Data processing and control methods and applications based on edge computing

Abstract

The present invention discloses a method and application of data processing and control based on edge computing, the method comprising the following steps: the edge node obtains the location information of the end node and the obstacle according to the data fusion algorithm; and the edge node controls the end node to execute the task issued by the cloud data center according to the location information, and reports the task data of the end node to the cloud data center. The method can perform data exploration based on cloud, edge, and end collaboration based on edge computing, realize data exploration without predicting terrain and other information while ensuring node mobility, solve the delay problem of data fusion, data processing, and giving feedback, and make the edge server also mobile while running a relatively simple data processing algorithm, which can automatically complete feedback control. At the same time, the operability of the entire system is taken into account, and the user can run the program on the host at any time to view data or send commands anywhere connected to the network.

Description

Technical Field

[0001] The present invention relates to the field of computers, and in particular to a method and application of data processing and control based on edge computing. Background Art

[0002] With the development of the information industry, more and more data are constantly generated. The emergence of sensor networks has brought the Internet of Things technology into a new field. How to effectively collect and use this data has been a focus of real-time data processing in recent years. Most of the research focuses on how to deploy networks to detect various sensors and how to design network protocols to meet different needs in a limited range of networks. However, there is no focus on how to process the data collected by the network. In recent years, the term "edge computing" has emerged, and a method of finding a nearby service point for data processing has been proposed. The processing and feedback of real-time data has been preliminarily realized. After the node explores the data, it is limited by its own computing resources and capabilities. The data needs to be transmitted to a server with certain computing power for data processing. This server is generally not mobile, and in addition to the basic data processing function, it also runs a set of traffic or resource balancing programs to schedule data processing requests sent by different nodes to avoid exceeding the processing capacity of this server. However, including the automation control system, the processing process and results of this data cannot be read remotely. To obtain the data, the operator must be in the corresponding sensor network. And the pre-set edge server cannot dynamically control the edge node according to the different needs of the user. On the server running the load balancing algorithm, data may be retransmitted to other servers or refused to operate due to the server being busy, which not only causes huge data transmission overhead to the network composed of edge nodes (re-finding edge servers and transmitting data), but also cannot guarantee real-time data feedback. Now there is an urgent need for a method that can not only maintain the simplicity of edge nodes (limited computing resources), but also ensure real-time data exploration (mobility of edge nodes), and ensure that data can be processed nearby and feedback control operations can be given (edge ​​computing). At the same time, the edge server also needs to have a certain data fusion capability, so that the overall situation after fusion can be reported to the user for observation, and at the same time receive scheduling and control commands from the user.

[0003] In order to meet the above needs, some magazines proposed using ultrasonic sensors as a way to measure distance for the car. They also proposed to establish a motion model for the car through fuzzy control algorithms and the formulation of fuzzy inference rules, solving the problem of sensor movement and real-time data collection in multiple locations.

[0004] Some papers have also proposed a new data fusion algorithm. After processing based on the Bar-Shalom formula, the processing results are significantly improved, but this is at the expense of reduced processing speed and requires high computing power for a single node, which is not suitable for real-time data processing. Especially when the car is moving at a high speed, it may cause untimely feedback and collision.

[0005] The cloud data processing center can collect and aggregate the collected data information, perform certain processing operations, and provide it to users. After the data is aggregated to the sink node, the node uploads the fused data to the cloud data center as feedback for the user or operator, allowing the user to directly view the operation status and required data of the sensor network in a remote location. However, renting a cloud data center as a transit point is not only expensive, but also has a high delay in providing feedback.

[0006] In view of this, it is necessary to provide a data exploration method that can run simple and efficient data processing and fusion algorithms at the edge without user control and report data in real time according to user regulations and accept real-time control. Based on edge computing-based data processing and control exploration, cloud, edge, and end collaborative control is realized. The cloud is responsible for user-end interaction and can run directly on the user host. The edge is responsible for real-time scheduling control and can receive commands and report data. The end is responsible for edge information collection and data exploration.

