Work instruction changing method and related equipment based on artificial intelligence

Abstract

The present application proposes a method, device, electronic device and storage medium for changing work instructions based on artificial intelligence. The method for changing work instructions based on artificial intelligence includes: building a performance analysis platform to obtain enterprise performance data, and the enterprise performance data includes the total revenue and order revenue of the enterprise; according to the order revenue, the average receivables of employees completing each order type are counted to obtain the matching degree between employees and order types; monitoring the total revenue of the enterprise to obtain a change signal of the work instruction; based on the change signal and the matching degree between the employee and the order type, the initial work matrix is ​​updated to obtain a significant work matrix, and the initial work matrix reflects the order type that the employee is responsible for before the work instruction is changed; based on the significant work matrix, work is allocated to obtain a preferred work instruction, and the significant work matrix reflects the order type that the employee is responsible for after the work instruction is changed. The present application can automatically change the work instruction and improve the efficiency of changing the work instruction.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to an artificial intelligence-based work instruction change method, device, electronic device and storage medium. Background Art

[0002] Corporate performance can directly reflect the overall financial status and operating results of the company, and is an important indicator of whether the corporate system is successful. Therefore, the rationality of the company's management strategy can be judged by analyzing corporate data to assist in the formulation of corporate management decisions.

[0003] Existing technologies usually analyze the company's performance data to judge the work performance of different teams or individuals in the company, and then manually assign work instructions to different teams or individuals. However, this method cannot adapt to the rapid changes in the market and the efficiency of work instruction assignment is low. Summary of the invention

[0004] In view of the above content, it is necessary to propose an artificial intelligence-based work instruction change method and related equipment to solve the technical problem of how to improve the efficiency of work instruction dispatching, wherein the related equipment includes an artificial intelligence-based work instruction change device, electronic equipment and storage medium.

[0005] The present application provides a method for changing work instructions based on artificial intelligence, the method comprising:

[0006] Building a performance analysis platform to obtain enterprise performance data, including total enterprise revenue and order revenue;

[0007] According to the order revenue, the average receivable amount of each order type completed by the employee is calculated to obtain the matching degree between the employee and the order type;

[0008] Monitoring the total revenue of the enterprise to obtain a change signal of the work order, wherein the change signal is used to change the work order;

[0009] Based on the change signal and the matching degree between the employee and the order type, a preset initial work matrix is ​​updated to obtain a significant work matrix, wherein the initial work matrix reflects the order type that the employee is responsible for before the work instruction is changed;

[0010] Work allocation is performed based on the significant work matrix to obtain a preferred work instruction, wherein the significant work matrix reflects the type of order that the employee is responsible for after the work instruction is changed.

[0011] In this way, the matching degree between employees and each order type is calculated through enterprise performance data, and based on the matching degree between employees and each order type, the optimal work instructions are obtained by using the optimization algorithm, so as to realize the automatic dispatch of work instructions and improve the dispatch efficiency of work instructions.

[0012] In some embodiments, the step of calculating the average receivable amount of each order type completed by the employee according to the order revenue to obtain the matching degree between the employee and the order type includes:

[0013] Filter order information based on employee ID and order type ID to obtain the order information set of each employee completing each order type;

[0014] Counting the order revenue of the order information set to obtain an average revenue of each type of order completed by each employee;

[0015] The employee-order type matching degree is calculated based on the average revenue of each order type completed by each employee. The employee-order type matching degree satisfies the relationship:

[0016]

[0017] Among them, the molecule represents the average revenue of employee m completing order type n, and the denominator represents the maximum value of the average revenue of all employees completing order type n. mn is the matching degree between employee m and order type n, and its value range is [0, 1].

[0018] In this way, the matching degree between each enterprise employee and each order type is obtained based on the order revenue, which enables accurate quantification of employees' proficiency in different order types.

[0019] In some embodiments, the monitoring of the total revenue of the enterprise to obtain a change signal of a work order, wherein the change signal is used to change the work order and includes:

[0020] Monitoring the total revenue of the enterprise to obtain a total revenue gain value of the enterprise;

[0021] The enterprise total revenue gain value is compared with a preset threshold to obtain the change signal.

[0022] In this way, the total revenue of the enterprise can be monitored in real time. When the revenue of the enterprise is abnormal, a change signal can be issued in time to realize the automatic change of work instructions and reduce the loss of the enterprise.

[0023] In some embodiments, updating the preset initial work matrix based on the change signal and the matching degree between the employee and the order type to obtain a significant work matrix includes:

[0024] The average matching degree between all employees and all order types is obtained based on the matching degree between each employee and the order type and the preset initial work matrix;

[0025] Updating the initial working matrix according to a custom optimization logic to obtain a significant average matching degree;

[0026] A working matrix corresponding to the significant average matching degree is obtained as the significant working matrix.

[0027] In this way, the significant work matrix can be automatically obtained. Under the actual situation of meeting the proportion of each order type of the enterprise, the corresponding relationship between each employee of the enterprise and the different order types can be obtained when the total revenue of the enterprise reaches the maximum.

[0028] In some embodiments, the average matching degree of all employees and all order types satisfies the relationship:

[0029]

[0030] Among them, P 1 is the initial work matrix, whose size is M×N, where M and N are the number of enterprise employees and the number of order types respectively; is the value of the mth row and nth column in the initial work matrix, reflecting whether employee m is responsible for order type n. If employee m is responsible for order type n, then The value is 1, otherwise The value is 0; α mn is the matching degree between employee m and order type n, F(P 1 ) is the average matching degree between all employees and all order types, and its value range is [0, 1]. The larger the value, the higher the total revenue of the enterprise.

[0031] In this way, the average matching degree between all employees and all order types can link the correspondence between the company's employees and order types with the company's total revenue, thereby quantifying the company's total revenue.

[0032] In some embodiments, the custom optimization logic includes an objective function, a first constraint, a second constraint, and a third constraint, and the objective function satisfies the relationship:

[0033] R = max(F(P 1 ))

[0034] The first constraint condition satisfies the relationship:

[0035]

[0036] The second constraint condition satisfies the relationship:

[0037]

[0038] The third constraint condition satisfies the relationship:

[0039]

[0040] in, is the value of the mth row and nth column in the initial working matrix P1, is the number of employees responsible for order type N, B 1 :B 2 :...B N The ratio of the number of employees between the N types of order preset by the enterprise, which is related to the number of orders of each type.

