Task Dispatching Method, Device, Electronic Device and Storage Medium

Abstract

The present application discloses a task dispatching method, device, electronic device and storage medium. The method includes: when receiving a task request, determining at least one user within a preset range of the task location who is to receive the task corresponding to the task request; based on preset metrics, determining the metric parameters corresponding to each user, where the metric parameters include the weight value and historical metric value of each metric; according to the metric parameters corresponding to each user, determining the score value of each user through the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS); and according to the score value, sending the task corresponding to the task request to the user with the highest score value. This embodiment ensures the fairness of task dispatching.

Description

Technical Field

[0001] This application belongs to the field of communication technologies, and particularly relates to a task dispatching method, apparatus, electronic device, and storage medium. Background Art

[0002] As the number of vehicle purchasers increases, the number of vehicles with faults or accidents also increases. When a vehicle has a fault or accident, rescue services such as towing, tire replacement, power connection, and fuel delivery are required. In the existing rescue solutions, tasks are usually dispatched to rescue personnel on a first-come, first-served basis.

[0003] The above-mentioned rescue solution does not consider the comprehensive situation of rescue personnel, resulting in the fact that the finally dispatched personnel may not necessarily be the optimal ones, and there is a problem of poor fairness in task dispatching. Summary of the Invention

[0004] Embodiments of this application provide a task dispatching method, apparatus, electronic device, and storage medium to solve the problem of poor fairness in task dispatching in the existing rescue solutions.

[0005] In a first aspect, embodiments of this application provide a task dispatching method, including:

[0006] When a task request is received, determining at least one user within a preset range of the task location who is to receive the task corresponding to the task request;

[0007] Based on pre-set metrics, determining the metric parameters corresponding to each user, where the metric parameters include the weight value and historical metric value of each metric;

[0008] According to the metric parameters corresponding to each user, determining the score value of each user through the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS);

[0009] According to the score value, sending the task corresponding to the task request to the user with the highest score value.

[0010] In a second aspect, embodiments of this application further provide a task dispatching apparatus, including:

[0011] A first determination module, configured to determine at least one user within a preset range of the task location who is to receive the task corresponding to the task request when a task request is received;

[0012] A second determination module, configured to determine the metric parameters corresponding to each user based on pre-set metrics, where the metric parameters include the weight value and historical metric value of each metric;

[0013] A third determination module, configured to determine a score value for each user by using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) according to the metric parameters corresponding to each user.

[0014] A sending module, configured to send the task corresponding to the task request to the user with the highest score value according to the score value.

[0015] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0016] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0017] The solution provided by the embodiment of the present application, when receiving a task request, determines at least one user within a preset range of the task location to receive the task corresponding to the task request; determines the metric parameters corresponding to each user based on preset metrics, where the metric parameters include the weight value and historical metric value of each metric; determines the score value for each user by using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) according to the metric parameters corresponding to each user; sends the task corresponding to the task request to the user with the highest score value according to the score value; realizes the comprehensive consideration of task distribution from multiple metric dimensions, so that the determined score value can represent the comprehensive situation of the user. In addition, the score value is determined by using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), so that the information of the metric parameters can be fully utilized, the determined score value has a high accuracy, and the calculated score value can accurately reflect the gap between users, thereby enabling the selection of the optimal user and improving the fairness of task distribution, and solving the problem of poor fairness of the existing distribution scheme. Description of the Drawings

[0018] Figure 1 is a schematic flowchart of the task distribution method in the embodiment of the present application;

[0019] Figure 2 is another schematic flowchart of the task distribution method in the embodiment of the present application;

[0020] Figure 3 is a structural diagram of the algorithm when determining the score value for each user in the embodiment of the present application;

[0021] Figure 4 is a schematic structural diagram of the task distribution device in the embodiment of the present application;

[0022] Figure 5It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0023] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present application.

[0024] The terms "first", "second", etc. in the description and claims of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are usually of the same category, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the description and claims means at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the associated objects before and after.

