AI Data Processing Method, System and Storage Medium Based on Edge Computing

Abstract

The present invention discloses an AI data processing method, system and storage medium based on edge computing, and relates to the field of AI data processing technology, including: obtaining multiple carrier devices ZT capable of performing edge computing; screening the carrier devices ZT and optimizing the screening process; performing data processing based on edge computing using the screened carrier devices ZT through AI, and replacing the carrier devices ZT performing edge computing in real time based on the data processing; the present invention is used to solve the problem of large response delay in the response speed of AI data processing, which will cause the AI ​​data processing to be unable to respond to emergencies in a timely manner, and at the same time, the response delay of AI in data processing will increase when facing a large amount of processing data, and the load on the AI ​​system will be increased.

Description

Technical Field

[0001] The present invention relates to the field of AI data processing technology, and specifically to an AI data processing method, system and storage medium based on edge computing. Background Art

[0002] AI data processing is a method that uses artificial intelligence technology to efficiently process and analyze large-scale data, aiming to extract valuable information from massive data and help companies make wise decisions. This method can automatically identify and organize structured and unstructured data, dig out hidden patterns and associations, and provide more accurate and comprehensive data insights.

[0003] Existing methods for AI data processing are usually optimized in terms of actual data analysis. For example, by improving the data processing of airborne intelligent sensor AI, the collection accuracy of drones during long-distance collection and the optimization of the collection path are improved. Although this improvement method can optimize the collection process of drones, there is still a large response delay in the response speed of AI data processing. This will result in that when the drone is far away from the terminal and there is an emergency such as entering a tunnel or misidentifying the collection object, the large response delay will cause the AI ​​data processing to be unable to respond to the emergency in time, causing the drone to be damaged and fall or erroneously collected. For example, in the Chinese patent application publication number Cn118072559A, a method for processing AI data based on drone airborne intelligent sensors is disclosed. This solution is to solve the problems of low accuracy of existing drone long-distance data transmission and unreasonable planning of collection routes, which leads to many invalid flight paths of drones. Other improvements for AI data processing include blurring the image through AI data processing in actual image processing to increase the image retention time. Although this improvement method can increase the image retention time, when a large number of pictures are received in a short period of time, the AI ​​data processing process for each picture needs to be processed through cloud computing, which will increase the response delay of AI when processing a large number of pictures and increase the load on the AI ​​system, resulting in the inability to convert the picture in time or even picture damage. In view of this, it is necessary to improve the existing AI data processing. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to a certain extent. By proposing an AI data processing method, system and storage medium based on edge computing, it is used to solve the problem that there is a large response delay in the response speed of AI data processing in the prior art. The large response delay will cause the AI ​​data processing to be unable to respond to emergencies in a timely manner. At the same time, facing a large amount of processing data, the response delay of AI during data processing will increase and the load on the AI ​​system will be increased.

[0005] To achieve the above object, in a first aspect, the present application provides an AI data processing method based on edge computing, including the following steps:

[0006] Obtain multiple carrier devices ZT capable of performing edge computing based on the location environment where the AI is located;

[0007] Use the carrier screening method to screen the multiple carrier devices ZT, and use the screening optimization method to optimize the screening process after each screening;

[0008] Through the AI, use the screened carrier device ZT to perform data processing based on edge computing, and based on the data processing results, replace the carrier device ZT for edge computing in real time.

[0009] Further, obtaining multiple carrier devices ZT capable of performing edge computing based on the location environment where the AI is located includes:

[0010] Establish a three-dimensional rectangular coordinate system, denoted as the carrier acquisition coordinate system. Among them, the units of the X-axis, Y-axis, and Z-axis of the three-dimensional rectangular coordinate system are all meters; fix the location of the electronic device where the AI is located at the coordinate origin of the carrier acquisition coordinate system, and use the coordinate origin as the center of the sphere and the standard scene radius as the radius to make a sphere, denoted as the carrier search sphere; use the AI to obtain the devices in the actual scene corresponding to the carrier search sphere that can be used as carriers for edge computing, and denote them as carrier devices ZT 1 to carrier device ZT n , where the device that can be used as a carrier for edge computing is a terminal with computing power.

[0011] Further, using the carrier screening method to screen the multiple carrier devices ZT, and using the screening optimization method to optimize the screening process after each screening includes:

[0012] Use the carrier screening method to screen the multiple carrier devices ZT;

[0013] After each screening, use the screening optimization method to optimize the screening process.

[0014] Further, denote the electronic device where the AI is located as the AI device. The carrier screening method includes:

[0015] Use the response sub-test method, the ability sub-test method, and the depth sub-test method to obtain the best response time, the best response quantity, and the best response depth of each carrier device ZT respectively; the response sub-test method includes: use the AI device to send ICMP echo request packets to all carrier devices ZT respectively, and record the time taken by the response packet corresponding to each carrier device ZT as the response time HY 1 to response time HY n; For any carrier device ZT n1 , the AI device repeatedly sends n ICMP echo request packets to the carrier device ZT n1 and obtains the response times HY corresponding to the n response packets n1 , which are respectively denoted as MS 1 to MS n . The response fluctuation of the carrier device ZT n1 is obtained using the response fluctuation algorithm. The response fluctuation algorithm includes: where B is the response fluctuation, bi is the i-th response time HY 1 to MS n , and bp is the average value of MS n1 to MS 1 to MS n ; The minimum value of MS 1 to MS n is denoted as the best response time, and the maximum value of MS 1 to MS n is denoted as MS max ;

[0016] Obtain the response fluctuations, MS max and the best response times corresponding to all carrier devices ZT; Denote the average value of all response fluctuations as the fluctuation mean value, and denote the carrier device ZT with the smallest best response time among the carrier devices ZT with response fluctuations less than the fluctuation mean value as the response preferred device.

