Object recommendation method and apparatus, electronic device, and storage medium

Abstract

The application relates to an object recommendation method and device, electronic equipment and a storage medium, and applies to the technical field of data recommendation. The method comprises the following steps: acquiring an execution parameter of a to-be-assigned task and an execution index value of the execution parameter; acquiring an execution capability value of each to-be-recommended object in a to-be-recommended object set to the execution parameter of a historical task; screening a to-be-recommended object sub-set in the to-be-recommended object set, wherein the execution capability value is in a preset range of the execution index value; and determining a target to-be-recommended object according to the execution capability value of the to-be-recommended object in the to-be-recommended object sub-set. The application solves the problems that the related personnel configures according to experience, the manual effort is large, and the deployment efficiency is low in the prior art.

Description

Technical Field

[0001] This application relates to the field of data recommendation technology, and in particular to an object recommendation method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the development of technology and the improvement of production levels, more and more enterprises are using corresponding mechanical equipment (such as machines or robots) to complete corresponding processes in order to improve production efficiency.

[0003] Because the mechanical equipment produced by different manufacturers has different structures, its output performance in the production process may vary. Therefore, it is necessary to make appropriate adjustments to different mechanical equipment.

[0004] In practical applications, the allocation of mechanical equipment is often configured by relevant personnel based on experience, which consumes a lot of manual labor and has low allocation efficiency. Summary of the Invention

[0005] This application provides an object recommendation method, apparatus, electronic device, and storage medium to solve the problems in the prior art where configuration by relevant personnel based on experience is labor-intensive and inefficient.

[0006] In a first aspect, embodiments of this application provide an object recommendation method, including:

[0007] Obtain the execution parameters of the task to be assigned and the execution index values ​​of the execution parameters;

[0008] Obtain the execution capability value of each object in the set of objects to be recommended for the execution parameters of historical tasks;

[0009] Filter the set of objects to be recommended, and select the subset of objects whose execution capability values ​​are within the preset range of the execution indicator values.

[0010] The target objects to be recommended are determined based on the execution capability values ​​of the objects to be recommended in the subset of objects to be recommended.

[0011] Optionally, the step of filtering the subset of recommended objects whose execution capability values ​​fall within a preset range of the execution metric values ​​from the set of recommended objects includes:

[0012] Determine the target execution parameter among the execution parameters, the target execution parameter indicating the execution parameter that has the greatest impact when recommending the object to be recommended;

[0013] Filter out the objects to be recommended whose execution capability value is greater than the execution index value, and obtain the first recommendation set;

[0014] Filter out the objects to be recommended in the first recommendation set whose execution capability values ​​of other execution parameters are less than the execution index value to obtain the subset of objects to be recommended. The other execution capability values ​​are the execution capability values ​​other than the target execution capability value.

[0015] Optionally, determining the target object to be recommended based on the execution capability value of the objects to be recommended in the subset of objects to be recommended includes:

[0016] For each of the objects to be recommended, a ratio value is configured for the target execution capability value of the target execution parameter, wherein the target execution parameter indicates the load of the object to be recommended;

[0017] The recommendation coefficient is determined based on the ratio value and the other performance capability values.

[0018] The recommendation coefficients are sorted in descending order to obtain the ranking results;

[0019] The top-ranked preset number of objects in the sorting results are identified as the target objects to be recommended.

[0020] Optionally, a recommendation coefficient is determined based on the ratio value and other performance capability values, including:

[0021] The proportion values ​​of each of the objects to be recommended are weighted to obtain a weighted value;

[0022] Calculate the product of the weighted value and the other execution capability values;

[0023] The product is determined to be the recommendation coefficient.

[0024] Optionally, the weighting of the proportion values ​​of each of the objects to be recommended to obtain a weighted value includes:

[0025] The weighted value is obtained by exponentiation of the ratio value, wherein the exponent of the exponentiation operation is a positive integer greater than 1.

[0026] Optionally, when the target execution parameter is the load, obtaining the execution capability value of each object to be recommended in the set of objects to be recommended for the execution parameters of historical tasks includes:

[0027] Obtain the average number of historical executions of the daily task volume of the recommended objects in the recommended set for the historical tasks;

[0028] Based on the historical average number of executions of the daily task volume, the historical number of executions of the objects to be recommended, and the execution index value of the daily task volume, the execution capacity value of the load volume of each object to be recommended is calculated.

