in Table 1 is a fault report; the work order type of work order p2 is a consultation; the work order types of work orders p3, p4, and p5 are complaints.

[0130] Among them, the numerical ranges and the work order types corresponding to the numerical ranges are set by the system or manually.

[0131] Exemplarily, the numerical ranges and the work order types corresponding to the numerical ranges are set based on the experience summarized from historical work order data.

[0132] In some embodiments, the process of classifying the work orders described above can be implemented through a classification model.

[0133] Optionally, the classification model includes: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), etc., but is not limited thereto, and the embodiments of the present application do not make specific limitations in this regard.

[0134] Optionally, when using the classification model to classify work orders, the classification model needs to be trained first, and the training process of the classification model includes the following steps 1-step 4.

[0135] Step 1: Obtain training set data.

[0136] Among them, the training set data includes the first sample attribute information of the sample work order, the data corresponding to the first sample attribute information, and the true type label of the sample work order.

[0137] For example: Table 2 shows some training set data.

[0138] Table 2: Training Set Data

[0139]

[0140] Step 2: Input the training set data into the classification model. The classification model calculates the predicted classification score corresponding to the sample work order based on the first sample attribute information, and obtains the predicted type of the sample work order based on the predicted classification score.

[0141] Exemplarily, the classification model calculates the predicted classification score corresponding to the sample work order based on the first sample attribute information and the classification weight coefficient corresponding to the first sample attribute information.

[0142] Step 3: Adjust the model parameters of the classification model based on the error between the true type label of the work order and the predicted type.

[0143] Among them, the model parameters of the classification model include the learning rate, the classification weight coefficient, and the threshold (δ). The learning rate is used to describe the adjustment degree of the classification weight coefficient, and the threshold is used to determine whether the error change of the model prediction is significant.

[0144] In some embodiments, the method for adjusting the classification weight coefficient based on the error between the true type label and the predicted type is as follows:

[0145] First, calculate the error between the true type label and the predicted type. For example:

[0146] In Table 2, the true type label and the predicted type of the sample work order p1 are both fault reporting, the prediction is correct, and the error is 0;

[0147] The true type label and the predicted type of the sample work order p2 are both consultation, the prediction is correct, and the error is 0;

[0148] The true type label and the predicted type of the sample work order p3 are both complaint, the prediction is correct, and the error is 0;

[0149] The true type label of the sample work order p4 is fault reporting, and the predicted type is complaint, the prediction is incorrect, and there is an error;

[0150] The true type label of the sample work order p5 is consultation, and the predicted type is complaint, the prediction is incorrect, and there is an error.

[0151] Therefore, adjust the classification weight coefficient based on the error between the true type label and the predicted type of the sample work orders p4 and p5.

[0152] Exemplarily, assume that the classification function f(W, P) = W * P, where W represents the classification weight coefficient vector, and P represents the attribute vector of the first sample attribute information. For example: the classification weight coefficient vector is [0.4, 0.3, 0.3], and the attribute vector of the sample work order p4 is [1, 2, 3].

[0153] Then, calculate the change in the function value and the gradient of the classification function. For example: the change in the function value of sample work order p4 = the number of the true type label - the number of the predicted type = 1 - 3 = -2, and the gradient of sample work order p4 = [1, 2, 3].

[0154] Finally, update the classification weight coefficient based on the change in the function value and the gradient. The formula for the classification weight coefficient is as follows:

[0155] W′ = W + η * Δf * ΔWf(W, P)

[0156] Where, W represents the classification weight coefficient vector, W′ represents the updated classification weight coefficient vector, P represents the attribute vector of the first sample attribute information, η represents the learning rate, Δf represents the change in the function value, and ΔWf(W, P) represents the gradient.

[0157] For example: when η is set to 0.02, the updated classification weight coefficient vector of sample work order p4 = [0.4, 0.3, 0.3] + 0.02 * (-2) * [1, 2, 3] = [0.4, 0.3, 0.3] + [-0.04, -0.08, -0.12] = [0.36, 0.22, 0.18], and the classification weight coefficient vector is adjusted from [0.4, 0.3, 0.3] to [0.36, 0.22, 0.18].

[0158] Exemplarily, based on the classification weight coefficient vector updated based on sample work order p4, continue to update based on sample work order p5.

