Work order distribution method and device and electronic equipment

Abstract

The invention discloses a work order distribution method and device and electronic equipment. The method comprises the following steps: obtaining a to-be-processed service work order of an operator, and obtaining service work order data corresponding to the to-be-processed service work order, the service work order data at least comprising attribute information of the to-be-processed service work order; sorting the business work orders to be processed according to the business work order data to obtain a first sorting result; and distributing the to-be-processed business work order to the target object according to the first sorting result. According to the invention, the technical problems of low efficiency and time and labor consumption due to the fact that most service work order processing systems of operators depend on manual work order selection in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, device and electronic device for allocating a work order. Background Art

[0002] In the related technologies, the business work order processing systems used by operators mostly rely on manual selection of work orders. Due to the lack of an automatic issuance mechanism, manual intervention, selection and allocation are required every time a work order is generated, which is not only time-consuming and labor-intensive, but also prone to human errors.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a work order allocation method, device and electronic device to at least solve the technical problem that most of the business work order processing systems of operators in the related art rely on manual selection of work orders, which is inefficient and time-consuming and labor-intensive.

[0005] According to one aspect of an embodiment of the present application, a method for allocating work orders is provided, including: obtaining an operator's pending business work order, and obtaining business work order data corresponding to the pending business work order, wherein the business work order data includes at least attribute information of the pending business work order; sorting the pending business work order based on the business work order data to obtain a first sorting result; and allocating the pending business work order to a target object based on the first sorting result.

[0006] In some embodiments of the present application, the business work orders to be processed are sorted according to the business work order data to obtain a first sorting result, including: determining the estimated processing time and urgency of each business work order to be processed from the business work order data, wherein the urgency is represented by a numerical value, and the larger the numerical value, the higher the urgency; determining the business work orders with an estimated processing time less than a first threshold and an urgency less than a second threshold from the business work orders to be processed as a first set of business work orders to be processed; and sorting the first set of business work orders to be processed according to the estimated processing time using a first sorting algorithm to obtain a first sorting result.

[0007] In some embodiments of the present application, obtaining the estimated processing time of each pending business work order includes: obtaining the operator's historical business work orders, wherein the historical business work orders are processed business work orders; determining first characteristic information corresponding to a first business work order, wherein the first business work order is any one of the pending business work orders, and the first characteristic information is used to describe the attributes of the first business work order; determining from the historical business work orders a target historical business work order whose similarity with the first characteristic information is greater than a preset threshold; obtaining the historical processing time of the target historical business work order; and determining the historical processing time of the target historical business work order as the estimated processing time of the first business work order.

[0008] In some embodiments of the present application, business work orders to be processed are sorted based on business work order data to obtain a first sorting result, including: determining the estimated processing time and urgency of each business work order to be processed from the business work order data; determining the business work orders with an estimated processing time greater than or equal to a first threshold and an urgency greater than or equal to a second threshold from the business work orders to be processed as a second set of business work orders to be processed; determining the weighted processing time of each business work order to be processed in the second set of business work orders to be processed based on the estimated processing time and the urgency; and sorting the second set of business work orders to be processed from small to large using a second sorting algorithm based on the weighted processing time to obtain a first sorting result.

[0009] In some embodiments of the present application, the pending business work orders are assigned to the target object based on the first sorting result, including: obtaining the business processing efficiency of the target object; sorting the target objects from high to low according to the business processing efficiency to obtain a second sorting result; and assigning the pending business work orders to the target object based on the first sorting result and the second sorting result.

[0010] In some embodiments of the present application, after allocating the pending business work orders to the target objects based on the first sorting result, the method further includes: obtaining the business processing efficiency and accuracy of the target objects; determining a score value for each target object based on the business processing efficiency and accuracy; obtaining the workload of the target objects, wherein the workload is used to represent the workload allocated to the target object; and adjusting the work order allocation strategy when the workload is greater than or equal to the score value corresponding to each target object.

[0011] In some embodiments of the present application, the work order allocation strategy is adjusted, including: obtaining target business work orders that need to be reallocated from the pending business work orders allocated to the target object; sorting the target business work orders from high to low according to the urgency, to obtain a third sorting result; determining the working status and business processing efficiency of the target object, wherein the working status is used to indicate the idleness of the target object; sorting the target objects whose workload is less than the corresponding score value based on the working status and the business processing efficiency, to obtain a fourth sorting result; and allocating the target business work order to the target object whose workload is less than the corresponding score value based on the third sorting result and the fourth sorting result.

[0012] According to another aspect of an embodiment of the present application, a work order allocation device is also provided, including: an acquisition module, used to acquire an operator's pending business work order, and to acquire business work order data corresponding to the pending business work order, wherein the business work order data at least includes attribute information of the pending business work order; a sorting module, used to sort the pending business work order according to the business work order data to obtain a first sorting result; and an allocation module, used to allocate the pending business work order to a target object according to the first sorting result.

[0013] According to another aspect of the embodiment of the present application, there is also provided an electronic device, including: a memory and a processor, the memory being used to store program instructions; the processor being connected to the memory and being used to execute the above-mentioned work order allocation method.

[0014] According to another aspect of the embodiments of the present application, a non-volatile storage medium is provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned work order allocation method by running the computer program.

[0015] According to another aspect of the embodiments of the present application, a computer program product is provided, including computer instructions, which implement the above-mentioned work order allocation method when executed by a processor.