[0029] Optionally, the execution parameters include at least one of execution difficulty, execution domain, execution workload, execution cycle, and execution interaction.

[0030] Secondly, embodiments of this application provide an object recommendation device, comprising:

[0031] The first acquisition module is used to acquire the execution parameters of the task to be assigned and the execution index values ​​of the execution parameters;

[0032] The second acquisition module is used to acquire the execution capability value of the execution parameters of each object to be recommended in the set of objects to be recommended for historical tasks;

[0033] The filtering module is used to filter the subset of objects to be recommended from the set of objects to be recommended, whose execution capability values ​​are within the preset range of the execution indicator values;

[0034] The determination module is used to determine the target object to be recommended based on the execution capability value of the objects to be recommended in the subset of objects to be recommended.

[0035] Thirdly, embodiments of this application provide an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0036] The memory is used to store computer programs;

[0037] The processor is configured to execute the program stored in the memory to implement the object recommendation method described in the first aspect.

[0038] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the object recommendation method described in the first aspect.

[0039] Compared with the prior art, the technical solution provided in this application has the following advantages: The method provided in this application obtains the execution parameters and execution index values ​​of the task to be assigned; obtains the execution capability value of each object to be recommended in the set of objects to be recommended for historical tasks; filters the set of objects to be recommended whose execution capability values ​​are within a preset range of the execution index values; and determines the target object to be recommended based on the execution capability values ​​of the objects to be recommended in the subset of objects to be recommended. Thus, after obtaining the execution parameters and execution index values ​​of the task to be assigned, and the execution capability values ​​of the objects to be recommended, the method automatically performs preliminary filtering based on the execution capability values ​​of the objects to be recommended to obtain a subset of objects to be recommended. Then, based on the execution capability values ​​of the objects to be recommended in the subset of objects to be recommended, the target object to be recommended can be further determined. This process requires no manual intervention and no human effort, thus improving allocation efficiency. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0042] Figure 1 This is an application scenario diagram of the object recommendation method provided in an embodiment of this application;

[0043] Figure 2 A flowchart illustrating an object recommendation method provided in an embodiment of this application;

[0044] Figure 3 A structural diagram of an object recommendation device provided in an embodiment of this application;

[0045] Figure 4 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] An object recommendation method is provided according to an embodiment of this application. Optionally, in this embodiment, the above-described object recommendation method can be applied to, for example... Figure 1 The hardware environment shown consists of terminal 101 and server 102. For example... Figure 1 As shown, server 102 is connected to terminal 101 via a network and can be used to provide services (such as application services) to the terminal or clients installed on the terminal. A database can be set up on the server or independently of the server to provide data storage services for server 102. The network mentioned above includes, but is not limited to, wide area network, metropolitan area network or local area network. Terminal 101 is not limited to PC, mobile phone, tablet computer, etc.

[0048] The object recommendation method of this application embodiment can be executed by server 102, terminal 101, or jointly by server 102 and terminal 101. Specifically, the object recommendation method of this application embodiment can be executed by terminal 101, or by a client installed on it.

[0049] Taking the object recommendation method of this application embodiment as an example, Figure 2 This is a flowchart illustrating an optional object recommendation method according to an embodiment of this application, such as... Figure 2 As shown, the process of this method may include the following steps:

[0050] Step 201: Obtain the execution parameters and execution index values ​​of the tasks to be assigned.

[0051] In some embodiments, when a new task (task to be assigned) needs to be assigned to a relevant object, it is often necessary to first evaluate the new task in multiple dimensions. For example, the execution status of the new task can be evaluated from the perspective of multiple execution parameters to obtain the execution index values ​​corresponding to different execution parameters.

[0052] The execution parameters can be set manually based on the actual situation of the tasks to be assigned, or the tasks to be assigned can be uploaded to the server, and the server can obtain the execution parameters through data analysis (for example, by identifying the title of the task to be assigned to determine the execution area of ​​the task to be assigned, or by comparing the data in the task to be assigned with historical tasks to determine the execution workload and execution difficulty of the task to be assigned).

[0053] The execution parameters may include, but are not limited to, execution difficulty, execution domain, execution proficiency, workload, execution cycle, and execution interaction capabilities.

