A method, apparatus, system, equipment, and storage medium for allocating cloud production resources.

Abstract

This invention discloses a method, apparatus, system, device, and storage medium for allocating cloud production resources, relating to the field of cloud computing technology. The method includes: acquiring task data of cloud production users in cloud production tasks; evaluating the user's job performance based on the task data to obtain a target user rating; determining the target resource allocation level for the cloud production user based on the target user rating and a preset mapping relationship between resource allocation levels and user job performance ratings, and then allocating resources accordingly. This method can rationally allocate cloud production resources, with a sound and balanced allocation mechanism, improving user experience and facilitating the promotion of cloud production technology.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of cloud computing technology, and in particular to a cloud production resource allocation method, apparatus, system, device and storage medium. Background Technology

[0002] With the continuous development of Internet+ technology and communication technology, cloud computing and cloud production technology have become a trend in technological development. Utilizing cloud production technology to crowdsource tasks that would otherwise require the involvement of industry professionals or collaborators to cloud production users can save on operating costs. However, existing technologies suffer from flawed and uneven resource allocation mechanisms for cloud production users, resulting in a poor user experience and hindering the widespread adoption of crowdsourcing cloud production technology for tasks. Summary of the Invention

[0003] This invention provides a cloud production resource allocation method, apparatus, system, equipment, and storage medium, which can reasonably allocate cloud production resources, with a sound and balanced allocation mechanism, which can improve user experience and facilitate the promotion of cloud production technology.

[0004] In a first aspect, embodiments of the present invention provide a method for allocating cloud production resources, the method comprising:

[0005] Obtain task data from cloud production users in cloud production tasks;

[0006] Based on the task data, the cloud production users are rated on the quality of their work to obtain the target user rating.

[0007] Based on the target user rating and the preset mapping relationship between resource allocation level and user job quality rating, the target resource allocation level of the cloud production user is determined, and resources are allocated.

[0008] Secondly, embodiments of the present invention also provide a cloud production system, which includes: a cloud production task data module, a cloud production task slicing module, and a cloud production business management module; wherein:

[0009] The cloud production task data module is used to record the task data of cloud production users in cloud production tasks.

[0010] The cloud production task slicing module is used to slice cloud production tasks and distribute task slices to cloud production users.

[0011] The cloud production business management module is used to obtain task data recorded in the cloud production task data module, perform user job quality rating on the cloud production user based on the task data, and obtain a target user rating; based on the target user rating and the preset mapping relationship between resource allocation level and user job quality rating, determine the target resource allocation level of the cloud production user, and perform resource allocation.

[0012] Thirdly, embodiments of the present invention also provide a cloud production resource allocation device, the device comprising:

[0013] The task data acquisition module is used to acquire task data of cloud production users in cloud production tasks;

[0014] The target user rating determination module is used to perform user job quality rating on the cloud production users based on the task data, and obtain the target user rating.

[0015] The resource allocation module is used to determine the target resource allocation level of the cloud production user based on the target user rating and the preset mapping relationship between the resource allocation level and the user job quality rating, and then allocate resources accordingly.

[0016] Fourthly, embodiments of the present invention also provide an electronic device, the device comprising:

[0017] One or more processors;

[0018] Storage device for storing one or more programs.

[0019] When the one or more programs are executed by the one or more processors, the one or more processors implement a cloud production resource allocation method as described in any embodiment of the present invention.

[0020] Fifthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for allocating cloud production resources as described in any embodiment of the present invention.

[0021] The technical solution of this invention obtains task data of cloud production users in cloud production tasks; evaluates the user's work quality based on the task data to obtain a target user rating; and determines the target resource allocation level of the cloud production user based on the target user rating and the preset mapping relationship between the resource allocation level and the user's work quality rating, and then allocates resources. This solves the resource allocation problem when crowdsourcing through cloud production technology, achieves reasonable allocation of cloud production resources, has a sound and balanced allocation mechanism, can improve user experience, and thus facilitates the promotion of cloud production technology. Attached Figure Description

[0022] Figure 1a This is a flowchart of a cloud production resource allocation method provided in Embodiment 1 of the present invention;

[0023] Figure 1b This is a resource allocation diagram provided in Embodiment 1 of the present invention;

[0024] Figure 2 This is a flowchart of a cloud production resource allocation method provided in Embodiment 2 of the present invention;

[0025] Figure 3 This is a flowchart of a cloud production resource allocation method provided in Embodiment 3 of the present invention;

[0026] Figure 4a This is a schematic diagram of the structure of a cloud production system provided in Embodiment 4 of the present invention;

[0027] Figure 4b This is a schematic diagram of the structure of another cloud production system provided in Embodiment 4 of the present invention;

[0028] Figure 4c This is a flowchart of a login logic processing method provided in Embodiment 4 of the present invention;

[0029] Figure 5 This is a schematic diagram of the structure of a cloud production resource allocation device provided in Embodiment 5 of the present invention;

[0030] Figure 6 This is a schematic diagram of the structure of an electronic device provided in Embodiment Six of the present invention. Detailed Implementation

[0031] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0032] Example 1

[0033] Figure 1a This is a flowchart of a cloud production resource allocation method provided in Embodiment 1 of the present invention. This embodiment is applicable to resource allocation to users when tasks to be processed are crowdsourced to users through cloud production technology, such as the allocation of resources for physical or virtual products. This method can be executed by a cloud production resource allocation device, which can be implemented through software and / or hardware. The device can be integrated into electronic devices, such as computers or mobile phones. Figure 1a As shown, the method specifically includes:

[0034] Step 110: Obtain task data of cloud production users in cloud production tasks.