[0007] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0008] The purpose of the present invention is to provide a method and application of data processing and control based on edge computing, so as to solve the problem that the existing data exploration requires certain terrain information to be known in advance, and the data fusion algorithm is combined with the sensor. When the data fusion algorithm has high requirements, the problems of limited node resources and unknown environment are not considered.

[0009] To achieve the above objectives, an embodiment of the present invention provides a method for data processing and control based on edge computing.

[0010] In one or more embodiments of the present invention, the method includes: the edge node obtains the location information of the end node and the obstacle according to the data fusion algorithm; and the edge node controls the end node to execute the task issued by the cloud data center according to the location information, and reports the task data of the end node to the cloud data center.

[0011] In one or more embodiments of the present invention, the edge node obtains the location information of the end node and the obstacle according to the data fusion algorithm, including: determining whether the edge node searches for the end node within a preset range; if not, the edge node establishes a connection with the cloud data center and reports the current status; if so, the edge node establishes a connection with the end node and makes the end nodes form an equilateral polygon to obtain the distance between the end nodes, and calculates the weight of the end node according to the distance and the data fusion algorithm.

[0012] In one or more embodiments of the present invention, the method further includes: determining whether the end node measures the current position of the obstacle for the first time; if so, saving the current position to the edge node; if not, calculating the latest position of the obstacle based on the current position, the historical position of the obstacle and the weight, and saving the latest position of the obstacle to the edge node.

[0013] In one or more embodiments of the present invention, the method further includes: calculating the displacement of the end node based on the speed of the end node and the current time to obtain the current position of the end node; or obtaining the relative position of the end node and the obstacle through a gyroscope to obtain the current position of the end node.

[0014] In one or more embodiments of the present invention, the method also includes: calculating the total energy required for the end node to process the task based on the energy of the end node for sending, receiving and processing data, the end node's movement energy and the energy consumed by the sensor; determining whether the total energy is less than the remaining energy of the end node; if so, marking the end node invalid; if not, the end node executing the task issued by the cloud data center.

[0015] In one or more embodiments of the present invention, the method further includes: the edge node generates a movement process of the end node according to the location information and task requirements, and converts the movement process into a command and sends it to the end node.

[0016] In another aspect of the present invention, a device for data processing and control based on edge computing is provided, which includes an acquisition module and a task module.

[0017] The acquisition module is used for edge nodes to obtain the location information of end nodes and obstacles according to the data fusion algorithm.

[0018] The task module is used for the edge node to control the end node to execute the task issued by the cloud data center according to the location information, and report the task data of the end node to the cloud data center.

[0019] In one or more embodiments of the present invention, the acquisition module is also used to: determine whether the edge node searches for the end node within a preset range; if not, the edge node establishes a connection with the cloud data center and reports the current status; if so, the edge node establishes a connection with the end node and makes the end nodes form an equilateral polygon to obtain the distance between the end nodes, and calculate the weight of the end node based on the distance and the data fusion algorithm.

[0020] In one or more embodiments of the present invention, the acquisition module is also used to: determine whether the end node measures the current position of the obstacle for the first time; if so, save the current position to the edge node; if not, calculate the latest position of the obstacle based on the current position, the historical position of the obstacle and the weight, and save the latest position of the obstacle to the edge node.

[0021] In one or more embodiments of the present invention, the acquisition module is also used to: calculate the displacement of the end node based on the speed of the end node and the current time to obtain the current position of the end node; or obtain the relative position of the end node and the obstacle through a gyroscope to obtain the current position of the end node.

[0022] In one or more embodiments of the present invention, the task module is also used to: calculate the total energy required for the end node to process the task based on the energy of the end node for sending, receiving and processing data, the end node's movement energy and the energy consumed by the sensor; determine whether the total energy is less than the remaining energy of the end node; if so, mark the end node invalid; if not, the end node executes the task issued by the cloud data center.

