Elastic Internet of Things gateway based on edge large model and data processing method

Abstract

The invention provides an elastic Internet of Things gateway based on an edge large model and a data processing method, and belongs to the technical field of data processing. The data monitoring module is responsible for monitoring operation data of different ports of the Internet of Things gateway, and the data processing module is responsible for processing model data between the target terminal and a deviation data terminal on the basis of an idle port of the Internet of Things gateway, so that the reliability of data interaction of an edge large model is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and particularly relates to an elastic Internet of Things gateway and a data processing method based on an edge large model. Background Art

[0002] The edge large model, also known as the end-side large model, is a large model set at the application side, which can greatly improve the data processing efficiency of the large model. Specifically, in the invention patent application CN202411529054.7, "Human-Computer Interaction Method, Device, Equipment and Medium", a construction method of the end-side large model is given, realizing the ability of the user side to carry out inference and decision-making by itself. Even in the case of no network, it can operate efficiently and ensure a continuous interaction experience. However, there are the following technical problems in the existing technical solutions: In the process of data processing based on the edge large model, data interaction inevitably exists among different edge terminals. This makes it an urgent technical problem to carry out elastic optimization processing of the Internet of Things gateway between edge terminals in combination with the application situation of the edge large model.

[0003] In view of the above technical problems, specifically, the present application provides an elastic Internet of Things gateway and a data processing method based on an edge large model. Summary of the Invention

[0004] To achieve the object of the present invention, the present invention adopts the following technical solutions: Specifically, in the first aspect, the present application provides a data processing method, which specifically includes: S1 Take the terminal deploying the edge large model as the target terminal, determine the processing deviation situation of the model interaction data between the target terminal and other target terminals of different types. When it is determined that there are deviation data terminals in the target terminal, proceed to the next step; S2 Take the Internet of Things gateway on the communication link between the target terminal and the deviation data terminal as the associated gateway, and determine the number of deviation data terminals associated with different associated gateways; S3 Based on the processing data deviation situation of the associated deviation data terminals, when it is determined that the association degree of the deviation terminals of the associated gateway meets the requirements, based on the historical processing data of the associated gateway, determine the idle periods of the associated gateway on different dates; S4 Take the communication link where the associated gateway has deviation data terminals as the deviation communication link, determine the similarity of the distribution of the idle periods of the Internet of Things gateway devices in the deviation communication link with the associated gateway on different dates, and use the distribution similarity situation to determine the elastic control method of the associated gateway. Based on the elastic control method, realize the data processing of the model interaction data between the target terminal and the deviation data terminal.

[0005] The beneficial effects of the present invention are as follows: Based on the processing data deviation situation of the associated deviation data terminals, it is determined whether the association degree of the deviation terminals of the associated gateway meets the requirements, thereby realizing the evaluation of the differences in the data processing requirements of the model interaction data of the associated gateway from the number of associated deviation data terminals of the associated gateway and the processing data deviation situations between different deviation data terminals and the target terminal, and also laying a foundation for generating a differential elastic control method according to the differences in data processing requirements, ensuring the reliability and real-time nature of data processing.

[0006] According to the similarity of the distribution of idle periods of the Internet of Things gateway devices in the deviation communication link by the associated gateway on different dates, the elastic control method of the associated gateway is determined, thereby avoiding the occurrence of the technical problem of low accuracy in the evaluation results of the available degree of the associated gateway at different times caused by solely considering the idle periods. Through the evaluation of the coincidence of the idle periods with the Internet of Things gateway devices in the deviation communication link, the accurate evaluation of the available degree of the associated gateway is ensured, and the output of a differential elastic control strategy is realized from the perspective of the available degree.

[0007] A further technical solution lies in that the different types of model interaction data include text, voice, image, and video.

[0008] A further technical solution lies in that the processing deviation situation includes the deviation data volume of different types of model interaction data within different time periods.