[0035] In this context, "cloud" refers to a decentralized and flexible organizational model accessible via the internet, unrestricted by time or space. "Cloud production" refers to an innovative, socialized, decentralized, and flexible intensive production organization model based on internet cloud technology and a shared platform. This model involves distributing centralized processing tasks to qualified industry professionals and social groups through a network-based collaborative approach. Specifically, workers can access the internet anytime, anywhere via various methods and channels to obtain and process specific tasks from the "cloud production task pool," and automatically perform production assessments and service fee settlements based on the processing results. A cloud production task refers to a task within the "cloud production task pool," which can specifically be a fragment of a centralized processing task. A cloud production user is someone who executes a cloud production task.

[0036] In this embodiment of the invention, task data can be data generated when a cloud production user performs a cloud production task. For example, a cloud production task can be a virtual pet feeding task implemented as a Hypertext 5 (HTML5, H5) mini-game. The virtual pet feeding task can integrate the promotion, introduction, display, and service of business tasks into the game, attracting users to complete tasks such as business registration, business consumption, and business Q&A by accepting virtual pets in a fun and motivating way, thus enhancing user stickiness. Task data can be data generated during virtual pet feeding. Specifically, task data can include the user's completed task status, virtual pet information (such as growth value, level information, feeding status, and leaderboard), user personal information (such as mobile phone number and application account), and resource allocation information.

[0037] Specifically, in an optional embodiment of the present invention, the task data includes at least one of the following: the user level of the cloud production user, the error rate of the cloud production task, and the correctness of the cloud production task.

[0038] The user level of a cloud production user can be determined based on a combination of factors, including the user's completion of cloud production tasks and the duration of their login to the cloud production system. Specifically, the user level of a cloud production user can be represented by the level information of their virtual pet.

[0039] The error rate of a cloud production task can be defined as the percentage of errors made by users when executing the task. Specifically, the error rate of a cloud production task can be represented by the error rate over a given period, such as the error rate over the past 10 days.

[0040] For example, on day t, the error rate of a cloud production task, expressed by the user's error rate over the past 10 days, can be determined by the following formula: Err = 1 - [(m1 - m2) + (n1 - n2)] / [(m t1 -m t2 )+(n t1 -n t2 []; where Err represents the error rate of cloud production tasks on day t; m1 represents the cumulative number of correct actual tasks on day (t-1); m2 represents the cumulative number of correct actual tasks on day (t-11); n1 represents the cumulative number of correct question bank tasks on day (t-1); n2 represents the cumulative number of correct question bank tasks on day (t-11); m t1 m represents the total number of actual tasks completed on day (t-1); t2 n represents the total cumulative actual tasks completed on day (t-11); t1 n represents the total number of questions in the question bank accumulated over (t-1) days; t2 This represents the total number of question bank tasks accumulated over day (t-11). In this embodiment, a real task can be a task that actually involves business processing, such as business registration or business consumption. Question bank tasks can be question-and-answer type business processing tasks, such as business question-and-answer.

[0041] The number of correct executions of a cloud production task can be defined as the number of times a user executes a cloud production task correctly. Specifically, the number of correct executions of a cloud production task can be represented by the number of correct executions within a range. For example, the number of correct executions of a cloud production task can be represented by the number of correct executions over the past 10 days.

[0042] For example, on day t, the correctness of cloud production tasks can be represented by the correctness of the user's actual tasks over the past 10 days. The correctness of cloud production tasks can be determined by the following formula: Tot = m1 - m2; where Tot is the correctness of cloud production tasks on day t.

[0043] Step 120: Based on the task data, perform user job quality rating for cloud production users to obtain target user ratings.

[0044] The user task quality rating can be determined by comprehensively evaluating task data. Specifically, it can be determined whether the weighted sum of the values ​​in the task data meets the conditions for each quality rating, or whether each value in the task data meets the conditions for each quality rating individually. The weighted sum or the quality rating corresponding to the condition when each value meets the conditions is determined based on the evaluation results.

[0045] In an optional embodiment of the present invention, the user job quality rating of cloud production users is performed based on task data to obtain a target user rating, including: determining whether at least one of the user level of the cloud production user, the error rate of the cloud production task, and the correctness of the cloud production task meets the corresponding threshold value, and obtaining a judgment result; and performing user job quality rating of cloud production users based on the judgment result to obtain a target user rating.

[0046] For example, quality ratings may include Level I, Level II, Level III, Level IV, Level V, and Level VI. Each quality rating can impose conditions on at least one of the following: the user level of the cloud production user, the error rate of the cloud production task, and the number of correct cloud production tasks. For example, the conditions for Level I may be that the user level of the cloud production user is greater than or equal to 40, the error rate of the cloud production task is less than or equal to 20%, and the number of correct cloud production tasks is greater than or equal to 10,000. The conditions for Level II may be that the user level of the cloud production user is greater than or equal to 30, the error rate of the cloud production task is less than or equal to 30%, the number of correct cloud production tasks is greater than or equal to 8,000, and the conditions for Level I are not met. Other quality ratings can be specifically set according to similar rules, and this embodiment of the invention does not specifically limit them, and will not describe them in detail here.

[0047] In this embodiment of the invention, the quality rating matched when at least one of the following conditions is met—the user level of the cloud production user, the error rate of the cloud production task, and the correct quantity of the cloud production task—can be determined as the target user rating based on the judgment result. For example, if the user level of the cloud production user is level 45, the error rate of the cloud production task is 15%, and the correct quantity of the cloud production task is 10,000, it can be determined that the cloud production user meets the conditions of level I, and the target user rating of the cloud production user is level I.