[0023] In one or more embodiments of the present invention, the task module is further used for: the edge node generates a movement process of the end node according to the location information and task requirements, and converts the movement process into a command and sends it to the end node.

[0024] In another aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory, wherein the memory stores instructions, and when the instructions are executed by the at least one processor, the at least one processor executes the method of data processing and control based on edge computing as described above.

[0025] In another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for data processing and control based on edge computing are implemented.

[0026] Compared with the prior art, the edge computing-based data processing and control method and application according to the embodiment of the present invention can perform data exploration based on edge computing for cloud, edge, and end collaboration, and realize data exploration without predicting terrain information while ensuring node mobility, solving the delay problem of data fusion, data processing, and feedback, making the edge server also mobile and running a relatively simple data processing algorithm at the same time, and can automatically complete feedback control. At the same time, taking into account the operability of the entire system, users can run the program on the host at any time to view data or send commands anywhere connected to the network.

[0027] According to the method and application of data processing and control based on edge computing in the implementation mode of the present invention, it is possible to combine edge computing with cloud, edge and terminal applications, taking into account the real-time performance of data while taking into account the user experience; simplify the data processing method and data distribution mode of the edge part, and highly complex data can be transmitted back to the cloud data center for processing; the end node requirements are low and the price is cheap, and the edge has the ability to process and explore data in real time; the edge nodes are creatively made mobile, which can adapt to a variety of environments and backgrounds compared to the relatively fixed location of traditional edge computing hosts; the edge nodes and end nodes discover each other and have high connection efficiency; the data processing method for data exploration is reasonably allocated, and data with low computational complexity can be processed in the edge computing process, and data with complex computation will be transmitted back to the cloud data center for collaborative processing; the edge part is creatively made to have a certain ability to automatically explore and store data without human intervention, thus linking edge computing with automated control. Real-time data exploration does not require prior maps and other information; the use of edge computing in conjunction with data fusion algorithms makes the measured data more reliable; the selected algorithms are efficient and take into account the real-time nature of the data; the pathfinding algorithm is accommodated, and the mobility of edge nodes, the close distance to remote nodes, and the possession of certain computing resources make it possible to use some more complex pathfinding algorithms when exploring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is an overall flow chart of a method for data processing and control based on edge computing according to an embodiment of the present invention;

[0029] Figure 2 is a system architecture diagram of a method for data processing and control based on edge computing according to an embodiment of the present invention;

[0030] Figure 3 is a command mode flow chart A of a method for data processing and control based on edge computing according to an embodiment of the present invention;

[0031] Figure 4is a command mode flow chart B of a method for data processing and control based on edge computing according to an embodiment of the present invention;

[0032] Figure 5 is a structural diagram of a device for data processing and control based on edge computing according to an embodiment of the present invention;

[0033] Figure 6 It is a hardware structure diagram of a computing device for data processing and control based on edge computing according to one embodiment of the present invention. DETAILED DESCRIPTION

[0034] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific implementation modes.

[0035] Unless explicitly stated otherwise, throughout the specification and claims, the term “comprise” or variations such as “include” or “comprising”, etc., will be understood to include the stated elements or components but not to exclude other elements or components.

[0036] Some concepts involved in the embodiments of the present invention are introduced below.

[0037] Edge computing: refers to an open platform that integrates network, computing, storage, and application core capabilities to provide the closest services on the side close to the source of objects or data.

[0038] Data fusion: When sensors sense and collect data, if the coverage of two sensors overlaps, redundant data will be generated. Redundant data should be processed to reduce the amount of data transmission and improve data credibility.

[0039] Cloud, edge, and end collaboration: Edge-cloud collaboration is the collaboration between the edge side and the central cloud required for most deployment and application scenarios of edge computing. The cloud data center issues commands to edge nodes and collects data for centralized computing and displays it to users. Edge nodes have certain self-processing capabilities and can control end nodes to perform some actions, such as data collection and data exploration. After the data is collected in the cloud data center, the algorithm or user performs different control operations on the edge nodes based on the data content to achieve cloud, edge, and end collaboration.

