A method and system for scheduling Internet of Things tasks based on mobile edge computing

Abstract

The present invention is applicable to the technical field of Internet of Things, and provides an Internet of Things task scheduling method and system based on mobile edge computing, the method comprising the following steps: receiving task data sent by multiple Internet of Things terminal devices, and forming a to-be-processed task queue according to the task data; analyzing each task data in the to-be-processed task queue in turn, and determining whether the task data is newly added task data; when the task data is newly added task data, adding the task data to a first processing queue, and when the task data is existing task data, adding the task data to a second processing queue; with the shortest total processing time of the first processing queue as the goal, using an artificial fish swarm algorithm to perform strategy optimization, and processing the first processing queue based on past experience; the present invention has the beneficial effects of greatly reducing the configuration requirements of the server and reducing the cost.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an Internet of Things task scheduling method and system based on mobile edge computing. Background Art

[0002] With the rapid development of the Internet of Things and mobile terminals, edge computing technology has emerged. By configuring computing and storage at the edge of the Internet, it can process large amounts of data generated by IoT terminals and respond to latency-sensitive application requests. In order to improve the efficiency of computing resource utilization and optimize performance indicators, the optimization of edge computing resource allocation and task scheduling has received widespread attention.

[0003] However, due to the influence of network transmission rate, available resources of cloud servers and edge nodes, and the complexity of each computing task, the load on each edge node is very uneven. For example, some edge nodes are idle, while the computing tasks, storage space, and communication capabilities of some edge nodes are overloaded. Therefore, how to reasonably schedule computing tasks and their related data and evenly distribute computing tasks to matching edge nodes is an urgent problem to be solved. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide an Internet of Things task scheduling method and system based on mobile edge computing, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0005] The embodiment of the present invention is implemented as follows: a method for scheduling Internet of Things tasks based on mobile edge computing, the method comprising the following steps:

[0006] Receive task data sent by multiple IoT terminal devices, and form a queue of tasks to be processed according to the task data;

[0007] Analyze each task data in the pending task queue in turn to determine whether the task data is newly added task data;

[0008] When the task data is newly added task data, the task data is added to the first processing queue; when the task data is existing task data, the task data is added to the second processing queue;

[0009] With the goal of minimizing the total processing time of the first processing queue, the artificial fish swarm algorithm is used to optimize the strategy and the first processing queue is processed based on past experience;

[0010] Upload each task data in the second processing queue to the cloud computing center in turn, and send the processing results of the cloud computing center to the Internet of Things terminal device.

[0011] As a further solution of the present invention: the step of sequentially analyzing each task data in the pending task queue to determine whether the task data is newly added task data specifically includes:

[0012] Obtaining each task data in the to-be-processed task queue in turn, and analyzing the task data to obtain relevant information of the task data including task type and attributes;

[0013] Calling an existing task database stored locally, wherein the existing task database is a collection of processed task data;

[0014] The task data in the pending task queue is compared and analyzed with the existing task database to determine whether the task data is newly added task data.

[0015] As a further solution of the present invention: the step of using the artificial fish swarm algorithm to optimize the strategy with the goal of minimizing the total processing time of the first processing queue and processing the first processing queue based on past experience specifically includes:

[0016] Using artificial fish swarm algorithm to optimize the strategy, an edge computing node is assigned to each task data in the first processing queue;

[0017] The task data is processed according to a task-result mapping relationship library, and a processing result is output, wherein the task-result mapping relationship library is obtained by processing existing identical task data.

[0018] As a further solution of the present invention: the step of sequentially uploading each task data in the second processing queue to the cloud computing center, and sending the processing result of the cloud computing center to the Internet of Things terminal device specifically includes:

[0019] Uploading each task data in the second processing queue to the cloud computing center in sequence;

[0020] Receive the processing results from the cloud computing center, and update the processing results and related information of the task data to the existing task database and the task-result mapping relationship library;

[0021] Send the processing results to the corresponding IoT terminal device.

[0022] As a further solution of the present invention: the step of receiving task data sent by multiple IoT terminal devices and forming a queue of tasks to be processed based on the task data also includes receiving a device load status sent by the IoT terminal device. When the IoT terminal device is in a protocol state and the device load state is lower than a set threshold, task data is assigned to the IoT terminal device for processing, and the protocol state is used to characterize that the IoT terminal device is registered as an edge computing node that can perform task processing.