[0048] Step 130: Based on the target user rating and the preset mapping relationship between the resource allocation level and the user job quality rating, determine the target resource allocation level for cloud production users and allocate resources accordingly.

[0049] The mapping relationship between resource allocation levels and user job quality ratings can be one-to-one and / or one-to-many. For example, a Level I cloud production user can allocate resources according to the rules corresponding to all resource allocation levels. Conversely, a Level VI cloud production user can allocate resources only according to the rules corresponding to resource allocation level F.

[0050] In an optional embodiment of the present invention, the target resource allocation level of the cloud production user is determined based on the target user rating and the preset mapping relationship between the resource allocation level and the user job quality rating, and resources are allocated accordingly. This includes: sorting the resource allocation levels in descending order of level; if the user job quality rating meets the level requirements of the target resource allocation level, then resources are allocated according to the target resource allocation level and resource allocation levels lower than the target resource allocation level.

[0051] For example, Figure 1b This is a resource allocation diagram provided in Embodiment 1 of the present invention. Figure 1b As shown, resource allocation levels can include Level A, Level B, Level C, Level D, Level E, and Level F, with each level decreasing in importance. When a cloud production user corresponds to multiple resource allocation levels, resources can be allocated first according to the highest level. Once the resources corresponding to the highest level have been allocated, resources will then be allocated according to the next lower level. For example, a Level I cloud production user can allocate resources according to the rules corresponding to all resource allocation levels. Specifically, for a Level I cloud production user, resources can first be allocated according to the resource allocation rules corresponding to Level A. If the resources corresponding to Level A have been allocated, resources can then be allocated according to the resource allocation rules corresponding to Level B. If the resources corresponding to Level B have been allocated, resources can then be allocated according to the resource allocation rules corresponding to Level C, and so on.

[0052] The technical solution of this embodiment obtains task data of cloud production users in cloud production tasks; evaluates the user's work quality based on the task data to obtain a target user rating; and determines the target resource allocation level of the cloud production user based on the target user rating and the preset mapping relationship between the resource allocation level and the user's work quality rating, and then allocates resources. This solves the resource allocation problem when crowdsourcing through cloud production technology, achieves reasonable allocation of cloud production resources, has a sound and balanced allocation mechanism, can improve user experience, and thus facilitates the promotion of cloud production technology.

[0053] Example 2

[0054] Figure 2 This is a flowchart of a cloud production resource allocation method provided in Embodiment 2 of the present invention. This embodiment is a further refinement of the above technical solution, and the technical solution in this embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the method includes:

[0055] Step 210: Obtain the data entered by the cloud production user for at least one task slice in the cloud production task; and verify each entered data according to the task slice and slice type of the cloud production task.

[0056] In this context, a task slice can be a subtask formed by splitting a cloud production task. Task slices can be related to specific business tasks. Optionally, in this embodiment of the invention, a task slice includes at least one of the following: preset selection-type data entry, numerical entry, date entry, string entry, professional domain Chinese character entry, professional domain number entry, uppercase amount entry, lowercase amount entry, and ID number entry.

[0057] The slice type can represent the type of task slice. Optionally, in this embodiment of the invention, the slice type includes: question bank type and business type. The business type refers to a user executing a cloud production task based on actual generated business, which can correspond to a real task. The question bank type refers to a user executing a cloud production task based on a question bank preset by the cloud generation system, which can correspond to a question bank task.

[0058] Verification of entered data can be performed to check whether the input data is correct or meets preset rules. Optionally, in this embodiment of the invention, verification of each entered data is performed according to the task slice and slice type of the cloud production task, including: if the slice type is a question bank type, the entered data is verified according to the preset value corresponding to the task slice of the cloud production task; if the slice type is a business type, the entered data is verified according to the verification code corresponding to the task slice of the cloud production task.

[0059] For question-based task slices, preset values ​​can be used to verify whether the entered data matches the preset values, thus determining whether the verification passes. For business-related task slices, which are typically based on actual business processes, the entered data may vary between users, and therefore preset values ​​are not available. For example, different cloud production users may enter different ID numbers when executing business-related task slices. Business-related task slices can be verified using CAPTCHAs. Specifically, matching CAPTCHAs can be set for different task slices, requiring users to enter the CAPTCHA along with their data. If the verification result is a correct CAPTCHA, the user's entered data is confirmed to be correct; if the verification result is an incorrect CAPTCHA, the user's entered data is confirmed to be incorrect. CAPTCHA settings can be specifically configured according to the data rules corresponding to different task slices. For example, the CAPTCHA can be set based on one or more of the following: data length, the number of numbers, letters, or Chinese characters in the data, or the meaning of the data itself. The CAPTCHA can occupy a preset data length. For example, the CAPTCHA length can be two digits, which can effectively verify the user's entered data and avoid the CAPTCHA being too long, thus affecting the user experience.

[0060] Step 220: Update the task data according to the preset task data update rules based on the verification results.

[0061] Optionally, task data may include at least one of the following: user level of cloud production user, error rate of cloud production task, and correctness of cloud production task.

[0062] Among these, updating task data can be done by verifying the correctness of user-entered data, and updating the user level of cloud production users, the error rate of cloud production tasks, and the correctness of cloud production tasks.

[0063] Step 230: Obtain task data of cloud production users in cloud production tasks.

[0064] Step 240: Based on the task data, perform user job quality rating for cloud production users to obtain target user ratings.