[0159] For example: Δf of sample work order p5 = 2 - 3 = -1,

[0160] Then the updated classification weight coefficient vector of sample work order p5 = [0.36, 0.22, 0.18] + 0.02 * (-1) * [2, 3, 2] = [0.36, 0.22, 0.18] + [-0.04, -0.06, -0.04] = [0.32, 0.16, 0.14], and the classification weight coefficient vector is adjusted from [0.36, 0.22, 0.18] to [0.32, 0.16, 0.14].

[0161] In some embodiments, the learning rate is adjusted based on the change rate of the prediction error of the sample work order.

[0162] Where, the prediction error refers to the number of sample work order type prediction errors.

[0163] For example: Assume that the previous prediction error (initial state) is 0 and the current error is 2 (2 work order type prediction errors). Then the change rate of the prediction error = (0 - 2) / 0. Since the previous prediction error is 0, the denominator is 0. In practical applications, this situation can be handled specially. For example, set a small initial error value or directly adjust the learning rate according to the error change.

[0164] Exemplarily, in the case where the change rate of the prediction error cannot be directly calculated, the learning rate can be appropriately increased according to the increase in the error. For example, adjust the learning rate to 0.03 to accelerate the adjustment speed of the classification weight coefficient and reduce the error as soon as possible.

[0165] When the change rate of the prediction error can be calculated, if the change rate of the prediction error > δ, increase the learning rate; if the change rate of the prediction error < -δ, decrease the learning rate. The initial value of δ can be set to 0.1, but it is not limited to this. When the change rate of the prediction error is greater than δ, it indicates that the adjustment direction of the current classification weight coefficient is correct, but the adjustment speed is slow, and the learning rate needs to be increased to accelerate the convergence speed; when the change rate of the prediction error is less than -δ, it indicates that the adjustment direction of the current classification weight coefficient may be incorrect, and the learning rate needs to be decreased to avoid over-adjustment.

[0166] Step 4: Repeat the above Step 1, Step 2, and Step 3 until the classification model converges.

[0167] Among them, the convergence of the classification model means reaching the maximum number of iterations or the prediction error is lower than the error threshold.

[0168] During the process of training the classification model, continuously iterate and update the classification weight coefficient and the learning rate, so that the classification model gradually approaches the true classification situation and improves the accuracy of work order classification.

[0169] After statistics on indicators such as classification accuracy and processing duration, compared with the traditional classification method, the classification accuracy of the classification method in the embodiment of the present application has increased by 25%, and the processing duration has been shortened by 15%, significantly improving the work order processing efficiency and customer satisfaction.

[0170] S303: The computer device obtains the sorting score corresponding to each work order in each category of work orders based on the second attribute information corresponding to each work order in each category of work orders and the sorting weight coefficient corresponding to the second attribute information, and sorts the work orders in each category of work orders according to the sorting score.

[0171] Among them, the sorting score is used to judge the assignment order of the work orders.

[0172] In some embodiments, the data corresponding to the second attribute information of the work order and the sorting weight coefficient corresponding to the second attribute information are weighted and summed to calculate the sorting score of the work order.

[0173] For example, Table 3 shows the second attribute information of a certain group of work orders and their corresponding data.

[0174] Table 3: Second Attribute Information and Their Corresponding Data

[0175]

[0176] Among them, customer level 1 indicates that the customer to whom the work order belongs is a low-level customer, customer level 2 indicates that the customer to whom the work order belongs is an average-level customer, and customer level 3 indicates that the customer to whom the work order belongs is a high-level customer; work order importance 1 indicates slight importance, work order importance 2 indicates medium importance, and work order importance 3 indicates severe importance; work order urgency level 1 indicates relatively low work order urgency, work order urgency level 2 indicates medium work order urgency, and work order urgency level 3 indicates relatively high work order urgency; the unit of the remaining processing time of the work order is hour (h); the development tendency 1 of the situation indicates that the development tendency of the work order situation slows down; the development tendency 2 of the situation indicates that the development tendency of the work order situation is stable, and the development tendency 3 of the situation indicates that the development tendency of the work order situation tightens.

