A flexible Internet of Things gateway and data processing method based on edge big model

Abstract

The present invention provides a flexible Internet of Things gateway and data processing method based on an edge large model, belonging to the field of data processing technology, and specifically includes: an idle port setting module responsible for setting and controlling the idle ports of the Internet of Things gateway, a data monitoring module responsible for monitoring and processing the operating data of different ports of the Internet of Things gateway, and a data processing module 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, thereby improving the reliability of data interaction of the edge large model.

Description

Technical Field

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

[0002] The edge big model, also known as the device-side big model, is a large model set up on the application side. It can greatly improve the efficiency of the big model's data processing. Specifically, the invention patent application CN202411529054.7 "Human-computer interaction method, device, equipment and medium" provides a method for building a device-side big model. This enables the user side to have its own reasoning and decision-making capabilities. Even in the absence of a network, it can operate efficiently and ensure a continuous interactive experience. However, the existing technical solutions have the following technical problems:

[0003] In the process of data processing based on edge big models, data interaction is inevitable between different edge terminals. This makes how to combine the application of edge big models and perform elastic optimization processing of IoT gateways between edge terminals a technical problem that needs to be solved urgently.

[0004] In response to the above technical problems, this application specifically provides a flexible Internet of Things gateway and data processing method based on an edge big model. Summary of the Invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] Specifically, in a first aspect, the present application provides a data processing method, which specifically includes:

[0007] S1 takes the terminal where the edge large model is deployed as the target terminal, determines the processing deviation of different types of model interaction data between the target terminal and other target terminals, and proceeds to the next step if it is determined that the target terminal has a deviation data terminal based on the processing deviation;

[0008] S2 uses the Internet of Things gateway on the communication link between the target terminal and the deviation data terminal as an associated gateway, and determines the number of deviation data terminals associated with different associated gateways;

[0009] S3: when determining that the association degree of the deviation terminal of the associated gateway meets the requirement based on the deviation of the processing data of the associated deviation data terminal, determining the idle time periods of the associated gateway on different dates based on the historical processing data of the associated gateway;

[0010] S4 takes the communication link of the associated gateway with the deviation data terminal as the deviation communication link, determines 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, and uses the distribution similarity to determine the elasticity control method of the associated gateway, and implements data processing of the model interaction data between the target terminal and the deviation data terminal based on the elasticity control method.

[0011] The beneficial effects of the present invention are:

[0012] Based on the processing data deviation of the associated deviation data terminal, it is determined whether the association degree of the deviation terminal of the associated gateway meets the requirements, thereby realizing the evaluation of the differences in 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 between different deviation data terminals and the target terminal, and also laying the foundation for generating differentiated elastic control methods according to the differences in data processing requirements, thereby ensuring the reliability and real-time performance of data processing.

[0013] Based on the similarity in the distribution of the idle periods of the associated gateways on different dates and those of the IoT gateway devices in the deviation communication links, the elasticity control method of the associated gateways is determined, thereby avoiding the technical problem of low accuracy in the evaluation results of the availability of the associated gateways in different time periods caused by the single consideration of the idle periods. By evaluating the overlap of the idle periods of the IoT gateway devices in the deviation communication links, the accurate evaluation of the availability of the associated gateways is guaranteed, and the output of differentiated elasticity control strategies from the perspective of availability is achieved.

[0014] A further technical solution is that the different types of model interaction data include text, voice, image and video.

[0015] A further technical solution is that the processing deviation situation includes the deviation data volume of different types of model interaction data in different time periods.

[0016] A further technical solution is that the method for determining the deviation data terminal of the target terminal is:

[0017] Determining the deviation data volume 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;

[0018] 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;

[0019] 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.

[0020] A further technical solution is 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 terminals are determined to be deviation data terminals of the target terminal.

[0021] A further technical solution is that, when the target terminal does not have a deviation data terminal, there is no need to optimize the physical network gateway of the target terminal.

[0022] A further technical solution is that the elastic control method of the associated gateway is determined by:

[0023] Determine the number of overlapping deviation communication links in different idle periods based on the similarity in the distribution of idle periods of the associated gateway and the IoT gateway device in the deviation communication link on different dates;

[0024] Determine the idle correlation coefficients of different idle periods based on 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 day idle correlation coefficients of different days based on the average of the idle correlation coefficients of different idle periods on different days;

[0025] According to the average values of the idle association coefficients of different dates, the average association coefficient of the association gateway is determined, and the elastic control method of the association gateway is determined by using the average association coefficient.