[0065] Step 250: Based on the target user rating and the preset mapping relationship between the resource allocation level and the user job quality rating, determine the target resource allocation level for cloud production users and allocate resources accordingly.

[0066] Based on the above implementation method, optionally, the cloud production resource allocation method further includes: obtaining the total number of verifications and / or the number of verification failures; if the number of verification failures is greater than a preset verification failure value, and / or the total number of verifications is greater than a preset verification value, then suspend the cloud production task of the cloud production user and resume it after a preset interval.

[0067] The total number of verifications can be the total number of times a user continuously executes a task slice and initiates verification. If the total number of verifications exceeds a preset verification value, it can be determined that the user has reached the upper limit for continuously executing task slices. The cloud production tasks of the cloud production user can be paused and resumed after a preset interval. The preset verification value can be a value set according to actual conditions, such as 200 times. The preset interval can also be a value set according to actual conditions, such as resuming task execution every 5 minutes. By controlling the execution of cloud production tasks based on the total number of verifications, the total number of times a user executes task slices within a specified time interval can be limited, preventing users from becoming addicted to tasks. The number of verification failures can be the total number of times a user executes a task slice and initiates verification with an incorrect result. If the number of verification failures exceeds a preset verification failure value, it can be determined that the effectiveness of the user's executed task slices is low. The cloud production tasks of the cloud production user can be paused and resumed after a preset interval. For example, if the number of verification failures is greater than 5, the number of verification failures can be reduced by one every 5 minutes. By controlling the execution of cloud production tasks by users based on the number of verification failures, the effectiveness of users executing task slices can be improved. The mechanism of recovery after a preset interval can increase users' enthusiasm for executing task slices.

[0068] Based on the above implementation method, optionally, the cloud production resource allocation method further includes: if the number of consecutive successful verifications reaches a preset consecutive success value, then one verification failure is offset.

[0069] The consecutive successful verification count can be the total number of times a user continuously executes a task slice and initiates verification, with consecutive correct verification results. The preset consecutive success value can be set based on actual conditions, such as 10 times. Offsetting one verification failure count involves subtracting one from the total number of failures, which can increase user motivation to execute task slices.

[0070] The technical solution of this embodiment obtains the input data entered by cloud production users for at least one task slice in cloud production tasks; verifies each input data according to the task slice and slice type of the cloud production task; updates the task data according to the verification results and preset task data update rules; obtains the task data of cloud production users in cloud production tasks; performs user job quality rating on cloud production users based on task data to obtain target user ratings; determines the target resource allocation level of cloud production users based on target user ratings and the preset mapping relationship between resource allocation levels and user job quality ratings, and allocates resources accordingly. This solves the resource allocation problem when crowdsourcing through cloud production technology, improves users' enthusiasm for performing cloud production tasks, and rationally allocates cloud production resources. The allocation mechanism is sound and balanced, which can improve user experience and facilitate the promotion of cloud production technology.

[0071] Example 3

[0072] Figure 3 This is a flowchart of a cloud production resource allocation method provided in Embodiment 3 of the present invention. This embodiment is a further refinement of the above technical solution, and the technical solution in this embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 3 As shown, the method includes:

[0073] Step 310: Obtain task data of cloud production users in cloud production tasks.

[0074] Step 320: Based on the task data, perform user job quality rating for cloud production users to obtain target user ratings.

[0075] The execution order of steps 320 and 330 can be interchanged. Specifically, step 320 can be executed when it is determined in step 340 that the number of successful attempts has reached the preset resource allocation threshold; if the number of successful attempts has not reached the preset resource allocation threshold, step 320 can be skipped.

[0076] Step 330: Obtain the number of times cloud production users have successfully allocated resources.

[0077] The success count can be the total number of times a cloud production user has successfully allocated resources. Specifically, the success count can be the total number of times resources have been successfully allocated under various resource allocation rules. Limiting the success count can prevent cloud production users from solely allocating resources according to the same rule, thus preventing other users from allocating resources according to the current rule. It can provide users with multiple resource allocation principles, thereby improving the overall balance of resource allocation for all users.

[0078] Step 340: If the number of successful attempts reaches the preset resource allocation threshold, then determine the target resource allocation level for the cloud production user based on the target user rating and the preset mapping relationship between the resource allocation level and the user job quality rating, and then allocate resources accordingly.

[0079] The resource allocation method based on the preset mapping relationship between resource allocation levels and user job quality ratings can be understood as a form of random resource allocation. Different resource allocation levels may correspond to different success rates, and / or different specific resources allocated. Specifically, higher levels generally have higher resource costs and / or higher success rates compared to lower levels. For example, level A may have a 20% success rate, while level B may have a 10% success rate. Similarly, level C may have higher resource costs than level D. When the number of successful allocations reaches a preset threshold, it indicates that the user has successfully allocated resources too many times, necessitating a reduction in the success rate or the cost of the allocated resources to improve the balance of resource allocation.

[0080] Step 350: If the number of successful allocations does not reach the preset resource allocation threshold, determine whether there are any precise resources to be allocated, and obtain the precise number of times the cloud production user has allocated resources according to the precise resource allocation rules.