[0054] Furthermore, after determining the execution parameters, relevant personnel can set the execution index values ​​of each execution parameter according to the actual situation of the tasks to be assigned.

[0055] For example, the execution parameters of the task to be assigned and their corresponding execution index values ​​can be: total task volume M, task execution cycle P, daily average task execution volume z (M / P), task execution difficulty C (requirements for ability, ranging from 0 to 1), execution interaction ability (related to the frequency and results of interaction with other objects, ranging from 0 to 1), execution proficiency D (related to the domain of tasks executed by the object, the more times the same domain is executed, the higher the proficiency), and stability I (the more overdue periods or accidents occur, the lower the stability, ranging from 0 to 1).

[0056] Step 202: Obtain the execution capability value of the execution parameters of each object to be recommended in the set of objects to be recommended for historical tasks.

[0057] In some embodiments, the set of objects to be recommended can be a set of objects selected by relevant personnel that are capable of performing the task to be assigned. For each object to be recommended, the execution capability value of the object to be recommended can be determined based on its execution parameters for historical tasks.

[0058] In the historical tasks executed by the object to be recommended, there may be multiple execution domains for the historical tasks. Therefore, the initial execution capability value of the object to be recommended can be calculated separately according to the different execution domains of the historical tasks. Then, the average of the initial execution capability values ​​calculated for different execution domains can be used as the execution capability value of the object to be recommended. Alternatively, the initial execution capability value of the object to be recommended can be stored separately according to different execution domains, and the initial execution capability value can be used as the execution capability value.

[0059] Step 203: Filter the set of objects to be recommended, and select the subset of objects whose execution capability values ​​are within the preset range of execution indicator values.

[0060] In some embodiments, after determining the execution capability value of each object to be recommended, the objects to be recommended can be filtered according to the execution capability value.

[0061] The object to be recommended can be any object that needs to be assigned a task, such as mechanical equipment or a robot.

[0062] For example, recommended objects can be filtered according to the execution domain of the task to be assigned.

[0063] In one optional embodiment, filtering the set of objects to be recommended that have execution capability values ​​within a preset range of execution metric values ​​includes:

[0064] Determine the target execution parameter in the execution parameters. The target execution parameter indicates the execution parameter that has the greatest impact when recommending the object to be recommended. Filter the objects to be recommended whose execution capability value of the target execution parameter is greater than a first preset range of the execution index value to obtain a first recommendation set. Filter the objects to be recommended in the first recommendation set whose execution capability values ​​of other execution parameters are less than a second preset range of the execution index value to obtain a subset of objects to be recommended. Other execution capability values ​​are execution capability values ​​other than the target execution capability value. The preset range includes the first preset range and the second preset range.

[0065] Specifically, the target execution parameters can be determined by the server from the execution indicator values ​​when it obtains the execution indicator values ​​of the task to be matched, or they can be set by relevant personnel according to actual needs.

[0066] After determining the target execution parameter, since this parameter has the greatest impact on the recommended objects, the objects are first filtered based on the execution capability value of this parameter. Objects with execution capability values ​​greater than the execution metric value are selected as the first recommendation set. Further, objects in the first recommendation set are filtered based on the fact that the execution capability values ​​of other execution parameters of the recommended objects are less than the execution metric value; these are then selected as a subset of the recommended objects.

[0067] There are several options for selecting the target execution parameters. For example, it could be the workload of the object to be recommended. The workload refers to the amount of tasks that the object to be recommended can complete. The larger the workload, the more tasks the object to be recommended can complete.

[0068] In an optional embodiment, when the target execution parameter is the workload; obtaining the execution capability value of each object to be recommended in the set of objects to be recommended for the execution parameters of historical tasks, including:

[0069] Obtain the average historical execution count of the daily task volume for each object in the recommended set; based on the average historical execution count of the daily task volume, the historical execution count of the recommended object, and the execution index value of the daily task volume, calculate the execution capacity value of the load for each object to be recommended.

[0070] Specifically, when the target execution parameter is the workload, it can be determined based on the execution volume of the task of the object to be recommended. In particular, the larger the execution volume of the task of the object to be recommended, the larger the amount of task to be completed by the object to be recommended, that is, the larger the workload.