[0016] In the embodiment of the present application, a method of analyzing work order data is adopted, by obtaining the work order data of the business work orders to be processed, sorting the business work orders according to the work order data, and then assigning the business work orders to the target objects, thereby achieving the purpose of intelligent sorting and allocation of work orders, thereby achieving the technical effect of improving the efficiency and accuracy of work order processing, and further solving the technical problem that the business work order processing systems of operators in related technologies mostly rely on manual selection of work orders, which is inefficient and time-consuming and labor-intensive. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 It is a hardware structure block diagram of a computer terminal of a work order allocation method according to an embodiment of the present application;

[0019] Figure 2 is a flow chart of a method for allocating work orders according to an embodiment of the present application;

[0020] Figure 3 is a system framework diagram of a work order allocation method according to an embodiment of the present application;

[0021] Figure 4 It is a schematic diagram of the overall process of a method for allocating work orders according to an embodiment of the present application;

[0022] Figure 5 It is a structural diagram of a work order distribution device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] In order to better understand the embodiments of the present application, the technical terms involved in the embodiments of the present application are explained as follows:

[0026] Shortest Job First (SJF): An algorithm used in the field of operating systems and task scheduling to optimize the order in which tasks are processed. The SJF algorithm sorts tasks according to their estimated execution time and prioritizes tasks with the shortest estimated execution time to reduce task waiting time and improve system efficiency.

[0027] Weighted Shortest Job First (WSJF): An improved version of the task scheduling algorithm, the WSJF algorithm adds weighted factors to the task based on SJF, such as the urgency, value or deadline of the task, to more comprehensively evaluate the priority of the task.

[0028] The resource allocation system used in the relevant technology is not equipped with algorithms and programs for intelligently issuing work orders, resulting in manual intervention in the issuance of work orders, which is inefficient and error-prone. First, manual selection of work orders is not only time-consuming, but also prone to errors due to human factors, affecting the accuracy and timeliness of work order processing. For example, manual selection tends to favor simple work orders, and employees tend to prioritize simple work orders and put complex work orders aside, resulting in complex work orders not being processed for a long time, causing work orders to pile up. Complex work orders usually require more time and energy to process, and employees tend to postpone them, and sometimes even completely ignore the existence of these work orders, resulting in these work orders being unprocessed for a long time. Secondly, the lack of a systematic automatic allocation mechanism leads to extremely uneven distribution of work orders, with some employees overburdened with work, while other employees have a lighter workload, and fail to make full use of human resources. This uneven distribution not only increases the work pressure of some employees, but may also lead to a decline in work quality. It can be seen that untimely and unbalanced work order processing will lead to interruption of the entire business process, especially during peak hours. If key work orders cannot be processed in time, it will cause delays in the entire business process, thereby affecting the overall operational efficiency of the operator.

[0029] In order to solve the above technical problems, the embodiments of the present application provide corresponding solutions, which are described in detail below.

[0030] The work order allocation method embodiment provided in the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a method for allocating work orders. Figure 1As shown, the computer terminal 10 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions connected via a wired and / or wireless network. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. Those skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0031] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0032] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for allocating work orders in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the method for allocating work orders described above is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0033] The transmission module 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0034] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0035] It should be noted that, in some optional embodiments, the above Figure 1 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0036] In the above-mentioned operating environment, an embodiment of the present application provides an embodiment of a method for allocating a work order. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0037] Figure 2 is a flow chart of a method for allocating work orders according to an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0038] Step S202: obtaining the to-be-processed service work order of the operator, and obtaining the service work order data corresponding to the to-be-processed service work order, wherein the service work order data at least includes the attribute information of the to-be-processed service work order.

[0039] In the above step S202, the pending business work order refers to the business work order that has not yet been processed or is waiting for allocation in the operator's internal system. The work order can come from the customer's request, service change requirements, network fault reports, etc., and contains various business and service problems that the operator needs to respond to and solve. In some embodiments of the present application, the API interface can be integrated with the operator's customer service system, fault reporting system, employee management system and other data sources to automatically obtain the latest list and detailed data of the pending business work order regularly. For example, the latest business work order information can be obtained from a database or other data source through a scheduled task (such as once every hour or every half an hour). This information usually includes the basic attributes of the work order, such as work order ID, creation time, work order type (such as fault report, service request, etc.), work order status (such as pending, processing, completed), etc. In addition, the system can also obtain other information related to the work order, such as work order description, estimated processing time, urgency, etc.

[0040] Business work order data refers to a collection of detailed information associated with a pending business work order, including the attribute information of the work order. Attribute information is a key component of business work order data and is used to describe the specific characteristics of the work order, such as the type of work order (service request, fault report, etc.), complexity (assessing the skills and time required to complete the work order), urgency (whether the work order needs to be processed immediately), etc.

[0041] (1) Work order type: Differentiate between different types of work orders, such as fault reports, service requests, and complaint handling.

[0042] (2) Complexity: Assess the difficulty of handling a work order, which can be quantified based on the work order description and historical processing records.

[0043] (3) Urgency: The work order is graded according to its urgency, for example, into three levels: high, medium, and low.

[0044] (4) Work order description: Detailed work order description information helps the system better understand the work order content.

[0045] (5) Estimated processing time: Estimate the processing time of the work order based on historical data and work order complexity.

[0046] In some embodiments of the present application, for each pending business work order, detailed data of the work order, including attribute information such as work order type, urgency, and estimated processing time, can be obtained by calling the API interface of the operator's internal system. API calls provide a direct access to data, ensuring the accuracy and real-time nature of information acquisition. In an optional embodiment, since the collected business work order data may come from different systems, the formats and fields may be inconsistent. After acquiring the data, data normalization processing can be performed to unify the attribute information of all work orders into a unified format and standard so that the subsequent intelligent sorting algorithm can correctly interpret and process it. For example, the words describing the degree of urgency in different systems (such as "very urgent", "immediate processing", "ordinary") are converted into a unified quantitative level (such as level 1-level 5) to facilitate algorithm calculation.