[0009] A further technical solution lies in that the method for determining the deviation data terminals of the target terminal is as follows: Based on the processing deviation situation, determine the deviation data volume of the target terminal and other target terminals within different time periods for different types of model interaction data; Based on the deviation data volume of different types of model interaction data within different time periods, determine the time periods in which the deviation data volume is not within the preset deviation data volume range, and use them as data deviation time periods; Determine whether the other target terminals are the deviation data terminals of the target terminal through the proportion of the number of the data deviation time periods.

[0010] A further technical solution lies in that when the proportion of the number of the data deviation time periods is greater than the proportion of the number of deviation time periods, it is determined that the other target terminals are the deviation data terminals of the target terminal.

[0011] A further technical solution lies in that when there are no deviation data terminals for the target terminal, there is no need to perform optimization processing on the physical network gateway of the target terminal.

[0012] A further technical solution lies in that the method for determining the elastic control method of the correlation gateway is as follows: Based on the similarity of the distribution of the idle periods of the Internet of Things gateway devices in the deviation communication link by the correlation gateway on different dates, determine the number of overlapping deviation communication links in different idle periods; Based on the proportion of the number of overlapping deviation communication links in different idle periods in the number of deviation communication links, determine the idle correlation coefficients of different idle periods, and use the average value of the idle correlation coefficients of different idle periods on different dates to determine the date idle correlation coefficients of different dates; Based on the average value of the date idle correlation coefficients of different dates, determine the mean correlation coefficient of the correlation gateway, and use the mean correlation coefficient to determine the elastic control method of the correlation gateway.

[0013] A further technical solution lies in that using the mean correlation coefficient to determine the elastic control method of the correlation gateway specifically includes: When the mean correlation coefficient is greater than the preset correlation coefficient threshold, there is no need to perform elastic optimization control on the correlation gateway; When the mean correlation coefficient is not greater than the preset correlation coefficient threshold, and when the mean correlation coefficient is less than the preset mean threshold, use the preset number of spare ports to determine the control number of the idle ports of the correlation gateway, and when data processing of model data is required, use the idle ports for data processing of the model data between the target terminal and the deviation data terminal; When the mean correlation coefficient is not less than the preset mean threshold, use the product of the mean correlation coefficient and the preset proportionality factor to determine the control number of the idle ports of the correlation gateway, and when data processing of model data is required, use the idle ports for data processing of the model data between the target terminal and the deviation data terminal.

[0014] In a second aspect, the present invention provides an elastic Internet of Things gateway based on an edge large model, which is applied to the above data processing method, and specifically includes: An idle port setting module, a data monitoring module, and a data processing module; Wherein the idle port setting module is responsible for performing setting control processing on the idle ports of the Internet of Things gateway; The data monitoring module is responsible for performing monitoring processing on the operation data of different ports of the Internet of Things gateway; The data processing module is responsible for performing data processing on the model data between the target terminal and the deviation data terminal based on the idle ports of the Internet of Things gateway.

[0015] Other features and advantages will be described in the following specification. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification and the accompanying drawings.

[0016] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specifically presents preferred embodiments and, in conjunction with the accompanying drawings, provides a detailed description as follows. Description of the Drawings

[0017] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more obvious.

[0018] Figure 1 is a flowchart of a data processing method; Figure 2 is a flowchart of a method for determining a deviation data terminal of a target terminal; Figure 3 is a flowchart for determining that the association degree of a deviation terminal of an associated gateway meets the requirements; Figure 4 is a flowchart of a method for determining an elastic control method of an associated gateway; Figure 5 is a framework diagram of an elastic Internet of Things gateway based on an edge large model. Detailed Embodiments

[0019] To enable those skilled in the art of the present technology to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this specification.

[0020] In this application, by using the idle situation of the Internet of Things gateway in the data communication link between an edge node and other edge nodes, the determination of an elastic control method for the Internet of Things gateway is realized, thereby ensuring the reliability of data interaction processing between the edge node and other edge nodes.

