Resource allocation method and device, equipment, medium and product

Abstract

The invention discloses a resource allocation method and device, equipment, a medium and a product, relates to the technical field of big data, can be applied to the field of financial science and technology, and comprises the following steps: determining a to-be-executed job task corresponding to each candidate user according to a resource allocation request initiated by at least one candidate user; determining a task priority of each to-be-executed job task, determining a task execution efficiency value of each candidate user, and determining a resource allocation weight value corresponding to each candidate user according to the task priority and the task execution efficiency value; and allocating resources to each candidate user according to the resource allocation weight value, so that each candidate user executes the corresponding job task to be executed by utilizing the allocated resources. According to the method, on the premise of ensuring basic execution of the job task of each candidate user, resources are preferentially allocated to the user with the job task with the high task priority or the user with the high task execution efficiency, and the overall resource utilization efficiency of the big data platform is improved.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a resource allocation method, device, equipment, medium and product. Background Art

[0002] Big data platform is a network platform that provides services through content sharing, resource sharing, channel co-construction and data sharing. Big data platform can make full use of big data resources to support innovation and development. In the era of digital economy, big data has not only become a new key production factor, but also a strong engine to promote economic and social development.

[0003] Current big data platforms usually allocate resources to each user on a first-come, first-served basis. However, due to the limited total amount of resources, this approach will result in irrational resource allocation, such as inefficient users occupying too many resources, which in turn affects the overall resource utilization efficiency of the big data platform. Summary of the invention

[0004] The present invention provides a resource allocation method, device, equipment, medium and product to solve the problem that the current big data platform adopts a first-come, first-served method to allocate resources, resulting in low overall resource utilization efficiency of the big data platform.

[0005] According to one aspect of the present invention, a method for allocating resources is provided, the method comprising:

[0006] Determine, according to a resource allocation request initiated by at least one candidate user, a job task to be executed corresponding to each candidate user;

[0007] Determine the task priority of each of the to-be-executed tasks, and determine the task execution efficiency value of each of the candidate users, and determine the resource allocation weight value corresponding to each of the candidate users according to the task priority and the task execution efficiency value;

[0008] Resources are allocated to each of the candidate users according to the resource allocation weight value, so that each of the candidate users can use the allocated resources to execute the corresponding job task to be executed.

[0009] According to another aspect of the present invention, a resource allocation device is provided, the device comprising:

[0010] A to-be-executed job task determination module, configured to determine the to-be-executed job tasks corresponding to each candidate user according to a resource allocation request initiated by at least one candidate user;

[0011] A resource allocation weight value determination module is used to determine the task priority of each of the to-be-executed tasks, and to determine the task execution efficiency value of each of the candidate users, and to determine the resource allocation weight value corresponding to each of the candidate users according to the task priority and the task execution efficiency value;

[0012] The resource allocation module is used to allocate resources to each candidate user according to the resource allocation weight value, so that each candidate user can use the allocated resources to execute the corresponding job task to be executed.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the resource allocation method described in any one of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement any resource allocation method of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any one of the resource allocation methods of the present invention.

[0019] The present invention determines the to-be-executed job tasks corresponding to each candidate user according to a resource allocation request initiated by at least one candidate user; determines the task priority of each to-be-executed job task, and determines the task execution efficiency value of each candidate user, and determines the resource allocation weight value corresponding to each candidate user according to the task priority and the task execution efficiency value; allocates resources to each candidate user according to the resource allocation weight value, so that each candidate user can use the allocated resources to execute the corresponding to-be-executed job tasks, thereby preferentially allocating resources to users with high task priority job tasks or users with high task execution efficiency on the premise of ensuring the basic execution of the job tasks of each candidate user, thereby improving the overall resource utilization efficiency of the big data platform.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A flowchart of a resource allocation method provided in Embodiment 1 of the present invention;

[0023] Figure 2 A flowchart of a resource allocation method provided in Embodiment 2 of the present invention;

[0024] Figure 3 A schematic diagram of the structure of a resource allocation device provided in Embodiment 3 of the present invention;

[0025] Figure 4 It is a structural diagram of an electronic device for implementing the resource allocation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1This is a flow chart of a resource allocation method provided in the first embodiment of the present invention. This embodiment is applicable to the case where a big data platform allocates resources to each user according to the task priority and task execution efficiency value. The method can be executed by a resource allocation device, which can be implemented in the form of hardware and / or software, such as by a big data platform. Figure 1 As shown, the method includes:

[0030] S101. Determine, according to a resource allocation request initiated by at least one candidate user, a job task to be executed corresponding to each candidate user.