[0177] Exemplarily, when the sorting weight coefficients corresponding to the second attribute information in Table 3 are 0.3, 0.25, 0.2, -0.15, and 0.1 respectively, the sorting scores of this group of work orders are:

[0178] R(p1) = 0.3 * 3 + 0.25 * 3 + 0.2 * 2 - 0.15 * 48 + 0.1 * 3 = -5.75;

[0179] R(p2) = 0.3 * 2 + 0.25 * 2 + 0.2 * 3 - 0.15 * 24 + 0.1 * 2 = -1.9;

[0180] R(p3) = 0.3 * 1 + 0.25 * 1 + 0.2 * 1 - 0.15 * 72 + 0.1 * 1 = -9.95;

[0181] R(p4) = 0.3 * 3 + 0.25 * 2 + 0.2 * 3 - 0.15 * 36 + 0.1 * 3 = -3.35;

[0182] R(p5) = 0.3 * 2 + 0.25 * 3 + 0.2 * 2 - 0.15 * 60 + 0.1 * 2 = -4.45.

[0183] Among them, R(p1) represents the sorting score of work order p1, R(p2) represents the sorting score of work order p2, R(p3) represents the sorting score of work order p3, R(p4) represents the sorting score of work order p4, and R(p5) represents the sorting score of work order p5.

[0184] Exemplarily, based on the sorting scores, the work orders in each category of work orders are sorted in descending order to determine the assignment priority of the work orders. For example: In Table 1, the order of the work orders sorted in descending order based on the sorting scores is work order p2 > work order p4 > work order p5 > work order p1 > work order p3. Then, when assigning work orders, work orders p2, p4, p5, p1, and p3 are assigned in this order.

[0185] Optionally, the sorting weight coefficient corresponding to the second attribute information is a default value, or a system / user-defined value, or an empirical value summarized from historical work order assignment data, and can be flexibly adjusted according to specific application scenarios.

[0186] In some embodiments, the sorting weight coefficient is dynamically adjusted according to one or more of the work order processing results, customer feedback data, or work order processing efficiency.

[0187] Among them, the work order processing result is used to indicate whether the problem described in the work order is solved and the solution effect of the problem.

[0188] The customer feedback data refers to the satisfaction and suggestions, etc., feedback by the customer based on the work order processing result.

[0189] The work order processing efficiency is used to describe the speed and quality, etc., of the target object in processing the target work order.

[0190] Dynamically adjusting the sorting weight coefficient according to one or more of the work order processing results, customer feedback data, or work order processing efficiency may include the following situations, but are not limited to:

[0191] Situation 1: If it is found based on the work order processing result, customer feedback data, and work order processing efficiency that the urgency of the work order has a greater impact on the work order processing efficiency, then the sorting weight coefficient value corresponding to the urgency of the work order can be appropriately increased. For example: The sorting weight coefficient value corresponding to the urgency of the work order in Table 1 is adjusted from 0.2 to 0.25.

[0192] Situation 2: If it is found based on the work order processing result, customer feedback data, and work order processing efficiency that the remaining processing time of the work order has a smaller impact on the work order processing efficiency, then the absolute value of the sorting weight coefficient value corresponding to the remaining processing time of the work order can be appropriately reduced. For example: The sorting weight coefficient value corresponding to the remaining processing time of the work order in Table 1 is adjusted from -0.15 to -0.1.

[0193] Situation 3: If it is found based on the work order processing result, customer feedback data, and work order processing efficiency that the customer feedback data (such as customer satisfaction) of the work order with a high customer level is high, then the sorting weight coefficient value corresponding to the work order with a high customer level can be increased. For example: The sorting weight coefficient values corresponding to the customer levels of work orders p1 and p2 in Table 1 are adjusted from 0.3 to 0.35.

[0194] As work orders are continuously assigned and processed, the work order data is constantly updated. After each new work order data is added, the above processes of calculating the sorting score and adjusting the sorting weight coefficient are repeated to gradually optimize the sorting weight coefficient, making the sorting result more in line with the applied work order assignment scenario and actual business requirements, and improving customer satisfaction and work order processing efficiency. According to statistics, compared with the traditional sorting method, the sorting method of the embodiment of the present application shortens the work order processing time by 25% and increases customer satisfaction by 30%, significantly improving the work order processing efficiency and customer satisfaction.

[0195] S304: The computer device obtains the work order processing efficiency and work order processing satisfaction of each object for processing the target work order.

[0196] Among them, the work order processing satisfaction refers to the degree of customer satisfaction obtained based on customer feedback data after the work order is processed; alternatively, it can also be the satisfaction evaluated or predicted by professionals / systems.

[0197] For example: Table IV shows the work order processing efficiency and work order processing satisfaction of each object for processing work orders.