[0023] Another object of an embodiment of the present invention is to provide an Internet of Things task scheduling system based on mobile edge computing, comprising:

[0024] A task data receiving module is used to receive task data sent by multiple IoT terminal devices and form a to-be-processed task queue according to the task data;

[0025] The task data determination module is used to analyze each task data in the to-be-processed task queue in turn to determine whether the task data is newly added task data; when the task data is newly added task data, the task data is added to the first processing queue; when the task data is existing task data, the task data is added to the second processing queue;

[0026] A first processing module, configured to optimize the strategy using an artificial fish swarm algorithm with the goal of minimizing the total processing time of a first processing queue, and process the first processing queue based on past experience; and

[0027] The second processing module is used to upload each task data in the second processing queue to the cloud computing center in sequence, and send the processing results of the cloud computing center to the Internet of Things terminal device.

[0028] As a further solution of the present invention: the task data determination module includes:

[0029] An acquisition unit, used to sequentially acquire each task data in the to-be-processed task queue, and analyze the task data to acquire relevant information of the task data including task type and attributes;

[0030] A database calling unit, used to call an existing task database stored locally, wherein the existing task database is a collection of processed task data; and

[0031] The comparison and analysis unit is used to compare and analyze the task data in the to-be-processed task queue with the existing task database to determine whether the task data is newly added task data.

[0032] As a further solution of the present invention: the first processing module includes:

[0033] an allocation unit, configured to use an artificial fish swarm algorithm to perform strategy optimization and allocate an edge computing node to each task data in the first processing queue; and

[0034] The processing unit is used to process the task data according to the task-result mapping relationship library and output the processing result, wherein the task-result mapping relationship library is obtained by processing the existing identical task data.

[0035] As a further solution of the present invention: the second processing module includes:

[0036] A task data uploading unit, used for uploading each task data in the second processing queue to the cloud computing center in sequence;

[0037] The result sending and updating unit is used to receive the processing results of the cloud computing center, update the processing results and related information of the task data to the existing task database and the task-result mapping relationship library, and send the processing results to the corresponding Internet of Things terminal devices.

[0038] As a further solution of the present invention: it also includes an auxiliary processing module, which is used to receive the device load status sent by the Internet of Things terminal device. When the Internet of Things terminal device is in a protocol state and the device load state is lower than a set threshold, task data is assigned to the Internet of Things terminal device for processing. The protocol state is used to characterize that the Internet of Things terminal device is registered as an edge computing node that can perform task processing.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: the embodiment of the present invention analyzes and determines each task data in the task queue to be processed, and performs a preliminary screening process. Task data that has been processed before or in prior knowledge can be quickly processed, and the resources consumed or occupied are also less, and the response speed is also faster. The remaining task data is uploaded to the cloud computing center for processing, and the powerful data processing capabilities of the cloud computing center are used for rapid processing, which can greatly reduce the configuration requirements of the server and reduce costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The flowchart of an IoT task scheduling method based on mobile edge computing is shown in FIG.

[0041] Figure 2 A flow chart for determining whether the task data is newly added task data.

[0042] Figure 3 The flowchart is for processing the first processing queue based on past experience.

[0043] Figure 4 The flowchart of processing the second processing queue is shown in FIG.

[0044] Figure 5 This is a structural diagram of an Internet of Things task scheduling system based on mobile edge computing.

[0045] Figure 6 This is a structural diagram of the task data determination module.

[0046] Figure 7It is a structural schematic diagram of the first processing module.

[0047] Figure 8 It is a structural schematic diagram of the second processing module.

[0048] Fig. 9 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0051] like Figure 1 As shown, it is a flowchart of an Internet of Things task scheduling method based on mobile edge computing provided by an embodiment of the present invention, which is applied to an Internet of Things terminal device cluster, a main control module, an edge computing node and a cloud computing center that can constitute a communication network. The method includes the following steps:

[0052] S100, receiving task data sent by multiple IoT terminal devices, and forming a queue of tasks to be processed according to the task data.

[0053] S200, analyzing each task data in the to-be-processed task queue in turn to determine whether the task data is newly added task data.

[0054] S300, when the task data is newly added task data, adding the task data to a first processing queue; when the task data is existing task data, adding the task data to a second processing queue.