[0040] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0041] Example 1

[0042] like Figures 1 to 4 As shown, a method for data processing and control based on edge computing in an embodiment of the present invention is introduced, and the method includes the following steps.

[0043] In step S101, the edge node obtains the location information of the end node and the obstacle according to the data fusion algorithm.

[0044] After the edge node runs the edge control process, it first enters the search mode and uses low-energy, short-distance communication methods such as Bluetooth / zigbee to search and discover surrounding end nodes. If the end node is not found after multiple rounds, it waits for the connection of the cloud data center and reports the current status after the connection is established. If the end node is found, a slave thread is generated to establish a connection with the end node to send, receive and control data. At the same time, it enters the initialization process, and the end nodes are made to form an equilateral polygon to measure the distance between the end nodes. This process is used to initialize the weight of each end node.

[0045]

[0046]

[0047]

[0048]

[0049] Where x is the distance between the end nodes and σ is the standard deviation.

[0050] Since this calculation process is not complicated, the result can be directly stored in the edge host after the edge node completes the calculation. However, if there are too many controlled end nodes and the edge host capacity is limited, it is necessary to report to the cloud data center for processing and then return the result.

[0051]

[0052] Among them, A i and A0 are the normal fuzzy sets of measured and estimated values ​​obtained at the i-th terminal node.

[0053] The sensors of n sensor end nodes are normalized and their weights are:

[0054]

[0055] Among them, ω i Represents the weight of each end node, saves the weight corresponding to the end node to the edge node, and notifies the cloud data center of the weight of the end node.

[0056] When the end node measures the obstacle position, if it is the first time to measure the current position, the current position will be directly reported to the edge node for storage. If it is not the first time to measure the current position, the obstacle position will be updated according to the following rules:

[0057] [x new ,y new]=[x get ,y get ]*[ω i ,ω i ]+[x pre ,y pre ]*[1-ω i , 1-ω i ]

[0058] Among them, x get ,y get is the current position of the obstacle measured by the end node, x pre ,y pre is the historical position of the obstacle measured by the end node.

[0059] The updated obstacle location is saved to the edge node, and the map information (obstacle location) is transmitted back when requested by the cloud data center.

[0060] The cloud data center uses a two-dimensional array to store two-dimensional plane obstacle information. The obstacle information fusion is completed by the edge host. When the user types a command to view the map, the content of the two-dimensional array is converted and displayed on the screen, and the content of the latest user data corresponding to each location is displayed. If the content is updated at this time, the update part is processed by the cloud data center. According to the size of the retention factor, the data update operation is:

[0061] d (t+1) =d t ·ρ+(1-ρ)·D

[0062] Among them, D is the newly measured weighted average data, d t is the old data, d (t+1) For new data, ρ is generally set to 0.75 to make data updates smooth and increase reliability.

[0063]

[0064] Among them, n is the n-times data measured by the end node, and ω is the weight of the end node.

[0065] In step S102, the edge node controls the end node to execute the task issued by the cloud data center according to the location information, and reports the task data of the end node to the cloud data center.

[0066] The cloud data center automatically initiates a connection request to the target program on the remote target machine (i.e., the edge computing control process running on the edge host) according to the set IP and port number, and connects in real time after the edge host starts the corresponding process. After the connection is completed, the cloud data center will start the self-check process, send a series of individual control commands, and determine whether all functions of the edge part are normal through the return value of the edge host. At this time, the status of the end node will not be reported to the cloud data center, but will be perceived and processed by the edge host itself when the edge host starts. If necessary, the fused status data information will be reported to the cloud data center.

[0067] The edge node receives the command from the cloud data center and forwards it to the corresponding end node, reporting data according to user requirements. During this process, since the map information (obstacle identification) is calculated at the edge node, the edge node can work independently from the cloud data center and has a certain ability to control the end node. The edge node also has a certain storage and computing capability, and can retain some information when the user is not online (the cloud data center process is not running), and report to the user when the user is back online. When the edge node is working independently, the edge node controls the end node to complete the corresponding work according to the pre-set completion. All ways to complete certain operations and explore data independently without user control are included.