[0198] Table IV: Work order processing efficiency and work order processing satisfaction

[0199] Work Order Number Object Number Work Order Processing Efficiency Work Order Processing Satisfaction Degree p1 a1 0.80 0.75 p1 a2 0.60 0.65 p1 a3 0.70 0.70 p2 a1 0.75 0.70 p2 a2 0.85 0.80 p2 a3 0.65 0.60 p3 a1 0.90 0.85 P3 a2 0.70 0.75 p3 a3 0.80 0.80 p4 a1 0.65 0.60 p4 a2 0.75 0.70 p4 a3 0.85 0.85 p5 a1 0.70 0.75 p5 a2 0.80 0.85 p5 a3 0.60 0.65

[0200] Table V shows the basic information of each object for processing work orders.

[0201] Table V: Basic information of the object

[0202] Object Number Object Name Object Skills a1 Zhang San Fault Report Handling, Complaint Handling a2 Li Si Consultation Answering, Order Processing a3 Wang Wu Package Change Processing, Refund Tracking Processing

[0203] Based on the basic information of each object for processing work orders, as well as the work order processing efficiency and work order processing satisfaction, comprehensively measure the ability of each object to process various work orders, so as to assign corresponding work orders to each object based on the ability to process work orders, thereby improving the matching degree between work orders and the objects processing work orders.

[0204] In some embodiments, the embodiment of the present application does not limit the order of step S304 and step S301.

[0205] S305: The computer device performs a weighted sum of the work order processing efficiency and work order processing satisfaction of each object for processing the target work order based on the order of the work orders sorted by priority, obtains the comprehensive score of each object for processing the target work order, filters out the target object for processing the target work order based on the comprehensive score, and assigns the target work order to the target object.

[0206] Among them, the comprehensive score is used to measure the ability of each object to process work orders and filter the objects for processing the target work order.

[0207] For example, Table VI shows the comprehensive scores for each object to process work orders, calculated based on the data in Table IV.

[0208] Table VI: Comprehensive Scores for Each Object to Process Work Orders

[0209] Work Order Number Object Number Comprehensive Score p1 a1 0.77 p1 a2 0.63 p1 a3 0.71 p2 a1 0.73 p2 a2 0.81 p2 a3 0.64 p3 a1 0.87 P3 a2 0.73 p3 a3 0.80 p4 a1 0.64 p4 a2 0.74 p4 a3 0.83 p5 a1 0.73 p5 a2 0.82 p5 a3 0.62

[0210] Among them, the weight of work order processing efficiency is 0.4, and the weight of work order processing satisfaction is 0.6. Based on the data of work order processing efficiency and their corresponding weights, as well as the data of work order processing satisfaction and their corresponding weights, the comprehensive score for each object to process each work order is calculated. For example: in Table VI, the comprehensive score of object a1 for processing work order p1 = 0.80 * 0.4 + 0.75 * 0.6 = 0.77.

[0211] Optionally, the weights of work order processing efficiency and work order processing satisfaction are default values, or are system / human - set values, or are empirical values summarized from historical data of processing work orders, and can be flexibly adjusted according to specific application scenarios.

[0212] In some embodiments, the steps of screening out the target object for processing the target work order based on the comprehensive score and allocating the target work order to the target object are as follows:

[0213] Step 1: Initialize an empty work order distribution list for each object processing the work order.

[0214] Step 2: Calculate / obtain the comprehensive score of each object processing the work order.

[0215] Step 3: Determine the target object corresponding to the target work order.

[0216] For example: when work order p2 is the target work order, according to Table V, the comprehensive score of object a2 for processing work order p2 is the highest. Therefore, the target object corresponding to work order p2 is object a2.

[0217] Step 4: Allocate the target work order to the corresponding target object.

[0218] Step 5: Update the status of the target object.

[0219] Exemplarily, after the target work order is allocated, the workload of its corresponding target object increases, and its status can be updated to busy or pending for subsequent adjustment of the work order allocation process. For example: when the status of an object is busy, no work order is temporarily allocated to this object; or when the number of work orders pending for an object exceeds the quantity threshold, no work order is temporarily allocated to this object.

[0220] Step 6: According to the order of work orders sorted by priority, repeat Steps 2 to 5 until all work orders are allocated.