[0031] Among them, candidate users refer to users with resource allocation requirements. Each candidate user corresponds to at least one job task to be executed, and the job task to be executed needs to rely on the resources allocated by the big data platform to be executed. The task type of the job task to be executed can be a production task, a high-value experimental task, a daily analysis task, etc. This embodiment does not limit the specific task type of the job task to be executed.

[0032] In one implementation, the big data platform obtains a resource allocation request initiated by at least one candidate user through a user client within a target time period, parses the resource allocation request to determine at least one job task identification information carried in the resource allocation request, and determines the job task to be executed corresponding to each candidate user according to the job task identification information. The target time period can be set and adjusted according to demand, such as setting the target time period to "days", "hours" or "minutes", etc. This embodiment does not limit the specific meaning of the target time period.

[0033] For example, assuming that the resource allocation request initiated by any candidate user carries the job task identification information "000001" and "000002", the big data platform parses the job task identification information "000001" and "000002". Further, the big data platform uses the pre-established association between the job task identification information and the job task, and searches and matches according to the job task identification information "000001" and "000002". If the job task identification information "000001" is searched and matched to "job task A", and the job task identification information "000002" is searched and matched to "job task B", the big data platform will use "job task A" and "job task B" as the job tasks to be executed corresponding to the candidate user.

[0034] S102: Determine the task priority of each of the to-be-executed tasks, and determine the task execution efficiency value of each of the candidate users, and determine the resource allocation weight value corresponding to each of the candidate users according to the task priority and the task execution efficiency value.

[0035] Among them, the task priority reflects the task importance of the pending task. The higher the task priority of any pending task, the more important the pending task is and the more priority it should be executed. For example, if the task priority of pending task A is higher than the task priority of pending task B, it means that the task importance of pending task A is higher than that of pending task B, so pending task A should be executed before pending task B.

[0036] The task execution efficiency value is a quantitative score used to evaluate the efficiency of candidate user job task execution. The higher the task execution efficiency value of any candidate user, the more efficient the candidate user is in executing the job task. Correspondingly, the lower the task execution efficiency value of any candidate user, the less efficient the candidate user is in executing the job task.

[0037] The resource allocation weight value reflects the relative value of the resource usage rights that the candidate user obtains from the big data platform. The candidate user with a higher resource allocation weight value obtains more resource usage rights; correspondingly, the candidate user with a lower resource allocation weight value obtains less resource usage rights.

[0038] In one embodiment, the big data platform determines the current task type of each pending job task, and determines the task priority of each pending job task according to the current task type of each pending job task. In addition, the big data platform determines the task execution efficiency value of each candidate user according to the time required for each candidate user to execute historical job tasks. Furthermore, the big data platform performs weighted calculation based on the task execution efficiency value of each candidate user and the task priority of each pending job task of each candidate user, and determines the resource allocation weight value corresponding to each candidate user according to the weighted calculation result.

[0039] In another embodiment, the big data platform determines the current role information of each candidate user, and determines the task priority of each candidate user's corresponding job task to be executed based on the current role information of each candidate user. In addition, the big data platform determines the task execution efficiency value of each candidate user by weighted calculation based on the time required for each candidate user to execute historical job tasks and the completion rate of historical job tasks. Further, the big data platform performs weighted calculation based on the task execution efficiency value of each candidate user and the task priority of each job task to be executed by each candidate user, and determines the resource allocation weight value corresponding to each candidate user based on the weighted calculation result.

[0040] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data comply with relevant laws, regulations and standards in relevant regions.

[0041] S103: Allocate resources to each of the candidate users according to the resource allocation weight value, so that each of the candidate users can use the allocated resources to execute the corresponding job task to be executed.