[0026] A further technical solution is to use the mean value of the association coefficient to determine the elastic control method of the association gateway, which specifically includes:

[0027] When the association coefficient mean is greater than a preset association coefficient threshold, there is no need to perform elastic optimization control of the association gateway;

[0028] When the mean value of the correlation coefficient is not greater than a preset correlation coefficient threshold value, or when the mean value of the correlation coefficient is less than a preset mean threshold value, a control number of idle ports of the association gateway is determined by using a preset number of spare ports, 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;

[0029] When the mean value of the association coefficient is not less than a preset mean value threshold, the product of the mean value of the association coefficient and a preset proportional factor is used to determine the control number of the idle ports of the association 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.

[0030] In a second aspect, the present invention provides a flexible Internet of Things gateway based on an edge large model, which is applied to the above-mentioned data processing method, specifically comprising:

[0031] Idle port setting module, data monitoring module, data processing module;

[0032] The idle port setting module is responsible for setting and controlling the idle ports of the IoT gateway;

[0033] The data monitoring module is responsible for monitoring and processing the operating data of different ports of the Internet of Things gateway;

[0034] 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.

[0035] Other features and advantages will be described in the following description. The objectives and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings.

[0038] Figure 1 It is a flow chart of a data processing method;

[0039] Figure 2 is a flow chart of a method for determining a deviation data terminal of a target terminal;

[0040] Figure 3 It is a flow chart for determining whether the degree of association of the deviation terminal of the associated gateway meets the requirements;

[0041] Figure 4 is a flow chart of a method for determining a method for elastic control of an associated gateway;

[0042] Figure 5 This is a framework diagram of an elastic IoT gateway based on an edge big model. DETAILED DESCRIPTION

[0043] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this specification without creative work should fall within the scope of protection of this specification.

[0044] In this application, the idle status of the Internet of Things gateway in the data communication link between the edge node and other edge nodes is utilized to determine the elastic control method of the Internet of Things gateway, thereby ensuring the reliability of data interaction processing between the edge node and other edge nodes.

[0045] The processing deviation situation is used to determine the deviation data volume of different types of model interaction data between the target terminal and other target terminals in different time periods. Based on the deviation data volume of different types of model interaction data in different time periods, the time period in which the deviation data volume is not within the preset deviation data volume range is determined and used as the data deviation period. When the number of data deviation time periods accounts for more than 0.6, the other target terminals are determined to be the deviation data terminals of the target terminal.

[0046] Based on the processing data deviation of the associated deviation data terminal, determine the data deviation period in which 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 number proportion of the 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 association coefficient of the associated gateway; 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.

[0047] Determine the number of overlapping deviation communication links in different idle periods based on the distribution similarity of the idle periods of the associated gateway and the IoT gateway device in the deviation communication link on different dates, determine the idle correlation coefficients 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 coefficients of different dates based on the average of the idle correlation coefficients of different idle periods on different dates;

[0048] When the average value of the correlation coefficient is greater than 0.6, there is no need to perform elastic optimization control of the correlation gateway;

[0049] When the mean value of the correlation coefficient is not greater than 0.6, and when the mean value of the correlation coefficient is less than 0.2, 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 data processing of the model data between the target terminal and the deviation data terminal is performed by using the idle ports;

[0050] When the mean value of the association coefficient is not less than 0.2, the product of the mean value of the association coefficient and the preset proportional factor is used to determine the control number 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.

[0051] Example 1

[0052] like Figure 1 As shown, the present application provides a data processing method, which specifically includes:

[0053] S1 takes the terminal where the edge large model is deployed as the target terminal, determines the processing deviation of different types of model interaction data between the target terminal and other target terminals, and proceeds to the next step if it is determined that the target terminal has a deviation data terminal based on the processing deviation;

[0054] S2 uses the Internet of Things gateway on the communication link between the target terminal and the deviation data terminal as an associated gateway, and determines the number of deviation data terminals associated with different associated gateways;

[0055] S3: when determining that the association degree of the deviation terminal of the associated gateway meets the requirement based on the deviation of the processing data of the associated deviation data terminal, determining the idle time periods of the associated gateway on different dates based on the historical processing data of the associated gateway;

[0056] S4 takes the communication link of the associated gateway with the deviation data terminal as the deviation communication link, determines 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, and uses the distribution similarity to determine the elasticity control method of the associated gateway, and implements data processing of the model interaction data between the target terminal and the deviation data terminal based on the elasticity control method.

[0057] Furthermore, the different types of model interaction data include text, voice, image and video.

[0058] Specifically, the processing deviation situation includes the deviation data volume of different types of model interaction data in different time periods.

[0059] It should be noted that if Figure 2As shown, the method for determining the deviation data terminal of the target terminal is:

[0060] Determining the deviation data volume 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;

[0061] 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;

[0062] 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.

[0063] Further, 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 terminals are determined to be deviation data terminals of the target terminal.