[0068] After the end node executes the command, the edge node reports the data of the end node to the cloud data center, including all valid user data required by the user and the status data that the edge host reports and requests the cloud data center to process, but it cannot or is difficult to process independently. The cloud data center performs data fusion processing on the data required by the user and visualizes it on the screen. This data includes geographic data and exploration data perceived by the end node sensors.

[0069] In addition, in this process, the user can switch to the command sending mode and send control commands to the edge host. There are two ways to send control commands. One is to send a single command, which is switched to this mode with B, and each command ends with E, which can only contain one instruction; the other is to send a command string, which is switched to this mode with A and ends with E. Each instruction is separated by S. After receiving the command, the car executes the command string in sequence. This method requires additional execution time after the command. During the execution process, you can switch to the display mode to view the reported user data. Among them, the A mode can be used for the end node to temporarily leave the communication range and automatically return after completing the task according to the instruction.

[0070] The edge node uses the energy priority principle to schedule the end node. The end node is generally not considered to have unlimited energy, so when the edge node issues tasks (instructions or instruction strings) to the cloud data center, it will prioritize scheduling the end node with high energy to complete the task. The total energy required by the end node to process the task is calculated as follows:

[0071] E send =E ing +E m +E s

[0072] E ing Represents the energy of node sending, receiving and processing data, E m represents the node movement energy, E s Indicates the energy consumed by the sensor, which can generally be ignored. send The total energy required to process the task. send <The remaining energy of the end node, then mark the end node invalid. Other scheduling algorithms can also be set, such as greedy algorithm end node distance priority, minimum energy algorithm, etc. All edge self-control priority scheduling algorithms are included in the present invention.

[0073] For end nodes, the computing power is generally low and the resources are severely limited. Therefore, the end node only needs to have the most basic functions: sending and receiving data, controlling sensor collection, controlling the movement of its own vehicle, and basic computing power. After the end node is started, there is no need to do any self-test work. It needs to wait for the establishment of a connection with the edge node. The end node actively searches for the edge node and requests a connection. Only after joining the edge part can it start to work normally. If the edge node cannot be found for a long time after startup, the end node automatically sleeps. After the connection is established, the end node performs self-test and initialization operations according to the commands sent by the edge node, and reports basic information (vehicle, power, initial distance, current status, etc.).

[0074] In addition, the end node needs to read the value of the ultrasonic distance sensor at a certain frequency. This value is not reported in full, but is used as a basis for judging whether there are obstacles. If there is an obstacle (for example, distance < 20cm) and it is in front of itself, it will immediately stop executing the forward command and report the information to update the map. Here, the end node does not need to do any processing on the original data. In this way, data exploration of the edge part can be carried out without the original geographic information.

[0075] In the case where a map already exists or map information has been explored, a pathfinding algorithm can be used based on the map information, such as Dijkstra, depth-first traversal algorithm, greedy algorithm, machine learning, etc. In this embodiment, a depth-first traversal algorithm is used, and the movement process of the end node is generated in the edge node part according to the task requirements, and it is converted into a command and sent to the end node. All pathfinding calculations are completed in the edge part, and the end node only needs to accept the ready-made command.

[0076] like Figure 5 As shown, a device for data processing and control based on edge computing according to a specific embodiment of the present invention is introduced.

[0077] In an embodiment of the present invention, the device for data processing and control based on edge computing includes an acquisition module 501 and a task module 502 .

[0078] The acquisition module 501 is used for the edge node to acquire the position information of the end node and the obstacle according to the data fusion algorithm.

[0079] The task module 502 is used for the edge node to control the end node to execute the task issued by the cloud data center according to the location information, and report the task data of the end node to the cloud data center.