[0017] Further, the ability sub-test method includes: Denote the maximum value in all MS max as the ability test time; During the ability test time, use the AI device to send ICMP echo request packets to all carrier devices ZT respectively, and immediately send again after receiving the response packets, and obtain the number of ICMP echo request packets sent by the AI device to all carrier devices ZT during the ability test time, which are respectively denoted as the best response numbers NL 1 to the best response number NL n , where the AI device sends ICMP echo request packets to only one carrier device ZT at the same time;

[0018] Denote the carrier device ZT corresponding to the maximum value among the best response numbers NL 1 to the best response number NL n as the ability preferred device;

[0019] The depth sub-test method includes: Randomly obtain a keyword using big data, denoted as the test keyword; Use the AI device to connect to the carrier device ZT 1 to the carrier device ZT nAfter searching for the test keywords, record the search results as the carrier search results SJ 1 to the carrier search results SJ n , establish a plane rectangular coordinate system, denoted as the in-depth analysis coordinate system. Among them, the unit of the X-axis of the in-depth analysis coordinate system is the search time, and the unit of the Y-axis is the data quantity; for any carrier search result SJ n1 , based on the time when the AI device is connected to the carrier device ZT n1 and then search for the test keywords, and the data quantity corresponding to the growth of the search time in the carrier search result SJ n1 draw a curve in the in-depth analysis coordinate system, denoted as the search depth curve QX n1 . Among them, for any point (x0, y0) on the search depth curve QX n1 , it means that when the time to search for the test keywords after the AI device is connected to the carrier device ZT n1 is x0, the data quantity searched is y0; draw the search depth curves QX corresponding to all carrier connection devices ZT in the same in-depth analysis coordinate system. Denote the ordinate corresponding to all search depth curves QX when X = the ability test time as the best response depth of the carrier device ZT corresponding to each search depth curve QX. Denote the minimum value of all best response depths as the reference data quantity, and denote the carrier device ZT corresponding to the search depth curve QX with the ordinate equal to the reference data quantity and the minimum abscissa as the depth-optimized device.

[0020] Furthermore, the carrier screening method further includes:

[0021] Establish a plane rectangular coordinate system, denoted as the comprehensive screening coordinate system. Among them, denote the coordinate points on the X-axis of the comprehensive screening coordinate system from the origin to the right as the carrier devices ZT 1 to the carrier device ZT n . The coordinates of the Y-axis of the comprehensive screening coordinate system are set to time, response quantity, and data quantity;

[0022] When the coordinate of the Y-axis of the comprehensive screening coordinate system is time, based on the best response time corresponding to each carrier device ZT, draw n bar charts at the coordinate points from the carrier device ZT 1 to the carrier device ZT n , denoted as the time bar chart. Among them, the width of each time bar chart is one unit length of the X-axis of the comprehensive screening coordinate system, and the length of the time bar chart is the best response time corresponding to each carrier device ZT;

[0023] When the coordinate of the Y-axis of the comprehensive screening coordinate system is the response quantity, based on the best response quantity NL corresponding to each carrier device ZT, at the coordinate points from the carrier device ZT 1 to the carrier device ZT nDraw n bar charts at this position, denoted as quantity bar charts. Among them, the width of each quantity bar chart is one unit length of the X-axis of the comprehensive screening coordinate system, and the length of the quantity bar chart is the optimal response quantity corresponding to each carrier device ZT;

[0024] When the coordinate of the Y-axis of the comprehensive screening coordinate system is the data quantity, based on the optimal response depth NL corresponding to each carrier device ZT, at the coordinate point being the carrier device ZT 1 to the carrier device ZT n Draw n bar charts at this position, denoted as depth bar charts. Among them, the width of each depth bar chart is one unit length of the X-axis of the comprehensive screening coordinate system, and the length of the depth bar chart is the optimal response depth corresponding to each carrier device ZT;

[0025] Denote the sum of the areas of the time bar chart, quantity bar chart, and depth bar chart corresponding to the carrier device ZT as the comprehensive screening value, and denote the average value of the comprehensive screening value as the screening average value; Denote the carrier device ZT whose comprehensive screening value is greater than the screening average value among the response preferred device, capacity preferred device, and depth preferred device as the screening preferred device, where the maximum value of the screening preferred device is 3; Sort the carrier devices ZT other than the screening preferred devices based on the comprehensive screening value from large to small, and denote them as screening standby devices BY 1 to the screening standby device BY f , where f is a positive integer less than or equal to n and greater than or equal to n - 3.

[0026] Furthermore, the screening optimization method includes: After each screening of the carrier device ZT, when any carrier device ZT n1 is simultaneously denoted as the response preferred device, capacity preferred device, and depth preferred device, denote the carrier device ZT n1 as the device not to be screened. When using the carrier screening method to screen the carrier device ZT again, do not screen the device not to be screened.

[0027] Furthermore, using the carrier device ZT obtained by screening through AI for data processing based on edge computing, and the carrier device ZT for edge computing is replaced in real time based on the data processing result, including:

[0028] When using the AI device for data processing, screen all the carrier devices ZT in the environment where the AI device is located based on the carrier screening method, and optimize the data processing of the AI device using the edge computing processing method. The edge computing processing method includes:

[0029] When there is a device not to be screened among all the carrier devices ZT, stop screening and use the device not to be screened as the terminal for data processing based on edge computing;

[0030] When there is no device that can be exempted from screening among all carrier devices ZT, the screening and preferred device is preferentially used as the terminal for data processing based on edge computing. When the screening result does not contain the screening and preferred device, the screening standby device BY is selected as the terminal in ascending order of its serial number and data processing is performed based on the edge time.

[0031] When the AI device uses any one of the carrier devices ZT as the terminal for data processing, the AI device sends an ICMP echo request packet to the carrier device ZT serving as the terminal at every ability test time. When the time taken for the echo packet to pass is greater than the ability test time, the carrier device ZT serving as the terminal is recorded as a high-latency terminal, and the edge computing processing method is used to analyze the carrier devices ZT other than the high-latency terminal and reselect the carrier device ZT serving as the terminal.

[0032] In a second aspect, the present application further provides an AI data processing system based on edge computing, including a carrier acquisition module, a carrier screening and optimization module, and a carrier processing module.

[0033] The carrier acquisition module is used to acquire multiple carrier devices ZT capable of performing edge computing based on the location environment where the AI is located.

[0034] The carrier screening and optimization module is used to screen the multiple carrier devices ZT using the carrier screening method, and optimize the screening process after each screening using the screening optimization method.

[0035] The carrier processing module is used to perform data processing based on edge computing through the AI using the screened carrier device ZT, and replace the carrier device ZT for edge computing in real time based on the data processing result.

[0036] In a third aspect, the present application provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method are run.