[0055] S400, with the goal of minimizing the total processing time of the first processing queue, using the artificial fish swarm algorithm to optimize the strategy, and processing the first processing queue based on past experience.

[0056] S500, upload each task data in the second processing queue to the cloud computing center in sequence, and send the processing result of the cloud computing center to the Internet of Things terminal device.

[0057] In an embodiment of the present invention, task data sent by an IoT terminal device can be output to a main control module in real time to form a queue of pending tasks. When the number of IoT terminal devices is large, the queue of pending tasks may be long and will take up more resources to process. In this embodiment, each task data in the queue of pending tasks is first judged. The newly added task data here refers to the same task data that has not been processed before or in prior knowledge. It can be screened out to form a second processing queue, and the rest forms a first processing queue. For the first processing queue, since it has been processed before or in prior knowledge, it can be quickly processed according to prior knowledge, so the resources consumed or occupied are less, and the response speed is also faster. The second processing queue is uploaded to the cloud computing center for processing.

[0058] The embodiment of the present invention analyzes and determines each task data in the task queue to be processed, and performs preliminary screening processing. Task data that has been processed before or in prior knowledge can be quickly processed, and the resources consumed or occupied are also less, and the response speed is also faster. The remaining task data is uploaded to the cloud computing center for processing, and the powerful data processing capabilities of the cloud computing center are used for rapid processing, which can greatly reduce the configuration requirements of the server and reduce costs.

[0059] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of sequentially analyzing each task data in the pending task queue to determine whether the task data is newly added task data specifically includes:

[0060] S201, sequentially acquiring each task data in the to-be-processed task queue, and analyzing the task data to acquire relevant information of the task data including task type and attributes.

[0061] In an embodiment of the present invention, the task data output by the IoT terminal device has certain task types and attributes, such as what the task type is a request, the attributes include various data, etc., and are repetitive. For example, in a certain event, the IoT terminal device outputs 1 task data. At another time point, for the same event, the task data output by the same IoT terminal device is very likely to be the same. Therefore, by analyzing the task data, it can be determined whether the task data has appeared or been processed before.

[0062] S202, calling an existing task database stored locally, where the existing task database is a collection of processed task data.

[0063] The existing task database described in the embodiment of the present invention refers to a collection of processed task data, that is, a collection of task data that has been processed before the current task is processed, or in other words, task data that has appeared or been processed before.

[0064] S203, comparing and analyzing the task data in the to-be-processed task queue with the existing task database to determine whether the task data is newly added task data.

[0065] When the embodiment of the present invention is applied in practice, by comparing and analyzing the task data in the task queue to be processed with the existing task database, it can be determined whether the task data is new. When the task data is new task data, the task data is added to the first processing queue; when the task data is existing task data, the task data is added to the second processing queue.

[0066] like Figure 3 As shown, as another preferred embodiment of the present invention, the step of taking the shortest total processing time of the first processing queue as the goal, using the artificial fish swarm algorithm to optimize the strategy, and processing the first processing queue based on past experience specifically includes:

[0067] S401, using an artificial fish swarm algorithm to perform strategy optimization and assign an edge computing node to each task data in the first processing queue.

[0068] In the embodiment of the present invention, the artificial fish school algorithm is a foraging method of fish schools. By constructing artificial fish to imitate the foraging, clustering and tail-chasing behaviors of fish schools, optimization is achieved. Because it has a fast convergence speed, it can be used to solve problems with real-time requirements. For some occasions where the precision requirements are not high, it can be used to quickly obtain a feasible solution. The artificial fish school algorithm belongs to the existing technology. Based on it, an edge computing node can be quickly assigned to each task data in the first processing queue.

[0069] S402, processing the task data according to a task-result mapping relationship library, and outputting a processing result, wherein the task-result mapping relationship library is obtained by processing existing identical task data.

[0070] In the embodiment of the present invention, since each task data in the first processing queue has been processed before, it has certain prior knowledge, and the previous processing method or processing result is also known, therefore, a task-result mapping relationship library can be established based on this, and the corresponding processing result can be directly obtained by directly inputting the task data. Its processing method responds very quickly, and each task data in the first processing queue can be quickly processed and responded to, effectively reducing the occurrence of delays.