[0071] For example, the sum of historical execution counts and daily task volume can be divided by the historical average execution count, and the resulting ratio can be used as the load of the object to be recommended. The load value is greater than 0. When the load value is greater than 1.5, it indicates that the load of the object to be recommended may be too heavy.

[0072] Based on the above embodiments, to avoid overloading the recommended objects, the recommended objects with the execution ability value of the target execution parameter greater than the execution index value can be filtered first. For example, if the execution index value of the load is 1, the recommended objects with the load greater than 1.5 can be filtered. Then, based on other execution parameters, such as execution difficulty, execution field, execution cycle, and execution interaction ability, filtering is performed respectively. Exemplarily, filter the recommended objects with the filtering ability c less than the execution difficulty C by 0.2 or more, that is, exclude c < C - 0.2; filter the recommended objects with the execution interaction ability f less than half of F, that is, exclude f < F / 2; filter the recommended objects with the execution d less than 75% of D, that is, exclude d < D * 0.75; filter the recommended objects with the development stability i less than the severity I, that is, i < I.

[0073] Step 204: Determine the target recommended object according to the execution ability value of the recommended object in the subset of recommended objects.

[0074] In an optional embodiment, determining the target recommended object according to the execution ability value of the recommended object in the subset of recommended objects includes:

[0075] Configure a proportional value for the target execution ability value of each recommended object for the target execution parameter, where the target execution parameter indicates the load of the recommended object; determine the recommendation coefficient according to the proportional value and other execution ability values; sort the recommendation coefficients in descending order to obtain a sorting result; determine the recommended objects ranked top in the preset number in the sorting result as the target recommended objects.

[0076] In some embodiments, when configuring the proportional value for the target execution ability value, different proportional values can be configured according to different intervals.

[0077] Exemplarily, when filtering the recommended objects, the recommended objects with a larger load can be filtered first to avoid overusing these recommended objects. For example, filter the recommended objects with a load greater than 1.5. Then, configure proportional values for the recommended objects with a load between 1.5 and 0 according to different intervals. For example, taking 0.3 as a step, divide the load into 5 intervals, configure the proportional value of the recommended objects between 1.5 - 1.2 to be 0.2, configure the proportional value of the recommended objects between 1.2 - 0.9 to be 0.4, and so on.

[0078] Alternatively, the recommended candidates can be sorted from lowest to highest workload, with the top 20% having the lowest workload assigned a percentage of 1, the 20%–40% having a percentage of 0.8, the 40%–60% having a percentage of 0.6, the 60%–80% having a percentage of 0.4, and the 80%–100% having a percentage of 0.2.

[0079] Furthermore, after determining the ratio, a recommendation coefficient can be determined based on this ratio and other performance capability values. The ratio and other performance capability values ​​are all positively correlated with the recommendation coefficient.

[0080] Specifically, the proportion value of each target to be recommended can be weighted to obtain a weighted value; the product of the weighted value and other performance capability values ​​can be calculated; and the product can be determined as the recommendation coefficient.

[0081] By weighting the proportions, the impact of the target execution parameters of the recommended object on the recommendation results can be amplified, thus making the recommendation results more focused on the target execution parameters.

[0082] There are several ways to weight the proportion value. For example, the proportion value can be exponentially multiplied to obtain the weighted value, where the exponent of the exponent is a positive integer greater than 1. For instance, the square of the proportion value can be used as the weighted value.

[0083] Furthermore, after obtaining the recommendation coefficients, the recommendation coefficients can be sorted in descending order to obtain the sorting results; then, the top preset number of objects to be recommended in the sorting results can be determined as the target objects to be recommended.

[0084] Alternatively, the obtained recommendation coefficients can be sent to a relevant screen for display, and staff can select based on the displayed recommendation coefficients to determine the target candidates for recommendation.

[0085] In one specific embodiment, this object recommendation method can be applied to recommending employees. Specifically, for newly assigned tasks, key roles within the department, such as department leaders, technical team leaders, and architects, review the task to determine multiple dimensions of information about the newly assigned task, including total task volume M, task duration P, average daily task volume z (M / P), difficulty C (requirements for skills, ranging from 0 to 1), required frequency of communication F (0 to 1), corresponding domain D (e.g., development, data mining, operations and maintenance, algorithms, etc.), and severity I (severity of penalties such as overdue penalties and accident penalties, ranging from 0 to 1).