[0221] For example, there are a total of five work orders to be assigned, and their assignment order is p3, p1, p5, p2, p4. Then, p3, p1, p5, p2, and p4 are used as the target work orders in sequence, and the target objects of these five work order objects are determined respectively, and the work orders are assigned to the corresponding target objects. As shown in Table 5, the comprehensive scores of object a1 for processing work order p1 and work order p3 are the highest, the comprehensive scores of object a2 for processing work order p2 and work order p5 are the highest, and the comprehensive score of object a3 for processing work order p4 is the highest. Therefore, based on the comprehensive scores in Table 5, the five work orders are assigned, and the work order distribution list for each object is as follows:

[0222] D(a1) = {p3, p1}, D(a2) = {p5, p2}, D(a3) = {p4}.

[0223] Among them, D(a1) represents the work order distribution list of object a1, D(a2) represents the work order distribution list of object a2, and D(a3) represents the work order distribution list of object a3.

[0224] In some embodiments, when assigning work orders according to the work order sequence sorted by priority, weight scores are additionally configured for the work orders, so as to flexibly adjust the assignment order of the work orders based on the weight scores.

[0225] For example: Table 7 shows the weight scores of some work orders.

[0226] Table 7: Weight scores of work orders

[0227] Work Order Number Weighted Score p1 0.85 p2 0.78 p3 0.92 p4 0.65 p5 0.80

[0228] As shown in Table 7, the weight score of work order p3 > the weight score of work order p1 > the weight score of work order p5 > the weight score of work order p2 > the weight score of work order p4. Then, the assignment order of the work orders can be p3, p1, p5, p2, p4. Another example: In the case where it is necessary to ensure that the assignment priority of work order p2 is the highest, the weight score of work order p2 can be adjusted to 0.95. At this time, the weight score of work order p2 is the highest, and its assignment priority is also the highest.

[0229] In summary, for the solution provided in this application, the work orders to be assigned are first classified by the first attribute information and its corresponding classification weight coefficient to obtain different types of work orders, and then the priority ranking is performed by the second attribute information and its corresponding ranking weight coefficient to determine the assignment priority of each type of work order; finally, the comprehensive ability of the object processing the work order is determined based on the work order processing efficiency and work order satisfaction degree; so that when assigning work orders, the work orders can be targeted to be assigned to the target objects with matching comprehensive abilities for processing according to the type and assignment priority of the work orders, improving the matching degree between the work orders and the work order processors, and further improving the work order processing efficiency and customer satisfaction. At the same time, the classification weight coefficient and ranking weight coefficient involved in the classification and ranking processes can be flexibly adjusted based on different application scenarios, historical work order assignment data, etc., to ensure the performance of work order classification and ranking.

[0230] Figure 4 FIG. 4 is a schematic structural diagram of a work order assignment framework provided for an exemplary embodiment. The framework includes a general work order pool, three sub-work order pools (sub-work order pool 1, sub-work order pool 2, sub-work order pool 3) and three teams (Team A, Team B, Team C) of objects processing work orders.

[0231] Among them, both the general work order pool and the sub-work order pools are used to store and manage the work orders to be assigned.

[0232] Exemplarily, the general work order pool is used to collect all the work orders to be assigned. For example: all the work orders sent to a certain department. Another example: the general work order pool receives work orders such as area work orders sent to provinces / local self-built orders / inter-provincial work orders / transfer orders from other channels, etc.

[0233] Exemplarily, the sub-work order pool is used to store the work orders to be assigned of the same type, and the work orders to be assigned in the general work order pool enter the sub-work order pool after classification.

[0234] Priority ranking 1 is used to rank the work orders in sub-work order pool 1, priority ranking 2 is used to rank the work orders in sub-work order pool 2, and priority ranking 3 is used to rank the work orders in sub-work order pool 3.

[0235] Team A is used to process the work orders in sub-work order pool 1, Team B is used to process the work orders in sub-work order pool 2, and Team C is used to process the work orders in sub-work order pool 1.

[0236] Exemplarily, the number, corresponding relationship, etc. of the sub-work order pools and teams are not limited to those shown in the figure.

[0237] Figure 5 FIG. 5 is a schematic flow diagram of a work order assignment method provided for an exemplary embodiment. This method can be executed by a computer device and can also be implemented based on the framework shown in FIG. 4. This method includes: Figure 4 shown in FIG. 4. This method includes:

[0238] Step S501: The computer device obtains the work order to be assigned, parses the work order to be assigned, and obtains the work order description information.

[0239] Among them, the work order description information includes the first attribute information, the second attribute information, etc. of the work order. The first attribute information refers to the attribute characteristics for classification corresponding to the work order, and the second attribute information refers to the attribute characteristics for sorting corresponding to the work order.