[0071] like Figure 4 As shown, as another preferred embodiment of the present invention, the steps of sequentially uploading each task data in the second processing queue to the cloud computing center and sending the processing result of the cloud computing center to the Internet of Things terminal device specifically include:

[0072] S501, uploading each task data in the second processing queue to the cloud computing center in sequence.

[0073] S502, receiving the processing result from the cloud computing center, and updating the processing result and related information of the task data to the existing task database and the task-result mapping relationship library.

[0074] S503: Send the processing result to the corresponding IoT terminal device.

[0075] In an embodiment of the present invention, after the task data is uploaded to the cloud computing center, it will rely on the powerful data processing capabilities of the cloud computing center to perform analysis and calculation to obtain processing results. The obtained processing results will be associated with the task data and returned and updated to the task database and task-result mapping relationship library, providing prior knowledge for subsequent task data processing.

[0076] As another preferred embodiment of the present invention, the step of receiving task data sent by multiple Internet of Things terminal devices and forming a queue of tasks to be processed according to the task data also includes receiving a device load status sent by the Internet of Things terminal device. When the Internet of Things terminal device is in a protocol state and the device load state is lower than a set threshold, task data is assigned to the Internet of Things terminal device for processing, and the protocol state is used to characterize that the Internet of Things terminal device is registered as an edge computing node that can perform task processing.

[0077] In the embodiment of the present invention, since some IoT terminal devices also have data processing and computing capabilities, task data can also be processed for IoT terminal devices that are in an idle or idle state, which can make more full use of computing resources in the network and improve computing speed and efficiency. The IoT terminal device here is in a protocol state, that is, the IoT terminal device is registered as an edge computing node that can process tasks, and the load state is lower than the set threshold, which indicates that the IoT terminal device is in an idle or idle state.

[0078] like Figure 5As shown, an embodiment of the present invention further provides an Internet of Things task scheduling system based on mobile edge computing, including a task data receiving module 100, a task data determination module 200, a first processing module 300 and a second processing module 400, wherein the task data receiving module 100 is used to receive task data sent by multiple Internet of Things terminal devices, and form a to-be-processed task queue according to the task data; the task data determination module 200 is used to analyze each task data in the to-be-processed task queue in turn, and determine whether the task data is newly added task data; when the task data is newly added task data, the task data is added to the first processing queue, and when the task data is existing task data, the task data is added to the second processing queue; the first processing module 300 is used to optimize the strategy using the artificial fish swarm algorithm with the goal of minimizing the total processing time of the first processing queue, and process the first processing queue based on past experience; the second processing module 400 is used to upload each task data in the second processing queue to the cloud computing center in turn, and send the processing results of the cloud computing center to the Internet of Things terminal device.

[0079] The embodiment of the present invention analyzes and determines each task data in the task queue to be processed, and performs preliminary screening processing. Task data that has been processed before or in prior knowledge can be quickly processed, and the resources consumed or occupied are also less, and the response speed is also faster. The remaining task data is uploaded to the cloud computing center for processing, and the powerful data processing capabilities of the cloud computing center are used for rapid processing, which can greatly reduce the configuration requirements of the server and reduce costs.

[0080] like Figure 6 As shown, as a preferred embodiment of the present invention, the task data determination module 200 includes an acquisition unit 201, a database calling unit 202 and a comparison and analysis unit 203, wherein the acquisition unit 201 is used to sequentially acquire each task data in the task queue to be processed, and analyze the task data to obtain relevant information of the task data including task type and attributes; the database calling unit 202 is used to call an existing task database stored locally, and the existing task database is a collection of processed task data; the comparison and analysis unit 203 is used to compare and analyze the task data in the task queue to be processed with the existing task database to determine whether the task data is newly added task data.

[0081] When the embodiment of the present invention is applied in practice, by comparing and analyzing the task data in the task queue to be processed with the existing task database, it can be determined whether the task data is new. When the task data is new task data, the task data is added to the first processing queue; when the task data is existing task data, the task data is added to the second processing queue.

[0082] like Figure 7 As shown, as another preferred embodiment of the present invention, the first processing module 300 includes an allocation unit 301 and a processing unit 302, the allocation unit 301 is used to use an artificial fish swarm algorithm to perform strategy optimization and allocate an edge computing node to each task data in the first processing queue; the processing unit 302 is used to process the task data according to a task-result mapping relationship library and output the processing result, wherein the task-result mapping relationship library is obtained based on processing of existing identical task data.

