Task processing method and device, computer equipment and storage medium

Abstract

The invention relates to a task processing method and device, computer equipment and a storage medium. The method comprises the steps of receiving a to-be-processed task, wherein the to-be-processed task comprises to-be-processed task data; obtaining a prediction data list, wherein the prediction data list comprises prediction data and a prediction data identifier corresponding to the prediction data; matching the prediction data list with the to-be-processed task to obtain to-be-processed task data matched with target prediction data; replacing to-be-processed task data matched with the target prediction data with a prediction data identifier corresponding to the target prediction data to obtain an updated to-be-processed task; and sending the updated to-be-processed task to an edge host to instruct the edge host to process. By adopting the method, the task processing efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of big data data processing, and in particular to a task processing method, apparatus, computer equipment, and storage medium. Background Art

[0002] With the continuous development of the Internet, the business scenarios using content delivery networks (CDNs) are becoming increasingly diverse, and the number of resource locators that the content management platform needs to send to edge machines for processing has reached hundreds of millions.

[0003] In related technologies, the content management platform directly sends the resource locators that need to be processed to the edge machine. When the number of tasks is large, the processing of the resource locators involves a large amount of data transmission, occupies a large amount of bandwidth, and increases resource consumption. Summary of the invention

[0004] Based on this, it is necessary to provide a task processing method, device, computer equipment, and storage medium to address the above technical problems.

[0005] In a first aspect, the present application provides a task processing method. The method comprises:

[0006] Receiving a task to be processed, wherein the task to be processed includes task data to be processed;

[0007] Acquire a prediction data list, wherein the prediction data list includes prediction data and prediction data identifiers corresponding to the prediction data;

[0008] Matching the predicted data list with the pending tasks to obtain pending task data that matches the target predicted data;

[0009] Replacing the to-be-processed task data that matches the target prediction data with the prediction data identifier corresponding to the target prediction data to obtain an updated to-be-processed task;

[0010] The updated to-be-processed tasks are sent to the edge host to instruct the edge host to process them.

[0011] In one embodiment, sending the updated pending tasks to the edge host includes:

[0012] The updated pending tasks are sent to the agent of the edge host, and the agent of the edge host restores the prediction data identifier in the updated pending tasks to the target prediction data in the prediction data list, and sends the target prediction data to the edge gateway for processing.

[0013] In one embodiment, the process of constructing the prediction data list includes:

[0014] Acquire historical task data, wherein the historical task data includes historical data, historical keywords, and historical data access frequency;

[0015] Obtaining forecast data based on a preset forecast model and the historical task data;

[0016] A prediction data list is generated based on the prediction data and a cache path or identifier corresponding to the prediction data.

[0017] In one of the embodiments, the updated to-be-processed task includes a task identifier, and the task identifier is used to determine whether the to-be-processed task data in the to-be-processed task matches the predicted data in the predicted data list.

[0018] In one of the embodiments, when the updated to-be-processed tasks include to-be-processed task data that does not match the predicted data, the to-be-processed task data that does not match the predicted data is sent to an edge gateway for processing.

[0019] In one of the embodiments, the prediction data list is constructed by periodically generated and / or real-time generated prediction data.

[0020] In a second aspect, the present application further provides a task processing device, the device comprising:

[0021] A receiving module, used for receiving a task to be processed, wherein the task to be processed includes task data to be processed;

[0022] An acquisition module, used for acquiring a prediction data list, wherein the prediction data list includes prediction data and prediction data identifiers corresponding to the prediction data;

[0023] A matching module, used for matching the predicted data list with the tasks to be processed to obtain the task data to be processed that matches the predicted data;

[0024] An updating module, used for replacing the to-be-processed task data matching the predicted data with the predicted data identifier corresponding to the predicted data, to obtain an updated to-be-processed task;

[0025] The processing module is used to send the updated to-be-processed tasks to the edge host for processing.