[0041] In this way, the work instruction change problem can be transformed into a problem of finding the optimal solution, and the mathematical model is constructed. The initial work instruction matrix P can be directly updated using the existing optimization algorithm. 1 , so that the average matching degree between the employee and the order type is maximized.

[0042] In some embodiments, performing work allocation based on the significant work matrix to obtain preferred work instructions includes:

[0043] Acquire a matching relationship between employees and order types based on the significant work matrix;

[0044] Work allocation is performed based on the matching relationship to obtain the preferred work instruction.

[0045] In this way, the preferred work instructions are automatically obtained based on the significant work matrix, and the automatic allocation of work instructions is realized, and human participation is no longer required, thereby improving the efficiency of enterprise work instruction allocation.

[0046] The embodiment of the present application also provides a work instruction changing device based on artificial intelligence, the device comprising:

[0047] An acquisition unit, used to build a performance analysis platform to acquire enterprise performance data, wherein the enterprise performance data includes the total revenue and order revenue of the enterprise;

[0048] A statistical unit, used for calculating the average receivable amount of each order type completed by the employee according to the order revenue to obtain the matching degree between the employee and the order type;

[0049] A monitoring unit, used for monitoring the total revenue of the enterprise to obtain a change signal of the work order, wherein the change signal is used to change the work order;

[0050] An updating unit, configured to update a preset initial work matrix based on the change signal and the matching degree between the employee and the order type to obtain a significant work matrix, wherein the initial work matrix reflects the order type that the employee is responsible for before the work instruction is changed;

[0051] An allocation unit is used to allocate work based on the significant work matrix to obtain preferred work instructions, and the significant work matrix reflects the type of orders that the employee is responsible for after the work instruction is changed.

[0052] The present application also provides an electronic device, the electronic device comprising:

[0053] A memory storing at least one instruction;

[0054] A processor executes the instructions stored in the memory to implement the artificial intelligence-based work instruction changing method.

[0055] An embodiment of the present application also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the artificial intelligence-based work instruction changing method. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of a preferred embodiment of the work instruction changing method based on artificial intelligence involved in this application.

[0057] Figure 2 This is a flowchart involved in this application for calculating the average receivables of each order type completed by employees based on the order revenue to obtain the matching degree between employees and order types.

[0058] Figure 3 It is a flowchart of a preferred embodiment of the present application for updating a preset initial work matrix based on the change signal and the matching degree between the employee and the order type to obtain a significant work matrix.

[0059] Figure 4 It is a functional module diagram of a preferred embodiment of the work instruction changing device based on artificial intelligence involved in this application.

[0060] Figure 5 It is a structural schematic diagram of an electronic device of a preferred embodiment of the work instruction changing method based on artificial intelligence involved in the present application. DETAILED DESCRIPTION

[0061] In order to more clearly understand the purpose, features and advantages of the present application, the present application is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other. In the following description, many specific details are set forth to facilitate a full understanding of the present application, and the embodiments described are only a part of the embodiments of the present application, rather than all of the embodiments.

[0062] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used herein in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0064] The embodiment of the present application provides a work instruction change method based on artificial intelligence, which can be applied to one or more electronic devices. The electronic device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0065] An electronic device can be any electronic product that can interact with a customer, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), a game console, an interactive network television (IPTV), a smart wearable device, etc.

[0066] The electronic device may also include a network device and / or a client device, wherein the network device includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud consisting of a large number of hosts or network servers based on cloud computing.

[0067] The network where the electronic device is located includes but is not limited to the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0068] like Figure 1As shown, it is a flow chart of a preferred embodiment of the method for changing work instructions based on artificial intelligence of the present application. According to different requirements, the order of the steps in the flow chart can be changed, and some steps can be omitted.

[0069] S10, building a performance analysis platform to obtain enterprise performance data, wherein the enterprise performance data includes the total revenue and order revenue of the enterprise.

[0070] In an optional embodiment, the performance analysis platform is first built through the FineBI tool. The FineBI tool can analyze enterprise data independently through business users. The performance analysis platform includes five parts: user terminal APP, order system, data middle platform, big data platform and operation management system. The terminal APP is used to obtain employee ID information, receive work instructions and upload the execution status of work instructions; the order system is used to obtain enterprise order data, and the enterprise order data contains multiple order information, each of which contains employee ID information, actual transaction price, transaction time, transaction cost and order type; the data middle platform is a central platform that integrates all data sources and data processing, and any data can be obtained through the data middle platform; the big data platform is used for analysis and storage of large amounts of data; the operation management system is used to monitor the execution status of work instructions.

[0071] In this optional embodiment, first, the enterprise order data is filtered according to the transaction time to obtain an order data set within a preset time period, and the enterprise performance data is related to each order information in the order data set, and the preset time period can be one quarter; further, the order revenue amount of each order information needs to be obtained, and the order revenue amount y of order information i i The calculation method is:

[0072] y i =w i -c i

[0073] Among them, w i is the actual transaction price of order i; c i is the transaction cost of order i, which includes all costs such as promotion and publicity spent to complete order i. Since different employees have different sales capabilities and sales methods, their actual transaction prices and transaction costs for completing orders are also different. Therefore, wi and c i Related to the employee ID information that completed the order i.

[0074] In this optional embodiment, the order revenues of all order information are summed up to obtain the total revenue of the enterprise in a quarter, and the total revenue of the enterprise in the quarter and the revenues of all orders are used as the quarterly performance data of the enterprise, and all the quarterly performance data of the enterprise constitute the said enterprise performance data.

[0075] For example, the order type is health insurance. Employee 1 gives away a 200 yuan gas card in the process of completing the order, and the final transaction is completed at a price of 950 yuan. The revenue of the order is y=950-200=750 yuan. According to the above method, the order revenue of all orders in the most recent quarter is obtained, and then the total revenue of the company in the corresponding quarter is obtained by summing them up.

[0076] In this way, the performance analysis platform built can achieve orderly management of large amounts of enterprise data, thereby obtaining accurate enterprise performance data and realizing real-time monitoring of the enterprise's operating conditions.