[0080] The acquisition module 501 is also used to: determine whether the edge node searches for the end node within a preset range; if not, the edge node establishes a connection with the cloud data center and reports the current status; if so, the edge node establishes a connection with the end node and makes the end nodes form an equilateral polygon to obtain the distance between the end nodes, and calculate the weight of the end node based on the distance and data fusion algorithm.

[0081] The acquisition module 501 is also used to: determine whether the end node measures the current position of the obstacle for the first time; if so, save the current position to the edge node; if not, calculate the latest position of the obstacle based on the current position, the historical position of the obstacle and the weight, and save the latest position of the obstacle to the edge node.

[0082] The acquisition module 501 is also used to: calculate the displacement of the end node according to the speed of the end node and the current time to obtain the current position of the end node; or obtain the relative position of the end node and the obstacle through the gyroscope to obtain the current position of the end node.

[0083] Task module 502 is also used to: calculate the total energy required for the end node to process the task based on the energy of the end node for sending, receiving and processing data, the end node's movement energy and the energy consumed by the sensor; determine whether the total energy is less than the remaining energy of the end node; if so, mark the end node invalid; if not, the end node executes the task issued by the cloud data center.

[0084] The task module 502 is also used for: the edge node generates the movement process of the end node according to the location information and task requirements, and converts the movement process into a command and sends it to the end node.

[0085] Figure 6 FIG. 6 shows a hardware structure diagram of a computing device 60 for edge computing-based data processing and control according to an embodiment of the present specification. Figure 6As shown, the computing device 60 may include at least one processor 601, a memory 602 (e.g., a non-volatile memory), a memory 603, and a communication interface 604, and the at least one processor 601, the memory 602, the memory 603, and the communication interface 604 are connected together via a bus 605. The at least one processor 601 executes at least one computer-readable instruction stored or encoded in the memory 602.

[0086] It should be understood that the computer executable instructions stored in the memory 602, when executed, cause at least one processor 601 to perform the above combined operations in various embodiments of this specification. Figure 1-6 Describes the various operations and functions.

[0087] In the embodiments of the present specification, the computing device 60 may include, but is not limited to, a personal computer, a server computer, a workstation, a desktop computer, a laptop computer, a notebook computer, a mobile computing device, a smart phone, a tablet computer, a cellular phone, a personal digital assistant (PDA), a handheld device, a messaging device, a wearable computing device, a consumer electronic device, and the like.

[0088] According to one embodiment, a program product such as a machine-readable medium is provided. The machine-readable medium may have instructions (i.e., the above-mentioned elements implemented in the form of software), which, when executed by a machine, causes the machine to perform the above-mentioned combination of various embodiments of this specification. Figure 1-6 Specifically, a system or device equipped with a readable storage medium may be provided, on which a software program code implementing the functions of any of the above-mentioned embodiments is stored, and a computer or processor of the system or device reads and executes the instructions stored in the readable storage medium.

[0089] According to the method and application of data processing and control based on edge computing in the implementation mode of the present invention, it is possible to combine edge computing with cloud, edge and terminal applications, taking into account the real-time performance of data while taking into account the user experience; simplify the data processing method and data distribution mode of the edge part, and highly complex data can be transmitted back to the cloud data center for processing; the end node requirements are low and the price is cheap, and the edge has the ability to process and explore data in real time; the edge nodes are creatively made mobile, which can adapt to a variety of environments and backgrounds compared to the relatively fixed location of traditional edge computing hosts; the edge nodes and end nodes discover each other and have high connection efficiency; the data processing method for data exploration is reasonably allocated, and data with low computational complexity can be processed in the edge computing process, and data with complex computation will be transmitted back to the cloud data center for collaborative processing; the edge part is creatively made to have a certain ability to automatically explore and store data without human intervention, thus linking edge computing with automated control. Real-time data exploration does not require prior maps and other information; the use of edge computing in conjunction with data fusion algorithms makes the measured data more reliable; the selected algorithms are efficient and take into account the real-time nature of the data; the pathfinding algorithm is accommodated, and the mobility of edge nodes, the close distance to remote nodes, and the possession of certain computing resources make it possible to use some more complex pathfinding algorithms when exploring data.