[0083] In the embodiment of the present invention, since each task data in the first processing queue has been processed before, it has certain prior knowledge, and the previous processing method or processing result is also known, therefore, a task-result mapping relationship library can be established based on this, and the corresponding processing result can be directly obtained by directly inputting the task data. Its processing method responds very quickly, and each task data in the first processing queue can be quickly processed and responded to, effectively reducing the occurrence of delays.

[0084] like Figure 8 As shown, as another preferred embodiment of the present invention, the second processing module 400 includes a task data uploading unit 401 and a result sending and updating unit 402, the task data uploading unit 401 is used to upload each task data in the second processing queue to the cloud computing center in turn; the result sending and updating unit 402 is used to receive the processing results of the cloud computing center, update the processing results and related information of the task data to the existing task database and the task-result mapping relationship library, and send the processing results to the corresponding Internet of Things terminal devices.

[0085] In an embodiment of the present invention, after the task data is uploaded to the cloud computing center, it will rely on the powerful data processing capabilities of the cloud computing center to perform analysis and calculation to obtain processing results. The obtained processing results will be associated with the task data and returned and updated to the task database and task-result mapping relationship library, providing prior knowledge for subsequent task data processing.

[0086] As another preferred embodiment of the present invention, it also includes an auxiliary processing module, which is used to receive the device load status sent by the Internet of Things terminal device. When the Internet of Things terminal device is in a protocol state and the device load state is lower than a set threshold, task data is assigned to the Internet of Things terminal device for processing. The protocol state is used to characterize that the Internet of Things terminal device is registered as an edge computing node that can perform task processing.

[0087] like Fig. 9As shown, an embodiment of the present invention further provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes:

[0088] S100, receiving task data sent by multiple IoT terminal devices, and forming a queue of tasks to be processed according to the task data.

[0089] S200, analyzing each task data in the to-be-processed task queue in turn to determine whether the task data is newly added task data.

[0090] S300, when the task data is newly added task data, adding the task data to a first processing queue; when the task data is existing task data, adding the task data to a second processing queue.

[0091] S400, with the goal of minimizing the total processing time of the first processing queue, using the artificial fish swarm algorithm to optimize the strategy, and processing the first processing queue based on past experience.

[0092] S500, upload each task data in the second processing queue to the cloud computing center in sequence, and send the processing result of the cloud computing center to the Internet of Things terminal device.

[0093] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes:

[0094] S100, receiving task data sent by multiple IoT terminal devices, and forming a queue of tasks to be processed according to the task data.

[0095] S200, analyzing each task data in the to-be-processed task queue in turn to determine whether the task data is newly added task data.

[0096] S300, when the task data is newly added task data, adding the task data to a first processing queue; when the task data is existing task data, adding the task data to a second processing queue.

[0097] S400, with the goal of minimizing the total processing time of the first processing queue, using the artificial fish swarm algorithm to optimize the strategy, and processing the first processing queue based on past experience.

[0098] S500, upload each task data in the second processing queue to the cloud computing center in sequence, and send the processing result of the cloud computing center to the Internet of Things terminal device.

[0099] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. 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 performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0100] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0101] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0102] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for scheduling Internet of Things tasks based on mobile edge computing, characterized in that: The method comprises the following steps: Receive task data sent by multiple IoT terminal devices, and form a queue of tasks to be processed according to the task data; Analyze each task data in the pending task queue in turn to determine whether the task data is newly added task data; When the task data is newly added task data, the task data is added to the second processing queue; when the task data is existing task data, the task data is added to the first processing queue; Among them, the newly added task data refers to the same task data that has not been processed before or in prior knowledge, and it is screened out to form the second processing queue, and the rest forms the first processing queue. As for the first processing queue, since it has been processed before or in prior knowledge, it is quickly processed according to prior knowledge; With the goal of minimizing the total processing time of the first processing queue, the artificial fish swarm algorithm is used to optimize the strategy and the first processing queue is processed based on past experience; Upload each task data in the second processing queue to the cloud computing center in turn, and send the processing results of the cloud computing center to the Internet of Things terminal device; The step of optimizing the strategy by using the artificial fish swarm algorithm with the goal of minimizing the total processing time of the first processing queue and processing the first processing queue based on past experience specifically includes: Using artificial fish swarm algorithm to optimize the strategy, an edge computing node is assigned to each task data in the first processing queue; According to the task-result mapping relationship library, the task data is processed and the processing result is output, wherein the task-result mapping relationship library is obtained by processing the existing identical task data; The step of sequentially uploading each task data in the second processing queue to the cloud computing center, and sending the processing result of the cloud computing center to the Internet of Things terminal device specifically includes: Uploading each task data in the second processing queue to the cloud computing center in sequence; Receive the processing results from the cloud computing center, and update the processing results and related information of the task data to the existing task database and the task-result mapping relationship library; Send the processing results to the corresponding IoT terminal device.