[0047] Step S204: sort the business work orders to be processed according to the business work order data to obtain a first sorting result.

[0048] In the above step S204, the first sorting result definition refers to the priority sorting list obtained by the work order management system after analyzing and processing the acquired pending business work order data according to the sorting algorithm. In some embodiments of the present application, the work orders can be sorted using an intelligent sorting algorithm based on factors such as the complexity and urgency of the work orders and the historical processing efficiency of the employees. The intelligent sorting algorithm comprehensively considers multiple factors, including the complexity and urgency of the work orders and the historical processing efficiency of the employees, which together determine the priority of the work orders. For example, the algorithm can assign a priority score to the work order based on the complexity and urgency of the work order, while taking into account the historical processing efficiency of the employees to ensure that the work order can be processed efficiently and orderly.

[0049] In order to give priority to simple and non-urgent work orders and avoid occupying resources for processing urgent work orders, a first sorting algorithm can be used for sorting. Specifically: the estimated processing time and urgency of each pending business work order are determined from the business work order data, where the urgency is represented by a numerical value, and the larger the numerical value, the higher the urgency; from the pending business work orders, the business work orders with an estimated processing time less than a first threshold and an urgency less than a second threshold are determined as a first set of pending business work orders; the first set of pending business work orders is sorted according to the estimated processing time using the first sorting algorithm to obtain a first sorting result.

[0050] The estimated processing time refers to the average time required to process a work order based on historical data, work order type, description, and complexity. The urgency is a numerical representation used to quantify the urgency of the work order. The larger the value, the higher the urgency of the work order. In some embodiments of the present application, the estimated processing time can be predicted by analyzing the time statistics of the past processing of the same type of work order, combined with the description and complexity of the current work order. For the urgency, the urgency value of each work order can be determined according to the preset urgency quantification rules, such as strong customer feedback, large business impact, etc. In other embodiments of the present application, machine learning models, such as regression analysis or neural networks, can also be used to predict the estimated processing time based on various attributes of the work order (such as type, description, historical processing time, etc.). The determination of the urgency can also be done by training a specific classification model to identify the key information in the work order description and automatically give the urgency value.

[0051] In order to more accurately evaluate the difficulty of processing a work order, the estimated processing time for each pending business work order can be obtained through the following steps: obtain the operator's historical business work orders, wherein the historical business work orders are processed business work orders; determine the first characteristic information corresponding to the first business work order, wherein the first business work order is any one of the pending business work orders, and the first characteristic information is used to describe the attributes of the first business work order; determine from the historical business work orders a target historical business work order whose similarity with the first characteristic information is greater than a preset threshold; obtain the historical processing time of the target historical business work order; and determine the historical processing time of the target historical business work order as the estimated processing time for the first business work order.

[0052] The first characteristic information refers to a set of attributes describing the first business work order, which may include the type, description, complexity, urgency, etc. of the work order, and is used to match and predict the processing time of the first business work order.

[0053] In some embodiments of the present application, all historical service work orders can be queried from the operator's database, and the first characteristic information of the service work order to be processed can be compared with the characteristic information of the historical work order. For each first service work order, a target historical service work order whose first characteristic information has a similarity higher than a preset threshold can be found, and the actual processing time of these work orders can be extracted. The expected processing time of the work order can be determined based on the average processing time of these similar work orders.

[0054] In order to improve efficiency, a machine learning model, such as a regression model or a neural network, can also be trained, using the first feature information of historical business work orders and their actual processing time as training data. When a new first business work order enters the system, the model can predict its processing time based on the first feature information of the work order, without directly matching the features of historical work orders, thereby improving processing efficiency and prediction accuracy. For example, the system can use deep learning technology to train a multi-layer neural network model, input the feature information of the first business work order, and output the predicted processing time. The training data of the model can include information such as the type, description, and processing time of historical work orders. By learning from a large amount of data, the model can accurately predict the processing time of new work orders.

[0055] In the process of work order management, in order to quickly screen out work orders with higher priorities, two numerical thresholds can be pre-set. The first threshold is used to limit the upper limit of the expected processing time, and the second threshold is used to limit the lower limit of the urgency. By setting the first threshold and the second threshold, those work orders that are both urgent and have relatively short processing times can be quickly identified to ensure that these work orders are processed with priority. The first set of pending business work orders refers to a set of work orders with high processing priority screened out according to the first threshold and the second threshold. By giving priority to these work orders, the business process can be further optimized and the waiting time for key businesses can be reduced. In some embodiments of the present application, the first threshold and the second threshold can be dynamically adjusted based on the current work order processing load and business needs. For example, during the peak period of work order processing, the system may lower the first threshold (i.e., shorten the upper limit of the processing time) to ensure that more urgent work orders can be processed quickly.

[0056] When sorting the first set of pending business work orders, a shortest job first (SJF) algorithm may be used, that is, the first set of pending business work orders is sorted in the order of the estimated processing time, ensuring that the work order with the shortest processing time is processed first. In some embodiments of the present application, a priority queue data structure may be used, and the estimated processing time may be used as the priority attribute of the work order. The work orders in the queue may be automatically sorted according to the priority, thereby generating the first sorting result.