[0081] When the number of successful resource allocations does not reach the preset resource allocation threshold, it can be understood that the number of successful resource allocations for a user is appropriate, and resource allocation rules with a higher success probability or higher allocation cost can continue to be used for resource allocation. Precise resource allocation rules can be understood as a method that guarantees successful resource allocation when preset conditions are met. Precise resources can have a certain quantity. If there are unallocated precise resources, resource allocation can continue according to the precise resource allocation rules; if there are no unallocated precise resources, resource allocation cannot be performed according to the precise resource allocation rules. Precise resources can be allocated on a first-come, first-served basis. Since resource allocation according to the precise resource allocation rules guarantees resource allocation, to prevent the same cloud production user from continuously using the precise resource allocation rules while other users cannot obtain resources, the number of precise allocations can be limited.

[0082] Step 360: If the number of accurate attempts has not reached the preset accuracy threshold, and there are accurate resources to be allocated, then obtain the location information of the cloud production user and allocate resources according to the accurate resource allocation rules corresponding to the location information.

[0083] The statement that "the number of accurate allocation attempts has not reached the preset accuracy threshold" can be understood as meaning that the number of times a user has allocated resources according to the accurate resource allocation rules is appropriate, and resource allocation can continue according to the accurate resource allocation rules. Specifically, the accurate resource allocation rules can be a set of rules for resource allocation based on regions, facilitating the organization of regional activities through the cloud production system. For example, if a user can allocate resources according to the accurate resource allocation rules, then when the location information determines that the user is in a designated region, the user can obtain resources according to the accurate resource allocation rules.

[0084] Step 370: If the number of accurate allocations reaches the preset accuracy threshold, and / or there are no accurate resources to be allocated, determine whether the cloud production user is in the whitelist, and obtain the number of times the cloud production user has allocated resources according to the whitelist resource allocation rules.

[0085] Among these, "reaching the preset precision threshold" can be understood as a user allocating resources too many times according to the precision resource allocation rules, requiring adjustments to the user's resource allocation plan. "No precision resources to be allocated" can be understood as resources corresponding to the precision resource allocation rules already being allocated, requiring a new resource allocation principle. "Users on the whitelist" can be understood as cloud production users being preset as superior users, and resources can be allocated according to the whitelist resource allocation rules.

[0086] Step 380: If the cloud production user is on the whitelist and the number of times the user has been on the whitelist has not reached the preset whitelist threshold, then resources will be allocated according to the whitelist resource allocation rules.

[0087] The statement that the number of times a user has been on the whitelist has not reached the preset whitelist threshold can be interpreted as the user having performed resource allocation according to the whitelist resource allocation rules a reasonable number of times, and that resource allocation can continue to be performed according to the whitelist resource allocation rules. The whitelist resource allocation rules can be understood as ensuring that users on the whitelist can obtain resources 100% of the time.

[0088] Step 390: If the cloud production user is not on the whitelist, or the number of times the user has been on the whitelist has reached the preset whitelist threshold, then resources will be allocated according to the compensation resource allocation rules.

[0089] In this context, "users not on the whitelist" can be understood as cloud production users not being pre-selected as "preferred" users and therefore ineligible for resource allocation according to the whitelist resource allocation rules. "Reaching the preset whitelist threshold" means the user has attempted resource allocation too many times according to the whitelist rules, necessitating adjustments to the allocation scheme. The compensation resource allocation rule can be understood as guaranteeing a successful resource allocation once after a preset number of allocations. For example, it could mean that for every X resource allocations made using the compensation resource allocation rule on a given day, one allocation is successful. Specifically, it could mean that for every X successful allocations on a given day, the count restarts from the (X+1)th allocation. The count restarts at midnight the following day.

[0090] The above resource allocation rules can be understood as follows: Priority is given to allocating resources according to the precise resource allocation rule. If there are no unallocated precise resources, and / or the number of precise allocations reaches a preset precision threshold, then resources are allocated according to the whitelist resource allocation rule. If the cloud production user is not on the whitelist, and / or the number of whitelist allocations reaches a preset whitelist threshold, then resources are allocated according to the compensation resource allocation rule. If the number of successful resource allocations according to the precise resource allocation rule, the whitelist resource allocation rule, and the compensation resource allocation rule reaches a preset resource allocation threshold, then resources are allocated based on the preset mapping relationship between resource allocation levels and user job quality ratings.

[0091] The aforementioned resource allocation rules can prevent users from successfully allocating resources multiple times in a short period, thus preventing other users from successfully allocating resources. This improves the user experience and makes the resource allocation mechanism more robust and fair. It should be noted that the above resource allocation may come at the cost of consuming the cloud production user's own resources. These resources can be the resource value obtained by the cloud production user through executing task slices. For example, the resource value can increase after a user successfully executes a task slice, and the specific increase rule can be related to the difficulty of the task slice. To improve the cloud production user experience, if the current task slice cannot be entered or the current task slice times out (e.g., exceeding 50 seconds), a refresh command can be obtained from the user, and the next task slice can be reassigned to the user. The verification result of the current task slice can be marked as invalid, and a refresh flag can be attached. To improve the quality of user execution of task slices, a maximum limit can be set for the number of consecutive refreshes, such as 3 times.