[0077] S11, according to the order revenue, the average receivable amount of each order type completed by the employee is counted to obtain the matching degree between the employee and the order type.

[0078] See also Figure 2 In an optional embodiment, counting the order revenue to obtain the matching degree between the employee and the order type includes:

[0079] S111, filtering order information according to employee ID and order type ID to obtain an order information set of each employee completing each order type.

[0080] In an optional embodiment, the employee ID can reflect the identity information of the employee within the enterprise, and the order type ID can distinguish different order types in the enterprise. The employee ID and the order type ID can be either letters or numbers, and this application does not impose any restrictions.

[0081] In this optional embodiment, due to the differences between different order types, employees have different proficiency in different order types, so that the same employee has different ability to generate revenue when completing different order types. In order to calculate the matching degree between each employee and each order type of the enterprise, it is first necessary to use an intelligent search engine to search the employee ID and order type ID information in the enterprise performance data to obtain an order information set, which includes all order information of the employee ID completing the order type ID. Among them, the intelligent search is a new generation of search engine combined with artificial intelligence technology. In addition to providing traditional fast retrieval, relevance sorting and other functions, the engine can also provide user role registration, automatic identification of user interests, semantic understanding of content, intelligent information filtering and push and other functions.

[0082] In this optional embodiment, the number of order information sets is M×N, where M is the number of employees in the enterprise, N is the number of all order types in the enterprise, and the order information set of employee m completing order type n is recorded as Q mn , Q mn Includes all order information for order type n completed by employee m.

[0083] S112, counting the order revenue of the order information set to obtain an average revenue of each employee completing each order type.

[0084] In this optional embodiment, the average revenue of each order type completed by each employee can be obtained by counting the average revenue of all orders in the order information set. For example, the calculation formula for the average revenue of employee m completing order type n is as follows:

[0085]

[0086] Where K represents the order information set Q mn The number of order information contained in y k is the order information set Q mn The order revenue of order information k, That is, the average revenue of employee m completing order type n.

[0087] S113, calculating the matching degree between the employee and the order type based on the average revenue of each order type completed by each employee.

[0088] In this optional embodiment, the matching degree α between employee m and order type n is mn The calculation formula is as follows:

[0089]

[0090] Among them, the molecule represents the average revenue of employee m completing order type n, and the denominator represents the maximum average revenue of M employees completing order type n, where M is the number of employees in the enterprise; mn is the matching degree between the employee m and the order type n, and the value range is [0, 1]. The larger the value, the more matching the employee m and the order type n are, and the higher the revenue obtained.

[0091] For example, if order type 2 is health insurance, employee 1 has completed 3 order information of order type 2, and the revenue of the 3 order information is 200, 230, and 300 respectively; then the average revenue of employee 1 completing order type 2 is Assuming that the maximum average revenue of all employees in the company who complete order type 2 is 300, the matching degree between employee 1 and order type 2 is

[0092] In this way, the matching degree between each enterprise employee and each order type is obtained based on the order revenue, which enables accurate quantification of employees' proficiency in different order types.

[0093] S12, monitoring the total revenue of the enterprise to obtain a change signal of the work order, wherein the change signal is used to change the work order.

[0094] In an optional embodiment, the total revenue of the enterprise is monitored to obtain a change signal of the work order, and the change signal is used to change the work order and includes:

[0095] S121, monitoring the total revenue of the enterprise to obtain a total revenue gain value of the enterprise.

[0096] In this optional embodiment, the enterprise's total revenue gain value can reflect the business conditions of the enterprise. When the enterprise's total revenue gain value is less than a preset threshold, it means that the current work instructions cannot meet the current situation of the enterprise and the work instructions need to be changed to increase the enterprise's total revenue.

[0097] In this optional embodiment, the ratio of the current total revenue to the historical total revenue is calculated as the total revenue gain value of the enterprise. The current total revenue is the total revenue of the enterprise in the current quarter, and the historical total revenue is the total revenue of the enterprise in the previous quarter. The calculation formula of the total revenue gain value r is as follows:

[0098]

[0099] Among them, Y and Y′ are the current total revenue of the enterprise and the historical total revenue of the enterprise respectively.

[0100] S122, comparing the enterprise total revenue gain value with a preset threshold to obtain the change signal.

[0101] In this optional embodiment, the enterprise total revenue gain value r is compared with a preset threshold to obtain an enterprise revenue evaluation result, and the enterprise revenue evaluation result includes meeting the standard and not meeting the standard. When the enterprise total revenue gain value is less than the preset threshold, the enterprise revenue evaluation result is not meeting the standard, indicating that the current work instruction cannot meet the current situation of the enterprise, and a work instruction change signal is issued to change the work instruction. The preset threshold is 0.05.

[0102] For example, the current total revenue of the enterprise is 1.1 million yuan, and the historical total revenue of the enterprise is 1.2 million yuan. Then the total revenue gain value of the enterprise is If it is less than the preset threshold of 0.05, a change signal of the work instruction is triggered.

[0103] In this way, the total revenue of the enterprise can be monitored in real time. When the revenue of the enterprise is abnormal, a change signal can be issued in time to realize the automatic change of work instructions and reduce the loss of the enterprise.

[0104] S13, based on the change signal and the matching degree between the employee and the order type, updating a preset initial work matrix to obtain a significant work matrix, wherein the initial work matrix reflects the order type that the employee is responsible for before the work instruction is changed.

[0105] See also Figure 3 In an optional embodiment, updating a preset initial work matrix based on the change signal and the matching degree between the employee and the order type to obtain a significant work matrix includes:

[0106] S131, obtaining the average matching degree between all employees and all order types according to the matching degree between each employee and the order type and the preset initial work matrix.

[0107] In an optional embodiment, the initial working matrix P 1 It reflects the order types that each company employee was responsible for before the work order was changed. It is an M×N matrix, where the rows represent the M employees of the company and the columns represent the N order types. If the company employee m was responsible for order type n before the work order was changed, then the value of the mth row and nth column otherwise And one employee can only be responsible for one order type.