[0240] Step S502: The computer device distributes the work orders in the total work order pool to the corresponding sub-work order pools.

[0241] Among them, the work order types in different sub-work order pools are different. For example: in Figure 4 , the work order type of the work orders in sub-work order pool 1 is fault reporting, the work order type of the work orders in sub-work order pool 2 is consultation, and the work order type of the work orders in sub-work order pool 3 is complaint.

[0242] Exemplarily, the computer device distributes the work orders in the total work order pool to the corresponding sub-work order pools based on the first attribute information.

[0243] Step S503: The computer device performs priority sorting on the work orders in each sub-work order pool.

[0244] Among them, the priority sorting refers to determining the assignment priority of the work orders.

[0245] Exemplarily, the computer device performs priority sorting on the work orders in each sub-work order pool based on the second attribute information.

[0246] Step S504: The computer device assigns the target work order to the target object based on the order of the work orders after priority sorting, as well as the work order processing efficiency and work order processing satisfaction.

[0247] Among them, the target object refers to the object that processes the target work order, and the target work order is any work order among the work orders after priority sorting.

[0248] Exemplarily, the computer device calculates the comprehensive score of the object that processes the work order based on the work order processing efficiency and work order processing satisfaction, and assigns the target work order to the target object based on the comprehensive score.

[0249] Among them, the comprehensive score is used to screen the object that processes the target work order.

[0250] Exemplarily, as Figure 4 shown, the objects that process the work orders can be pre-divided into multiple teams, and different teams are respectively corresponding to process different types of work orders.

[0251] The above mainly introduces the solution provided by the present application. Correspondingly, the present application also provides a work order assignment device, and this device is used to implement the above method embodiments.

[0252] As Figure 6 shown in the schematic structural diagram of the work order allocation device, the work order allocation device may include an acquisition module 601, a classification module 602, a sorting module 603, and an allocation module 604. Among them, the acquisition module 601 is used to execute the operation of step S201 in the method shown in Figure 2 and the operations of step S301 and step S304 in the method shown in Figure 3 ; the classification module 602 is used to execute the operation of step S202 in Figure 2 and the operation of step S302 in Figure 3 ; the sorting module 603 is used to execute the operation of step S203 in Figure 2 and the operation of step S303 in Figure 3 ; the allocation module 604 is used to execute the operation of step S204 in Figure 2 and the operation of step S305 in Figure 3 .

[0253] In some embodiments, in order to implement the above functions, the work order allocation device includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, combining the units and algorithm steps of each example described in the embodiments disclosed herein, this application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0254] The embodiments of this application can divide the work order allocation device into functional modules according to the above method embodiments. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. It should be noted that the division of modules in the embodiments of this application is illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0255] As Figure 7 shown, the computer device provided by the embodiments of this application may include a processor 701, a bus 702, a communication interface 703, and a memory 704. The processor 701, the memory 704, and the communication interface 703 communicate through the bus 702. It should be understood that this application does not limit the number of processors and memories in the network device.

[0256] The bus 702 can be a PCI bus, an Extended Industry Standard Architecture (EISA) bus, or a UB bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 only one line is used in Figure 7 , but it does not mean that there is only one bus or one type of bus. The bus 702 can include a path for transmitting information between various components of the network device (for example, the memory 704, the processor 701, and the communication interface 703).

[0257] The processor 701 can include any one or more of processors such as a CPU, a Graphics Processing Unit (GPU), a Micro Processor (MP), or a Digital Signal Processor (DSP).

[0258] The memory 704 can include volatile memory, such as Random Access Memory (RAM). The processor 701 can also include non-volatile memory, such as Read-Only Memory (ROM), flash memory, a Hard Disk Drive (HDD), or a Solid State Drive (SSD).

[0259] The communication interface 703 uses a transceiver module such as, but not limited to, a network interface card or a transceiver to implement communication between the network device and other devices or a communication network.

[0260] The executable program code is stored in the memory 704, and the processor 701 executes the executable program code to respectively implement the functions of the foregoing method embodiments. That is, instructions for executing the foregoing work order allocation method are stored on the memory 704.

[0261] In another aspect, a computer-readable storage medium is provided. At least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor to implement the work order allocation method provided in the foregoing method embodiments.

[0262] In another aspect, a computer program product is provided. The computer program product includes a computer program or instructions. When the computer program or instructions are executed by a processor, the work order allocation method provided in the foregoing method embodiments is implemented.