[0021] Based on the processing of the deviation situation, determine the deviation data volume of the target terminal and other target terminals in different time periods and for different types of model interaction data. Based on the deviation data volume of different types of model interaction data in different time periods, determine the time periods in which the deviation data volume is not within the preset deviation data volume range, and use them as data deviation time periods. When the proportion of the number of data deviation time periods is greater than 0.6, then determine that the other target terminal is the deviation data terminal of the target terminal.

[0022] To process the data deviation of the associated deviation data terminal, determine the data deviation period when the deviation data volume between the associated deviation data terminal and the target terminal is not within the preset deviation data volume range. Based on the proportion of the number of data deviation periods of different associated deviation data terminals, determine the data deviation coefficients of different associated deviation data terminals. Through the weighted sum of the data deviation coefficients of different associated deviation data terminals, determine the deviation correlation coefficient of the associated gateway. When the deviation correlation coefficient is greater than the preset correlation coefficient threshold, it is determined that the association degree of the deviation terminals of the associated gateway does not meet the requirements.

[0023] Based on the similarity of the distribution of the idle periods of the associated gateway and the IoT gateway devices in the deviation communication link on different dates, determine the number of overlapping deviation communication links in different idle periods. Based on the proportion of the number of overlapping deviation communication links in different idle periods in the number of deviation communication links, determine the idle correlation coefficients of different idle periods, and use the average value of the idle correlation coefficients of different idle periods in different dates to determine the date idle correlation coefficients of different dates; When the average value of the correlation coefficients is greater than 0.6, there is no need to perform elastic optimization control on the associated gateway; When the average value of the correlation coefficients is not greater than 0.6, and when the average value of the correlation coefficients is less than 0.2, use the preset number of spare ports to determine the control number of the idle ports of the associated gateway, and when data processing of model data is required, use the idle ports to perform data processing of the model data between the target terminal and the deviation data terminal; When the average value of the correlation coefficients is not less than 0.2, use the product of the average value of the correlation coefficients and the preset proportional factor to determine the control number of the idle ports of the associated gateway, and when data processing of model data is required, use the idle ports to perform data processing of the model data between the target terminal and the deviation data terminal.

[0024] Embodiment 1 As Figure 1 shown, the present application provides a data processing method, which specifically includes: S1 Use the terminal deploying the edge large model as the target terminal, determine the processing deviation of the target terminal and other target terminals in different types of model interaction data. When it is determined that there is a deviation data terminal for the target terminal based on the processing deviation, proceed to the next step; S2 Use the IoT gateway on the communication link between the target terminal and the deviation data terminal as the associated gateway, and determine the number of deviation data terminals associated with different associated gateways; When S3 processes the data deviation situation of the associated deviation data terminal and determines that the association degree of the deviation terminal of the associated gateway meets the requirements, based on the historical processing data of the associated gateway, it determines the idle periods of the associated gateway on different dates; S4 takes the communication link of the associated gateway with the deviation data terminal as the deviation communication link, determines the similarity of the distribution of the idle periods of the associated gateway and the Internet of Things gateway devices in the deviation communication link on different dates, and uses the distribution similarity to determine the elastic control method of the associated gateway. Based on the elastic control method, it realizes the data processing of the model interaction data between the target terminal and the deviation data terminal.

[0025] Further, the different types of model interaction data include text, voice, image, and video.

[0026] Specifically, the processing of the deviation situation includes the deviation data volume of different types of model interaction data in different time periods.

[0027] It should be noted that, as Figure 2 shown, the method for determining the deviation data terminal of the target terminal is: Based on the processing of the deviation situation, it determines the deviation data volume of the target terminal and other target terminals in different time periods for different types of model interaction data; Based on the deviation data volume of different types of model interaction data in different time periods, it determines the time periods in which the deviation data volume is not within the preset deviation data volume range, and uses them as data deviation periods; Through the proportion of the number of the data deviation periods, it determines whether the other target terminals are the deviation data terminals of the target terminal.