[0042] In one implementation, the big data platform determines the amount of resources to be allocated corresponding to each candidate user according to the resource allocation weight value of each candidate user, and allocates resources to each candidate user according to the amount of resources to be allocated corresponding to each candidate user. Further, each candidate user uses the resources allocated by the big data platform to execute the corresponding job task to be executed.

[0043] For example, assuming that the resource allocation weight value of candidate user 1 is x, the resource allocation weight value of candidate user 2 is 0.5x, and the resource allocation weight value of candidate user 3 is 2x. assuming that the big data platform determines that the amount of resources to be allocated corresponding to candidate user 1 is A, the amount of resources to be allocated corresponding to candidate user 2 is 0.5A, and the resource allocation weight value of candidate user 3 is 2A, then the big data platform allocates resources with an amount of A to candidate user 1, the big data platform allocates resources with an amount of 0.5A to candidate user 2, and the big data platform allocates resources with an amount of 2A to candidate user 3.

[0044] The embodiment of the present invention determines the to-be-executed job tasks corresponding to each candidate user according to a resource allocation request initiated by at least one candidate user; determines the task priority of each to-be-executed job task, and determines the task execution efficiency value of each candidate user, and determines the resource allocation weight value corresponding to each candidate user according to the task priority and the task execution efficiency value; allocates resources to each candidate user according to the resource allocation weight value, so that each candidate user can use the allocated resources to execute the corresponding to-be-executed job tasks, and on the premise of ensuring the basic execution of the job tasks of each candidate user, preferentially allocates resources to users with high task priority job tasks, or users with high task execution efficiency, thereby improving the overall resource utilization efficiency of the big data platform.

[0045] Optionally, the method further includes:

[0046] Determine whether the current time has reached the weight value update time;

[0047] If so, redetermine the update task priority of each to-be-executed job task, redetermine the update task execution efficiency value of each candidate user, and update the resource allocation weight value corresponding to each candidate user according to the update task priority and the update task execution efficiency value.

[0048] The weight value update time can be set and adjusted according to demand. For example, if the weight value update time is set to "day", the resource allocation weight value corresponding to each candidate user is updated every day.

[0049] By determining whether the current moment has reached the weight value update time; if so, redetermining the update task priority of each job task to be executed, and redetermining the update task execution efficiency value of each candidate user, and updating the resource allocation weight value corresponding to each candidate user according to the update task priority and the update task execution efficiency value, the effect of periodic updating of the resource allocation weight value is achieved, the real-time and credibility of the resource allocation weight value is guaranteed, and the accuracy of resource allocation is further guaranteed.

[0050] Embodiment 2

[0051] Figure 2 This is a flow chart of a resource allocation method provided in Embodiment 2 of the present invention. This embodiment further optimizes and expands the above embodiment and can be combined with the above optional implementation modes. Figure 2 As shown, the method includes:

[0052] S201. Determine to-be-executed job tasks corresponding to each candidate user according to a resource allocation request initiated by at least one candidate user.

[0053] S202: Determine the current task type of each job task to be executed; and determine the task priority of each job task to be executed according to the current task type and the association between the task type and the task priority.

[0054] The current task type indicates the task type of the task to be executed, including but not limited to production tasks, high-value experimental tasks, daily analysis tasks, etc. Task priorities can be divided into high priority, medium priority, or low priority, and can also be divided into first priority, second priority, third priority, etc. This embodiment does not limit the form of expression of task priority.

[0055] In one implementation, the current task type of each to-be-executed job task is matched with the association relationship between the task type and the task priority, and the task priority of each to-be-executed job task is determined according to the matching result.

[0056] For example, assuming that the current task type of any job task to be executed is "production task", and assuming that in the association relationship between task type and task priority, the task priority associated with "production task" is "high priority", then the task priority of the job task to be executed is determined to be "high priority".

[0057] By determining the current task type of each task to be executed; and determining the task priority of each task to be executed according to the current task type and the relationship between the task type and the task priority, the beneficial effects are:

[0058] First, since the importance of pending tasks of different task types is different, the corresponding task priority is determined according to the different current task types, which ensures the rationality and accuracy of the task priority determination.