[0057] To facilitate understanding of the above sorting process, the following is explained through a specific embodiment. Assume that data of three work orders are collected from the database, work order A, work order B, and work order C. The estimated processing time of work order A is 2 hours, the estimated processing time of work order B is 1 hour, and the estimated processing time of work order C is 3 hours. The collected data also includes attributes such as the type and urgency of the work order. According to the shortest job first (SJF) algorithm, these three work orders are sorted according to the length of the estimated processing time. The sorted order is: work order B (1 hour) -> work order A (2 hours) -> work order C (3 hours). According to the sorting results, work order B can be assigned to the first available employee for processing. After completing work order B, the next one to be processed is work order A, and the last one to be processed is work order C. In this way, it is ensured that the work order with the shortest processing time is processed first, thereby reducing the waiting time of customers and improving the efficiency of the customer service center.

[0058] For complex and urgent work orders, in order to ensure that important work orders can be processed in a timely manner, the following steps can be used to sort the pending business work orders: determine the estimated processing time and urgency of each pending business work order from the business work order data; determine the business work orders with an estimated processing time greater than or equal to a first threshold and an urgency greater than or equal to a second threshold from the pending business work orders as a second set of pending business work orders; determine the weighted processing time of each pending business work order in the second set of pending business work orders based on the estimated processing time and the urgency; and sort the second set of pending business work orders from small to large using a second sorting algorithm based on the weighted processing time to obtain a first sorting result.

[0059] The second set of pending business work orders refers to a set of work orders that are screened out based on the estimated processing time and urgency, and that require special attention and priority processing. The second set of pending business work orders includes work orders that are highly complex and urgent to process, and are the focus of processing by the work order management system. In some embodiments of the present application, work orders with an estimated processing time greater than or equal to the first threshold and a degree of urgency greater than or equal to the second threshold can be screened out based on the preset first threshold and second threshold to form a second set of pending business work orders. For example, if the first threshold is 4 hours and the second threshold is 4 points (out of 5 points), all work orders with an estimated processing time ≥ 4 hours and a degree of urgency ≥ 4 points are screened out as a set. Similarly, according to the current work order processing load and business needs, the first threshold and the second threshold are dynamically adjusted to ensure that the processing of the work orders in the set matches the system resources.

[0060] The weighted processing time refers to the processing time obtained by recalculating each work order in the second set of pending business work orders by combining the urgency and the estimated processing time. In some embodiments of the present application, for each work order in the second set of pending business work orders, the product or weighted sum of the estimated processing time and the urgency can be used as the weighted processing time. For example, if the estimated processing time of work order A is 6 hours and the urgency is 4 points (out of 5 points), the weighted processing time may be 24 (i.e. 6 hours × 4 points). In addition, a custom weighted function can also be designed to calculate the estimated processing time and urgency with different weight coefficients to adapt to more complex business scenarios. For example, the system can set the weight coefficient of the urgency to 2 and the weight coefficient of the estimated processing time to 1, then the weighted processing time of the work order is the urgency weight coefficient × urgency + estimated processing time weight coefficient × estimated processing time.

[0061] After the weighted processing time is obtained, the weighted shortest job first (WSJF) algorithm can be used to sort the second set of pending business work orders. For example, all work orders in the second set of pending business work orders are sorted from small to large according to the weighted processing time. A priority queue data structure can also be used to take the weighted processing time as the priority attribute of the work order. The work orders in the queue will be automatically sorted according to the priority, so as to sort the second set of pending business work orders and obtain the first sorting result.

[0062] In order to facilitate the understanding of the above sorting process, the following is explained through a specific embodiment. Assume that there are three work orders, namely work order X, work order Y, and work order Z. The estimated processing time of work order X is 2 hours, and the urgency is high; the estimated processing time of work order Y is 1 hour, and the urgency is medium; the estimated processing time of work order Z is 3 hours, and the urgency is low. The average processing time of employee A is 2 hours / order, and the average processing time of employee B is 1.5 hours / order. The collected data also includes attributes such as the type and urgency of the work order. In this case, a weight value can be assigned to each work order, and the weight value can be calculated based on the urgency and estimated processing time of the work order. For example, the weight of a work order with high urgency can be defined as 2, the weight of a work order with medium urgency can be defined as 1, and the weight of a work order with low urgency can be defined as 0.5. Then calculate the weighted processing time, work order X = 2 hours * 2 = 4; work order Y = 1 hour * 1 = 1; work order Z = 3 hours * 0.5 = 1.5. Sorting by weighted processing time from small to large, the resulting order is: work order Y (1) -> work order Z (1.5) -> work order X (4). Considering the processing efficiency of employees, assuming that employees A and B are both available, we can choose employee B with higher processing efficiency to handle the first work order Y. After completing work order Y, we can assign work order Z to employee A (at this time, employee B may have completed work order Y and can accept new work orders). Finally, work order X is assigned to idle employees. This method of comprehensively considering the urgency and employee processing efficiency can not only give priority to urgent work orders, but also maximize the use of employee work efficiency, further improving the service quality and response speed of the customer service center.

[0063] Step S206: assign the pending business work orders to the target object according to the first sorting result.

[0064] In the above step S206, the target object refers to an account that can receive and process the business work order or a node in the entire service process.

[0065] By considering the efficiency of the processing personnel, work orders can be allocated more reasonably and waste of resources can be avoided. Specifically: the business processing efficiency of the target object is obtained; the target objects are sorted from high to low according to the business processing efficiency to obtain a second sorting result; based on the first sorting result and the second sorting result, the to-be-processed business work orders are allocated to the target object.

[0066] Business processing efficiency refers to quantifiable indicators such as the speed and / or quality of the target object in processing business work orders. In some embodiments of the present application, the business work orders processed by the target object in the past period of time are analyzed, and the average processing time, work order completion rate, customer satisfaction and other indicators are counted to comprehensively evaluate the business processing efficiency. For example, by analyzing all the work orders processed by employee A in the past week, the system can calculate his average processing time, the proportion of unprocessed work orders, and the customer satisfaction score of his service, so as to evaluate his business processing efficiency.