[0092] The technical solution of this embodiment involves: acquiring task data of cloud production users in cloud production tasks; evaluating the user's job quality based on the task data to obtain a target user rating; acquiring the number of times the cloud production user successfully allocated resources; if the number of successful allocations reaches a preset resource allocation threshold, determining the target resource allocation level for the cloud production user based on the target user rating and a preset mapping relationship between resource allocation levels and user job quality ratings, and then allocating resources; if the number of successful allocations does not reach the preset resource allocation threshold, determining whether there are any precise resources to be allocated, and acquiring the precise number of times the cloud production user has allocated resources according to the precise resource allocation rules; if the precise number of allocations does not reach the preset precision threshold, and there are precise resources to be allocated, acquiring the cloud production user's location information, and... Resource allocation is performed based on the precise resource allocation rules corresponding to location information. If the number of precise allocations reaches the preset precision threshold, and / or there are no precise resources to be allocated, it is determined whether the cloud production user is in the whitelist, and the number of times the cloud production user has allocated resources according to the whitelist resource allocation rules is obtained. If the cloud production user is in the whitelist and the number of whitelist allocations has not reached the preset whitelist threshold, resource allocation is performed according to the whitelist resource allocation rules. If the cloud production user is not in the whitelist, or the number of whitelist allocations has reached the preset whitelist threshold, resource allocation is performed according to the compensation resource allocation rules. This solves the resource allocation problem when crowdsourcing through cloud production technology, achieves reasonable allocation of cloud production resources, and has a sound and balanced allocation mechanism that can improve user experience, thereby facilitating the promotion of cloud production technology.

[0093] Example 4

[0094] Figure 4a This is a schematic diagram of the structure of a cloud production system provided in Embodiment 4 of the present invention. Figure 4a As shown, the cloud production system includes: a cloud production task data module 410, a cloud production task slicing module 420, and a cloud production business management module 430; wherein:

[0095] The cloud production task data module 410 is used to record task data of cloud production users in cloud production tasks. Specifically, the cloud production task data module 410 can provide application function entry points and front-end displays of business processing for the cloud production system.

[0096] Figure 4b This is a schematic diagram of the structure of another cloud production system provided in Embodiment 4 of the present invention. Figure 4bAs shown, for example, the cloud production task data module can have the following functions: configuration of information such as task slicing, resource allocation rules, user-virtual pet interaction dialogue, and activity rules; querying and updating information such as virtual pet nicknames, cloud production user user levels, cloud production task error rates, cloud production task correctness, and leaderboards; login logic processing for multiple login channels; modification and querying of user basic information or virtual pet basic information; querying resource allocation status; and querying interactive dialogue content by channel.

[0097] Figure 4c This is a flowchart of a login logic processing method provided in Embodiment 4 of the present invention. Figure 4c As shown, user identification information can be generated for different cloud production users. This user identification information can be mapped to the user's identity information (such as mobile phone number, ID number, or social application account). Cloud production users can choose from multiple channels to log in to the cloud production system. When a user logs in via a social application account, authorized login permissions can be obtained; when a user logs in via mobile phone number, the user's entered mobile phone number and password, or mobile phone number and verification code, can be used for verification. The user's login information can be passed to the backend, where it is mapped and returns some or all of the information from the cloud production task data module.

[0098] The cloud production task slicing module 420 is used to slice cloud production tasks and distribute task slices to cloud production users.

[0099] The cloud production business management module 430 is used to obtain task data recorded in the cloud production task data module, perform user job quality rating on cloud production users based on task data, and obtain target user rating; based on the target user rating and the preset mapping relationship between resource allocation level and user job quality rating, determine the target resource allocation level of cloud production users, and perform resource allocation.

[0100] like Figure 4b As shown, the cloud production business management module can maintain and query parameters related to task slicing, resource allocation, and interactive dialogue. It can also support report downloads and better display multi-dimensional information of the cloud production system.

[0101] The technical solution of this embodiment solves the resource allocation problem when crowdsourcing through cloud production technology by setting up a cloud production system with a cloud production task data module, a cloud production task slicing module, and a cloud production business management module. It enables the recording and querying of multi-dimensional information of the cloud production system, and the reasonable allocation of cloud production resources. The allocation mechanism is sound and balanced, which can improve the user experience and facilitate the promotion of cloud production technology.

[0102] Example 5

[0103] Figure 5 This is a schematic diagram of the structure of a cloud production resource allocation device provided in Embodiment 5 of the present invention. Combined with... Figure 5 The device includes: a task data acquisition module 510, a target user rating determination module 520, and a resource allocation module 530. Wherein:

[0104] The task data acquisition module 510 is used to acquire task data of cloud production users in cloud production tasks.

[0105] The target user rating determination module 520 is used to rate the user operation quality of cloud production users based on task data to obtain the target user rating.

[0106] The resource allocation module 530 is used to determine the target resource allocation level of cloud production users and allocate resources based on the target user rating and the preset mapping relationship between the resource allocation level and the user job quality rating.

[0107] Optionally, the task data may include at least one of the following: the user level of the cloud production user, the error rate of the cloud production task, and the correctness of the cloud production task.

[0108] Target user rating determination module 520 includes:

[0109] The task data judgment unit is used to determine whether at least one of the following—the user level of the cloud production user, the error rate of the cloud production task, and the correctness of the cloud production task—meets the corresponding threshold value, and obtains the judgment result.

[0110] The target user rating determination unit is used to rate the user operation quality of cloud production users based on the judgment results, and obtain the target user rating.

[0111] Optionally, the device may also include:

[0112] The data entry verification module is used to obtain the data entered by the cloud production user for at least one task slice in the cloud production task before obtaining the task data of the cloud production user in the cloud production task; and to verify each data entry according to the task slice and slice type of the cloud production task.

[0113] The task data update module is used to update task data according to the verification results and preset task data update rules.

[0114] Optional slice types include: question bank type and business type;

[0115] The data entry verification module includes:

[0116] The first verification unit is used to verify the entered data according to the preset value corresponding to the task slice of the cloud production task if the slice type is a question bank type.

[0117] The second verification unit is used to verify the entered data based on the verification code corresponding to the task slice of the cloud production task if the slice type is business-type.