[0037] The beneficial effects of the present invention: The present invention first acquires multiple carrier devices ZT capable of performing edge computing based on the location environment where the AI is located; then screens the multiple carrier devices ZT using the carrier screening method, and optimizes the screening process after each screening using the screening optimization method. The advantage of this is that by screening multiple carrier devices ZT using the carrier screening method, it can provide support for the subsequent improvement of AI data processing, ensuring that the screened carrier devices are the optimal carrier devices within the obtainable range when using edge computing to assist the AI in data processing, thereby effectively improving the processing efficiency during edge computing.

[0038] The present invention also uses the screened carrier device ZT through AI to perform data processing based on edge computing, and replaces the carrier device ZT for edge computing in real time based on the data processing results. The advantage of this is that by optimizing the AI data processing using edge computing based on the carrier device ZT, the ability to endow local devices with data processing capabilities during AI data processing can be achieved, that is, there is no need to upload data to the cloud for computing during AI data processing, which can improve the response speed during AI data processing, prevent problems where the data processing of AI cannot respond to emergencies in a timely manner due to large response delays, and at the same time, the faster response speed during AI data processing allows the AI system to always maintain a low response delay and reduce the load on the AI system when facing a large amount of data. In addition, by replacing the carrier device ZT for edge computing in real time based on the data processing results, the optimal carrier device ZT can be flexibly selected to achieve edge computing during the AI data processing process, so that the data processing of AI always maintains a fast response delay, ensuring the sustainability of AI data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a schematic block diagram of the system of the present invention;

[0040] Figure 2 is a flowchart of the steps of the method of the present invention;

[0041] Figure 3 is a schematic diagram of obtaining the depth optimization device of the present invention;

[0042] Figure 4 is a schematic diagram of the comprehensive screening coordinate system with the Y-axis coordinate of the present invention being time;

[0043] Figure 5 is a connection block diagram of the electronic device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] Embodiment 1, in the first aspect, please refer to Figure 1 As shown, the present application provides an AI data processing system based on edge computing, including a carrier acquisition module, a carrier screening and optimization module, and a carrier processing module;

[0046] The carrier acquisition module is used to acquire multiple carrier devices ZT capable of performing edge computing based on the location environment where the AI is located.

[0047] The carrier acquisition module includes a carrier device grasping unit, and the carrier device grasping unit is configured with a carrier device grasping strategy. The carrier device grasping strategy includes: establishing a three-dimensional rectangular coordinate system, denoted as the carrier acquisition coordinate system, where the units of the X-axis, Y-axis, and Z-axis of the three-dimensional rectangular coordinate system are all meters; fixing the position of the electronic device where the AI is located at the coordinate origin of the carrier acquisition coordinate system, and making a sphere with the coordinate origin as the center of the sphere and a radius of the standard scene radius, denoted as the carrier search sphere;

[0048] In the specific implementation process, the standard scene radius is the effective distance for edge computing. In this embodiment, the standard scene radius is set to 1000m. In actual situations, the standard scene radius can be adjusted according to the signal transmission situation in the actual scene. For example, in an environment with low signal reception efficiency, the standard scene radius can be reduced to ensure that all carrier devices ZT within the carrier search sphere can achieve good signal transmission with the AI device;

[0049] Use the AI to obtain the devices in the actual scene corresponding to the carrier search sphere that can be used as carriers for edge computing, and denote them as carrier devices ZT 1 to carrier device ZT n where the devices that can be used as carriers for edge computing are terminals with a certain computing ability.

[0050] In the specific implementation process, terminals with a certain computing ability can be mobile phones, gateways, routers, servers, and other terminals capable of data processing. For example, when an AI intelligent vehicle is driving without a driver, the mobile phones carried by the people in the car can be used as carriers to support edge computing, and the mobile phones can be denoted as carrier devices ZT;

[0051] The carrier screening and optimization module is used to screen multiple carrier devices ZT using the carrier screening method, and use the screening and optimization method to optimize the screening process after each screening.

[0052] The carrier screening and optimization module is configured with a carrier device screening unit and a carrier screening and optimization unit; the carrier device screening unit is configured with a carrier device screening strategy, and the carrier device screening strategy includes:

[0053] Screen multiple carrier devices ZT using the carrier screening method, and denote the electronic device where the AI is located as the AI device. The carrier screening method includes:

[0054] Use the response sub-test method, the ability sub-test method, and the depth sub-test method to obtain the best response time, the best response quantity, and the best response depth of each carrier device ZT respectively.

[0055] The response sub-test method includes: using the AI ​​device to send ICMP response request packets to all carrier devices ZT respectively, and the time taken for the response packet corresponding to each carrier device ZT is recorded as the response time HY 1 Response timeHY n ; For any carrier device ZT n1 , the AI ​​device repeatedly sends the data to the carrier device ZT n1 Send n ICMP echo request packets and obtain the corresponding response time of n echo packets n1 , respectively denoted as MS 1 To MS n , use the response fluctuation algorithm to obtain the carrier device ZT n1 The response fluctuation algorithm includes: Among them, B is the response fluctuation, bi is MS 1 To MS n The i-th response time HY n1 , bp is MS 1 To MS n The average value of MS 1 To MS n The minimum value of MS 1 To MS n The maximum value is recorded as MS max , sending ICMP response request packets is a test method for testing response speed. The shorter the time taken for the response packets after sending the ICMP response request packets, the lower the delay between the two. For example, in actual applications, when the AI ​​smart car is unmanned, the number of carrier devices ZT is 3 and they are all mobile phones. The AI ​​system sends 3 ICMP response request packets to 3 mobile phones and obtains the time taken for the response packets corresponding to the 3 mobile phones. Through data analysis, the 3 response times HY of the first mobile phone are 2ms, 1ms and 3ms respectively, the 3 response times HY of the second mobile phone are 20ms, 20ms and 20ms respectively, and the 3 response times HY of the third mobile phone are 10ms, 9ms and 11ms respectively. Then, the response fluctuations of the 3 mobile phones are calculated to be 0.67, 0 and 0.67 respectively; by obtaining the response time HY and response fluctuations of the same carrier device ZT multiple times, the network fluctuation of the carrier device ZT can be obtained. By selecting the carrier device ZT with smaller network fluctuations, it can be ensured that the signal transmission during edge computing is more stable;

[0056] Get the response fluctuations and MS corresponding to ZT of all carrier devices max and the best response time; the average value of all response fluctuations is recorded as the fluctuation mean, and the carrier device ZT with the smallest best response time among the carrier devices ZT whose response fluctuations are less than the fluctuation mean is recorded as the preferred response device.