[0067] The second sorting result refers to the result obtained after sorting the target object's business processing efficiency from high to low. In some embodiments of the present application, the target object's business processing efficiency can be quantified as a specific numerical index, and a sorting algorithm (such as quick sort, merge sort, etc.) is used to sort the target object from high to low according to the efficiency index to generate a second sorting result. For example, the average processing time of employees, the work order completion rate, and the customer satisfaction score are comprehensively calculated to obtain a total score of business processing efficiency, and then sorted in descending order according to the total score.

[0068] By continuously monitoring the work status of processing personnel and the accuracy of work order processing, the allocation strategy can be dynamically adjusted to ensure continuous optimization of work order processing. Specifically: obtain the business processing efficiency and accuracy of the target object; determine the score value of each target object based on the business processing efficiency and accuracy; obtain the workload of the target object, where the workload is used to represent the workload allocated to the target object; when the workload is greater than or equal to the score value corresponding to each target object, adjust the work order allocation strategy.

[0069] The accuracy rate refers to the accuracy measure of the result of the work order processed by the target object, which can be determined based on customer feedback after the work order is processed, the reopening rate of the work order, or the internal audit results. In some embodiments of the present application, the work order data processed by the target object in the past, including processing time, processing results, and customer feedback, are analyzed to calculate the business processing efficiency and accuracy rate. For example, for employee Z, all the work orders processed by him in the past month can be counted, the average processing time can be calculated to determine the business processing efficiency, and the percentage of customer satisfaction feedback obtained from the processing results can be counted to determine the accuracy rate.

[0070] The score value refers to the numerical value obtained by quantitatively evaluating each target object based on the comprehensive business processing efficiency and accuracy, and is used to guide the priority of work order allocation. In some embodiments of the present application, different weights are assigned according to the business processing efficiency and accuracy, and the score value of the target object is calculated. For example, the business processing efficiency is assigned a weight of 0.6, and the accuracy is assigned a weight of 0.4, and the comprehensive score value of employee Z is calculated. In addition, a trained machine learning model can also be used to predict the score value of each target object based on the business processing efficiency, accuracy and historical work order data of the target object. For example, the system can use a random forest model, input the historical processing data of the target object, and output the predicted score value.

[0071] Workload refers to the number of work orders currently being processed or assigned but not completed by the target object, and the complexity of the work order can also be considered. When allocating work orders, the system can monitor the current workload of each employee (i.e., the target object) in real time. For example, if an employee is currently processing a large number of work orders or the complexity of the work orders in progress is high, the system can try to avoid assigning new work orders to him. In some embodiments of the present application, the current work order list of the target object is continuously tracked, including the work orders being processed, the work orders to be processed, and the completed work orders, and its workload is calculated. For example, for employee A, the number of work orders being processed can be counted, and the workload can be estimated based on the estimated processing time of the work order. In addition, a prediction model can also be established to predict its workload based on the working time of the target object and the processing time of the assigned work orders. For example, based on employee B's work log and work order processing history, the system uses a time series analysis model to predict its workload for a period of time in the future.

[0072] When the workload is greater than or equal to the score value corresponding to each target object, the work order allocation strategy can be adjusted. In some embodiments of the present application, a workload-to-score ratio threshold can be set. When the ratio of the workload of the target object to the score value reaches or exceeds the threshold, the work order allocation strategy is automatically adjusted to reduce the number of work orders assigned to the target object. For example, if the ratio of employee C's workload to the score value reaches 1.2, the system reduces the number of new work orders assigned to him. In addition, optimization algorithms, such as genetic algorithms, can also be used to dynamically adjust the work order allocation according to the workload and score value of the target object to ensure the balance and efficiency of the work order allocation. For example, the system uses a genetic algorithm, takes the matching degree of the workload and score value of the target object as the fitness function, and continuously iterates and adjusts the work order allocation plan to find the optimal allocation strategy.

[0073] In order to ensure that urgent work orders are handled with priority and to avoid excessive load on processing personnel, the work order allocation strategy can be adjusted in the following ways: obtain the target business work orders that need to be reallocated from the pending business work orders assigned to the target object; sort the target business work orders from high to low according to the degree of urgency to obtain a third sorting result; determine the working status and business processing efficiency of the target object, wherein the working status is used to indicate the idleness of the target object; sort the target objects whose workload is less than the corresponding score value based on the working status and business processing efficiency to obtain a fourth sorting result; and assign the target business work order to the target object whose workload is less than the corresponding score value based on the third sorting result and the fourth sorting result.

[0074] The target business work order refers to the work order that needs to be reallocated or prioritized from the target object in the work order allocation strategy adjustment. In some embodiments of the present application, when the workload of the target object exceeds its score value, or its business processing efficiency decreases, the priority of the pending business work orders can be adjusted, and those high-priority work orders can be screened as target business work orders and reallocated to more suitable processing objects.

[0075] The work status is used to reflect the current workload, idleness and working capacity of the target object. In some embodiments of the present application, the work status data of the target object can be collected in real time, including the number of assigned work orders, work order processing time, number of work orders to be processed, etc., as well as the latest business processing efficiency information to ensure the real-time and accuracy of the data. In addition, a mechanism can also be designed to allow the target object to regularly feedback its work status, including idle time, fatigue, processing capacity, etc., as well as self-assessment of business processing efficiency, to supplement the data of automatic monitoring and provide more comprehensive status information.