[0028] Further, when the proportion of the number of the data deviation periods is greater than the proportion of the number of deviation periods, it determines that the other target terminals are the deviation data terminals of the target terminal.

[0029] It can be understood that when there is no deviation data terminal for the target terminal, there is no need to perform the optimization process on the physical network gateway of the target terminal.

[0030] In another possible embodiment, the method for determining the deviation data terminal of the target terminal is: Based on the processing of the deviation situation, it determines the total deviation data volume of the model interaction data between the target terminal and other target terminals in different divided time periods; Based on the total deviation data volume in different divided time periods, it determines the interaction demand periods in the divided time periods; Determine whether the other target terminal is the deviation data terminal of the target terminal according to the number of the interactive demand periods.

[0031] Furthermore, the interactive demand period is a divided period when the total amount of deviation data does not meet the requirements.

[0032] Specifically, when the number of the interactive demand periods is greater than the preset demand period threshold, it is determined that the other target terminal is the deviation data terminal of the target terminal.

[0033] Specifically, when the interactive demand quantity between the other target terminal and the target terminal is greater than the preset demand quantity threshold, it is determined that the other target terminal does not belong to the deviation data terminal of the target terminal.

[0034] It should be noted that the deviation data terminal associated with the associated gateway is determined according to whether the associated gateway is on the communication link between the deviation data terminal and the target terminal.

[0035] It can be understood that as Figure 3 shown, determining that the association degree of the deviation terminal of the associated gateway meets the requirements specifically includes: Determine the data deviation period when the deviation of the processed data of the associated deviation data terminal is such that the deviation data volume between the associated deviation data terminal and the target terminal is not within the preset deviation data volume range; Based on the proportion of the number of data deviation periods of different associated deviation data terminals, determine the data deviation coefficients of different associated deviation data terminals; Determine the deviation association coefficient of the associated gateway through the weighted sum of the data deviation coefficients of different associated deviation data terminals, and use the deviation association coefficient to determine whether the association degree of the deviation terminal of the associated gateway meets the requirements.

[0036] Furthermore, when the deviation association coefficient is greater than the preset association coefficient threshold, it is determined that the association degree of the deviation terminal of the associated gateway does not meet the requirements.

[0037] In addition, it should be noted that when the association degree of the deviation terminal of the associated gateway does not meet the requirements, the control quantity of the idle ports of the associated gateway is determined by using the preset number of standby ports, and when data processing of model data is required, the idle ports are used for data processing of the model data between the target terminal and the deviation data terminal.

[0038] Optionally, determining that the association degree of the deviation terminal of the associated gateway meets the requirements specifically includes: Based on the data deviation situation of the associated deviation data terminal, determine the data deviation period when the deviation data volume between the associated deviation data terminal and the target terminal is not within the preset deviation data volume range; Take the sum of the proportion of the number of data deviation periods of different deviation data terminals in different time periods as the sum of the proportion; Determine the average value of the proportion sum in different time periods to obtain the average proportion sum of the gateway. Use the average proportion sum to determine whether the association degree of the deviation terminals of the gateway meets the requirements.

[0039] Furthermore, when the average proportion sum is greater than the preset proportion value, it is determined that the association degree of the deviation terminals of the gateway does not meet the requirements.

[0040] In another possible embodiment, determining that the association degree of the deviation terminals of the gateway meets the requirements specifically includes: S21 Obtain the number of deviation data terminals associated with the gateway. Determine the data deviation period when the deviation data volume between the associated deviation data terminal and the target terminal is not within the preset deviation data volume range. Based on the proportion of the number of data deviation periods of different associated deviation data terminals, determine the data deviation coefficients of different associated deviation data terminals. Based on the number of deviation data terminals and the data deviation coefficients of different deviation data terminals, determine the basic deviation coefficient; S22 Based on the proportion of the number of data deviation periods of different deviation data terminals in different time periods, determine the deviation coefficients of different deviation data terminals in different time periods. Use the deviation coefficients of different deviation data terminals in different time periods to determine the distribution aggregation coefficient of the deviation periods; S23 Determine the deviation association coefficient of the gateway through the average value of the basic deviation coefficient and the distribution aggregation coefficient. Use the deviation association coefficient to determine whether the association degree of the deviation terminals of the gateway meets the requirements.