[0059] Secondly, since the association relationship between the task type and the task priority is preset, the task priority of each job task to be executed can be directly determined by utilizing the association relationship, thereby improving the efficiency of task priority determination.

[0060] S203: Determine at least one historical job task executed by each candidate user, and the task completion rate and task completion time corresponding to each historical job task.

[0061] Among them, historical tasks refer to the tasks that the candidate user has completed at a historical moment. The task completion rate reflects the degree of completion of the historical tasks. For example, if the historical tasks are completed by 80%, the task completion rate is 80%; if the historical tasks are completed by 90%, the task completion rate is 90%, etc. The task completion time refers to the time it takes to complete the historical tasks.

[0062] S204: Determine the task difficulty coefficient corresponding to each historical task according to the historical task type of each historical task, and determine the first execution efficiency score corresponding to each historical task according to the task difficulty coefficient and the task completion rate.

[0063] The task difficulty coefficient reflects the difficulty of executing the historical task. It can be understood that the greater the task difficulty coefficient, the greater the difficulty of executing the historical task; correspondingly, the smaller the task difficulty coefficient, the smaller the difficulty of executing the historical task. The historical task type refers to the task type corresponding to the historical task.

[0064] In one implementation, the big data platform determines the task difficulty coefficient associated with each historical task type according to the historical task type of each historical task and the correlation between the task type and the task difficulty coefficient, as the task difficulty coefficient corresponding to each historical task. Further, the first execution efficiency score corresponding to each historical task is determined according to the product result between the task difficulty coefficient corresponding to each historical task and the task completion rate.

[0065] S205: Determine the second execution efficiency score corresponding to each historical task according to the task completion time corresponding to each historical task.

[0066] In one implementation, the big data platform uses the task completion time corresponding to each historical job task as the second execution efficiency score corresponding to each historical job task.

[0067] S206. Determine the total execution efficiency score corresponding to each historical job task according to the weighted calculation result between the first execution efficiency score and the second execution efficiency score, and determine the task execution efficiency value of each candidate user according to the total execution efficiency score.

[0068] In one embodiment, a first weight value corresponding to a first execution efficiency score and a second weight value corresponding to a second execution efficiency score are determined. It can be understood that since a higher task completion rate indicates a higher task execution efficiency, the first weight value corresponding to the first execution efficiency score is a positive number; and since a longer task completion time indicates a lower task execution efficiency, the second weight value corresponding to the second execution efficiency score is a negative number.

[0069] A weighted sum is performed according to the first weight value, the second weight value, the first execution efficiency score, and the second execution efficiency score, and the total execution efficiency score corresponding to each historical job task is determined according to the weighted summation result. Further, the average value of the total execution efficiency score of each candidate user corresponding to each historical job task is determined as the task execution efficiency value of each candidate user.

[0070] For example, assuming that according to the weighted summation results, the total execution efficiency scores corresponding to historical job task 1 and historical job task 2 are 85 points and 75 points respectively, and assuming that historical job task 1 and historical job task 2 belong to candidate user A, then the task execution efficiency value of candidate user A is determined to be (85+75) / 2=80.

[0071] By determining at least one historical operation task performed by each candidate user, and the task completion rate and task completion time corresponding to each historical operation task; determining the task difficulty coefficient corresponding to each historical operation task according to the historical task type of each historical operation task, and determining the first execution efficiency score corresponding to each historical operation task according to the task difficulty coefficient and the task completion rate; determining the second execution efficiency score corresponding to each historical operation task according to the task completion time corresponding to each historical operation task; determining the total execution efficiency score corresponding to each historical operation task according to the weighted calculation result between the first execution efficiency score and the second execution efficiency score, and determining the task execution efficiency value of each candidate user according to the total execution efficiency score, the beneficial effects are:

[0072] First, since the first execution efficiency score is determined based on the task difficulty coefficient and the task completion rate, the calculation of the first execution efficiency score introduces the consideration of task difficulty, avoiding the problem of insufficient rationality and reliability caused by determining the first execution efficiency score only based on the task completion rate, thereby improving the rationality and reliability of the first execution efficiency score.