[0108] In this optional embodiment, the higher the matching degree between employee m and order type n, the greater the revenue obtained by employee m when he is responsible for order type n. Based on the initial work matrix P 1 The average matching degree between all employees and all order types in the enterprise before the work instruction change is calculated by combining the matching degree between each employee and the order type F(P 1 ), F(P 1 ) is larger, the total revenue of the enterprise is larger. The average matching degree calculation formula of all employees and all order types is as follows:

[0109]

[0110] Among them, P 1 is the initial work matrix, whose size is M×N, where M and N are the number of enterprise employees and the number of order types respectively; is the value of the mth row and nth column in the initial work matrix, reflecting whether employee m is responsible for order type n. If employee m is responsible for order type n, then The value is 1, otherwise The value is 0; α mn is the matching degree between employee m and order type n, F(P 1 ) is the average matching degree between all employees and all order types, and its value range is [0, 1]. The larger the value, the higher the total revenue of the enterprise.

[0111] S132, updating the initial working matrix according to the custom optimization logic to obtain a significant average matching degree.

[0112] In this optional embodiment, the purpose of changing the work instruction is to maximize the total revenue of the enterprise under the constraints of meeting the actual situation and the enterprise's requirements. The maximum total revenue of the enterprise means that the average matching degree between all employees and all order types is maximized. The actual situation means that an employee must be responsible for and can only be responsible for one type of order. The enterprise requirement means that the number of employees between different order types must meet a certain ratio, which is pre-set by the enterprise according to the number of orders of each type. In this way, with the help of custom optimization logic, the problem of changing the work instruction can be transformed into the problem of finding the optimal solution to maximize the average matching degree between all employees and all order types.

[0113] In this optional embodiment, the custom optimization logic is: when the constraint conditions are met, update the initial working matrix P 1 In order to maximize the average matching degree between all employees and all order types, the objective function is set as follows:

[0114] R = max(F(P 1 ))

[0115] The first constraint is as follows:

[0116]

[0117] The second constraint is as follows:

[0118]

[0119] The third constraint is as follows:

[0120]

[0121] in, is the value of the mth row and nth column in the initial working matrix P1, is the number of employees responsible for order type N, B 1 :B 2 :...B NThe ratio of the number of employees between the N types of orders preset by the enterprise is related to the number of orders of each type. The first constraint and the second constraint are to make the initial work matrix P 1 The actual situation is met during the updating process, that is, an employee must be responsible for and can only be responsible for one type of order; the third constraint is to make the initial work matrix P 1 During the updating process, the enterprise requirement is met, that is, the number of employees between different order types must meet a certain ratio.

[0122] In this optional embodiment, the initial working matrix P is continuously updated using an optimization algorithm. 1 , further calculate the average matching degree between all employees and all order types based on the updated work matrix, obtain the maximum value of the average matching degree during the updating process, and take the maximum value of the average matching degree as the significant average matching degree.

[0123] In this optional embodiment, the optimization algorithm refers to a method for searching for a good parameter so that the system is in the best state. The optimization algorithms that can be selected in the prior art include linear programming algorithm, simulated annealing algorithm, genetic algorithm, etc. The optimization algorithm selected in this solution is simulated annealing algorithm, and the simulated annealing algorithm includes:

[0124] Step 1: Set the initialization parameters, initial temperature T and the number of iterations L for each T value. In this optional embodiment, the initialization parameters are the initial working matrix P 1 , initial temperature T = 1000, number of iterations L = 10;

[0125] Step 2: Give the initial working matrix P 1 Add random disturbance to generate a new working matrix P'. In this optional embodiment, the random disturbance is to change the initial working matrix P 1 The values ​​of each parameter are randomly set to 0 or 1;

[0126] Step 3: Evaluate whether P′ is better than P 1 Better, that is, ΔT = F(P′)-F(P 1 ) is greater than 0. If so, P′ is accepted as the new current solution. Otherwise, P′ is accepted as the current solution with a certain probability. The probability calculation formula is:

[0127] Step 4: When no new current solution is found after the number of iterations L, the average matching degree of all employees and all order types corresponding to this time is the significant average matching degree. Otherwise, reduce T and continue to iterate, and continuously update the working matrix until the significant average matching degree is obtained.

[0128] S133, obtaining a working matrix corresponding to the significant average matching degree as the significant working matrix.

[0129] In this optional embodiment, the working matrix corresponding to the significant average matching degree is the significant working matrix P 2 The significant work matrix is ​​also an M×N matrix, where the rows represent the M employees of the enterprise and the columns represent the N order types, which can reflect the order types that each enterprise employee is responsible for after the work instruction is changed.

[0130] In this way, the significant work matrix can be automatically obtained. Under the actual situation of meeting the proportion of each order type of the enterprise, the corresponding relationship between each employee of the enterprise and the different order types can be obtained when the total revenue of the enterprise reaches the maximum.

[0131] S14, performing work allocation based on the significant work matrix to obtain preferred work instructions, wherein the significant work matrix reflects the type of orders that the employee is responsible for after the work instructions are changed.

[0132] In an optional embodiment, performing work allocation based on the significant work matrix to obtain preferred work instructions includes:

[0133] S141, obtaining a matching relationship between employees and order types based on the significant work matrix.

[0134] In this optional embodiment, the significant work matrix can reflect the matching relationship between each employee and the order type when the total revenue of the enterprise reaches the maximum, and the preferred work instructions can be obtained by allocating work based on the matching relationship.

[0135] For example, when obtaining the matching relationship between employee m and order type, first obtain the N values ​​of the mth row of the significant work matrix, and then obtain the column n corresponding to 1 in the N values. * , n * This is the type of order that enterprise employee m should be responsible for after the work order is changed.

[0136] S142: Perform work allocation based on the matching relationship to obtain the preferred work instruction.

[0137] In this optional embodiment, the work content of each employee is allocated based on the matching relationship. When the order type corresponding to the enterprise employee m in the matching relationship is n * , then the work change instruction for employee m is: employee m is responsible for n * The order type represented. According to the above method, the work change instructions of each employee are obtained. In this solution, the work change instructions of all employees are used as the preferred work instructions. After the preferred work instructions are executed, the total revenue of the enterprise can be increased.