[0026] In one embodiment, sending the updated pending tasks to the edge host includes:

[0027] The updated pending tasks are sent to the agent of the edge host, and the agent of the edge host restores the prediction data identifier in the updated pending tasks to the target prediction data in the prediction data list, and sends the target prediction data to the edge gateway for processing.

[0028] In one embodiment, the process of constructing the prediction data list includes:

[0029] Acquire historical task data, wherein the historical task data includes historical data, historical keywords, and historical data access frequency;

[0030] Obtaining forecast data based on a preset forecast model and the historical task data;

[0031] A prediction data list is generated based on the prediction data and a cache path or identifier corresponding to the prediction data.

[0032] In one of the embodiments, the updated to-be-processed task includes a task identifier, and the task identifier is used to determine whether the to-be-processed task data in the to-be-processed task matches the predicted data in the predicted data list.

[0033] In one of the embodiments, when the updated to-be-processed tasks include to-be-processed task data that does not match the predicted data, the to-be-processed task data that does not match the predicted data is sent to an edge gateway for processing.

[0034] In one of the embodiments, the prediction data list is constructed by periodically generated and / or real-time generated prediction data.

[0035] In a third aspect, the present disclosure further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the task processing method when executing the computer program.

[0036] In a fourth aspect, the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the task processing method when executed by a processor.

[0037] In a fifth aspect, the present disclosure further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the task processing method are implemented.

[0038] The above task processing method has at least the following beneficial effects:

[0039] The embodiment scheme provided by the present disclosure uses a big data platform as a data center to generate a prediction data list. After analyzing the data, more model analysis can be performed on the user's behavior and expectations in the future, which helps to continuously optimize the prediction algorithm and make predictions and take precautions against data peaks based on these data. When the prediction data list matches the task to be processed, the prediction data identifier is used to replace the repeated keywords of a longer string, occupying less processor resources and improving the task processing efficiency.

[0040] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the conventional technology, the drawings required for use in the embodiments or the conventional technology descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 An application environment diagram of a task processing method in an embodiment;

[0043] Figure 2 is a flowchart of a task processing method in one embodiment;

[0044] Figure 3 A schematic diagram of a big data center pushing a prediction data list in an embodiment;

[0045] Figure 4 A schematic diagram of a process for using a prediction data list replacement in one embodiment;

[0046] Figure 5 A schematic diagram of task processing in one embodiment;

[0047] Figure 6 A schematic diagram of constructing a prediction data list in one embodiment;

[0048] Figure 7 is a schematic diagram of task processing in one embodiment;

[0049] Figure 8 is a structural block diagram of a task processing device in one embodiment;

[0050] Fig. 9 is an internal structure diagram of a computer device in one embodiment;

[0051] Fig.10 The figure is a diagram of the internal structure of a server in one embodiment. DETAILED DESCRIPTION

[0052] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0053] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure 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 disclosure described here can be implemented in an order other than those illustrated or described here. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are only examples of devices and methods consistent with some aspects of the present disclosure as detailed in the attached claims. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, product or device including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements inherent to such process, method, product or device. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. For example, if the words first, second, etc. are used to represent the name, they do not represent any specific order.

[0054] The present disclosure provides a task processing method, which can be applied to Figure 1 In the application environment shown. Among them, the components of the content distribution network may include a content management and control platform 100, a task distribution center 102, an edge host agent 104, an edge gateway 106, a big data center 108, and a statistical analysis center 110. The content management and control platform 100 can send the pending tasks to the big data center 108, and the statistical analysis center 110 regularly judges and predicts the data of the big data center 108, obtains a prediction data list, and sends it to the content management and control platform 100, so that the content management and control platform 100 replaces the pending task data in the pending tasks with the prediction data in the prediction data list, obtains the updated pending tasks, and sends them to the task distribution center 102, and the task distribution center 102 pushes the updated pending tasks to the edge host agent 104, restores the data in the updated pending tasks, and sends them to the edge gateway 106 for processing.