[0073] Second, since the task execution efficiency value is obtained by weighted calculation based on the first execution efficiency score and the second execution efficiency score, it is possible to jointly determine the task execution efficiency value based on multiple dimensions such as "task completion time", "task difficulty coefficient" and "task completion rate", thereby improving the accuracy and rationality of the determination of the task execution efficiency value.

[0074] S207. Determine a first weight score according to the task priority and the first weight coefficient, and determine a second weight score according to the task execution efficiency value and the second weight coefficient; determine the resource allocation weight value corresponding to each candidate user according to the sum of the first weight score and the second weight score.

[0075] Among them, the first weighting coefficient and the second weighting coefficient can be set and adjusted according to needs. For example, if the developer believes that the task priority has a greater impact on the resource allocation weight value, the first weighting coefficient corresponding to the task priority can be increased; for example, if the developer believes that the task execution efficiency value has a greater impact on the resource allocation weight value, the second weighting coefficient corresponding to the task execution efficiency value can be increased.

[0076] In one implementation, the big data platform determines the first weight score based on the product of the task priority and the first weight coefficient, and determines the second weight score based on the product of the task execution efficiency value and the second weight coefficient. Further, the resource allocation weight value corresponding to each candidate user is determined based on the sum of the first weight score and the second weight score.

[0077] By determining the first weight score according to the task priority and the first weight coefficient, and determining the second weight score according to the task execution efficiency value and the second weight coefficient; determining the resource allocation weight value corresponding to each candidate user according to the sum of the first weight score and the second weight score, the resource allocation weight value is jointly determined according to the two parameter dimensions of "task priority" and "task execution efficiency value", ensuring the rationality and accuracy of the determination of the resource allocation weight value, further ensuring the rationality of resource allocation, and improving the overall resource utilization efficiency of the big data platform.

[0078] S208, determining the amount of resources to be allocated corresponding to each candidate user according to the resource allocation weight value and the total amount of resources to be allocated; and allocating resources to the candidate resource pool corresponding to each candidate user according to the amount of resources to be allocated corresponding to each candidate user.

[0079] Among them, the big data platform allocates a corresponding candidate resource pool to each candidate user, and the big data platform allocates resources to each candidate resource pool so that the corresponding candidate user can use the resources in the candidate resource pool to perform job tasks.

[0080] Exemplarily, it is assumed that the candidate resource pool corresponding to candidate user 1 is candidate resource pool 1, the candidate resource pool corresponding to candidate user 2 is candidate resource pool 2, and the candidate resource pool corresponding to candidate user 3 is candidate resource pool 3.

[0081] Assume that the resource allocation weight value corresponding to candidate user 1 is 0.2, the resource allocation weight value corresponding to candidate user 2 is 0.5, and the resource allocation weight value corresponding to candidate user 3 is 0.3. The total amount of resources to be allocated is A, then the amount of resources to be allocated corresponding to candidate user 1 is 0.2A, the amount of resources to be allocated corresponding to candidate user 2 is 0.5A, and the amount of resources to be allocated corresponding to candidate user 3 is 0.3A.

[0082] Furthermore, the big data platform allocates 0.2A of resources to candidate resource pool 1, 0.5A of resources to candidate resource pool 2, and 0.3A of resources to candidate resource pool 3.

[0083] By determining the amount of resources to be allocated corresponding to each candidate user according to the resource allocation weight value and the total amount of resources to be allocated; and allocating resources to the candidate resource pool corresponding to each candidate user according to the amount of resources to be allocated corresponding to each candidate user, the beneficial effects are:

[0084] First, the amount of resources to be allocated is adapted to the resource allocation weight value of each candidate user, thereby achieving the effect of preferentially allocating resources to users with high task priority tasks or users with high task execution efficiency, thereby improving the overall resource utilization efficiency of the big data platform.

[0085] Second, since resources are allocated to the candidate resource pool corresponding to the candidate users, it is convenient for each candidate user to use the resources, thereby improving the user experience of the big data platform.