[0025] Specifically, in the process of dispatching rescue tasks, in the related art, tasks are usually dispatched to rescue personnel in a first-come, first-served manner, which fails to comprehensively consider the actual situation of the rescue personnel, resulting in that the finally dispatched personnel may not necessarily be the optimal ones, and there is a problem of poor fairness in task dispatching. In addition, for users who have purchased auto insurance policies, usually the insurance company cooperates with a third-party rescue platform. After the insurance company receives the rescue request from the customer, it sends the rescue task to the third-party rescue platform, and the third party notifies the rescue personnel to provide rescue services. This results in the fact that the actual rescue details of the third party are shielded from the insurance company, and the insurance company cannot track the rescue situation in real time.

[0026] In view of the above problems, in this application, when a task request is received, at least one user within a preset range of the task location who is to receive the task corresponding to the task request is determined. Based on preset metrics, the metric parameters corresponding to each user are determined, where the metric parameters include the weight value and historical metric value of each metric. According to the metric parameters corresponding to each user, the scoring value of each user is determined by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). According to the scoring value, the task corresponding to the task request is sent to the user with the highest scoring value; this realizes considering task distribution comprehensively from multiple metric dimensions, enabling the determined scoring value to represent the comprehensive situation of the user. In addition, by using the TOPSIS to determine the scoring value, the information of the metric parameters can be fully utilized, the determined scoring value has a high accuracy, and the calculated scoring value can accurately reflect the gap between each user, so that the optimal user can be selected, improving the fairness of task distribution and solving the problem of poor fairness in the existing distribution scheme; in addition, this embodiment can be applied to the client of the task distributor, enabling the task distributor to directly perform task distribution, so that the task implementation situation can be tracked in real time.

[0027] The following will, with reference to the accompanying drawings, explain in detail the task distribution method provided by the embodiments of this application through specific embodiments and their application scenarios.

[0028] Figure 1 A task distribution method provided by an embodiment of the present invention is shown, and this method includes the following steps:

[0029] Step 101: When a task request is received, determine at least one user within a preset range of the task location who is to receive the task corresponding to the task request.

[0030] Specifically, this embodiment can be executed by a client, which can be the client of the task distributor that dispatches the task request, enabling the task distributor to directly understand the task distribution process through this embodiment and facilitating tracking the task implementation situation.

[0031] The task request can be a rescue task request. For example, in the scenario of a vehicle breakdown, the task request can be a tow truck request; the task request can also be a consultation task request. For example, in the insurance consultation scenario, the task request can be an insurance type consultation request. It should be noted that the specific content of the task request is not specifically limited here.

[0032] Specifically, when a task request is received, at least one user within a preset range of the task location who is to receive the task can be determined. The preset range of the task location can be determined according to the actual situation. For example, the value of the preset range can be 10 kilometers. By determining at least one user within the preset range of the task location who is to receive the task, the user who finally receives the task request can be limited within the preset range, avoiding the problem of untimely task execution caused by users at relatively long distances preemptively receiving the task, and reducing the access pressure on the client.

[0033] Step 102: Based on the preset metrics, determine the metric parameters corresponding to each of the users.

[0034] Specifically, different metrics can be set for different application scenarios. For example, as an example, assuming the application scenario is a vehicle rescue scenario, the metrics can include service price, distance from the task location, on-time rate, task complaint rate, implementation rate, etc. As another embodiment, assuming the application scenario is an insurance type consultation scenario, the metrics can include service price, task success rate, task complaint rate, etc.

[0035] The metric parameters include the weight value and historical metric value of each of the metrics, enabling the situation of the user under this metric to be known through the historical metric value, and the importance of the metric to be indicated through the weight value of the metric. Of course, it should be noted that the sum of the weight values of all the metrics is 1.

[0036] Step 103: According to the metric parameters corresponding to each user, determine the score value of each of the users by the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).

[0037] Specifically, in this embodiment, according to the metric parameters corresponding to each user, the score value of each user can be determined by the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), realizing scoring from multiple metric dimensions comprehensively, so that the determined score value can represent the comprehensive situation of the user. In addition, determining the score value by the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) enables the full use of the information of the metric parameters, ensuring the accuracy of the calculated score value, and the calculated score value can accurately reflect the gap between each user, so that the optimal user can be selected, improving the fairness of task distribution.