[0057] The ability sub - test method includes: recording the maximum value among all MS max as the ability test time; within the ability test time, using the AI device to send ICMP echo request packets to all carrier devices ZT respectively, and immediately sending again after receiving the echo packet, obtaining the number of ICMP echo request packets sent by the AI device to all carrier devices ZT within the ability test time, and respectively recording them as the optimal response quantity NL 1 to the optimal response quantity NL n , where when the AI device sends ICMP echo request packets, it only sends ICMP echo request packets to one carrier device ZT at the same time. Through the ability sub - test method, the response ability of the carrier device ZT can be tested. For example, some carrier devices ZT may have a faster reaction speed only in the first response. If multiple response tests are carried out frequently, the response will become sluggish. Selecting such carrier devices ZT for edge computing of AI data processing will cause the AI data processing to become sluggish or even stuck when facing a large amount of data; for example, in actual application, when the AI intelligent vehicle is driving without a driver, the carrier device ZT is 3 mobile phones, and the ability test time is 50ms. Then, within 3 * 50ms, the AI device can be used to send ICMP echo request packets to 3 mobile phones respectively, and immediately send again after receiving the echo packet, so as to obtain the optimal response quantity NL. The more the optimal response quantity NL is, the more frequently the mobile phone can respond and transmit data when communicating with the AI device;

[0058] Taking the carrier device ZT corresponding to the maximum value among the optimal response quantity NL 1 to the optimal response quantity NL n as the ability - preferred device.

[0059] The depth sub - test method includes: randomly obtaining a keyword using big data, and recording it as the test keyword; using the AI device to connect to the carrier device ZT 1 to the carrier device ZT n respectively, and then searching for the test keyword. The search results are respectively recorded as the carrier search results SJ 1 to the carrier search results SJ n . Establish a plane rectangular coordinate system, denoted as the depth analysis coordinate system. Among them, the unit of the X - axis of the depth analysis coordinate system is the search time, and the unit of the Y - axis is the data quantity; for any one carrier search result SJ n1 , based on the time when the AI device connects to the carrier device ZT n1 and then searches for the test keyword, and the data quantity corresponding to the growth of the search time in the carrier search result SJ n1 , draw a curve in the depth analysis coordinate system, denoted as the search depth curve QXn1 , where, for any point (x0, y0) on the search depth curve QX n1 in it, it means that when the time for searching the test keyword is x0 after the AI device is connected to the carrier device ZT n1 , the number of data searched is y0; all the search depth curves QX corresponding to the carrier-connected devices ZT are plotted in the same depth analysis coordinate system. When X = the ability test time, the ordinate corresponding to all the search depth curves QX is recorded as the best response depth of the carrier device ZT corresponding to each search depth curve QX. The minimum value of all the best response depths is recorded as the reference data volume. The carrier device ZT corresponding to the search depth curve QX with the ordinate equal to the reference data volume and the minimum abscissa is recorded as the depth-optimized device.

[0060] In the specific implementation process, when the AI intelligent vehicle is in driverless mode, traffic signs can be selected as keywords. By obtaining the number of data that each carrier device ZT can search within the ability test time, the data retrieval ability of the carrier device ZT within a short period of time can be obtained. When the AI intelligent vehicle in driverless mode identifies an unrecorded traffic sign, the more data that can be searched within a short period of time, the more likely it is to retrieve the meaning represented by the unrecorded traffic sign, thus avoiding vehicle accidents; for example, in a data analysis, the obtained depth analysis coordinate system is shown in Figure 3 as shown, where Z1 is the test ability time, and the curves LL1 to LL3 are the search depth curves QX 1 to the search depth curve QX 3 , the ordinate Z2 of the point DD1 is the reference data volume. Through analysis, it can be obtained that the search depth curve QX with the ordinate equal to Z2 and the minimum abscissa is the search depth curve QX 1 , then the carrier device ZT corresponding to the search depth curve QX 1 is the depth-optimized device;

[0061] The carrier screening method also includes: establishing a plane rectangular coordinate system, denoted as the comprehensive screening coordinate system. Among them, the coordinate points on the X-axis of the comprehensive screening coordinate system from the origin to the right are respectively denoted as the carrier device ZT 1 to the carrier device ZT n , and the coordinates of the Y-axis of the comprehensive screening coordinate system are set as time, response quantity, and data quantity;

[0062] When the coordinate of the Y-axis of the comprehensive screening coordinate system is time, based on the best response time corresponding to each carrier device ZT at the coordinate point as the carrier device ZT 1 to the carrier device ZT nDraw n bar charts at this position, denoted as time bar charts. Among them, the width of each time bar chart is one unit length of the X-axis of the comprehensive screening coordinate system, and the length of the time bar chart is the optimal response time corresponding to each carrier device ZT;

[0063] When the coordinate of the Y-axis of the comprehensive screening coordinate system is the response quantity, based on the optimal response quantity NL corresponding to each carrier device ZT, at the coordinate point from carrier device ZT 1 to carrier device ZT n Draw n bar charts at this position, denoted as quantity bar charts. Among them, the width of each quantity bar chart is one unit length of the X-axis of the comprehensive screening coordinate system, and the length of the quantity bar chart is the optimal response quantity corresponding to each carrier device ZT;

[0064] When the coordinate of the Y-axis of the comprehensive screening coordinate system is the data quantity, based on the optimal response depth NL corresponding to each carrier device ZT, at the coordinate point from carrier device ZT 1 to carrier device ZT n Draw n bar charts at this position, denoted as depth bar charts. Among them, the width of each depth bar chart is one unit length of the X-axis of the comprehensive screening coordinate system, and the length of the depth bar chart is the optimal response depth corresponding to each carrier device ZT;

[0065] In the specific implementation process, for example, during the driverless operation of an AI intelligent vehicle, by analysis, the optimal response times corresponding to 3 mobile phones as carrier devices ZT 1 to carrier device ZT 3 are 2ms, 10ms, and 8ms respectively. Then, for the corresponding comprehensive screening coordinate system and time bar chart, please refer to Figure 4 as shown. Among them, the times corresponding to C1 to C5 are 2ms, 4ms, 6ms, 8ms, and 10ms respectively, and V1 to V3 are carrier devices ZT 1 to carrier device ZT 3 ;

[0066] Record the sum of the areas of the time bar chart, quantity bar chart, and depth bar chart corresponding to carrier device ZT as the comprehensive screening value, and record the average value of the comprehensive screening value as the screening average value; Record the carrier device ZT with a comprehensive screening value greater than the screening average value among the response preferred device, ability preferred device, and depth preferred device as the screening preferred device. Among them, the maximum value of the screening preferred device is 3; Sort the carrier devices ZT other than the screening preferred device in descending order based on the comprehensive screening value, and denote them as screening backup devices BY 1 to screening backup device BY f , where f is a positive integer less than or equal to n and greater than or equal to n - 3.