[0055] In some embodiments of the present disclosure, Figure 2 As shown, a task processing method is provided, which is applied to Figure 1The content control platform in the example of processing the pending task is used to illustrate. In a specific embodiment, the method may include the following steps:

[0056] S202: Receive a task to be processed, where the task to be processed includes task data to be processed.

[0057] The content management and control platform receives pending tasks, which may include content update, cache refresh, content preheating, etc. The pending task data may include task type, task identifier, resource locator (URL, Uniform ResourceLocator) list, cache strategy, execution time, etc.

[0058] S204: Acquire a prediction data list, wherein the prediction data list includes prediction data and prediction data identifiers corresponding to the prediction data.

[0059] The big data center queries the most recent analysis results through data analysis and machine learning models to generate prediction data. The prediction data may include user behavior predictions, content access predictions, network traffic predictions, etc., and generates a unique identifier for each prediction data. The prediction data and the prediction data identifier corresponding to the prediction data constitute the prediction data list. Figure 3 This is a schematic diagram of a big data center pushing a prediction data list in an embodiment. The big data center pushes the prediction data list to the content management platform and the edge host agent. The edge host agent receives the prediction data list and performs corresponding processing according to the prediction data, such as content preheating, cache strategy adjustment, etc. The big data platform is used as a data center. After analyzing the data, more model analysis can be done for the user's behavior and expectations in the future, which is helpful for continuous optimization of the prediction algorithm, and the data peak period can be predicted and prevented based on these data, thereby improving the task processing efficiency.

[0060] S206: Match the predicted data list with the tasks to be processed to obtain task data to be processed that matches the target predicted data.

[0061] The prediction data list includes the prediction data and the prediction data identifier corresponding to the prediction data. The pending tasks include the pending task data. A matching algorithm is designed to match the prediction data with the pending data to find the pending task data that matches the target prediction data.

[0062] S208: Replace the to-be-processed task data that matches the target prediction data with the prediction data identifier corresponding to the target prediction data to obtain an updated to-be-processed task.

[0063] S210: Send the updated to-be-processed task to the edge host to instruct the edge host to process it.

[0064] The pending task data matching the target prediction data is replaced with the prediction data identifier corresponding to the target prediction data to obtain an updated pending task, which includes the prediction data identifier corresponding to the target prediction data and the unreplaced pending task data. Figure 4 This is a flow chart of using a prediction data list replacement in one embodiment. After receiving a refresh URL task, it is determined whether there is matching prediction data in the current time period. If there is a match, the URL is quickly replaced using the prediction data and sent to the edge host agent. If there is no match, the original data is sent to the edge host agent.

[0065] In the above task processing method, the big data platform is used as the data center to generate a list of predicted data. After the data is analyzed, more model analysis can be done for the user's behavior and expectations in the future, which is helpful for continuous optimization of the prediction algorithm, and the data peak period can be predicted and prevented based on these data. When the predicted data list matches the task to be processed, the predicted data identifier is used to replace the repeated keywords of the longer string, which occupies less processor resources and improves the efficiency of task processing.

[0066] In some embodiments of the present disclosure, sending the updated to-be-processed task to the edge host includes:

[0067] The updated pending tasks are sent to the agent of the edge host, and the agent of the edge host restores the prediction data identifier in the updated pending tasks to the target prediction data in the prediction data list, and sends the target prediction data to the edge gateway for processing.

[0068] Figure 5 The figure is a schematic diagram of task processing in an embodiment. When a content control platform receives a task to be processed, if it is judged to be in prediction mode, it can perform a process of matching the prediction data list with the task to be processed. After performing relevant business processing internally, the task to be processed is pushed to the task distribution center, which then pushes it to the agent of the edge host for processing. The agent of the edge host restores the prediction data identifier in the updated task to be processed to the target prediction data in the prediction data list, and sends the target prediction data to the edge gateway for processing.