[0090] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0092] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0094] The foregoing description of specific exemplary embodiments of the present invention is for the purpose of illustration and demonstration. These descriptions are not intended to limit the present invention to the precise form disclosed, and it is clear that many changes and variations can be made based on the above teachings. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the present invention and its practical application, so that those skilled in the art can realize and utilize various different exemplary embodiments of the present invention and various different selections and changes. The scope of the present invention is intended to be limited by the claims and their equivalents.

Claims

1. A method for data processing and control based on edge computing, characterized in that: The method comprises: The edge node obtains the location information of the end node and the obstacle according to the data fusion algorithm, including: determining whether the edge node searches for the end node within a preset range; if not, the edge node establishes a connection with the cloud data center and reports the current status; if so, the edge node establishes a connection with the end node and makes the end nodes form an equilateral polygon to obtain the distance between the end nodes, and calculates the weight of the end node according to the distance and the data fusion algorithm; and The edge node controls the end node to execute the task issued by the cloud data center according to the location information, and reports the task data of the end node to the cloud data center; Wherein, calculating the weight of the end node according to the distance and data fusion algorithm includes: Where x is the distance between the end nodes and σ is the standard deviation; Among them, A i and the normal fuzzy set of measured and estimated values ​​obtained at the ith terminal node of A0; The sensors of n sensor end nodes are normalized and their weights are: Among them, ω i Represents the weight of each end node.

2. The method for data processing and control based on edge computing according to claim 1, characterized in that: The method further comprises: Determine whether the end node measures the current position of the obstacle for the first time; if so, Save the current location to the edge node; if not, The latest position of the obstacle is calculated according to the current position, the historical position of the obstacle and the weight, and the latest position of the obstacle is saved to the edge node.

3. The method for data processing and control based on edge computing according to claim 2, characterized in that: The method further comprises: Calculating the displacement of the end node according to the speed of the end node and the current time to obtain the current position of the end node; or The relative position of the end node and the obstacle is acquired through a gyroscope to obtain the current position of the end node.

4. The method for data processing and control based on edge computing according to claim 1, characterized in that: The method further comprises: Calculate the total energy required for the end node to process the task based on the energy of the end node for sending, receiving and processing data, the end node movement energy and the energy consumed by the sensor; Determine whether the total energy is less than the residual energy of the end node; if so, Mark the end node as invalid; if not, The end node executes the task issued by the cloud data center.

5. The method for data processing and control based on edge computing according to claim 1, characterized in that: The method further comprises: The edge node generates a movement process of the end node according to the location information and task requirements, and converts the movement process into a command and sends it to the end node.

6. A device for data processing and control based on edge computing, characterized in that: The device comprises: An acquisition module, used for the edge node to acquire the location information of the end node and the obstacle according to the data fusion algorithm, including: determining whether the edge node has searched for the end node within a preset range; if not, the edge node establishes a connection with the cloud data center and reports the current status; if so, the edge node establishes a connection with the end node and makes the end nodes form an equilateral polygon to obtain the distance between the end nodes, and calculates the weight of the end node according to the distance and the data fusion algorithm; and A task module, configured for the edge node to control the end node to execute the task issued by the cloud data center according to the location information, and report the task data of the end node to the cloud data center; Wherein, calculating the weight of the end node according to the distance and data fusion algorithm includes: Where x is the distance between the end nodes and σ is the standard deviation; Among them, A i and the normal fuzzy set of measured and estimated values ​​obtained at the ith terminal node of A0; The sensors of n sensor end nodes are normalized and their weights are: Among them, ω i Represents the weight of each end node.

7. An electronic device, characterized in that: include: at least one processor; as well as A memory storing instructions, which, when executed by the at least one processor, enables the at least one processor to perform the method for data processing and control based on edge computing as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for data processing and control based on edge computing as described in any one of claims 1 to 5 are implemented.

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