2. The method for scheduling Internet of Things tasks based on mobile edge computing according to claim 1, characterized in that: The step of sequentially analyzing each task data in the to-be-processed task queue to determine whether the task data is newly added task data specifically includes: Obtaining each task data in the to-be-processed task queue in turn, and analyzing the task data to obtain relevant information of the task data including task type and attributes; Calling an existing task database stored locally, wherein the existing task database is a collection of processed task data; The task data in the pending task queue is compared and analyzed with the existing task database to determine whether the task data is newly added task data.

3. A method for scheduling Internet of Things tasks based on mobile edge computing according to any one of claims 1-2, characterized in that: The step of receiving task data sent by multiple IoT terminal devices and forming a queue of tasks to be processed based on the task data also includes receiving a device load status sent by the IoT terminal device. When the IoT terminal device is in a protocol state and the device load state is lower than a set threshold, task data is allocated to the IoT terminal device for processing. The protocol state is used to characterize that the IoT terminal device is registered as an edge computing node that can perform task processing.

4. An Internet of Things task scheduling system based on mobile edge computing, characterized in that: include: A task data receiving module is used to receive task data sent by multiple IoT terminal devices and form a to-be-processed task queue according to the task data; The task data determination module is used to analyze each task data in the to-be-processed task queue in turn to determine whether the task data is newly added task data; when the task data is newly added task data, the task data is added to the second processing queue; when the task data is existing task data, the task data is added to the first processing queue; Among them, the newly added task data refers to the same task data that has not been processed before or in prior knowledge, and it is screened out to form the second processing queue, and the rest forms the first processing queue. As for the first processing queue, since it has been processed before or in prior knowledge, it is quickly processed according to prior knowledge; A first processing module, configured to optimize the strategy using an artificial fish swarm algorithm with the goal of minimizing the total processing time of a first processing queue, and process the first processing queue based on past experience; and The second processing module is used to upload each task data in the second processing queue to the cloud computing center in sequence, and send the processing result of the cloud computing center to the Internet of Things terminal device; The first processing module comprises: an allocation unit, configured to use an artificial fish swarm algorithm to perform strategy optimization and allocate an edge computing node to each task data in the first processing queue; and A processing unit, used to process the task data according to a task-result mapping relationship library and output a processing result, wherein the task-result mapping relationship library is obtained by processing existing identical task data; The second processing module comprises: A task data uploading unit, used for uploading each task data in the second processing queue to the cloud computing center in sequence; The result sending and updating unit is used to receive the processing results of the cloud computing center, update the processing results and related information of the task data to the existing task database and the task-result mapping relationship library, and send the processing results to the corresponding Internet of Things terminal devices.

5. The Internet of Things task scheduling system based on mobile edge computing according to claim 4 is characterized in that: The task data determination module comprises: An acquisition unit, used to sequentially acquire each task data in the to-be-processed task queue, and analyze the task data to acquire relevant information of the task data including task type and attributes; A database calling unit, used to call an existing task database stored locally, wherein the existing task database is a collection of processed task data; and The comparison and analysis unit is used to compare and analyze the task data in the to-be-processed task queue with the existing task database to determine whether the task data is newly added task data.

6. An Internet of Things task scheduling system based on mobile edge computing according to any one of claims 4-5, characterized in that: It also includes an auxiliary processing module, which is used to receive the device load status sent by the Internet of Things terminal device. When the Internet of Things terminal device is in a protocol state and the device load state is lower than a set threshold, task data is assigned to the Internet of Things terminal device for processing. The protocol state is used to characterize that the Internet of Things terminal device is registered as an edge computing node that can perform task processing.

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