[0041] Furthermore, the idle period is the period when the data processing volume of the gateway is within the preset data processing volume range.

[0042] Specifically, as Figure 4 shown, the determination method of the elastic control method of the gateway is: Based on the similarity of the distribution of the idle periods of the gateway and the Internet of Things gateway devices in the deviation communication links on different dates, determine the number of overlapping deviation communication links in different idle periods; Determine the idle correlation coefficient of different idle periods based on the proportion of the number of overlapping deviation communication links in different idle periods to the number of deviation communication links, and determine the date idle correlation coefficient of different dates based on the average value of the idle correlation coefficients of different idle periods in different dates; Determine the average value of the correlation coefficients of the correlation gateway based on the average value of the date idle correlation coefficients of different dates, and use the average value of the correlation coefficients to determine the elastic control method of the correlation gateway.

[0043] Further, using the average value of the correlation coefficients to determine the elastic control method of the correlation gateway specifically includes: When the average value of the correlation coefficients is greater than the preset correlation coefficient threshold, there is no need to perform elastic optimization control on the correlation gateway; When the average value of the correlation coefficients is not greater than the preset correlation coefficient threshold, and when the average value of the correlation coefficients is less than the preset average threshold, determine the control quantity of the idle ports of the correlation gateway using the preset number of backup ports, and when data processing of model data is required, use the idle ports for data processing of the model data between the target terminal and the deviation data terminal; When the average value of the correlation coefficients is not less than the preset average threshold, determine the control quantity of the idle ports of the correlation gateway using the product of the average value of the correlation coefficients and the preset proportionality factor, and when data processing of model data is required, use the idle ports for data processing of the model data between the target terminal and the deviation data terminal.

[0044] In another possible embodiment, the method for determining the elastic control method of the correlation gateway is: Based on the similarity of the distribution of the idle periods of the Internet of Things gateway devices in the deviation communication links of the correlation gateway in different dates, determine the number of overlapping deviation communication links in different idle periods. When the proportion of the number of overlapping deviation communication links in different idle periods is greater than the preset deviation link number proportion on average in different dates, it is determined that there is no need to perform elastic optimization control on the correlation gateway; When there are idle periods in which the proportion of the number of overlapping deviation communication links is not greater than the preset deviation link number proportion on average in different dates: obtain the number of idle periods in which the proportion of the number of overlapping deviation communication links is not greater than the preset deviation link number proportion on average in different dates. When the number of idle periods in which the proportion of the number of overlapping deviation communication links is not greater than the preset deviation link number proportion on average in different dates is greater than the preset number of idle periods, determine the control quantity of the idle ports of the correlation gateway using the preset number of backup ports, and when data processing of model data is required, use the idle ports for data processing of the model data between the target terminal and the deviation data terminal; When the number of idle periods in which the average value on different dates is not greater than the proportion of the number of preset deviation links is not greater than the preset number of idle periods: Determine the idle correlation coefficient of different idle periods based on the proportion of the number of overlapping deviation communication links in different idle periods in the number of the deviation communication links, and determine the date idle correlation coefficient of different dates based on the average value of the idle correlation coefficients of different idle periods in different dates. When the date idle correlation coefficients of different dates are all greater than the preset idle correlation coefficient threshold, it is determined that there is no need to perform elastic optimization control on the associated gateway; When there is a date whose date idle correlation coefficient is not greater than the preset idle correlation coefficient threshold: Obtain the proportion of the number of dates whose date idle correlation coefficient is not greater than the preset idle correlation coefficient threshold. When the proportion of the number of dates whose date idle correlation coefficient is not greater than the preset idle correlation coefficient threshold does not meet the requirements, determine the control quantity of the idle ports of the associated gateway using the preset number of spare ports, and when data processing of model data is required, use the idle ports for data processing of the model data between the target terminal and the deviation data terminal; When the proportion of the number of dates whose date idle correlation coefficient is not greater than the preset idle correlation coefficient threshold meets the requirements.