[0067] In the specific implementation process, the larger the comprehensive screening value is, the stronger the comprehensive ability of the carrier device ZT in terms of response time, the number of responses within a short period of time, and the search quantity within a short period of time. The response-optimized device, ability-optimized device, and depth-optimized device are the carrier devices ZT with the fastest response, the most frequent responses within a short period of time, and the strongest search ability among all carrier devices ZT. They can be preferentially used as carriers to assist the AI device in performing edge computing, so as to ensure that the data processing terminal with the fastest response, the most frequent responses within a short period of time, or the strongest search ability within the optional range can be obtained during AI data processing;

[0068] The carrier screening and optimization unit is configured with a carrier screening and optimization strategy, and the carrier screening and optimization strategy includes:

[0069] After each screening, the screening optimization method is used to optimize the screening process.

[0070] The screening optimization method includes: after each screening of the carrier device ZT, when any one carrier device ZT n1 is simultaneously recorded as the device to be preferentially selected, the ability-optimized device, and the depth-optimized device, the carrier device ZT n1 is recorded as the device not to be screened. When the carrier screening method is used to screen the carrier device ZT again, the device not to be screened is not screened. When the carrier device ZT is simultaneously recorded as the device to be preferentially selected, the ability-optimized device, and the depth-optimized device, it means that the carrier device ZT is the device with the strongest data processing ability within the optional range of the AI system. In the subsequent screening process, this device can be preferentially used as the data processing terminal for the AI device to perform edge computing without screening this device;

[0071] The carrier processing module is used to perform data processing based on edge computing by using the screened carrier device ZT through the AI, and to replace the carrier device ZT for edge computing in real time based on the data processing results.

[0072] The carrier processing module is configured with a carrier collaborative processing unit, and the carrier collaborative processing unit is configured with a carrier collaborative processing strategy. The carrier collaborative processing strategy includes: when using the AI device to perform data processing, all carrier devices ZT in the environment where the AI device is located are screened based on the carrier screening method, and the edge computing processing method is used to optimize the data processing of the AI device. The edge computing processing method includes:

[0073] When there is a device not to be screened among all carrier devices ZT, the screening is stopped and the device not to be screened is used as the terminal for data processing based on edge computing;

[0074] When there is no device that can be exempted from screening among all carrier devices ZT, the screening preferred device is preferentially used as the terminal for data processing based on edge computing. When the screening result does not contain the screening preferred device, the screening backup device BY is selected as the terminal in ascending order of its serial number and data processing is performed based on the edge time.

[0075] When an AI device uses any one of the carrier devices ZT as the terminal for data processing, an ICMP echo request packet is sent from the AI device to the carrier device ZT serving as the terminal every capability test time. When the time taken for the echo packet to pass is greater than the capability test time, the carrier device ZT serving as the terminal is recorded as a high-latency terminal, and the carrier devices ZT other than the high-latency terminal are analyzed using the edge computing processing method and a new carrier device ZT serving as the terminal is selected.

[0076] In the specific implementation process, by still sending an ICMP echo request packet from the AI device to the carrier device ZT serving as the terminal every capability test time during AI data processing, the real-time response of the carrier device ZT serving as the carrier for edge computing can be detected in real time. When the carrier device ZT has a large delay due to excessive use, the carrier device ZT can be replaced in a timely manner to ensure that the edge computing of the AI device can always maintain low latency and high stability. For example, in the actual application process, when an AI intelligent vehicle is driving without a driver, the capability test time is 50 ms. Among the 3 carrier devices ZT that are mobile phones for screening, the first mobile phone is recorded as the device that can be exempted from screening, the second mobile phone is recorded as the screening preferred device, and the third mobile phone is recorded as the screening backup device BY. And during the data processing of edge computing using the first mobile phone by the AI intelligent vehicle, the time taken for an echo packet to pass is 100 ms, then the first mobile phone is recorded as a high-latency terminal and the second mobile phone is used to continue assisting the AI intelligent vehicle in edge computing to prevent accidents from occurring during the driving of the AI intelligent vehicle due to the excessive delay of the first mobile phone.

[0077] Embodiment 2, Second aspect, please refer to Figure 2 As shown, the present application also provides an AI data processing method based on edge computing, including the following steps:

[0078] Step S1, obtain multiple carrier devices ZT capable of performing edge computing based on the location environment where the AI is located.

[0079] Step S1 includes: establishing a three-dimensional rectangular coordinate system, denoted as the carrier acquisition coordinate system, where the units of the X-axis, Y-axis, and Z-axis of the three-dimensional rectangular coordinate system are all meters; fixing the position of the electronic device where the AI is located at the coordinate origin of the carrier acquisition coordinate system, and making a sphere with the coordinate origin as the center of the sphere and the standard scene radius as the radius, denoted as the carrier search sphere; using the AI to obtain the devices that can be used as carriers for edge computing in the actual scene corresponding to the carrier search sphere, and respectively denoted as carrier devices ZT 1 to carrier device ZT n , where the devices that can be used as carriers for edge computing are terminals with certain computing capabilities.

[0080] Step S2, use the carrier screening method to screen multiple carrier devices ZT, and use the screening optimization method to optimize the screening process after each screening.

[0081] Step S2 includes: Step S201, using the carrier screening method to screen multiple carrier devices ZT. Denote the electronic device where the AI is located as the AI device. The carrier screening method includes:

[0082] Step S20101, use the response sub-test method, the ability sub-test method, and the depth sub-test method to obtain the best response time, the best response quantity, and the best response depth of each carrier device ZT respectively.