[0086] Optionally, determine the task priority of each job task to be executed, including:

[0087] Determine the current role information corresponding to each candidate user; and determine the task priority of the to-be-executed job task corresponding to each candidate user based on the current role information and the association between the role information and the task priority.

[0088] Among them, the current role information reflects the identity of the candidate user. The big data platform has pre-associated corresponding task priorities for different role information. That is, the task priority of the job tasks to be executed by the candidate user of any role information is the task priority pre-associated with the role information.

[0089] In one implementation, the current role information of each candidate user is matched with the association between the role information and the task priority, and the task priority of the to-be-executed job task corresponding to each candidate user is determined according to the matching result.

[0090] For example, assuming that the current role information of any candidate user is "Role A", and assuming that in the association relationship between role information and task priority, the task priority associated with "Role A" is "high priority", then the task priority of the pending job task corresponding to the candidate user is determined to be "high priority". For example, assuming that the candidate user corresponds to pending job task 1 and pending job task 2, then the task priority of pending job task 1 and pending job task 2 is determined to be "high priority".

[0091] By determining the current role information corresponding to each candidate user; determining the task priority of the to-be-executed job tasks corresponding to each candidate user based on the current role information and the association between the role information and the task priority, the effect of determining the task priority of the corresponding to-be-executed job tasks based on the role information of the candidate users is achieved. On the one hand, it ensures the adaptability of the task priority and the role information, and optimizes the rationality of the task priority determination; on the other hand, it makes it possible to determine the task priority based on both the "task type" and the "role information", thereby improving the diversity of the task priority determination methods.

[0092] Embodiment 3

[0093] Figure 3 This is a schematic diagram of a resource allocation device provided in Embodiment 3 of the present invention, which can be applied to a situation where a big data platform allocates resources to each user according to task priority and task execution efficiency value, such as Figure 3As shown, the device comprises:

[0094] The to-be-executed job task determination module 31 is used to determine the to-be-executed job task corresponding to each candidate user according to a resource allocation request initiated by at least one candidate user;

[0095] The resource allocation weight value determination module 32 is used to determine the task priority of each of the to-be-executed tasks, and the task execution efficiency value of each of the candidate users, and determine the resource allocation weight value corresponding to each of the candidate users according to the task priority and the task execution efficiency value;

[0096] The resource allocation module 33 is used to allocate resources to each candidate user according to the resource allocation weight value, so that each candidate user can use the allocated resources to execute the corresponding job task to be executed.

[0097] Optionally, the resource allocation weight value determination module 32 is specifically configured to:

[0098] Determine the current task type of each of the to-be-performed tasks;

[0099] The task priority of each of the to-be-executed job tasks is determined according to the current task type and the association between the task type and the task priority.

[0100] Optionally, the resource allocation weight value determination module 32 is further configured to:

[0101] Determine the current role information corresponding to each of the candidate users;

[0102] The task priority of the to-be-executed job task corresponding to each of the candidate users is determined according to the current role information and the association relationship between the role information and the task priority.

[0103] Optionally, the resource allocation weight value determination module 32 is further configured to:

[0104] Determine at least one historical job task performed by each of the candidate users, and a task completion rate and a task completion time respectively corresponding to each of the historical job tasks;

[0105] Determine, according to the historical task type of each of the historical task, the task difficulty coefficient corresponding to each of the historical task, and determine, according to the task difficulty coefficient and the task completion rate, the first execution efficiency score corresponding to each of the historical task;

[0106] Determine the second execution efficiency score corresponding to each of the historical operation tasks according to the task completion time corresponding to each of the historical operation tasks;

[0107] According to the weighted calculation result between the first execution efficiency score and the second execution efficiency score, the total execution efficiency score corresponding to each of the historical job tasks is determined, and the task execution efficiency value of each of the candidate users is determined according to the total execution efficiency score.

[0108] Optionally, the resource allocation weight value determination module 32 is further configured to:

[0109] Determine a first weight score according to the task priority and the first weight coefficient, and determine a second weight score according to the task execution efficiency value and the second weight coefficient;

[0110] The resource allocation weight value corresponding to each of the candidate users is determined according to the sum of the first weight score and the second weight score.