[0138] In this way, the preferred work instructions are automatically obtained based on the significant work matrix, and the automatic allocation of work instructions is realized, and human participation is no longer required, thereby improving the efficiency of enterprise work instruction allocation.

[0139] See also Figure 4 , Figure 4 1 is a functional module diagram of a preferred embodiment of the work instruction change device based on artificial intelligence of the present application. The work instruction change device based on artificial intelligence 11 includes an acquisition unit 110, a statistical unit 111, a monitoring unit 112, an update unit 113, and an allocation unit 114. The module / unit referred to in the present application refers to a series of computer-readable instruction segments that can be executed by the processor 13 and can complete fixed functions, which are stored in the memory 12. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0140] In an optional embodiment, the acquisition unit 110 is used to build a performance analysis platform to obtain enterprise performance data, where the enterprise performance data includes the total revenue and order revenue of the enterprise.

[0141] In an optional embodiment, the performance analysis platform is first built through the FineBI tool. The FineBI tool can analyze enterprise data independently through business users. The performance analysis platform includes five parts: user terminal APP, order system, data middle platform, big data platform and operation management system. The terminal APP is used to obtain employee ID information, receive work instructions and upload the execution status of work instructions; the order system is used to obtain enterprise order data, and the enterprise order data contains multiple order information, each of which contains employee ID information, actual transaction price, transaction time, transaction cost and order type; the data middle platform is a central platform that integrates all data sources and data processing, and any data can be obtained through the data middle platform; the big data platform is used for analysis and storage of large amounts of data; the operation management system is used to monitor the execution status of work instructions.

[0142] In this optional embodiment, first, the enterprise order data is filtered according to the transaction time to obtain an order data set within a preset time period, and the enterprise performance data is related to each order information in the order data set. The preset time period may be one quarter. Further, the order revenue of each order information needs to be obtained. The calculation method of the order revenue yi of order information i is:

[0143] y i =w i -c i

[0144] Among them, w iis the actual transaction price of order i; c i is the transaction cost of order i, which includes all costs such as promotion and publicity spent to complete order i. Since different employees have different sales capabilities and sales methods, their actual transaction prices and transaction costs for completing orders are also different. Therefore, w i and c i Related to the employee ID information that completed the order i.

[0145] In this optional embodiment, the order revenues of all order information are summed up to obtain the total revenue of the enterprise in a quarter, and the total revenue of the enterprise in the quarter and the revenues of all orders are used as the quarterly performance data of the enterprise, and all the quarterly performance data of the enterprise constitute the said enterprise performance data.

[0146] For example, the order type is health insurance. Employee 1 gives away a 200 yuan gas card in the process of completing the order, and the final transaction is completed at a price of 950 yuan. The revenue of the order is y=950-200=750 yuan. According to the above method, the order revenue of all orders in the most recent quarter is obtained, and then the total revenue of the company in the corresponding quarter is obtained by summing them up.

[0147] In an optional embodiment, the statistical unit 111 is used to calculate the average receivable amount of each order type completed by the employee based on the order revenue to obtain the matching degree between the employee and the order type.

[0148] In an optional embodiment, the average receivable amount of each order type completed by the employee is counted according to the order revenue to obtain the matching degree between the employee and the order type:

[0149] Filter order information based on employee ID and order type ID to obtain the order information set of each employee completing each order type;

[0150] Counting the order revenue of the order information set to obtain an average revenue of each type of order completed by each employee;

[0151] The matching degree between employees and order types is calculated based on the average revenue of each order type completed by each employee.

[0152] In an optional embodiment, the employee ID can reflect the identity information of the employee within the enterprise, and the order type ID can distinguish different order types in the enterprise. The employee ID and the order type ID can be either letters or numbers, and this application does not impose any restrictions.

[0153] In this optional embodiment, due to the differences between different order types, employees have different proficiency in different order types, so that the same employee has different ability to generate revenue when completing different order types. In order to calculate the matching degree between each employee and each order type of the enterprise, it is first necessary to use an intelligent search engine to search the employee ID and order type ID information in the enterprise performance data to obtain an order information set, which includes all order information of the employee ID completing the order type ID. Among them, the intelligent search is a new generation of search engine combined with artificial intelligence technology. In addition to providing traditional fast retrieval, relevance sorting and other functions, the engine can also provide user role registration, automatic identification of user interests, semantic understanding of content, intelligent information filtering and push and other functions.

[0154] In this optional embodiment, the number of order information sets is M×N, where M is the number of employees in the enterprise, N is the number of all order types in the enterprise, and the order information set of employee m completing order type n is recorded as Q mn , Q mn Includes all order information for order type n completed by employee m.

[0155] In this optional embodiment, the average revenue of each order type completed by each employee can be obtained by counting the average revenue of all orders in the order information set. For example, the calculation formula for the average revenue of employee m completing order type n is as follows:

[0156]

[0157] Where K represents the order information set Q mn The number of order information contained in y k is the order revenue of order information k in the order information set Qmn, That is, the average revenue of employee m completing order type n.

[0158] In this optional embodiment, the matching degree α between employee m and order type n is mn The calculation formula is as follows:

[0159]

[0160] Among them, the molecule represents the average revenue of employee m completing order type n, and the denominator represents the maximum average revenue of M employees completing order type n, where M is the number of employees in the enterprise; mn is the matching degree between the employee m and the order type n, and the value range is [0, 1]. The larger the value, the more matching the employee m and the order type n are, and the higher the revenue obtained.

[0161] For example, if order type 2 is health insurance, employee 1 has completed 3 order information of order type 2, and the revenue of the 3 order information is 200, 230, and 300 respectively; then the average revenue of employee 1 completing order type 2 is Assuming that the maximum average revenue of all employees in the company who complete order type 2 is 300, the matching degree between employee 1 and order type 2 is

[0162] In an optional embodiment, the monitoring unit 112 is used to monitor the total revenue of the enterprise to obtain a change signal of the work order, and the change signal is used to change the work order.

[0163] In an optional embodiment, the monitoring of the total revenue of the enterprise to obtain a change signal of the work order, wherein the change signal is used to change the work order and includes:

[0164] Monitoring the total revenue of the enterprise to obtain a total revenue gain value of the enterprise;

[0165] The enterprise total revenue gain value is compared with a preset threshold to obtain the change signal.