[0045] Determine the gateway idle association value of the associated gateway according to the date idle correlation coefficients of different dates, and determine the elastic control method of the associated gateway using the gateway idle association value.

[0046] Specifically, determining the elastic control method of the associated gateway using the correlation coefficient mean value specifically includes: When the gateway idle association value is greater than the preset association threshold, there is no need to perform elastic optimization control on the associated gateway; When the gateway idle association value is not greater than the preset association threshold, and when the gateway idle association value is less than the preset idle threshold, determine the control quantity of the idle ports of the associated gateway using the preset number of spare ports, and when data processing of model data is required, use the idle ports for data processing of the model data between the target terminal and the deviation data terminal; When the gateway idle association value is not less than the preset idle threshold, determine the control quantity of the idle ports of the associated gateway using the product of the gateway idle association value and the preset proportional factor, and when data processing of model data is required, use the idle ports for data processing of the model data between the target terminal and the deviation data terminal.

[0047] Embodiment 2 Second aspect, as Figure 5As shown in the figure, the present invention provides an elastic Internet of Things gateway based on an edge large model, which is applied to the above-mentioned data processing method, and specifically includes: An idle port setting module, a data monitoring module, and a data processing module; Among them, the idle port setting module is responsible for setting and controlling the idle ports of the Internet of Things gateway; The data monitoring module is responsible for monitoring the operation data of different ports of the Internet of Things gateway; The data processing module is responsible for processing the model data between the target terminal and the deviation data terminal based on the idle ports of the Internet of Things gateway.

[0048] Optionally, the method for determining the deviation data terminal of the target terminal is as follows: Obtain the data volume of model interaction data of other target terminals in different time periods. When the number of time periods in which the data volume of model interaction data of other target terminal models is less than the preset data volume threshold is greater than the preset number of time periods, it is determined that the other target terminals do not belong to the deviation data terminals of the target terminal; When the number of time periods in which the data volume of model interaction data of other target terminal models is less than the preset data volume threshold is not greater than the preset number of time periods: Take the ratio of the data volume of model interaction data of other target terminals in different time periods to the data volume of the target time period as the data volume ratio. When the average value of the data volume ratios of other target terminals in different time periods is less than the preset data volume ratio threshold, it is determined that the other target terminals do not belong to the deviation data terminals of the target terminal; When the average value of the data volume ratios of other target terminals in different time periods is not less than the preset data volume ratio threshold: Based on the processing deviation situation, determine the deviation data volume of the target terminal and other target terminals in different time periods for different types of model interaction data. Based on the deviation data volume of different types of model interaction data in different time periods, determine the time periods in which the deviation data volume is not within the preset deviation data volume interval, and use them as data deviation time periods. When the proportion of the number of data deviation time periods of other target terminals and the target terminal is greater than the preset proportion of deviation time periods, it is determined that the other target terminals belong to the deviation data terminals of the target terminal; When the proportion of the number of data deviation time periods of other target terminals and the target terminal is not greater than the preset proportion of deviation time periods: Determine the deviation data volume of the target terminal and other target terminals in different time periods for different types of model interaction data based on the processing deviation situation, and combine the proportion of the number of data deviation time periods in different time periods to determine the interaction demand coefficient in different time periods. When the number of time periods with an interaction demand coefficient greater than the preset demand coefficient threshold meets the requirement, it is determined that the other target terminal belongs to the deviation data terminal of the target terminal; When the number of time periods with an interaction demand coefficient greater than the preset demand coefficient threshold does not meet the requirement: Determine the interaction demand volume between the other target terminal and the target terminal based on the interaction demand coefficient in different time periods, and use the interaction demand volume to determine whether the other target terminal is the deviation data terminal of the target terminal.