[0076] After determining the working state and business processing efficiency of the target object, the working state and business processing efficiency of all target objects can be analyzed. For target objects whose workload is less than the score value, a sorting algorithm (such as quick sort) is used to sort them according to their business processing efficiency and idleness to obtain a fourth sorting result. In some embodiments of the present application, if the working state of the target object changes, the system can adjust its score value to ensure that the score value can accurately reflect the current processing capacity and workload of the target object. For example, if the workload of the target object increases, but the business processing efficiency remains unchanged, the system may adjust its score value to reflect the decline in its actual processing capacity.

[0077] After obtaining the third and fourth sorting results, a matching algorithm can be used to match the target business work orders with high urgency in the third sorting results with the target objects that are idle and have high processing efficiency in the fourth sorting results, and prioritize the work orders to these target objects to ensure that the work orders can be processed promptly and efficiently.

[0078] Through the above implementation method, the work order management system can identify those target business work orders that need to be reallocated, sort these work orders by urgency, and sort the target objects based on the working status and business processing efficiency of the target objects, and finally assign the target business work orders to the most suitable target objects, ensuring efficient, fair and timely work order processing.

[0079] Through the above steps S202 to S206, the work order data is analyzed, the work order data of the business work orders to be processed is obtained, the business work orders are sorted according to the work order data, and then the business work orders are assigned to the target objects, thereby achieving the purpose of intelligent sorting and allocation of work orders, thereby achieving the technical effect of improving the efficiency and accuracy of work order processing, and further solving the technical problem in related technologies that most of the business work order processing systems of operators rely on manual selection of work orders, which is inefficient and time-consuming and labor-intensive.

[0080] Figure 3 is a system framework diagram of a work order allocation method according to an embodiment of the present application, such as Figure 3 As shown, in some embodiments of the present application, the system may include the following modules:

[0081] Module S302: Data collection module, used to periodically obtain the latest business work order information from the database or other data sources, including but not limited to work order ID, creation time, work order type, work order status, work order description, estimated processing time and urgency, etc. At the same time, module S302 can also collect employee information related to work order processing, such as employee ID, professional skills, historical processing efficiency and current workload, etc.

[0082] In some embodiments of the present application, updated data may be obtained through regular database queries or message queues. For example, newly generated work order information may be obtained from the work order database through SQL queries every half hour, and employee status updates may be obtained through the employee database.

[0083] Module S304: Intelligent sorting module, used to receive data from S302 and sort the work orders using intelligent sorting algorithms (such as SJF / WSJF). The algorithm can comprehensively consider factors such as the complexity and urgency of the work orders and the historical processing efficiency of the employees to generate a work order priority list.

[0084] In some embodiments of the present application, the WSJF algorithm can be used to first calculate the weight according to the urgency and complexity of the work order, and then sort according to the weight and expected processing time. If the SJF algorithm is used, the sorting is directly based on the expected processing time to ensure that the work order with the short processing time is prioritized.

[0085] Module S306: database / data source, used to store all business work orders and employee information, including historical processing records of work orders and employee work performance data. Module S306 interacts with data collection module S302 to provide the latest work order and employee status data.

[0086] In some embodiments of the present application, a relational database (such as MySQL) or a non-relational database (such as MongoDB) can be used to store data to ensure high concurrent access and data integrity. The data source includes work order processing history and employee work records, which are used as the calculation basis for the intelligent sorting algorithm.

[0087] Module S308: Work order allocation module, used to automatically allocate work orders to employee modules in S314 based on the work order sorting results of S304 and the monitoring information of S312. The allocation strategy can take into account the current workload and processing efficiency of employees to ensure the balance and efficiency of work order allocation.

[0088] In some embodiments of the present application, a priority queue data structure may be used to assign work orders to employees with the smallest workload and the highest score one by one according to the work order sorting results. The work status of employees is updated in real time to reflect the latest situation after the work order is assigned.

[0089] Module S310: Work order sorting results, used to receive and store the work order sorting list generated by the S304 intelligent sorting module, which contains the priority information of all work orders to be processed.

[0090] In some embodiments of the present application, a memory data structure (such as an array or a list) may be used to store the sorting results to ensure fast access and updating. When allocating work orders, S310 provides the priority information of the work order to S308 as a basis for the work order allocation strategy.

[0091] Module S312: Monitoring module, used to monitor the system operation status and employee work status in real time, including the progress of work order processing, employee workload and system resource usage. The monitoring module provides the information feedback required for dynamically adjusting the work order allocation strategy. According to the information fed back by the monitoring module, the system can dynamically adjust the work order allocation strategy. For example, if it is found that the workload of an employee suddenly increases, the system can reallocate the work orders and assign some of the work orders to other employees to ensure that the workload of each employee remains at a reasonable level to avoid excessive burden.

[0092] In some embodiments of the present application, real-time data analysis technology can be used to continuously monitor changes in work order status in the database and the workload of the employee module. When encountering sudden changes in work orders or employee status, S308 and S304 are notified in time to adjust the strategy.

[0093] Module S314: Employee (ie target object) module, used to receive and process the work order assigned by S308. Each employee module contains information such as employee ID, work status, professional skills and historical processing efficiency.

[0094] In some embodiments of the present application, employees can receive assigned work orders through the work order management system interface. After starting to process the work order, the employee module updates its work status to S308 and S312 in real time to facilitate monitoring and subsequent work order allocation decisions.

[0095] Module S316: System resource module, used to monitor and manage the hardware and software resources required for the operation of the work order management system, including computing resources, storage resources, and network resources, etc. S316 interacts with the S312 monitoring module to provide real-time feedback on the use of system resources.