[0083] Step S20102, the response sub-test method includes: using the AI device to send ICMP echo request packets to all carrier devices ZT respectively, and record the time taken for the echo packets corresponding to each carrier device ZT as the response time HY 1 to response time HY n ; for any one carrier device ZT n1 , the AI device repeatedly sends n ICMP echo request packets to the carrier device ZT n1 and obtain the response times HY corresponding to the n echo packets n1 , and respectively denoted as MS 1 to MS n , use the response fluctuation algorithm to obtain the response fluctuation of the carrier device ZT n1 , and the response fluctuation algorithm includes: where B is the response fluctuation, bi is the i-th response time HY among MS 1 to MS n , and bp is the average value of MS n1 to MS 1 ; record the minimum value of MS n to MS 1 as the best response time, and record the maximum value of MS n to MS 1 as MS n ​max ;

[0084] Step S20103: Obtain the response fluctuations, MS max and the optimal response time corresponding to all carrier devices ZT; Denote the average value of all response fluctuations as the fluctuation mean value, and denote the carrier device ZT with the minimum optimal response time among the carrier devices ZT whose response fluctuations are less than the fluctuation mean value as the response preferred device.

[0085] Step S20104: The ability sub - test method includes: Denote the maximum value among all MS max as the ability test time; During the ability test time, use the AI device to send ICMP response request packets to all carrier devices ZT respectively, and immediately send them again after receiving the response packets, and obtain the number of ICMP response request packets sent by the AI device to all carrier devices ZT during the ability test time, which are respectively denoted as the optimal response quantity NL 1 to the optimal response quantity NL n , where when the AI device sends ICMP response request packets, it only sends ICMP response request packets to one carrier device ZT at the same time;

[0086] Denote the carrier device ZT corresponding to the maximum value among the optimal response quantity NL 1 to the optimal response quantity NL n as the ability preferred device.

[0087] Step S20105: The depth sub - test method includes: Randomly obtain a keyword using big data, denoted as the test keyword; Use the AI device to connect to the carrier device ZT 1 to the carrier device ZT n respectively, and then search for the test keyword, and denote the search results as the carrier search results SJ 1 to the carrier search results SJ n , establish a plane rectangular coordinate system, denoted as the depth analysis coordinate system, where the unit of the X - axis of the depth analysis coordinate system is the search time, and the unit of the Y - axis is the data quantity; For any one carrier search result SJ n1 , based on the time when the AI device connects to the carrier device ZT n1 and then searches for the test keyword, and the data quantity corresponding to the growth of the search time in the carrier search result SJ n1 , plot a curve in the depth analysis coordinate system, denoted as the search depth curve QX n1 , where for any point (x0, y0) on the search depth curve QX n1 , it represents that when the AI device connects to the carrier device ZT n1When the time for searching the test keyword later is x0, the number of data retrieved is y0; in the same depth analysis coordinate system, plot the search depth curves QX corresponding to all carrier-connected devices ZT, and mark the ordinate corresponding to all search depth curves QX when X = the ability test time as the optimal response depth of the carrier device ZT corresponding to each search depth curve QX. Denote the minimum value of all optimal response depths as the reference data volume, and denote the carrier device ZT corresponding to the search depth curve QX with the ordinate equal to the reference data volume and the minimum abscissa as the depth-optimized device.

[0088] Step S20106, the carrier screening method further includes: establishing a plane rectangular coordinate system, denoted as the comprehensive screening coordinate system. Among them, mark the coordinate points on the X-axis of the comprehensive screening coordinate system from the origin to the right as carrier devices ZT 1 to carrier device ZT n , and set the coordinates of the Y-axis of the comprehensive screening coordinate system as time, response quantity, and data quantity;

[0089] Step S20107, when the coordinate of the Y-axis of the comprehensive screening coordinate system is time, based on the optimal response time corresponding to each carrier device ZT, plot n bar charts at the coordinate points from carrier device ZT 1 to carrier device ZT n , denoted as time bar charts. Among them, the width of each time bar chart is one unit length of the X-axis of the comprehensive screening coordinate system, and the length of the time bar chart is the optimal response time corresponding to each carrier device ZT;

[0090] Step S20108, when the coordinate of the Y-axis of the comprehensive screening coordinate system is the response quantity, based on the optimal response quantity NL corresponding to each carrier device ZT, plot n bar charts at the coordinate points from carrier device ZT 1 to carrier device ZT n , denoted as quantity bar charts. Among them, the width of each quantity bar chart is one unit length of the X-axis of the comprehensive screening coordinate system, and the length of the quantity bar chart is the optimal response quantity corresponding to each carrier device ZT;

[0091] Step S20109, when the coordinate of the Y-axis of the comprehensive screening coordinate system is the data quantity, based on the optimal response depth NL corresponding to each carrier device ZT, plot n bar charts at the coordinate points from carrier device ZT 1 to carrier device ZT n , denoted as depth bar charts. Among them, the width of each depth bar chart is one unit length of the X-axis of the comprehensive screening coordinate system, and the length of the depth bar chart is the optimal response depth corresponding to each carrier device ZT;

[0092] Step S20110: Denote the sum of the areas of the time bar chart, quantity bar chart, and depth bar chart corresponding to the carrier device ZT as the comprehensive screening value, and denote the average value of the comprehensive screening value as the screening average value; Denote the carrier device ZT with a comprehensive screening value greater than the screening average value among the response preferred device, ability preferred device, and depth preferred device as the screening preferred device, where the maximum number of screening preferred devices is 3; Sort the carrier devices ZT other than the screening preferred devices in descending order based on the comprehensive screening value, and denote them as screening backup devices BY 1 to the screening backup device BY f , where f is a positive integer less than or equal to n and greater than or equal to n - 3.

[0093] Step S202: After each screening, use the screening optimization method to optimize the screening process.

[0094] The screening optimization method includes: After each screening of the carrier device ZT, when any carrier device ZT n1 is simultaneously recorded as the response preferred device, ability preferred device, and depth preferred device, denote the carrier device ZT n1 as the device not to be screened. When using the carrier screening method to screen the carrier device ZT again, do not screen the device not to be screened.