[0166] In an optional embodiment, the enterprise's total revenue gain value can reflect the business conditions of the enterprise. When the enterprise's total revenue gain value is less than a preset threshold, it means that the current work instructions cannot meet the current situation of the enterprise and the work instructions need to be changed to increase the enterprise's total revenue.

[0167] In this optional embodiment, the ratio of the current total revenue to the historical total revenue is calculated as the total revenue gain value of the enterprise. The current total revenue is the total revenue of the enterprise in the current quarter, and the historical total revenue is the total revenue of the enterprise in the previous quarter. The calculation formula of the total revenue gain value r is as follows:

[0168]

[0169] Among them, Y and Y′ are the current total revenue of the enterprise and the historical total revenue of the enterprise respectively.

[0170] In this optional embodiment, the enterprise total revenue gain value r is compared with a preset threshold to obtain an enterprise revenue evaluation result, and the enterprise revenue evaluation result includes meeting the standard and not meeting the standard. When the enterprise total revenue gain value is less than the preset threshold, the enterprise revenue evaluation result is not meeting the standard, indicating that the current work instruction cannot meet the current situation of the enterprise, and a work instruction change signal is issued to change the work instruction. The preset threshold is 0.05.

[0171] For example, the current total revenue of the enterprise is 1.1 million yuan, and the historical total revenue of the enterprise is 1.2 million yuan. Then the total revenue gain value of the enterprise is If it is less than the preset threshold of 0.05, a change signal of the work instruction is triggered.

[0172] In an optional embodiment, the updating unit 113 is used to update a preset initial work matrix based on the change signal and the matching degree between the employee and the order type to obtain a significant work matrix, wherein the initial work matrix reflects the order type that the employee is responsible for before the work instruction is changed.

[0173] In an optional embodiment, updating a preset initial work matrix based on the change signal and the matching degree between the employee and the order type to obtain a significant work matrix includes:

[0174] Obtaining the average matching degree between all employees and all order types according to the matching degree between the employee and the order type and a preset initial work matrix;

[0175] Updating the initial working matrix according to a custom optimization logic to obtain a significant average matching degree;

[0176] A working matrix corresponding to the significant average matching degree is obtained as the significant working matrix.

[0177] In an optional embodiment, the initial working matrix P 1 It reflects the order types that each company employee was responsible for before the work order was changed. It is an M×N matrix, where the rows represent the M employees of the company and the columns represent the N order types. If the company employee m was responsible for order type n before the work order was changed, then the value of the mth row and nth column otherwise And one employee can only be responsible for one order type.

[0178] In this optional embodiment, the higher the matching degree between employee m and order type n, the greater the revenue obtained by employee m when he is responsible for order type n. Based on the initial work matrix P 1 The average matching degree between all employees and all order types in the enterprise before the work instruction change is calculated by combining the matching degree between each employee and the order type F(P 1 ), F(P 1 ) is larger, the total revenue of the enterprise is larger. The average matching degree calculation formula of all employees and all order types is as follows:

[0179]

[0180] Among them, P 1 is the initial work matrix, whose size is M×N, where M and N are the number of enterprise employees and the number of order types respectively; is the value of the mth row and nth column in the initial work matrix, reflecting whether employee m is responsible for order type n. If employee m is responsible for order type n, then The value is 1, otherwise The value is 0; α mn is the matching degree between employee m and order type n, F(P 1 ) is the average matching degree between all employees and all order types, and its value range is [0, 1]. The larger the value, the higher the total revenue of the enterprise.

[0181] In this optional embodiment, the purpose of changing the work instruction is to maximize the total revenue of the enterprise under the constraints of meeting the actual situation and the enterprise's requirements. The maximum total revenue of the enterprise means that the average matching degree between all employees and all order types is maximized. The actual situation means that an employee must be responsible for and can only be responsible for one type of order. The enterprise requirement means that the number of employees between different order types must meet a certain ratio, which is pre-set by the enterprise according to the number of orders of each type. In this way, with the help of custom optimization logic, the problem of changing the work instruction can be transformed into the problem of finding the optimal solution to maximize the average matching degree between all employees and all order types.

[0182] In this optional embodiment, the custom optimization logic is: when the constraint conditions are met, update the initial working matrix P 1 In order to maximize the average matching degree between all employees and all order types, the objective function is set as follows:

[0183] R = max(F(P 1 ))

[0184] The first constraint is as follows:

[0185]

[0186] The second constraint is as follows:

[0187]

[0188] The third constraint is as follows:

[0189]

[0190] in, is the value of the mth row and nth column in the initial working matrix P1, is the number of employees responsible for order type N, B 1 :B 2 :...B NThe ratio of the number of employees between the N types of orders preset by the enterprise is related to the number of orders of each type. The first constraint and the second constraint are to make the initial work matrix P 1 The actual situation is met during the updating process, that is, an employee must be responsible for and can only be responsible for one type of order; the third constraint is to make the initial work matrix P 1 During the updating process, the enterprise requirement is met, that is, the number of employees between different order types must meet a certain ratio.

[0191] In this optional embodiment, the initial working matrix P is continuously updated using an optimization algorithm. 1 , further calculate the average matching degree between all employees and all order types based on the updated work matrix, obtain the maximum value of the average matching degree during the updating process, and take the maximum value of the average matching degree as the significant average matching degree.

[0192] In this optional embodiment, the optimization algorithm refers to a method for searching for a good parameter so that the system is in the best state. The optimization algorithms that can be selected in the prior art include linear programming algorithm, simulated annealing algorithm, genetic algorithm, etc. The optimization algorithm selected in this solution is simulated annealing algorithm, and the simulated annealing algorithm includes:

[0193] Step 1: Set the initialization parameters, initial temperature T and the number of iterations L for each T value. In this optional embodiment, the initialization parameters are the initial working matrix P 1 , initial temperature T = 1000, number of iterations L = 10;

[0194] Step 2: Give the initial working matrix P 1 Add random disturbance to generate a new working matrix P'. In this optional embodiment, the random disturbance is to change the initial working matrix P 1 The values ​​of each parameter are randomly set to 0 or 1;

[0195] Step 3: Evaluate whether P′ is better than P 1 Better, that is, ΔT = F(P′)-F(P 1 ) is greater than 0. If so, P′ is accepted as the new current solution. Otherwise, P′ is accepted as the current solution with a certain probability. The probability calculation formula is:

[0196] Step 4: When no new current solution is found after the number of iterations L, the average matching degree of all employees and all order types corresponding to this time is the significant average matching degree. Otherwise, reduce T and continue to iterate, and continuously update the working matrix until the significant average matching degree is obtained.