[0096] In some embodiments of the present application, cloud computing platform resource monitoring technology, such as AWS CloudWatch or Azure Monitor, can be used to monitor key indicators such as CPU utilization, memory usage, disk I / O, and network traffic in real time to ensure stable system operation and timely response to work order allocation and processing needs.

[0097] Through the collaboration of the above modules, an automated and intelligent work order processing process can be realized, which effectively solves problems such as work order accumulation, uneven processing and business process interruption, and improves the overall efficiency of work order processing within the operator and customer satisfaction.

[0098] Figure 4 is a schematic diagram of the overall process of a method for allocating a work order according to an embodiment of the present application, such as Figure 4 As shown, in some embodiments of the present application, Figure 3 Based on the system framework shown, work order allocation can be performed through the following steps:

[0099] Step S402: data collection, including step S4021 and step S4022.

[0100] Step S4021: Obtain work order information from the data source. This step obtains the latest business work order information from the data source regularly or in real time by interacting with the database / data source module S306. The data collection module S302 executes SQL queries or calls the data source API to extract the newly generated work order and its detailed attributes, such as the type, description, urgency, and estimated processing time of the work order.

[0101] Step S4022: Collecting work order attributes. Module S302 further analyzes and organizes the detailed attributes of each work order, including but not limited to the complexity, urgency, processing difficulty and associated customer information of the work order, as input parameters for the subsequent intelligent sorting algorithm.

[0102] Step S404: Intelligent sorting, including step S4041 and step S4042.

[0103] Step S4041: Applying the SJF algorithm. The intelligent sorting module S304 applies the shortest job first (SJF) algorithm to sort the work orders according to the estimated processing time of the collected work orders. The work orders with the shortest processing time will be processed first to improve the system response speed and customer satisfaction.

[0104] Step S4042: Apply WSJF algorithm. In addition to the SJF algorithm, the intelligent sorting module S304 can also apply the weighted shortest job first (WSJF) algorithm, which comprehensively considers the urgency of the work order and the processing efficiency of the employee, and can generate a more comprehensive work order sorting list through weight calculation to ensure that urgent work orders with short processing time are processed first.

[0105] Step S406: Work order allocation, including step S4061 and step S4062.

[0106] Step S4061: Automatically assign work orders to employees. The work order assignment module S308 automatically assigns work orders to employees in the employee module S314 based on the intelligent sorting results. The work order assignment takes into account the professional skills, historical processing efficiency and current working status of the employees to ensure that the work orders can be processed efficiently and evenly.

[0107] Step S4062: Consider the current workload of employees. When allocating work orders, S308 also monitors the current workload of each employee to avoid over-allocation. If the workload of an employee has reached a preset upper limit, the system will temporarily stop allocating new work orders to him until the workload is reduced.

[0108] Step S408: dynamic adjustment, including step S4081, step S4082 and step S4083.

[0109] Step S4081: monitor system operation status. The monitoring module S312 continuously monitors the operation status of the work order distribution system, including the work order processing progress, employee work status, system resource usage, etc., for subsequent policy adjustment and system optimization.

[0110] Step S4082: Dynamically adjust the allocation strategy. Based on the system operation status monitored in S312, S308 can dynamically adjust the work order allocation strategy. For example, if the system detects a processing peak, the allocation strategy may give priority to employees with high processing efficiency, even if they currently have a high workload.

[0111] Step S4083: Respond to emergencies. When encountering an emergency work order or other emergencies, such as the emergence of an emergency work order, a system failure, or an employee suddenly leaving their post, the monitoring module S312 can immediately notify the intelligent sorting module S304 and the work order allocation module S308. The system can re-evaluate the work order sorting and allocation strategy to ensure that emergency work orders can be processed as a priority, and in emergencies, the normal process of work order processing can be quickly restored. For example, when an emergency work order appears, the emergency work order can be processed as a priority to ensure that important tasks can be resolved in a timely manner. The system can readjust the work order sorting according to the priority of the emergency work order, and assign these work orders to the most appropriate employees to ensure the continuity and stability of the business process.

[0112] This application can greatly improve the efficiency of work order processing by introducing an automated allocation mechanism. First, the intelligent sorting algorithm is used to automatically allocate work orders, which reduces the links of manual intervention, significantly improves the speed and accuracy of work order processing, and ensures that each work order can quickly enter the processing process. Secondly, the intelligent sorting algorithm can achieve fairness in work order processing and avoid the problem of uneven burden on employees. By comprehensively considering factors such as the complexity and urgency of work orders and the historical processing efficiency of employees, the intelligent sorting and allocation of work orders are realized, ensuring that the workload of each employee is reasonably distributed and improving the overall collaboration efficiency of the team. Thirdly, the accumulation of complex work orders is effectively reduced through dynamic adjustment strategies. Complex work orders often require more time and energy to process. The intelligent sorting algorithm can dynamically adjust the allocation order of work orders according to the urgency and difficulty of the work orders, ensuring that complex work orders can also be processed in a timely manner, avoiding the negative impact of work order accumulation. Finally, through the intelligent sorting algorithm, the system can give priority to key work orders, ensure that important tasks are responded to and processed in a timely manner, ensure business continuity, avoid business process interruptions, and improve customer satisfaction and overall enterprise operational efficiency.

[0113] Figure 5 is a structural diagram of a work order distribution device according to an embodiment of the present application, such as Figure 5 As shown, the device comprises:

[0114] The acquisition module 502 is used to acquire the to-be-processed service work order of the operator, and acquire the service work order data corresponding to the to-be-processed service work order, wherein the service work order data at least includes the attribute information of the to-be-processed service work order;

[0115] A sorting module 504 is used to sort the pending business work orders according to the business work order data to obtain a first sorting result;

[0116] The allocation module 506 is used to allocate the pending business work orders to the target objects according to the first sorting result.