[0049] Embodiment 3 Optionally, the above step S21 includes the following content: S211 Obtain the number of deviation data terminals associated with the related gateway. When the number of deviation data terminals associated with the related gateway is greater than the preset deviation terminal number, it is determined that the association degree of the deviation terminals of the related gateway does not meet the requirement. When the number of deviation data terminals associated with the related gateway is not greater than the preset deviation terminal number, proceed to step S212; S212 Determine the data deviation time periods when the deviation data volume between the associated deviation data terminal and the target terminal is not within the preset deviation data volume range. Based on the proportion of the number of data deviation time periods of different associated deviation data terminals, determine the data deviation coefficient of different associated deviation data terminals. When there is a deviation data terminal with a data deviation coefficient greater than the set deviation coefficient threshold, proceed to step S213. When there is no deviation data terminal with a data deviation coefficient greater than the set deviation coefficient threshold, proceed to step S214; S213 When the number of deviation data terminals with a data deviation coefficient greater than the set deviation coefficient threshold does not meet the requirement, it is determined that the association degree of the deviation terminals of the related gateway does not meet the requirement. When the number of deviation data terminals with a data deviation coefficient greater than the set deviation coefficient threshold meets the requirement, proceed to step S214; S214 Determine the basic deviation coefficient based on the number of deviation data terminals and the data deviation coefficients of different deviation data terminals. When the basic deviation coefficient does not meet the requirement, it is determined that the association degree of the deviation terminals of the related gateway does not meet the requirement. When the basic deviation coefficient meets the requirement, proceed to step S215; S215 When the basic deviation coefficient is within the preset deviation coefficient range, proceed to step S22. When the basic deviation coefficient is not within the preset deviation coefficient range, determine that the association degree of the deviation terminals of the associated gateway meets the requirements.

[0050] Optionally, the following content is included in the above step S22: S221 Based on the proportion of the number of data deviation time periods of different deviation data terminals in different time periods, determine the deviation coefficients of different deviation data terminals in different time periods. Determine the deviation data distribution aggregation coefficient in different time periods based on the deviation coefficients of different deviation data terminals in different time periods. When there is a time period in which the deviation data distribution aggregation coefficient does not meet the requirements, proceed to step S222. When there is no time period in which the deviation data distribution aggregation coefficient does not meet the requirements, proceed to step S223; S222 When the number of time periods in which the deviation data distribution aggregation coefficient does not meet the requirements does not meet the requirements, determine that the association degree of the deviation terminals of the associated gateway does not meet the requirements. When the number of time periods in which the deviation data distribution aggregation coefficient does not meet the requirements meets the requirements, proceed to step S223; S223 Determine the distribution aggregation coefficient of the deviation time periods based on the deviation coefficients of different deviation data terminals in different time periods. When the distribution aggregation coefficient of the deviation time periods does not meet the requirements, determine that the association degree of the deviation terminals of the associated gateway does not meet the requirements. When the distribution aggregation coefficient of the deviation time periods meets the requirements, proceed to step S23.