[0197] In this optional embodiment, the working matrix corresponding to the significant average matching degree is the significant working matrix P 2 The significant work matrix is ​​also an M×N matrix, where the rows represent the M employees of the enterprise and the columns represent the N order types, which can reflect the order types that each enterprise employee is responsible for after the work instruction is changed.

[0198] In an optional embodiment, the allocation unit 114 is used to perform work allocation based on the significant work matrix to obtain a preferred work instruction, wherein the significant work matrix reflects the type of order that the employee is responsible for after the work instruction is changed.

[0199] In an optional embodiment, performing work allocation based on the significant work matrix to obtain preferred work instructions includes:

[0200] Acquire a matching relationship between employees and order types based on the significant work matrix;

[0201] Work allocation is performed based on the matching relationship to obtain the preferred work instruction.

[0202] In an optional embodiment, the significant work matrix can reflect the matching relationship between each employee and the order type when the total revenue of the enterprise reaches a maximum, and the preferred work instructions can be obtained by allocating work based on the matching relationship.

[0203] For example, when obtaining the matching relationship between employee m and order type, first obtain the N values ​​of the mth row of the significant work matrix, and then obtain the column n corresponding to 1 in the N values. * , n * This is the type of order that enterprise employee m should be responsible for after the work order is changed.

[0204] In this optional embodiment, the work content of each employee is allocated based on the matching relationship. When the order type corresponding to the enterprise employee m in the matching relationship is n * , then the work change instruction for employee m is: employee m is responsible for n * The order type represented. According to the above method, the work change instructions of each employee are obtained. In this solution, the work change instructions of all employees are used as the preferred work instructions. After the preferred work instructions are executed, the total revenue of the enterprise can be increased.

[0205] It can be seen from the above technical solutions that this application calculates the matching degree between employees and order types through enterprise performance data, and uses an optimization algorithm based on the matching degree between employees and order types to obtain the optimal work instructions, realize the automatic dispatch of work instructions, and improve the dispatch efficiency of work instructions.

[0206] See also Figure 5, is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 1 includes a memory 12 and a processor 13. The memory 12 is used to store computer-readable instructions, and the processor 13 is used to execute the computer-readable instructions stored in the memory to implement the artificial intelligence-based work instruction change method described in any of the above embodiments.

[0207] In an optional embodiment, the electronic device 1 further includes a bus, a computer program stored in the memory 12 and executable on the processor 13, such as a work instruction change program based on artificial intelligence.

[0208] Figure 5 Only the electronic device 1 having the memory 12 and the processor 13 is shown, and those skilled in the art can understand that Figure 5 The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0209] Combination Figure 1 The memory 12 in the electronic device 1 stores a plurality of computer-readable instructions to implement a method for changing a work instruction based on artificial intelligence, and the processor 13 can execute the plurality of instructions to implement:

[0210] Building a performance analysis platform to obtain enterprise performance data, including total enterprise revenue and order revenue;

[0211] According to the order revenue, the average receivable amount of each order type completed by the employee is calculated to obtain the matching degree between the employee and the order type;

[0212] Monitoring the total revenue of the enterprise to obtain a change signal of the work order, wherein the change signal is used to change the work order;

[0213] Based on the change signal and the matching degree between the employee and the order type, a preset initial work matrix is ​​updated to obtain a significant work matrix, wherein the initial work matrix reflects the order type that the employee is responsible for before the work instruction is changed;

[0214] Work allocation is performed based on the significant work matrix to obtain a preferred work instruction, wherein the significant work matrix reflects the type of order that the employee is responsible for after the work instruction is changed.

[0215] Specifically, the specific implementation method of the processor 13 for the above instructions can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0216] Those skilled in the art will appreciate that the schematic diagram is merely an example of the electronic device 1 and does not constitute a limitation on the electronic device 1. The electronic device 1 may be a bus structure or a star structure. The electronic device 1 may also include more or less other hardware or software than shown in the diagram, or a different arrangement of components. For example, the electronic device 1 may also include input and output devices, network access devices, etc.

[0217] It should be noted that the electronic device 1 is only an example, and other existing or future electronic products that are suitable for the present application should also be included in the protection scope of the present application and included here by reference.

[0218] Among them, the memory 12 includes at least one type of readable storage medium, and the readable storage medium can be non-volatile or volatile. The readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 12 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. The memory 12 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the work instruction change program based on artificial intelligence, but also can be used to temporarily store data that has been output or is to be output.

[0219] In some embodiments, the processor 13 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 13 is the control core (Control Unit) of the electronic device 1, and uses various interfaces and lines to connect the various components of the entire electronic device 1, and executes or executes programs or modules stored in the memory 12 (for example, executing a work instruction change program based on artificial intelligence, etc.), and calls the data stored in the memory 12 to execute various functions of the electronic device 1 and process data.

[0220] The processor 13 executes the operating system of the electronic device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the above-mentioned embodiments of the work instruction change method based on artificial intelligence, for example Figures 1 to 3 Steps shown.

[0221] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to complete the present application. The one or more modules / units may be a series of computer-readable instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into an acquisition unit 110, a statistics unit 111, a monitoring unit 112, an update unit 113, and an allocation unit 114.

[0222] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, and includes a number of instructions for enabling a computer device (which can be a personal computer, a computer device, or a network device, etc.) or a processor to execute a part of the artificial intelligence-based work instruction change method described in each embodiment of the present application.

[0223] If the module / unit integrated in the electronic device 1 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 present application implements all or part of the processes in the above-mentioned embodiment method, and can also instruct the relevant hardware devices to complete through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented.

[0224] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory and other memory, etc.

[0225] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the blockchain node, etc.