[0038] Step 104: According to the score value, send the task corresponding to the task request to the user with the highest score value.

[0039] After obtaining the score value corresponding to each user, the task corresponding to the task request can be sent to the user with the highest score value, thereby ensuring the fairness of task distribution and the execution quality of the task.

[0040] In this sample embodiment, when a task request is received, at least one user within the preset range of the task location who is to receive the task corresponding to the task request is determined. Based on the preset metrics, the metric parameters corresponding to each user are determined. According to the metric parameters corresponding to each user, by using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the score value of each user is determined. According to the score values, the task corresponding to the task request is sent to the user with the highest score value, realizing the comprehensive consideration of task distribution from multiple metric dimensions, enabling the determined score values to represent the comprehensive situation of users. In addition, by using TOPSIS to determine the score values, the information of the metric parameters can be fully utilized, the determined score values have a high accuracy, and the calculated score values can accurately reflect the differences among users, so that the optimal user can be selected, improving the fairness of task distribution and solving the problem of poor fairness in the existing distribution scheme.

[0041] In addition, in one embodiment, when determining at least one user within the preset range of the task location who is to receive the task corresponding to the task request, the following steps may be included:

[0042] Obtain a preset number of users to be dispatched within the preset range of the task location; send task information to the users to be dispatched, and based on the operations of the users to be dispatched on the task information within a preset time period, determine at least one user within the users to be dispatched who is to receive the task corresponding to the task request.

[0043] Specifically, after receiving a task request, the online preset number of users to be dispatched within the preset range of the task location can be searched. The value of the preset number can be determined according to the actual situation, and the preset number is the maximum concurrency of this task. In addition, the client sends task information to the users to be dispatched. At this time, the users to be dispatched can perform a task snatching operation within the preset time period. Specifically, this task snatching operation can be multiple clicks on the task information. After the client receives the operations of the users to be dispatched on the task information within the preset time period, at least one user within the users to be dispatched who is to receive the task corresponding to the task request can be determined. Specifically, the at least one user can be a specified number of users who operate first within the preset time period.

[0044] For example, as an example, the preset range can be a 10-kilometer range, and the preset time period can be 30s. In addition, this embodiment can record the information of the users to be dispatched found into the Redis database, record the number of operations of the users to be dispatched on the task information into the Redis database, and record information such as metric parameters and the determined score values into the Redis database.

[0045] By determining at least one user who is to receive the task corresponding to the task request from the users to be dispatched within the preset range of the task location, it is possible to avoid the situation where users far from the task location cannot complete the task in time when they preemptively operate to obtain the task, and it also reduces the concurrent access pressure on the client.

[0046] In addition, in one embodiment, as Figure 2 shown, when determining the score value of each user by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) according to the index parameters corresponding to each user, the following steps may be included:

[0047] Step 201: Determine the initial matrix according to the historical index values corresponding to each user.

[0048] Specifically, when determining the score value of each user by the TOPSIS, a three-layer structure can be established. The first layer is the target layer of the task dispatch result, the second layer is the index layer, and the third layer is the solution layer composed of users. Taking the application scenario of vehicle rescue as an example, the indexes include service price, distance from the task location, on-time rate, complaint task rate, implementation rate, and the number of users is N. Then the hierarchical structure is as Figure 3 shown, each user u n is a solution, and the final task dispatch result can be determined according to indexes such as service price, distance from the task location, on-time rate, complaint task rate, implementation rate in the index layer.

[0049] In addition, specifically, the element in the i-th row and j-th column of the initial matrix is the historical index value of the j-th index of the i-th user.

[0050] Taking the application scenario of vehicle rescue as an example, assume the index parameters are as shown in the following table:

[0051]

[0052] According to the above index parameters, the initial matrix can be determined as:

[0053]

[0054] where the element in the i-th row and j-th column of the above initial matrix is the historical index value of the j-th index of the i-th user.

[0055] Step 202: Perform positive normalization on the initial matrix to obtain a positive normalized matrix, and perform standardization on the positive normalized matrix to obtain a standardized matrix.