[0118] Optional, resource allocation module 530 includes:

[0119] The ranking unit is used to sort resource allocation levels from highest to lowest.

[0120] The first resource allocation unit is used to allocate resources according to the target resource allocation level and resource allocation levels lower than the target resource allocation level if the user's job quality rating meets the level requirements of the target resource allocation level.

[0121] Optionally, the resource allocation module 530 also includes:

[0122] The success count acquisition unit is used to acquire the number of times that cloud production users have successfully allocated resources.

[0123] The second resource allocation unit is used to determine the target resource allocation level of the cloud production user and allocate resources based on the target user rating and the preset mapping relationship between the resource allocation level and the user's job quality rating if the number of successful attempts reaches the preset resource allocation threshold.

[0124] Optionally, the device may also include:

[0125] The precise number acquisition unit is used to determine whether there are any precise resources to be allocated if the number of successful allocations has not reached the preset resource allocation threshold, and to acquire the precise number of times that cloud production users have allocated resources according to the precise resource allocation rules.

[0126] The third resource allocation unit is used to obtain the location information of the cloud production user and allocate resources according to the precise resource allocation rules corresponding to the location information if the number of precise attempts has not reached the preset precision threshold and there are precise resources to be allocated.

[0127] Optionally, the device may also include:

[0128] The whitelist count acquisition unit is used to determine whether a cloud production user is in the whitelist if the number of accurate counts reaches a preset accuracy threshold and / or there are no accurate resources to be allocated, and to acquire the number of times the cloud production user has allocated resources according to the whitelist resource allocation rules.

[0129] The fourth resource allocation unit is used to allocate resources according to the whitelist resource allocation rules if the cloud production user is in the whitelist and the number of times the whitelist is not reached.

[0130] The fifth resource allocation unit is used to allocate resources according to the compensation resource allocation rules if the cloud production user is not in the whitelist or the number of times the whitelist is reached.

[0131] Optionally, the device may also include:

[0132] The verification count acquisition module is used to obtain the total number of verifications and / or the number of verification failures;

[0133] The cloud production task pause / resume module is used to pause the cloud production user's cloud production task if the number of verification failures exceeds a preset verification failure value, and / or the total number of verifications exceeds a preset verification value, and then resume the task after a preset interval.

[0134] Optionally, the device may also include:

[0135] The count cancellation module is used to cancel one verification failure if the number of consecutive successful verifications reaches a preset consecutive success value.

[0136] Optionally, the task slices include at least one of the following: preset selection data entry, numerical entry, date entry, string entry, professional field Chinese character entry, professional field number entry, uppercase amount entry, lowercase amount entry, and ID number entry.

[0137] The cloud production resource allocation device provided in this embodiment of the invention can execute the cloud production resource allocation method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0138] Example 6

[0139] Figure 6 This is a schematic diagram of the structure of an electronic device provided in Embodiment Six of the present invention, as shown below. Figure 6 As shown, the device includes:

[0140] One or more processors 610, Figure 6 Take the 610 processor as an example;

[0141] Memory 620;

[0142] The device may further include an input device 630 and an output device 640.

[0143] The processor 610, memory 620, input device 630, and output device 640 in the device can be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0144] Memory 620, as a non-transitory computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to a cloud production resource allocation method in this embodiment of the invention (e.g., attached...). Figure 5 The task data acquisition module 510, target user rating determination module 520, and resource allocation module 530 are shown. The processor 610 executes various functional applications and data processing of the computer device by running software programs, instructions, and modules stored in the memory 620, thereby implementing a cloud production resource allocation method according to the above method embodiment.

[0145] Obtain task data from cloud production users in cloud production tasks;

[0146] Based on the task data, the cloud production users are rated on the quality of their work to obtain the target user rating.

[0147] Based on the target user rating and the preset mapping relationship between resource allocation level and user job quality rating, the target resource allocation level of the cloud production user is determined, and resources are allocated.

[0148] The memory 620 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 620 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 620 may optionally include memory remotely located relative to the processor 610, and these remote memories can be connected to the terminal device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0149] Input device 630 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the computer device. Output device 640 may include display devices such as a display screen.

[0150] Example 7

[0151] Embodiment 7 of the present invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a cloud production resource allocation method as provided in this embodiment of the present invention:

[0152] Obtain task data from cloud production users in cloud production tasks;

[0153] Based on the task data, the cloud production users are rated on the quality of their work to obtain the target user rating.

[0154] Based on the target user rating and the preset mapping relationship between resource allocation level and user job quality rating, the target resource allocation level of the cloud production user is determined, and resources are allocated.

[0155] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0156] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0157] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0158] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0159] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for allocating cloud production resources, characterized in that, The method comprises: obtaining input data input by a cloud production user in a cloud production task for at least one task slice; and verifying each of the input data according to a task slice and a slice type of the cloud production task; updating the task data according to a preset task data updating rule according to a verification result; wherein the slice type comprises a question bank type and a business type; verifying each of the input data according to the task slice and the slice type of the cloud production task comprises: if the slice type is the question bank type, verifying the input data according to a preset value corresponding to the task slice of the cloud production task; and if the slice type is the business type, verifying the input data according to a verification code corresponding to the task slice of the cloud production task; obtaining task data of the cloud production user in the cloud production task; performing user job quality rating on the cloud production user according to the task data to obtain a target user rating; determining a target resource allocation level of the cloud production user according to the target user rating and a preset mapping relationship between resource allocation levels and user job quality ratings, and performing resource allocation; The method further comprises: obtaining a total number of verifications and / or a number of failed verifications; if the number of failed verifications is greater than a preset failed verification value and / or the total number of verifications is greater than a preset verification value, suspending the cloud production task of the cloud production user and resuming after a preset time interval.