[0095] Step S3: Use the screened carrier device ZT through AI for data processing based on edge computing, and replace the carrier device ZT for edge computing in real time based on the data processing results.

[0096] Step S3 includes: Step S301: When using the AI device for data processing, screen all the carrier devices ZT in the environment where the AI device is located based on the carrier screening method, and use the edge computing processing method to optimize the data processing of the AI device. The edge computing processing method includes:

[0097] Step S3011: When there is a device not to be screened among all the carrier devices ZT, stop screening and use the device not to be screened as the terminal for data processing based on edge computing;

[0098] Step S3012: When there is no device not to be screened among all the carrier devices ZT, preferentially use the screening preferred device as the terminal for data processing based on edge computing. When the screening result does not contain the screening preferred device, select the screening backup device BY as the terminal in ascending order of the serial number of the screening backup device BY and perform data processing based on the edge time.

[0099] Step S302: When the AI device uses any carrier device ZT as a terminal for data processing, the AI device sends an ICMP echo request packet to the carrier device ZT serving as the terminal at intervals of the capability test time. When the time taken for the echo packet to pass is greater than the capability test time, the carrier device ZT serving as the terminal is recorded as a high-latency terminal, and the edge computing processing method is used to analyze the carrier devices ZT other than the high-latency terminal and re-select the carrier device ZT serving as the terminal.

[0100] Embodiment 3. Please refer to Figure 5 As shown, the present application also provides an electronic device, which may include: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in the AI data processing method based on edge computing are run to implement the following functions: First, based on the location environment where the AI is located, multiple carrier devices ZT capable of performing edge computing are obtained; then, the carrier screening method is used to screen the multiple carrier devices ZT, and the screening optimization method is used to optimize the screening process after each screening; finally, the AI uses the screened carrier device ZT to perform data processing based on edge computing, and the carrier device ZT for performing edge computing is replaced in real time based on the data processing results.

[0101] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software function units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0102] Embodiment 4. The present application also provides a computer-readable storage medium. The present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above AI data processing method based on edge computing are run. Through the above technical solution, when the computer program is executed by a processor, the method in any optional implementation manner of the above embodiment is executed to achieve the following functions: First, based on the location environment where the AI is located, multiple carrier devices ZT capable of performing edge computing are obtained; then the carrier screening method is used to screen the multiple carrier devices ZT, and the screening optimization method is used to optimize the screening process after each screening; finally, the AI uses the screened carrier device ZT to perform data processing based on edge computing, and the carrier device ZT for edge computing is replaced in real time based on the data processing result.

[0103] Through the description of the above embodiments, the embodiments of the present invention can be provided as a method, a system or a computer program product. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0104] In the embodiments provided by the present application, it should be understood that the disclosed system or method can be implemented in other ways. The above-described embodiments are merely illustrative. For example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of the system, module and unit can be in an electrical, mechanical or other form.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. The AI ​​data processing method based on edge computing is characterized by: The steps include: Acquire multiple carrier devices ZT capable of edge computing based on the location environment of AI; Using a carrier screening method to screen multiple carrier device ZTs, and using a screening optimization method to optimize the screening process after each screening; The carrier device ZT obtained through AI screening performs data processing based on edge computing, and replaces the carrier device ZT performing edge computing in real time based on the data processing results; The electronic device where AI is located is recorded as AI device. The carrier screening method includes: The response sub-test method, the capability sub-test method and the depth sub-test method are used to obtain the best response time, the best response quantity and the best response depth of each carrier device ZT respectively; the response sub-test method includes: using the AI ​​device to send ICMP response request packets to all carrier devices ZT respectively, and the time taken by the response packet corresponding to each carrier device ZT is recorded as response time HY1 to response time HY n ; For any carrier device ZT n1 , the AI ​​device repeatedly sends the data to the carrier device ZT n1 Send n ICMP echo request packets and obtain the corresponding response time of n echo packets n1 , respectively denoted as MS1 to MS n , use the response fluctuation algorithm to obtain the carrier device ZT n1 The response fluctuation algorithm includes: , where B is the response fluctuation and bi is the range from MS1 to MS n The i-th response time HY n1 , bp is MS1 to MS n The average value of MS1 to MS n The minimum value of is recorded as the optimal response time, and the minimum value of MS1 to MS n The maximum value is recorded as MS max ; Get the response fluctuations and MS corresponding to ZT of all carrier devices max and the best response time; the average value of all response fluctuations is recorded as the fluctuation mean, and the carrier device ZT with the smallest best response time among the carrier devices ZT whose response fluctuations are less than the fluctuation mean is recorded as the preferred response device.

2. The AI ​​data processing method based on edge computing according to claim 1, characterized in that: Based on the location environment of AI, multiple carrier devices ZT capable of edge computing are obtained, including: A three-dimensional rectangular coordinate system is established, denoted as the carrier acquisition coordinate system, where the units of the X-axis, Y-axis, and Z-axis of the three-dimensional rectangular coordinate system are all meters; the position of the electronic device where the AI ​​is located is fixed at the coordinate origin of the carrier acquisition coordinate system, and a sphere is made with the coordinate origin as the center and the radius as the standard scene radius, denoted as the carrier search sphere; the devices that can be used as edge computing carriers in the actual scene corresponding to the AI ​​acquisition carrier search sphere are denoted as carrier devices ZT1 to carrier devices ZT n , among which, the device that can serve as a carrier for edge computing is a terminal with computing capabilities.

3. The AI ​​data processing method based on edge computing according to claim 2, characterized in that: Use the carrier screening method to screen multiple carrier device ZTs, and use the screening optimization method to optimize the screening process after each screening, including: Screening multiple carrier devices ZT using a carrier screening method; After each screening run, the screening process was optimized using the screening optimization method.