[0056] Specifically, when performing positive normalization on the initial matrix to obtain a positive normalized matrix, all the original elements can be uniformly converted into elements under the extremely large type index. The formula for converting the extremely small type index into the extremely large type index is: yi = max - x j 。

[0057] Continuing with the above example, according to the meaning of the indicators, it can be obtained that both "distance" and "complaint task rate" are extremely small indicators, and the rest are extremely large indicators. Then, the original matrix X is transformed into a positive matrix Y:

[0058]

[0059] In addition, by standardizing the positive matrix Y, the influence of different indicator dimensions can be eliminated to form a standardized matrix Z. The element Z in the standardized matrix ij The calculation formula is:

[0060]

[0061] Continuing with the above example, the standardized matrix obtained after standardizing the positive matrix Y is:

[0062]

[0063] Step 203: Determine the score value of each user according to the element value of the standardized matrix and the weight value of each indicator.

[0064] Specifically, in one embodiment, when determining the score value of each user according to the element value of the standardized matrix and the weight value of each indicator, the maximum vector and the minimum vector can be determined according to the standardized matrix, where the maximum vector includes the maximum element value corresponding to each indicator, and the minimum vector includes the minimum element value corresponding to each indicator;

[0065] Determine the first distance between each user and the maximum vector according to the maximum vector and the weight value of each indicator, and determine the second distance between each user and the minimum vector according to the minimum vector and the weight value of each indicator; Determine the score value of each user according to the first distance and the second distance.

[0066] Specifically, the maximum vector includes the maximum element value corresponding to each indicator, and the minimum vector includes the minimum element value corresponding to each indicator, that is

[0067]

[0068] Continuing with the above example, then:

[0069]

[0070]

[0071] In addition, in one embodiment, when determining the first distance between each user and the maximum vector according to the maximum vector and the weight value of each index, the first distance between the i-th user and the maximum vector can be determined according to the maximum vector and the weight value of each index through the following formula:

[0072]

[0073] Wherein, represents the first distance between the i-th user and the maximum vector, m represents the total number of the indexes, W j represents the weight value of the j-th index, represents the maximum vector, Z ij represents the element value of the i-th row and j-th column in the normalization matrix.

[0074] Continuing with the above example, the first distance is expressed as:

[0075]

[0076] In addition, in one embodiment, when determining the second distance between each user and the minimum vector according to the minimum vector and the weight value of each index, the second distance between each user and the minimum vector can be determined according to the minimum vector and the weight value of each index through the following formula:

[0077]

[0078] Wherein, represents the second distance between the i-th user and the minimum vector, m represents the total number of the indexes, W j represents the weight value of the j-th index, represents the minimum vector, Z ij represents the element value of the i-th row and j-th column in the normalization matrix.

[0079] Continuing with the above example, the second distance is expressed as:

[0080]

[0081] In addition, in one embodiment, determining the score value of each user according to the first distance and the second distance includes: determining the score value of each user according to the first distance and the second distance through the following formula:

[0082]

[0083] Wherein, S i represents the score value of the i-th user, represents the first distance between the i-th user and the maximum vector, represents the second distance between the i-th user and the minimum vector.

[0084] Continuing with the above example, the scoring value is expressed as:

[0085] S i =(0.465978093 0.399222254 0.658988107)

[0086] Of course, it should be noted that in order to compare different users, generally, a normalized score is calculated based on the scoring value, and the task is sent to the user with the highest normalized score. Among them, the calculation formula for the normalized score is:

[0087]

[0088] Continuing with the above example, the normalized score is expressed as:

[0089]

[0090] According to the above results, user C can be determined as the user who finally receives the task, and the task is sent to user C.

[0091] In this way, this embodiment realizes the comprehensive selection of users suitable for the current task from multiple dimensional indicators, improves the quality of task execution, and determines the scoring value of each user through the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, ensuring the accuracy of the scoring value, and further ensuring the fairness of the determined user who receives the task.