[0069] In some embodiments of the present disclosure, the process of constructing the prediction data list includes:

[0070] Acquire historical task data, wherein the historical task data includes historical data, historical keywords, and historical data access frequency;

[0071] Obtaining forecast data based on a preset forecast model and the historical task data;

[0072] A prediction data list is generated based on the prediction data and a cache path or identifier corresponding to the prediction data.

[0073] Figure 6 The following is a schematic diagram of constructing a prediction data list in an embodiment. The content management platform receives a refresh service request, which includes a URL list that needs to be refreshed. If the big data prediction function is enabled, these URL data are pushed to the big data center, and the statistical analysis center regularly judges and predicts the data in the big data center. The prediction data can be obtained based on historical task data and a preset prediction model. The historical task data can be the latest hot news, keywords visited by historical users, etc.

[0074] In some embodiments of the present disclosure, the updated to-be-processed task includes a task identifier, and the task identifier is used to determine whether the to-be-processed task data in the to-be-processed task matches the predicted data in the predicted data list.

[0075] Figure 7 The figure is a schematic diagram of task processing in one embodiment. After the pending task is updated, it can carry the task identifier. When the agent of the edge host receives the pending task, it determines whether it is a hit prediction data according to the task identifier. If it is a hit, it quickly restores the pending task data according to the prediction data. If it is not a hit, it is the original data itself and can be used directly. Then the original data is sent to the gateway for processing.

[0076] In some embodiments of the present disclosure, the prediction data list is constructed by periodically generated and / or real-time generated prediction data.

[0077] Using big data as reference data, a custom algorithm is used to generate a list of predicted data for the data that may be generated during the next time range of access, and the list is sent to the edge node machine in advance. The content control platform can use the predicted data list for replacement and distinguish between hit and non-hit pending task data. The hit rate can also be pushed to the big data platform for the next prediction calculation. The prediction data can be automatically updated, and the normal business process will not be affected when the prediction data is incorrect, that is, the pending task data can be processed normally without replacement, which only occupies a small amount of computing resources and reduces network transmission overhead.

[0078] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0079] Based on the same inventive concept, the embodiment of the present disclosure also provides a task processing device for implementing the task processing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the task processing device embodiment provided below can refer to the limitations of the task processing method above, and will not be repeated here.

[0080] The device may include a system (including a distributed system), software (application), module, component, server, client, etc. that uses the method described in the embodiments of this specification and is combined with necessary implementation hardware. Based on the same innovative concept, the device in one or more embodiments provided in the embodiments of the present disclosure is as described in the following embodiments. Since the implementation scheme and method of the device to solve the problem are similar, the implementation of the specific device in the embodiments of this specification can refer to the implementation of the aforementioned method, and the repetitions will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0081] In one embodiment, Figure 8 As shown, a task processing device 800 is provided, and the device may be the aforementioned server, or a module, component, device, unit, etc. integrated in the server. The device 800 may include:

[0082] A receiving module 802 is used to receive a task to be processed, wherein the task to be processed includes task data to be processed;

[0083] An acquisition module 804 is used to acquire a prediction data list, wherein the prediction data list includes prediction data and prediction data identifiers corresponding to the prediction data;

[0084] A matching module 806 is used to match the predicted data list with the tasks to be processed to obtain the task data to be processed that matches the predicted data;

[0085] An updating module 808, configured to replace the to-be-processed task data matching the predicted data with the predicted data identifier corresponding to the predicted data, to obtain an updated to-be-processed task;

[0086] The processing module 810 is used to send the updated pending tasks to the edge host for processing. In one embodiment, sending the updated pending tasks to the edge host includes:

[0087] The updated pending tasks are sent to the agent of the edge host, and the agent of the edge host restores the prediction data identifier in the updated pending tasks to the target prediction data in the prediction data list, and sends the target prediction data to the edge gateway for processing.