4. The AI ​​data processing method based on edge computing according to claim 3 is characterized in that: The ability subtest method includes: all MS max The maximum value in is recorded as the capability test time; during the capability test time, use the AI ​​device to send ICMP response request packets to all carrier devices ZT respectively, and send them again immediately after receiving the response packets, and obtain the number of ICMP response request packets sent by the AI ​​device to all carrier devices ZT during the capability test time, which are recorded as the best response number NL1 to the best response number NL n , wherein, when the AI ​​device sends an ICMP response request packet, it only sends an ICMP response request packet to one carrier device ZT at the same time; The best response number NL1 to the best response number NL n The carrier device ZT corresponding to the maximum value in is recorded as the capability-optimized device; The deep sub-test method includes: using big data to randomly obtain a keyword, which is recorded as the test keyword; using AI devices to connect the carrier device ZT1 to the carrier device ZT n Then, the test keyword is searched and the search results are recorded as carrier search results SJ1 to carrier search results SJ n , establish a plane rectangular coordinate system, recorded as the depth analysis coordinate system, where the unit of the X-axis of the depth analysis coordinate system is the search time, and the unit of the Y-axis is the number of data; for any carrier search result SJ n1 , based on using AI devices to connect carrier devices ZT n1 The time of the post-search test keyword and the carrier search results SJ n1 The number of searched data corresponding to the increase in search time is plotted in the depth analysis coordinate system, which is recorded as the search depth curve QX n1 , where for the search depth curve QX n1 Any point (x0, y0) in the AI ​​device is connected to the carrier device ZT n1 When the time for searching the test keyword is x0, the amount of data searched is y0; draw the search depth curves QX corresponding to all carrier connection devices ZT in the same depth analysis coordinate system, mark the vertical coordinates corresponding to all search depth curves QX when X=capability test time as the optimal response depth of the carrier device ZT corresponding to each search depth curve QX, record the minimum value of all optimal response depths as the reference data volume, and record the carrier device ZT corresponding to the search depth curve QX with the vertical coordinate equal to the reference data volume and the smallest horizontal coordinate as the depth preferred device.

5. The AI ​​data processing method based on edge computing according to claim 4 is characterized in that: Vector screening also includes: A plane rectangular coordinate system is established, which is recorded as the comprehensive screening coordinate system, wherein the coordinate points from the origin to the right in the X-axis of the comprehensive screening coordinate system are recorded as carrier device ZT1 to carrier device ZT n ,The Y-axis coordinates of the comprehensive screening coordinate system are set to time, number of responses, and number of data; When the Y-axis coordinate of the comprehensive screening coordinate system is time, based on the optimal response time corresponding to each carrier device ZT, the coordinate points are carrier device ZT1 to carrier device ZT n Draw n bar graphs at , recorded as time bar graphs, where the width of each time bar graph is a unit length of the X axis of the comprehensive screening coordinate system, and the length of the time bar graph is the optimal response time corresponding to each carrier device ZT; When the coordinate of the Y axis of the comprehensive screening coordinate system is the number of responses, the coordinate points are from the carrier device ZT1 to the carrier device ZT based on the optimal number of responses NL corresponding to each carrier device ZT. n Draw n bar graphs at , which are recorded as quantity bar graphs, where the width of each quantity bar graph is a unit length of the X axis of the comprehensive screening coordinate system, and the length of the quantity bar graph is the number of best responses corresponding to each carrier device ZT; When the coordinate of the Y axis of the comprehensive screening coordinate system is the number of data, based on the optimal response depth NL corresponding to each carrier device ZT, the coordinate points are carrier device ZT1 to carrier device ZT n Draw n bar graphs at , recorded as depth bar graphs, where the width of each depth bar graph is a unit length of the X axis of the comprehensive screening coordinate system, and the length of the depth bar graph is the optimal response depth corresponding to each carrier device ZT; The sum of the areas of the time bar graph, quantity bar graph, and depth bar graph corresponding to the carrier device ZT is recorded as the comprehensive screening value, and the average value of the comprehensive screening value is recorded as the screening mean value; the carrier device ZT whose comprehensive screening value among the response preferred device, the capability preferred device, and the depth preferred device is greater than the screening mean value is recorded as the screening preferred device, where the maximum value of the screening preferred device is 3; the carrier devices ZT other than the screening preferred device are sorted from large to small based on the comprehensive screening value, and are recorded as screening spare device BY1 to screening spare device BY2 respectively. f , where f is a positive integer less than or equal to n and greater than or equal to n-3.

6. The AI ​​data processing method based on edge computing according to claim 5, characterized in that: The screening optimization method includes: after each screening of the carrier device ZT, when any carrier device ZT n1 When the carrier device ZT is recorded as the preferred device, the capability preferred device, and the depth preferred device at the same time, n1 It is recorded as the screening-free device. When the carrier screening method is used again to screen the carrier device ZT, the screening-free device is not screened.

7. The AI ​​data processing method based on edge computing according to claim 6, characterized in that: The carrier device ZT obtained through AI screening performs data processing based on edge computing, and the carrier device ZT for edge computing is replaced in real time based on the data processing results, including: When using AI devices for data processing, all carrier devices ZT in the environment of the AI ​​devices are screened based on the carrier screening method, and the data processing of the AI ​​devices is optimized using the edge computing processing method. The edge computing processing method includes: When there are screening-free devices among all carrier devices ZT, the screening is stopped and the screening-free devices are used as terminals for data processing based on edge computing; When there is no screening-free device among all carrier devices ZT, the preferred screening device is preferentially used as the terminal for data processing based on edge computing. When the screening result does not contain the preferred screening device, the screening backup device BY is selected as the terminal based on the sequence number of the screening backup device BY from small to large, and data processing is performed based on edge time; When the AI ​​device uses any carrier device ZT as a terminal for data processing, the AI ​​device sends an ICMP response request packet to the carrier device ZT serving as the terminal every capability test time. When the time taken for the response packet is greater than the capability test time, the carrier device ZT serving as the terminal is recorded as a high-latency terminal. The edge computing method is used to analyze the carrier devices ZT except the high-latency terminals and reselect the carrier device ZT as the terminal.

8. An AI data processing system based on edge computing, used to implement the AI ​​data processing method based on edge computing according to any one of claims 1 to 7, characterized in that: It includes a vector acquisition module, a vector screening and optimization module, and a vector processing module; The carrier acquisition module is used to acquire multiple carrier devices ZT capable of edge computing based on the location environment of the AI; The carrier screening optimization module is used to screen multiple carrier devices ZT using a carrier screening method, and to optimize the screening process after each screening using a screening optimization method; The carrier processing module is used to perform data processing based on edge computing using the carrier device ZT screened through AI, and to replace the carrier device ZT performing edge computing in real time based on the data processing results.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are executed.

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