[0092] Figure 4 shows a schematic structural diagram of a task dispatching device provided by an embodiment of the present invention. As Figure 4 shown, the task dispatching device includes:

[0093] A first determination module 401, configured to determine at least one user within the preset range of the task location who is to receive the task corresponding to the task request when receiving the task request;

[0094] A second determination module 402, configured to determine the index parameters corresponding to each user based on preset indexes, where the index parameters include the weight value and historical index value of each index;

[0095] A third determination module 403, configured to determine the scoring value of each user through the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method according to the index parameters corresponding to each user;

[0096] A sending module 404, configured to send the task corresponding to the task request to the user with the highest scoring value according to the scoring value.

[0097] In one embodiment, the first determination module 401 is configured to obtain a preset number of users to be assigned within a preset range of the task location; send task information to the users to be assigned, and determine at least one user who is to receive the task corresponding to the task request from the users to be assigned according to the operations of the users to be assigned on the task information within a preset time period.

[0098] In one embodiment, the second determination module 401 is configured to determine an initial matrix according to the historical index values corresponding to each user, where the element in the i-th row and j-th column of the initial matrix is the historical index value of the j-th index of the i-th user; perform a positive normalization process on the initial matrix to obtain a positive normalized matrix, and perform a standardization process on the positive normalized matrix to obtain a standardized matrix; determine the score value of each user according to the element values of the standardized matrix and the weight values of each index.

[0099] The task assignment device provided by the embodiments of the present application can implement Figures 1-3 each process implemented by the method embodiments. To avoid repetition, details are not described herein again.

[0100] It should be noted that the embodiments of the task assignment device in this specification and the embodiments of the task assignment method in this specification are based on the same inventive concept. Therefore, for the specific implementation of the embodiments of the task assignment device, reference may be made to the corresponding embodiments of the task assignment method described above, and the repeated parts are not described again.

[0101] The task assignment device in the embodiments of the present application may be a device, or a component, an integrated circuit, or a chip in a terminal. The device may be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device may be a mobile phone, a tablet computer, a laptop computer, a handheld computer, an in-vehicle electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc., and the non-mobile electronic device may be a server, a Network Attached Storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, etc. The embodiments of the present application do not make specific limitations.

[0102] The task dispatching device in the embodiments of the present application can be a device with an operating system. The operating system can be the Android operating system, the iOS operating system, or other possible operating systems, which are not specifically limited in the embodiments of the present application.

[0103] Based on the same technical concept, the embodiments of the present application further provide an electronic device, which is used to execute the above-mentioned task dispatching method. Figure 5 FIG. is a schematic structural diagram of an electronic device for implementing various embodiments of the present application. The electronic device may vary greatly due to different configurations or performances, and may include a processor 510, a communication interface 520, a memory 530, and a communication bus 540. Among them, the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call a computer program stored in the memory 530 and executable on the processor 510 to execute the following steps:

[0104] In the case of receiving a task request, determining at least one user within a preset range of the task location who is to receive the task corresponding to the task request;

[0105] Based on preset metrics, determining the metric parameters corresponding to each user, where the metric parameters include the weight value and historical metric value of each metric;

[0106] According to the metric parameters corresponding to each user, determining the score value of each user by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS);

[0107] According to the score value, sending the task corresponding to the task request to the user with the highest score value.

[0108] In one embodiment, the determining at least one user within a preset range of the task location who is to receive the task corresponding to the task request includes: obtaining a preset number of users to be dispatched within the preset range of the task location; sending task information to the users to be dispatched, and determining at least one user among the users to be dispatched who is to receive the task corresponding to the task request according to the operations of the users to be dispatched on the task information within a preset time period.

[0109] In one embodiment, determining the score value of each user according to the index parameters corresponding to each user by the Technique for Order of Preference by Similarity to Ideal Solution includes: determining an initial matrix according to the historical index values corresponding to each user, where the element in the i-th row and j-th column of the initial matrix is the historical index value of the j-th index of the i-th user; performing a positive normalization process on the initial matrix to obtain a positive normalized matrix, and performing a standardization process on the positive normalized matrix to obtain a standardized matrix; determining the score value of each user according to the element values of the standardized matrix and the weight value of each index.