[0263] Through the description of the above embodiments, those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the module is divided into different functional modules to complete all or part of the functions described above. For the specific working processes of the system, module, and unit described above, reference can be made to the corresponding processes in the foregoing method embodiments, which will not be elaborated here.

[0264] Since the work order allocation module, computer-readable storage medium, and computer program product in the embodiments of the present invention can be applied to the above method, the technical effects that can be obtained thereby can also refer to the above method embodiments, and will not be elaborated here in the embodiments of the present invention.

[0265] The method steps in this embodiment can be implemented in a hardware manner or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in a random access memory (RAM), flash memory, read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), register, hard disk, removable hard disk, CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a network device. Of course, the processor and the storage medium can also exist as discrete components in the network device.

[0266] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions of the embodiments of the present application are executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, or other programmable modules. The computer program or instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer program or instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless manner. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); or it can be a semiconductor medium, such as a solid state drive (SSD). As described above, the above are only specific implementation manners of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A work order allocation method, characterized in that: The method comprises: Get the work orders to be assigned; Classifying the work order based on first attribute information corresponding to the work order, where the first attribute information refers to an attribute feature corresponding to the work order for classification; Prioritizing the work orders in each category of the classified work orders, wherein the priority ranking refers to determining the allocation priority of the work orders; Based on the order of the work orders after the priority sorting, the target work order is assigned to a target object, where the target object refers to an object that processes the target work order, and the target work order is any work order among the work orders after the priority sorting.

2. The method according to claim 1, characterized in that The classifying the work order based on the first attribute information corresponding to the work order includes: Based on the first attribute information and the classification weight coefficient corresponding to the first attribute information, obtaining a classification score corresponding to the work order, wherein the classification score is used to determine the work order type to which the work order belongs; The work order is classified according to the classification score.

3. The method according to claim 2, characterized in that Classifying the work order according to the classification score includes: The work order is classified based on the work order type corresponding to the numerical range to which the classification score belongs, and different numerical ranges correspond to different work order types.

4. The method according to any one of claims 1 to 3, characterized in that: The prioritizing of the work orders in each category of the classified work orders includes: Based on the second attribute information corresponding to each work order in each category of work orders and the sorting weight coefficient corresponding to the second attribute information, obtaining a sorting score corresponding to each work order in each category of work orders, wherein the second attribute information refers to an attribute feature corresponding to the work order for sorting, and the sorting score is used to determine the order of allocation of the work orders; The work orders in each category of work orders are prioritized according to the ranking scores.

5. The method according to claim 4, characterized in that The step of prioritizing the work orders in each category of work orders according to the ranking scores includes: Based on the ranking scores, the work orders in each category of work orders are sorted in descending order.

6. The method according to claim 4 or 5, characterized in that: The ranking weight coefficient is dynamically adjusted according to one or more of the work order processing result, customer feedback data or work order processing efficiency.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: Obtaining the work order processing efficiency and work order processing satisfaction of each object in processing the target work order; The allocating the target work order to the target object based on the order of the work orders after the priority sorting includes: Based on the order of the work orders after the priority sorting, the work order processing efficiency and the work order processing satisfaction of each object in processing the target work order are weighted and summed to obtain a comprehensive score of each object in processing the target work order, and the comprehensive score is used to screen the object that processes the target work order; Based on the comprehensive score, screening out the target object that processes the target work order; The target work order is assigned to the target object.

8. A work order distribution device, characterized in that: The device includes: The acquisition module is used to obtain the work orders to be assigned; A classification module, used to classify the work order based on first attribute information corresponding to the work order, where the first attribute information refers to an attribute feature corresponding to the work order for classification; A sorting module is used to prioritize the work orders in each category of the classified work orders, wherein the priority sorting refers to determining the allocation priority of the work orders; The allocation module is used to allocate the target work order to the target object based on the order of the work orders after the priority sorting. The target object refers to the object that processes the target work order, and the target work order is any work order among the work orders after the priority sorting.

9. A computer device, characterized in that: The computer device comprises: a processor and a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement the work order allocation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: At least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by the processor to implement the work order allocation method according to any one of claims 1 to 7.

11. A computer program product, characterized in that The computer program product comprises a computer program or instructions, and when the computer program or instructions are executed by a processor, the work order allocation method according to any one of claims 1 to 7 is implemented.

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