[0117] It should be noted that Figure 5 The work order distribution device shown is used to execute Figure 2 The work order allocation method shown, therefore Figure 2 The explanations in the work order allocation method in also apply to Figure 5 The distribution device of the work order shown will not be described in detail here.

[0118] An embodiment of the present application also provides an electronic device, which includes a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute the steps of the work order allocation method in each embodiment of the present application.

[0119] For example, the processor performs the following functions by executing program instructions stored in the memory:

[0120] Obtain the operator's pending business work orders, and obtain the business work order data corresponding to the pending business work orders, wherein the business work order data at least includes attribute information of the pending business work orders; sort the pending business work orders according to the business work order data to obtain a first sorting result; and assign the pending business work orders to a target object according to the first sorting result.

[0121] An embodiment of the present application further provides a non-volatile storage medium, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the steps of the work order allocation method in each embodiment of the present application by running the computer program.

[0122] An embodiment of the present application further provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the work order allocation method in each embodiment of the present application.

[0123] The embodiments of the present application also provide a computer program, which, when executed by a processor, implements the steps of the work order allocation method in each embodiment of the present application.

[0124] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0125] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0126] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0127] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0128] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0129] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0130] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for allocating a work order, characterized in that: include: Acquire a pending service work order of an operator, and acquire service work order data corresponding to the pending service work order, wherein the service work order data at least includes attribute information of the pending service work order; Sorting the pending business work orders according to the business work order data to obtain a first sorting result; The to-be-processed business work orders are allocated to a target object according to the first sorting result.

2. The method according to claim 1, characterized in that Sorting the pending business work orders according to the business work order data to obtain a first sorting result, including: Determining the estimated processing time and urgency of each pending business work order from the business work order data, wherein the urgency is represented by a numerical value, and a larger numerical value indicates a higher urgency; Determine, from the pending business work orders, business work orders whose estimated processing time is less than a first threshold and whose urgency is less than a second threshold as a first pending business work order set; The first set of to-be-processed business work orders is sorted according to the estimated processing time using a first sorting algorithm to obtain the first sorting result.

3. The method according to claim 2, characterized in that Get the estimated processing time for each pending business work order, including: Acquire historical service work orders of the operator, wherein the historical service work orders are processed service work orders; Determine first characteristic information corresponding to a first business work order, wherein the first business work order is any one of the to-be-processed business work orders, and the first characteristic information is used to describe an attribute of the first business work order; Determine, from the historical business work orders, a target historical business work order whose similarity with the first characteristic information is greater than a preset threshold; Obtain the historical processing time of the target historical business work order; The historical processing duration of the target historical business work order is determined as the estimated processing duration of the first business work order.

4. The method according to claim 2, characterized in that: Sorting the pending business work orders according to the business work order data to obtain a first sorting result, including: Determining the estimated processing time and urgency of each pending business work order from the business work order data; Determine, from the to-be-processed business work orders, business work orders whose estimated processing time is greater than or equal to the first threshold and whose urgency is greater than or equal to the second threshold as a second to-be-processed business work order set; Determining a weighted processing time for each pending business work order in the second pending business work order set according to the estimated processing time and the urgency; According to the weighted processing time, the second set of to-be-processed business work orders is sorted from small to large using a second sorting algorithm to obtain the first sorting result.

5. The method according to claim 4, characterized in that Allocating the pending business work order to a target object according to the first sorting result includes: Obtaining the business processing efficiency of the target object; Sorting the target objects from high to low according to the business processing efficiency to obtain a second sorting result; The to-be-processed business work order is allocated to the target object according to the first sorting result and the second sorting result.

6. The method according to claim 1, characterized in that After allocating the pending business work orders to a target object according to the first sorting result, the method further includes: Obtaining the business processing efficiency and accuracy of the target object; Determine a score for each target object based on the business processing efficiency and the accuracy rate; Acquire a workload of the target object, wherein the workload is used to represent the workload allocated to the target object; When the workload is greater than or equal to the score value corresponding to each target object, the work order allocation strategy is adjusted.

7. The method according to claim 6, characterized in that Adjust the ticket allocation strategy, including: Obtain a target business work order that needs to be reassigned from the pending business work orders assigned to the target object; Sort the target business work orders from high to low according to the urgency to obtain a third sorting result; Determine the working state and service processing efficiency of the target object, wherein the working state is used to indicate the idleness of the target object; According to the working status and the business processing efficiency, the target objects whose workloads are less than the corresponding score values ​​are sorted to obtain a fourth sorting result; According to the third sorting result and the fourth sorting result, the target business work order is allocated to the target object whose workload is less than the corresponding score value.

8. A work order distribution device, characterized in that: include: An acquisition module, used to acquire a pending service work order of an operator, and acquire service work order data corresponding to the pending service work order, wherein the service work order data at least includes attribute information of the pending service work order; A sorting module, used to sort the pending business work orders according to the business work order data to obtain a first sorting result; An allocation module is used to allocate the pending business work orders to a target object according to the first sorting result.

9. An electronic device, characterized in that: include: A memory for storing program instructions; A processor, connected to the memory, is used to execute program instructions to implement the following functions: obtain the operator's pending business work order, and obtain the business work order data corresponding to the pending business work order, wherein the business work order data at least includes the attribute information of the pending business work order; sort the pending business work order according to the business work order data to obtain a first sorting result; and assign the pending business work order to a target object according to the first sorting result.

10. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the work order allocation method according to any one of claims 1 to 7 by running the computer program.

11. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by a processor, the work order allocation method described in any one of claims 1 to 7 is implemented.

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