[0086] For each employee, the system calculates the current average daily task volume a (the sum of the average daily task volumes of all current employees, i.e., M1 / P1 + M2 / P2 + … + Mi / Pi), and calculates the average average daily task volume A of the employees in the department (A = (a1 + a2 + a3 + …… + ai) / i); the working ability c is the difficulty of the most difficult project that the employee has been responsible for developing; the communication ability f (rated by the department leader for each employee, with a value ranging from 0 to 1); the proficiency d in the corresponding field (with a value ranging from 0 to 1. If there has been a task development in the corresponding field before, 0 times is 0.25, 1 time is 0.5, and for each additional time, the d value increases by 0.1); the development stability i (with a value ranging from 0 to 1, the initial score is 1, and for each overdue penalty or accident penalty related to the task within one year, the score is reduced by 0.1).

[0087] Input the new task into the system, calculate the comparison x between the average daily task volume of each employee if they take over the current task and the average task volume of the current department, x = (a + z) / A. Exclude all employees with x > 1.5 (the primary consideration is whether the workload is too heavy). For the remaining employees, sort them in ascending order of x to obtain the workload y after taking over the task. For the first 20% with the smallest x, take y = 1; for the 20% - 40% of the x ranking, take y = 0.8; for the 40% - 60% of the x ranking, take y = 0.6; for the 60% - 80% of the x ranking, take y = 0.4; for the 80% - 100% of the x ranking, take y = 0.2; exclude employees with the working ability c less than the difficulty C by 0.2 or more, that is, exclude c < C - 0.2; exclude employees with the communication ability f less than half of F, that is, exclude f < F / 2; exclude employees with the proficiency d less than 75% of D, that is, exclude d < D * 0.75; exclude employees with the development stability i less than the severity I, that is, i < I.

[0088] Calculate the suitability s of the remaining employees = 1 * y 2 * c * f * d * i. Sort the s of the selected employees from largest to smallest and integrate them into a list for the department leader to select.

[0089] Based on the same concept, in the embodiments of the present application, an object recommendation device is provided. The specific implementation of this device can be referred to the description in the method embodiment part, and the repeated parts will not be elaborated. As Figure 3 shown, this device mainly includes:

[0090] The first acquisition module 301 is used to acquire the execution parameters of the task to be assigned and the execution index values of the execution parameters;

[0091] The second acquisition module 302 is used to acquire the execution ability values of each recommended object in the recommended object set for the execution parameters of the historical tasks;

[0092] The screening module 303 is used to screen the sub - set of recommended objects in the recommended object set whose execution ability values are within the preset range of the execution index values;

[0093] The determination module 304 is used to determine the target object to be recommended based on the execution capability value of the objects to be recommended in the subset of objects to be recommended.

[0094] Based on the same concept, this application also provides an electronic device, such as... Figure 4 As shown, the electronic device mainly includes a processor 401, a memory 402, and a communication bus 403. The processor 401 and the memory 402 communicate with each other via the communication bus 403. The memory 402 stores programs that can be executed by the processor 401. The processor 401 executes the programs stored in the memory 402 to achieve the following steps:

[0095] Obtain the execution parameters and execution index values ​​of the tasks to be assigned;

[0096] Obtain the execution capability value of each object in the set of objects to be recommended for the execution parameters of historical tasks;

[0097] Filter the set of objects to be recommended, and select the subset of objects whose execution capability values ​​are within the preset range of execution indicator values.

[0098] The target objects to be recommended are determined based on the execution capability values ​​of the objects to be recommended in the subset of objects to be recommended.

[0099] The communication bus 403 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 403 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0100] The memory 402 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor 401.

[0101] The processor 401 mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., or a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0102] In another embodiment of this application, a computer-readable storage medium is provided, which stores a computer program that, when run on a computer, causes the computer to perform the object recommendation method described in the above embodiments.