[0064] It is understandable that when the target terminal does not have a deviation data terminal, there is no need to perform optimization processing on the physical network gateway of the target terminal.

[0065] In another possible embodiment, the method for determining the deviation data terminal of the target terminal is:

[0066] Determining the total amount of deviation data of model interaction data between the target terminal and other target terminals in different divided time periods based on the processing deviation situation;

[0067] Determining an interaction demand period in the divided time periods based on the total amount of deviation data in the different divided time periods;

[0068] Whether the other target terminal is a deviation data terminal of the target terminal is determined according to the number of the interaction requirement periods.

[0069] Furthermore, the interaction requirement period is a divided period in which the total amount of deviation data does not meet the requirement.

[0070] Specifically, when the number of the interaction demand periods is greater than a preset demand period threshold, the other target terminals are determined to be deviation data terminals of the target terminal.

[0071] Specifically, when the interaction demand between the other target terminals and the target terminal is greater than a preset demand threshold, it is determined that the other target terminals do not belong to the deviation data terminals of the target terminal.

[0072] It should be noted that the deviation data terminal associated with the association gateway is determined according to whether the association gateway is on the communication link between the deviation data terminal and the target terminal.

[0073] It is understandable that if Figure 3 As shown, determining whether the association degree of the deviation terminal of the association gateway meets the requirements specifically includes:

[0074] Determining a data deviation period in which the deviation data amount between the associated deviation data terminal and the target terminal is not within a preset deviation data amount range based on the processing data deviation of the associated deviation data terminal;

[0075] Determining data deviation coefficients of different associated deviation data terminals based on a proportion of the number of data deviation periods of different associated deviation data terminals;

[0076] The deviation correlation coefficient of the association gateway is determined by summing the weights of the data deviation coefficients of different associated deviation data terminals, and the deviation correlation coefficient is used to determine whether the correlation degree of the deviation terminal of the association gateway meets the requirements.

[0077] Furthermore, when the deviation association coefficient is greater than a preset association coefficient threshold, it is determined that the association degree of the deviation terminal of the association gateway does not meet the requirement.

[0078] It should also be noted that when the degree of association of the deviation terminal of the associated gateway does not meet the requirements, the preset number of spare ports is used to determine the control number 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.

[0079] Optionally, determining whether the association degree of the deviation terminal of the association gateway meets the requirement specifically includes:

[0080] Determining a data deviation period in which the deviation data amount between the associated deviation data terminal and the target terminal is not within a preset deviation data amount range based on the processing data deviation of the associated deviation data terminal;

[0081] The sum of the number of data deviation periods of different deviation data terminals in different time periods is taken as the sum of the number of percentages;

[0082] The average value of the number proportion of the associated gateway is determined by summing the average values of the number proportions in different time periods, and the average value of the number proportion is used to determine whether the association degree of the deviation terminal of the associated gateway meets the requirements.

[0083] Further, when the average value of the quantity proportion is greater than the preset value of the quantity proportion, it is determined that the degree of association of the deviation terminal of the associated gateway does not meet the requirement.

[0084] In another possible embodiment, determining whether the association degree of the deviation terminal of the association gateway meets the requirement specifically includes:

[0085] S21: obtaining the number of deviation data terminals associated with the associated gateway, determining a data deviation period in which the deviation data volume between the associated deviation data terminal and the target terminal is not within a preset deviation data volume range, determining data deviation coefficients of different associated deviation data terminals based on a proportion of the number of data deviation periods of different associated deviation data terminals, and determining a basic deviation coefficient based on the number of deviation data terminals and the data deviation coefficients of different deviation data terminals;

[0086] S22 determines the deviation coefficients of different deviation data terminals in different time periods based on the proportion of the number of data deviation periods of different deviation data terminals in different time periods, and determines the distribution clustering coefficients of the deviation periods based on the deviation coefficients of different deviation data terminals in different time periods;

[0087] S23 determines the deviation correlation coefficient of the association gateway by the average value of the basic deviation coefficient and the distribution clustering coefficient, and uses the deviation correlation coefficient to determine whether the correlation degree of the deviation terminal of the association gateway meets the requirement.

[0088] Furthermore, the idle period is a period during which the processing data volume of the associated gateway is within a preset processing data volume range.

[0089] Specifically, such as Figure 4 As shown, the method for determining the elastic control method of the associated gateway is:

[0090] Determine the number of overlapping deviation communication links in different idle periods based on the similarity in the distribution of idle periods of the associated gateway and the IoT gateway device in the deviation communication link on different dates;

[0091] Determine the idle correlation coefficients of different idle periods based on 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 day idle correlation coefficients of different days based on the average of the idle correlation coefficients of different idle periods on different days;

[0092] According to the average values of the idle association coefficients of different dates, the average association coefficient of the association gateway is determined, and the elastic control method of the association gateway is determined by using the average association coefficient.