2. The method of claim 1, wherein, The task data comprises at least one of a user level of the cloud production user, an error rate of the cloud production task, and a correct amount of the cloud production task; performing user job quality rating on the cloud production user according to the task data to obtain a target user rating comprises: determining whether at least one of the user level of the cloud production user, the error rate of the cloud production task, and the correct amount of the cloud production task meets a corresponding threshold value to obtain a determination result; performing user job quality rating on the cloud production user according to the determination result to obtain a target user rating.

3. The method of claim 1, wherein, determining a target resource allocation level of the cloud production user according to the target user rating and a preset mapping relationship between resource allocation levels and user job quality ratings, and performing resource allocation, comprises: sorting the resource allocation levels in order from a high level to a low level; if the user job quality rating meets the level requirement of the target resource allocation level, performing resource allocation according to the target resource allocation level and resource allocation levels lower than the target resource allocation level.

4. The method of claim 1, wherein, determining a target resource allocation level of the cloud production user according to the target user rating and a preset mapping relationship between resource allocation levels and user job quality ratings, and performing resource allocation, further comprises: obtaining a number of successful resource allocations of the cloud production user; if the number of successful resource allocations reaches a preset resource allocation threshold, determining a target resource allocation level of the cloud production user according to the target user rating and a preset mapping relationship between resource allocation levels and user job quality ratings, and performing resource allocation.

5. The method of claim 4, wherein, Further comprising: If the success number does not reach a preset resource allocation threshold, it is determined whether there is a precise resource to be allocated, and the precise number of times of resource allocation by the cloud production user according to a precise resource allocation rule is obtained; If the precise number does not reach a preset precise threshold, and there is a precise resource to be allocated, the location information of the cloud production user is obtained, and resource allocation is performed according to the precise resource allocation rule corresponding to the location information.

6. The method of claim 5, wherein, Further comprising: If the precise number reaches a preset precise threshold, and / or there is no precise resource to be allocated, it is determined whether the cloud production user is in a white list, and the white list number of times of resource allocation by the cloud production user according to a white list resource allocation rule is obtained; If the cloud production user is in the white list, and the white list number of times does not reach a preset white list threshold, resource allocation is performed according to the white list resource allocation rule; If the cloud production user is not in the white list, or the white list number of times reaches a preset white list threshold, resource allocation is performed according to a compensation resource allocation rule.

7. The method of claim 1, wherein, Further comprising: If it is detected that the number of consecutive verifications succeeds reaches a preset consecutive success value, the number of times of verification failure is offset once.

8. The method of claim 1, wherein, The task slice includes at least one of the following: preset selected data entry, numerical value entry, date entry, character string entry, professional field Chinese character entry, professional field digital entry, upper case amount entry, lower case amount entry, and certificate number entry.

9. A cloud production system, characterized by, Including: A cloud production task data module, a cloud production task slice module, and a cloud production business management module; wherein: A cloud production system is configured to obtain entry data entered by a cloud production user for at least one task slice in a cloud production task, verify each of the entry data according to a task slice and a slice type of the cloud production task, and update task data according to a preset task data update rule based on a verification result; The slice type includes a question bank type and a business type. The cloud production system is configured to verify the entry data according to a preset value corresponding to the task slice of the cloud production task if the slice type is the question bank type, or verify the entry data according to a verification code corresponding to the task slice of the cloud production task if the slice type is the business type. The cloud production task data module is configured to record task data of the cloud production user in the cloud production task. The cloud production task slice module is configured to slice the cloud production task and distribute task slices to the cloud production user. The cloud production business management module is configured to obtain the task data recorded in the cloud production task data module, rate a user's work quality of the cloud production user based on the task data to obtain a target user rating, determine a target resource allocation level of the cloud production user based on a mapping relationship between a preset resource allocation level and the user's work quality rating, and perform resource allocation. The cloud production system is further configured to: acquire a total number of verifications and / or a number of failed verifications; if the number of failed verifications is greater than a preset number of failed verifications and / or the total number of verifications is greater than a preset total number of verifications, suspend a cloud production task of the cloud production user, and resume the cloud production task after a preset time interval.

10. An apparatus for allocating cloud production resources, characterized by The method comprises: acquiring input data entered by the cloud production user in the cloud production task for at least one task slice; verifying the input data according to the task slice and the slice type of the cloud production task; updating the task data according to the verification result and a preset task data updating rule; wherein the slice type comprises a question bank type and a business type; the input data verification module comprises a first verification unit configured to verify the input data according to a preset value corresponding to the task slice of the cloud production task if the slice type is the question bank type, and a second verification unit configured to verify the input data according to a verification code corresponding to the task slice of the cloud production task if the slice type is the business type; acquiring task data of the cloud production user in the cloud production task; determining a target user rating of the cloud production user according to the task data; determining a target resource allocation level of the cloud production user according to the target user rating and a preset mapping relationship between resource allocation levels and user job quality ratings, and allocating resources; The device further comprises: a number acquisition module configured to acquire a total number of verifications and / or a number of failed verifications; and a cloud production task suspension / resume module configured to suspend a cloud production task of the cloud production user if the number of failed verifications is greater than a preset number of failed verifications and / or the total number of verifications is greater than a preset total number of verifications, and resume the cloud production task after a preset time interval.

11. An electronic device, comprising: The device comprises: one or more processors; a storage device configured to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-8.

12. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1-8.

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