[0111] Optionally, the resource allocation module 33 is specifically configured to:

[0112] Determine the amount of resources to be allocated corresponding to each of the candidate users according to the resource allocation weight value and the total amount of resources to be allocated;

[0113] According to the amount of resources to be allocated corresponding to each candidate user, resources are allocated to the candidate resource pool corresponding to each candidate user.

[0114] The resource allocation device provided in the embodiment of the present invention can execute the resource allocation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0115] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0116] Embodiment 4

[0117] Figure 4 A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0118] like Figure 4As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0119] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0120] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as a resource allocation method.

[0121] In some embodiments, the resource allocation method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the resource allocation method described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to execute the resource allocation method in any other appropriate manner (e.g., by means of firmware).

[0122] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0123] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0124] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0125] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0126] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0127] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0128] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0129] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A resource allocation method, characterized in that: The method comprises: Determine, according to a resource allocation request initiated by at least one candidate user, a job task to be executed corresponding to each candidate user; Determine the task priority of each of the to-be-executed tasks, and determine the task execution efficiency value of each of the candidate users, and determine the resource allocation weight value corresponding to each of the candidate users according to the task priority and the task execution efficiency value; Resources are allocated to each of the candidate users according to the resource allocation weight value, so that each of the candidate users can use the allocated resources to execute the corresponding job task to be executed.

2. The method according to claim 1, characterized in that The step of determining the task priority of each of the to-be-performed tasks includes: Determine the current task type of each of the to-be-performed tasks; The task priority of each of the to-be-executed job tasks is determined according to the current task type and the association between the task type and the task priority.

3. The method according to claim 1, characterized in that The step of determining the task priority of each of the to-be-performed tasks includes: Determine the current role information corresponding to each of the candidate users; The task priority of the to-be-executed job task corresponding to each of the candidate users is determined according to the current role information and the association relationship between the role information and the task priority.

4. The method according to claim 1, characterized in that: The determining the task execution efficiency value of each candidate user includes: Determine at least one historical job task performed by each of the candidate users, and a task completion rate and a task completion time respectively corresponding to each of the historical job tasks; Determine, according to the historical task type of each of the historical task, the task difficulty coefficient corresponding to each of the historical task, and determine, according to the task difficulty coefficient and the task completion rate, the first execution efficiency score corresponding to each of the historical task; Determine the second execution efficiency score corresponding to each of the historical operation tasks according to the task completion time corresponding to each of the historical operation tasks; According to the weighted calculation result between the first execution efficiency score and the second execution efficiency score, the total execution efficiency score corresponding to each of the historical job tasks is determined, and the task execution efficiency value of each of the candidate users is determined according to the total execution efficiency score.

5. The method according to claim 1, characterized in that The determining, according to the task priority and the task execution efficiency value, a resource allocation weight value corresponding to each of the candidate users comprises: Determine a first weight score according to the task priority and the first weight coefficient, and determine a second weight score according to the task execution efficiency value and the second weight coefficient; The resource allocation weight value corresponding to each of the candidate users is determined according to the sum of the first weight score and the second weight score.

6. The method according to claim 1, characterized in that Allocating resources to each of the candidate users according to the resource allocation weight value includes: Determine the amount of resources to be allocated corresponding to each of the candidate users according to the resource allocation weight value and the total amount of resources to be allocated; According to the amount of resources to be allocated corresponding to each candidate user, resources are allocated to the candidate resource pool corresponding to each candidate user.

7. A resource allocation device, characterized in that: The device comprises: A to-be-executed job task determination module, configured to determine the to-be-executed job tasks corresponding to each candidate user according to a resource allocation request initiated by at least one candidate user; A resource allocation weight value determination module is used to determine the task priority of each of the to-be-executed tasks, and to determine the task execution efficiency value of each of the candidate users, and to determine the resource allocation weight value corresponding to each of the candidate users according to the task priority and the task execution efficiency value; The resource allocation module is used to allocate resources to each candidate user according to the resource allocation weight value, so that each candidate user can use the allocated resources to execute the corresponding job task to be executed.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the resource allocation method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the resource allocation method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the resource allocation method according to any one of claims 1 to 6.

Images