[0226] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0227] The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one arrow is used in the figure, but it does not mean that there is only one bus or one type of bus. The bus is configured to realize the connection and communication between the memory 12 and at least one processor 13, etc.

[0228] An embodiment of the present application also provides a computer-readable storage medium (not shown), in which computer-readable storage medium is stored computer-readable instructions, and the computer-readable instructions are executed by a processor in an electronic device to implement the artificial intelligence-based work instruction changing method described in any of the above embodiments.

[0229] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0230] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0231] In addition, each functional module in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0232] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the specification can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any specific order.

[0233] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical solution of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present application.

Claims

1. A method for changing work instructions based on artificial intelligence, characterized in that: The method comprises: Building a performance analysis platform to obtain enterprise performance data, including total enterprise revenue and order revenue; According to the order revenue, the average receivable amount of each order type completed by the employee is calculated to obtain the matching degree between the employee and the order type; Monitoring the total revenue of the enterprise to obtain a change signal of the work order, wherein the change signal is used to change the work order; Based on the change signal and the employee-order type matching degree, a preset initial work matrix is ​​updated to obtain a significant work matrix, wherein the initial work matrix reflects the order type that the employee is responsible for before the work instruction is changed, wherein the updating of the preset initial work matrix based on the change signal and the employee-order type matching degree to obtain the significant work matrix includes: obtaining the average matching degree of all employees and all order types according to the matching degree of each employee and the order type and the preset initial work matrix; updating the initial work matrix according to the custom optimization logic to obtain the significant average matching degree; obtaining the work matrix corresponding to the significant average matching degree as the significant work matrix, and the average matching degree of all employees and all order types satisfies the relationship: in, is the initial work matrix, whose size is M×N, where M and N are the number of enterprise employees and the number of order types respectively; is the value of the mth row and nth column in the initial work matrix, reflecting whether employee m is responsible for order type n. If employee m is responsible for order type n, then The value is 1, otherwise The value is 0; is the matching degree between employee m and order type n, is the average matching degree between all employees and all order types, with a value range of [0,1]. A larger value indicates a higher total revenue of the enterprise. Work allocation is performed based on the significant work matrix to obtain a preferred work instruction, wherein the significant work matrix reflects the type of order that the employee is responsible for after the work instruction is changed.

2. The method for changing work instructions based on artificial intelligence as claimed in claim 1, characterized in that: The method of calculating the average receivable amount of each order type completed by the employee according to the order revenue to obtain the matching degree between the employee and the order type includes: Filter order information based on employee ID and order type ID to obtain the order information set of each employee completing each order type; Counting the order revenue of the order information set to obtain an average revenue of each type of order completed by each employee; The employee-order type matching degree is calculated based on the average revenue of each order type completed by each employee. The employee-order type matching degree satisfies the relationship: Among them, the molecule represents the average revenue of employee m completing order type n, and the denominator represents the maximum value of the average revenue of all employees completing order type n. is the matching degree between employee m and order type n, and its value range is [0,1].

3. The method for changing work instructions based on artificial intelligence as claimed in claim 1, characterized in that: The monitoring of the total revenue of the enterprise to obtain a change signal of the work order, wherein the change signal is used to change the work order and includes: Monitoring the total revenue of the enterprise to obtain a total revenue gain value of the enterprise; The enterprise total revenue gain value is compared with a preset threshold to obtain the change signal.

4. The method for changing work instructions based on artificial intelligence as claimed in claim 1, characterized in that: The custom optimization logic includes an objective function, a first constraint condition, a second constraint condition, and a third constraint condition, and the objective function satisfies the relationship: The first constraint condition satisfies the relationship: The second constraint condition satisfies the relationship: The third constraint condition satisfies the relationship: in, is the initial working matrix The value of the mth row and nth column in is the number of employees responsible for order type N, The ratio of the number of employees between the N types of order preset by the enterprise, which is related to the number of orders of each type.

5. The method for changing work instructions based on artificial intelligence as claimed in claim 1, characterized in that: The performing of work allocation based on the significant work matrix to obtain preferred work instructions comprises: Acquire a matching relationship between employees and order types based on the significant work matrix; Work allocation is performed based on the matching relationship to obtain the preferred work instruction.

6. A work instruction changing device based on artificial intelligence, characterized in that: The device comprises: An acquisition unit, used to build a performance analysis platform to acquire enterprise performance data, wherein the enterprise performance data includes the total revenue and order revenue of the enterprise; A statistical unit, used for calculating the average receivable amount of each order type completed by the employee according to the order revenue to obtain the matching degree between the employee and the order type; A monitoring unit, used for monitoring the total revenue of the enterprise to obtain a change signal of the work order, wherein the change signal is used to change the work order; An updating unit is used to update a preset initial work matrix based on the change signal and the employee-order type matching degree to obtain a significant work matrix, wherein the initial work matrix reflects the order type that the employee is responsible for before the work instruction is changed, wherein the updating of the preset initial work matrix based on the change signal and the employee-order type matching degree to obtain the significant work matrix includes: obtaining the average matching degree of all employees and all order types based on the matching degree of each employee and the order type and the preset initial work matrix; updating the initial work matrix based on a custom optimization logic to obtain a significant average matching degree; obtaining a work matrix corresponding to the significant average matching degree as the significant work matrix, wherein the average matching degree of all employees and all order types satisfies the relationship: in, is the initial work matrix, whose size is M×N, where M and N are the number of enterprise employees and the number of order types respectively; is the value of the mth row and nth column in the initial work matrix, reflecting whether employee m is responsible for order type n. If employee m is responsible for order type n, then The value is 1, otherwise The value is 0; is the matching degree between employee m and order type n, is the average matching degree between all employees and all order types, with a value range of [0,1]. A larger value indicates a higher total revenue of the enterprise. An allocation unit is used to allocate work based on the significant work matrix to obtain preferred work instructions, and the significant work matrix reflects the type of orders that the employee is responsible for after the work instruction is changed.

7. An electronic device, characterized in that: The electronic device comprises: a memory storing computer-readable instructions; and A processor executes computer-readable instructions stored in the memory to implement the artificial intelligence-based work instruction changing method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the artificial intelligence-based work instruction changing method according to any one of claims 1 to 5.

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