[0110] In one embodiment, determining the score value of each user according to the element values of the standardized matrix and the weight value of each index includes: determining a maximum vector and a minimum vector according to the standardized matrix, where the maximum vector includes the maximum element value corresponding to each index, and the minimum vector includes the minimum element value corresponding to each index; determining a first distance between each user and the maximum vector according to the maximum vector and the weight value of each index, and determining a second distance between each user and the minimum vector according to the minimum vector and the weight value of each index;

[0111] Determining the score value of each user according to the first distance and the second distance.

[0112] In one embodiment, determining a first distance between each user and the maximum vector according to the maximum vector and the weight value of each index includes: determining a first distance between the i-th user and the maximum vector according to the maximum vector and the weight value of each index through the following formula:

[0113]

[0114] where, represents the first distance between the i-th user and the maximum vector, m represents the total number of the indexes, W j represents the weight value of the j-th index, represents the maximum vector, Z ij represents the element value in the i-th row and j-th column of the standardized matrix.

[0115] In one embodiment, determining a second distance between each user and the minimum vector according to the minimum vector and the weight value of each index includes: determining a second distance between each user and the minimum vector according to the minimum vector and the weight value of each index through the following formula:

[0116]

[0117] where, represents the second distance between the i-th user and the minimum vector, m represents the total number of the metrics, and W j represents the weight value of the j-th metric, represents the minimum vector, and Z ij represents the element value at the i-th row and j-th column in the normalization matrix.

[0118] In one embodiment, determining the score value of each user according to the first distance and the second distance includes: determining the score value of each user according to the first distance and the second distance through the following formula:

[0119]

[0120] wherein, S i represents the score value of the i-th user, represents the first distance between the i-th user and the maximum vector, represents the second distance between the i-th user and the minimum vector.

[0121] The specific implementation steps can refer to the steps of the above-described embodiment of the task distribution method, and the same technical effects can be achieved. To avoid repetition, it will not be elaborated here.

[0122] It should be noted that the electronic device in the embodiment of the present application includes: a server, a terminal, or other devices other than the terminal.

[0123] The above structure of the electronic device does not limit the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements. For example, the input unit may include a Graphics Processing Unit (GPU) and a microphone, and the display unit may be configured with a display panel in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit includes at least one of a touch panel and other input devices. The touch panel is also called a touch screen. Other input devices may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, which will not be elaborated here.

[0124] The memory can be used to store software programs and various data. The memory may mainly include a first storage area for storing programs or instructions and a second storage area for storing data. Among them, the first storage area can store an operating system, application programs or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory can include volatile memory or non-volatile memory, or the memory can include both volatile and non-volatile memory. Among them, the non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM), a Static RAM (SRAM), a Dynamic RAM (DRAM), a Synchronous DRAM (SDRAM), a Double Data Rate SDRAM (DDR SDRAM), an Enhanced SDRAM (ESDRAM), a Synchlink DRAM (SLDRAM), and a Direct Rambus RAM (DRRAM).

[0125] The processor may include one or more processing units; optionally, the processor integrates an application processor and a modem processor. Among them, the application processor mainly processes operations related to the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above-mentioned modem processor may not be integrated into the processor either.

[0126] The embodiments of the present application also provide a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, it implements each process of the above-mentioned task dispatching method embodiment and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0127] Among them, the processor is the processor in the electronic device described in the foregoing embodiment. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disc, etc.

[0128] Another embodiment of the present application provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is configured to run programs or instructions to implement each process of the foregoing method embodiment, and can achieve the same technical effects. To avoid repetition, details are not described herein again.

[0129] It should be understood that the chip mentioned in the embodiments of the present application may also be referred to as a system-on-chip, system chip, chip system, or system-on-chip, etc.

[0130] It should be noted that in this article, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including that element. In addition, it should be pointed out that the methods and devices in the embodiments of the present application are not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0131] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods in the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0132] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them fall within the protection scope of the present application.