[0088] In one embodiment, the process of constructing the prediction data list includes:

[0089] Acquire historical task data, wherein the historical task data includes historical data, historical keywords, and historical data access frequency;

[0090] Obtaining forecast data based on a preset forecast model and the historical task data;

[0091] A prediction data list is generated based on the prediction data and a cache path or identifier corresponding to the prediction data.

[0092] In one of the embodiments, the updated to-be-processed task includes a task identifier, and the task identifier is used to determine whether the to-be-processed task data in the to-be-processed task matches the predicted data in the predicted data list.

[0093] In one of the embodiments, when the updated to-be-processed tasks include to-be-processed task data that does not match the predicted data, the to-be-processed task data that does not match the predicted data is sent to an edge gateway for processing.

[0094] In one of the embodiments, the prediction data list is constructed by periodically generated and / or real-time generated prediction data.

[0095] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0096] Each module in the task processing device described above can be implemented in whole or in part by software, hardware, or a combination thereof. Each module described above can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module described above.

[0097] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store a list of prediction data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a task processing method is implemented.

[0098] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.10 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, the task processing method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse, etc.

[0099] Those skilled in the art will understand that Fig. 9 , Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present disclosure, and does not constitute a limitation on the computer device to which the scheme of the present disclosure is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0100] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described in any embodiment of the present disclosure is implemented.

[0101] In one embodiment, a computer program product is provided, including a computer program, which implements the method described in any embodiment of the present disclosure when executed by a processor.

[0102] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present disclosure can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided by the present disclosure may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided by the present disclosure may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, etc., but are not limited to this.

[0103] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0104] The above-described embodiments only express several implementation methods of the present disclosure, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present disclosure, and these all belong to the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the attached claims.

Claims

1. A task processing method, characterized in that: Applied to a content management platform, the method includes: Receiving a task to be processed, wherein the task to be processed includes task data to be processed; Acquire a prediction data list, wherein the prediction data list includes prediction data and prediction data identifiers corresponding to the prediction data; Matching the predicted data list with the pending tasks to obtain pending task data that matches the target predicted data; Replacing the to-be-processed task data that matches the target prediction data with the prediction data identifier corresponding to the target prediction data to obtain an updated to-be-processed task; The updated to-be-processed tasks are sent to the edge host to instruct the edge host to process them.

2. The method according to claim 1, characterized in that The sending of the updated pending tasks to the edge host comprises: The updated pending tasks are sent to the agent of the edge host, and the agent of the edge host restores the prediction data identifier in the updated pending tasks to the target prediction data in the prediction data list, and sends the target prediction data to the edge gateway for processing.

3. The method according to claim 1, characterized in that The process of constructing the prediction data list includes: Acquire historical task data, wherein the historical task data includes historical data, historical keywords, and historical data access frequency; Obtaining forecast data based on a preset forecast model and the historical task data; A prediction data list is generated based on the prediction data and a cache path or identifier corresponding to the prediction data.

4. The method according to claim 1, characterized in that: The updated to-be-processed task includes a task identifier, and the task identifier is used to determine whether the to-be-processed task data in the to-be-processed task matches the predicted data in the predicted data list.

5. The method according to claim 1, characterized in that In the case where the updated tasks to be processed include task data to be processed that does not match the predicted data, the task data to be processed that does not match the predicted data is sent to the edge gateway for processing.

6. The method according to claim 1, characterized in that The prediction data list is constructed by using prediction data generated periodically and / or in real time.

7. A task processing device, characterized in that: The device comprises: A receiving module, used for receiving a task to be processed, wherein the task to be processed includes task data to be processed; An acquisition module, used for acquiring a prediction data list, wherein the prediction data list includes prediction data and prediction data identifiers corresponding to the prediction data; A matching module, used for matching the predicted data list with the tasks to be processed to obtain the task data to be processed that matches the target predicted data; An updating module, used to replace the to-be-processed task data matching the target prediction data with the prediction data identifier corresponding to the target prediction data, to obtain an updated to-be-processed task; The processing module is used to send the updated to-be-processed tasks to the edge host for processing.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images