[0051] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0052] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the specific order or continuous order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0053] The above description is only for one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, various changes and modifications can be made to one or more embodiments of this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A data processing method, characterized in that: Specifically include: The terminal where the edge large model is deployed is used as the target terminal, and the processing deviation of the target terminal and other target terminals in different types of model interaction data is determined. When it is determined that the target terminal has a deviation data terminal based on the processing deviation, the next step is entered; The Internet of Things gateway on the communication link between the target terminal and the deviation data terminal is used as an associated gateway, and the number of deviation data terminals associated with different associated gateways is determined; When determining that the association degree of the deviation terminal of the associated gateway meets the requirement based on the processing data deviation of the associated deviation data terminal, determine the idle time periods of the associated gateway on different dates based on the historical processing data of the associated gateway; The communication link of the associated gateway with the deviation data terminal is used as the deviation communication link, and the distribution similarity of the idle time periods of the associated gateway and the Internet of Things gateway device in the deviation communication link on different dates is determined, and the elastic control method of the associated gateway is determined by using the distribution similarity, and data processing of the model interaction data between the target terminal and the deviation data terminal is implemented based on the elastic control method.

2. The data processing method according to claim 1, characterized in that: The different types of model interaction data include text, voice, image and video.

3. The data processing method according to claim 1, characterized in that: The processing deviation situation includes the deviation data amount of different types of model interaction data in different time periods.

4. The data processing method according to claim 1, characterized in that: The method for determining the deviation data terminal of the target terminal is: Determine the deviation data amount of the target terminal and other target terminals in different types of model interaction data in different time periods based on the processing deviation situation; Based on the deviation data amount of different types of model interaction data in different time periods, determine the time period in which the deviation data amount is not within the preset deviation data amount range, and use it as the data deviation period; Whether the other target terminals are deviation data terminals of the target terminal is determined according to the proportion of the number of the data deviation periods.

5. The data processing method according to claim 4, characterized in that: When the proportion of the number of data deviation time periods is greater than the proportion of the number of deviation time periods, the other target terminal is determined to be a deviation data terminal of the target terminal.

6. The data processing method according to claim 4, characterized in that: When there is no deviation data terminal in the target terminal, there is no need to perform optimization processing on the physical network gateway of the target terminal.

7. The data processing method according to claim 1, characterized in that: The idle period is a period during which the processing data volume of the associated gateway is within a preset processing data volume range.

8. The data processing method according to claim 1, characterized in that: The method for determining the elastic control method of the associated gateway is: Determine the number of overlapping deviation communication links in different idle periods based on the similarity of the distribution of idle periods of the associated gateway and the Internet of Things gateway device in the deviation communication link on different dates; Determine the idle correlation coefficients of different idle periods by the proportion of the number of overlapping deviation communication links in different idle periods to the number of the deviation communication links, and determine the date idle correlation coefficients of different dates by the average value of the idle correlation coefficients of different idle periods on different dates; According to the average values ​​of the idle association coefficients of different dates, the mean value of the association coefficient of the association gateway is determined, and the elastic control method of the association gateway is determined by using the mean value of the association coefficient.

9. The data processing method according to claim 8, characterized in that: The elastic control method of determining the associated gateway by using the association coefficient mean value specifically includes: When the association coefficient mean value is greater than a preset association coefficient threshold value, there is no need to perform elastic optimization control of the association gateway; When the mean value of the association coefficient is not greater than a preset association coefficient threshold value, and when the mean value of the association coefficient is less than a preset mean threshold value, the control number of the idle ports of the association gateway is determined by using the preset number of spare ports, and when data processing of the model data is required, the model data between the target terminal and the deviation data terminal is processed by using the idle ports; When the mean value of the association coefficient is not less than a preset mean threshold, the product of the mean value of the association coefficient and a preset proportional factor is used to determine the control quantity of the idle ports of the associated gateway, and when data processing of the model data is required, the idle ports are used to process the model data between the target terminal and the deviation data terminal.

10. An elastic Internet of Things gateway based on an edge big model, applied to a data processing method according to any one of claims 1 to 9, characterized in that: Specifically include: Idle port setting module, data monitoring module, data processing module; The idle port setting module is responsible for setting and controlling the idle ports of the IoT gateway; The data monitoring module is responsible for monitoring and processing the operating data of different ports of the Internet of Things gateway; The data processing module is responsible for data processing of the model data between the target terminal and the deviation data terminal based on the idle port of the Internet of Things gateway.

Images