Claims

1. A task distribution method, characterized in that, including: when receiving a task request, determining at least one user within a preset range of the task location who is to receive the task corresponding to the task request; determining, based on preset metrics, the metric parameters corresponding to each user, where the metric parameters include the weight value and historical metric value of each metric; determining, according to the metric parameters corresponding to each user, the score value of each user by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS); sending the task corresponding to the task request to the user with the highest score value according to the score value; The determining of at least one user within a preset range of the task location who is to receive the task corresponding to the task request includes: obtaining a preset number of users to be dispatched within the preset range of the task location; sending task information to the users to be dispatched, and determining, according to the operations of the users to be dispatched on the task information within a preset time period, at least one user within the users to be dispatched who is to receive the task corresponding to the task request.

2. The task distribution method according to claim 1, wherein The determining, according to the metric parameters corresponding to each user, the score value of each user by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) includes: determining an initial matrix according to the historical metric values corresponding to each user, where the element in the i-th row and j-th column of the initial matrix is the historical metric value of the j-th metric of the i-th user; performing positive normalization on the initial matrix to obtain a positive normalized matrix, and performing standardization on the positive normalized matrix to obtain a standardized matrix; determining the score value of each user according to the element values of the standardized matrix and the weight value of each metric.

3. The task distribution method according to claim 2, wherein The determining the score value of each user according to the element values of the standardized matrix and the weight value of each metric includes: determining a maximum vector and a minimum vector according to the standardized matrix, where the maximum vector includes the maximum element value corresponding to each metric, and the minimum vector includes the minimum element value corresponding to each metric; determining a first distance between each user and the maximum vector according to the maximum vector and the weight value of each metric, and determining a second distance between each user and the minimum vector according to the minimum vector and the weight value of each metric; determining the score value of each user according to the first distance and the second distance.

4. The task dispatching method according to claim 3, wherein The determining a first distance between each user and the maximum vector according to the maximum vector and the weight value of each metric includes: determining, according to the maximum vector and the weight value of each metric, the first distance between the i-th user and the maximum vector through the following formula: ； wherein, represents the first distance between the i-th user and the maximum vector, m represents the total number of the metrics, represents the weight value of the j-th metric, represents the maximum vector, represents the element value of the i-th row and the j-th column in the normalization matrix.

5. The task dispatching method according to claim 3, wherein The determining a second distance between each user and the minimum vector according to the minimum vector and the weight value of each metric includes: determining, according to the minimum vector and the weight value of each metric, the second distance between each user and the minimum vector through the following formula: ； Among them, represents the second distance between the i-th user and the minimum vector, m represents the total number of the metrics, represents the weight value of the j-th metric, represents the minimum vector, represents the element value of the i-th row and j-th column in the normalization matrix.

6. The task dispatching method according to claim 3, wherein The determining the score value of each user according to the first distance and the second distance includes: determining, according to the first distance and the second distance, the score value of each user through the following formula: ； wherein, represents the rating value of the i-th user, represents the first distance between the i-th user and the maximum vector, represents the second distance between the i-th user and the minimum vector.

7. A task dispatching device, characterized in that, including: A first determination module, configured to determine at least one user within a preset range of a task location to receive the task corresponding to the task request when receiving the task request; A second determination module, configured to determine an index parameter corresponding to each user based on a preset index, where the index parameter includes a weight value and a historical index value of each index; A third determination module, configured to determine a score value of each user by using a technique for order preference by similarity to ideal solution (TOPSIS) according to the index parameter corresponding to each user; A sending module, configured to send the task corresponding to the task request to the user with the highest score value according to the score value; The first determination module is specifically further configured to obtain a preset number of users to be dispatched within the preset range of the task location; Send task information to the users to be dispatched, and determine at least one user to receive the task corresponding to the task request from the users to be dispatched according to the operations of the users to be dispatched on the task information within a preset time period.

8. An electronic device, characterized in that, It includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the task dispatching method according to any one of claims 1-6 are implemented.

9. A readable storage medium, characterized in that, A program or instruction is stored on the readable storage medium. When the program or instruction is executed by the processor, the steps of the task dispatching method according to any one of claims 1-6 are implemented.

Images