[0103] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape, etc.), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0104] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0105] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. An object recommendation method, characterized in that, include: Obtain the execution parameters of the task to be assigned and the execution index values ​​of the execution parameters; Obtain the execution capability value of each object in the set of objects to be recommended for the execution parameters of historical tasks; Filter the set of objects to be recommended, and select the subset of objects whose execution capability values ​​are within the preset range of the execution indicator values. Based on the execution capability values ​​of the objects to be recommended in the subset of objects to be recommended, the target objects to be recommended are determined; The step of determining the target object to be recommended based on the execution capability value of the objects to be recommended in the subset of objects to be recommended includes: Based on the load of each recommended object in the subset of recommended objects, a ratio value is configured. The ratio value is set to zero for the proportion of recommended objects in the subset of recommended objects whose load is greater than a preset threshold, and the load of recommended objects with load between zero and the preset threshold is divided into different intervals. The larger the load, the smaller the ratio value. The recommendation coefficient is determined based on the ratio value and the execution capability value; The recommendation coefficients are sorted in descending order to obtain the ranking results; The top-ranked preset number of objects in the sorting results are identified as the target objects to be recommended.

2. The object recommendation method according to claim 1, characterized in that, The filtering of the set of objects to be recommended, specifically the subset of objects whose execution capability values ​​fall within the preset range of the execution metric values, includes: Determine the target execution parameter among the execution parameters, the target execution parameter indicating the execution parameter that has the greatest impact when recommending the object to be recommended; Filter out the objects to be recommended whose execution capability value of the target execution parameter is greater than a first preset range of the execution index value to obtain a first recommendation set; Filter out the objects to be recommended in the first recommendation set whose execution capability values ​​of other execution parameters are less than the second preset range of execution index values ​​to obtain the subset of objects to be recommended. The other execution capability values ​​are the execution capability values ​​other than the target execution capability value.

3. The object recommendation method according to claim 2, characterized in that, The recommendation coefficient is determined based on the aforementioned ratio value and other performance capability values, including: The proportion values ​​of each of the objects to be recommended are weighted to obtain a weighted value; Calculate the product of the weighted value and the other execution capability values; The product is determined to be the recommendation coefficient.

4. The object recommendation method according to claim 3, characterized in that, The step of weighting the proportion values ​​of each of the objects to be recommended to obtain a weighted value includes: The weighted value is obtained by exponentiation of the ratio value, wherein the exponent of the exponentiation operation is a positive integer greater than 1.

5. The object recommendation method according to claim 2, characterized in that, When the target execution parameter is the load amount; obtaining the execution capability value of each object to be recommended in the set of objects to be recommended for the execution parameters of historical tasks includes: Obtain the average number of historical executions of the daily task volume of the recommended objects in the set of recommended objects for the historical task; Based on the historical average number of executions of the daily task volume, the historical number of executions of the objects to be recommended, and the execution index value of the daily task volume, the execution capacity value of the load volume of each object to be recommended is calculated.

6. The object recommendation method according to claim 1, characterized in that, The execution parameters include at least one of the following: execution difficulty, execution domain, workload, execution cycle, and execution interaction.

7. An object recommendation device, characterized in that, include: The first acquisition module is used to acquire the execution parameters of the task to be assigned and the execution index values ​​of the execution parameters; The second acquisition module is used to acquire the execution capability value of the execution parameters of each object to be recommended in the set of objects to be recommended for historical tasks; The filtering module is used to filter the subset of objects to be recommended from the set of objects to be recommended, whose execution capability values ​​are within the preset range of the execution indicator values; The determination module is used to determine the target object to be recommended based on the execution capability value of the objects to be recommended in the subset of objects to be recommended; The step of determining the target object to be recommended based on the execution capability value of the objects to be recommended in the subset of objects to be recommended includes: Based on the load of each recommended object in the subset of recommended objects, a ratio value is configured. The ratio value is set to zero for the proportion of recommended objects in the subset of recommended objects whose load is greater than a preset threshold, and the load of recommended objects with load between zero and the preset threshold is divided into different intervals. The larger the load, the smaller the ratio value. The recommendation coefficient is determined based on the ratio value and the execution capability value; The recommendation coefficients are sorted in descending order to obtain the ranking results; The top-ranked preset number of objects in the sorting results are identified as the target objects to be recommended.

8. An electronic device, characterized in that, include: The processor, communication interface, memory, and communication bus are connected, with the processor, communication interface, and memory communicating with each other via the communication bus. The memory is used to store computer programs; The processor is configured to execute the program stored in the memory to implement the object recommendation method according to any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the object recommendation method according to any one of claims 1-6.

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