[0093] Furthermore, the elastic control method of the association gateway is determined by using the association coefficient mean, specifically including:

[0094] When the association coefficient mean is greater than a preset association coefficient threshold, there is no need to perform elastic optimization control of the association gateway;

[0095] When the mean value of the correlation coefficient is not greater than a preset correlation coefficient threshold value, or when the mean value of the correlation coefficient is less than a preset mean threshold value, a control number of idle ports of the association gateway is determined by using a preset number of spare ports, 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;

[0096] When the mean value of the association coefficient is not less than a preset mean value threshold, the product of the mean value of the association coefficient and a preset proportional factor is used to determine the control number of the idle ports of the association 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.

[0097] In another possible embodiment, the elastic control method of the associated gateway is determined by:

[0098] Determining the number of overlapping deviation communication links in different idle periods based on the similarity in the distribution of idle periods of the associated gateway and the IoT gateway device in the deviation communication link on different dates; and determining that elastic optimization control of the associated gateway is unnecessary when the average value of the percentage of overlapping deviation communication links in different idle periods on different dates is greater than a preset percentage of the number of deviation links.

[0099] When there is an idle period in which the average value of the number of overlapping deviation communication links on different dates is not greater than the preset percentage of the number of deviation links: obtaining the number of idle periods in which the average value on different dates is not greater than the preset percentage of the number of deviation links; when the number of idle periods in which the average value on different dates is not greater than the preset percentage of the number of deviation links is greater than the preset number of idle periods, determining the control number of idle ports of the associated gateway using the preset number of spare ports, and when data processing of model data is required, using the spare ports to process the model data between the target terminal and the deviation data terminal;

[0100] When the average value on different days is not greater than the preset deviation link number and the number of idle periods is not greater than the preset number of idle periods:

[0101] Determining idle association coefficients for different idle periods based on the proportion of the number of overlapping deviation communication links in different idle periods to the number of the deviation communication links, and determining daily idle association coefficients for different dates based on the average of the idle association coefficients for different idle periods on different dates; when the daily idle association coefficients for different dates are all greater than a preset idle association coefficient threshold, determining that elastic optimization control of the association gateway is unnecessary;

[0102] When there is a date whose idle correlation coefficient is not greater than the preset idle correlation coefficient threshold:

[0103] Obtaining a percentage of dates whose idle correlation coefficients are not greater than a preset idle correlation coefficient threshold; if the percentage of dates whose idle correlation coefficients are not greater than the preset idle correlation coefficient threshold does not meet the requirement, determining a control number of idle ports of the association gateway using a preset number of spare ports, and using the spare ports to process the model data between the target terminal and the deviation data terminal when data processing of the model data is required;

[0104] When the proportion of dates whose idle correlation coefficient is not greater than the preset idle correlation coefficient threshold meets the requirement.

[0105] According to the date idle association coefficients of different dates, a gateway idle association value of the associated gateway is determined, and the gateway idle association value is used to determine a flexible control method of the associated gateway.

[0106] Specifically, the elastic control method for determining the association gateway using the association coefficient mean value specifically includes:

[0107] When the gateway idle association value is greater than a preset association threshold, there is no need to perform elastic optimization control of the associated gateway;

[0108] When the gateway idle association value is not greater than a preset association threshold, or when the gateway idle association value is less than a preset idle threshold, a preset number of spare ports is used to determine a control number of idle ports of the associated gateway, and when data processing of model data is required, the idle ports are used to process the model data between the target terminal and the deviation data terminal;

[0109] When the gateway idle association value is not less than a preset idle threshold, the product of the gateway idle association value 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.

[0110] Example 2

[0111] Second, as Figure 5 As shown, the present invention provides a flexible Internet of Things gateway based on an edge large model, which is applied to the above-mentioned data processing method, specifically including:

[0112] Idle port setting module, data monitoring module, data processing module;

[0113] The idle port setting module is responsible for setting and controlling the idle ports of the IoT gateway;

[0114] The data monitoring module is responsible for monitoring and processing the operating data of different ports of the Internet of Things gateway;

[0115] 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.

[0116] Optionally, the method for determining the deviation data terminal of the target terminal is:

[0117] Obtaining data volumes of model interaction data of other target terminals in different time periods, and determining that the other target terminals are not deviation data terminals of the target terminal when the number of time periods in which the data volume of the interaction data of the model of the other target terminals is less than a preset data volume threshold is greater than a preset number of time periods;

[0118] When the amount of interaction data of the other target terminal models is less than the preset data amount threshold, the number of time periods is not greater than the preset time periods:

[0119] The ratio of the data volume of the model interaction data of the other target terminals in different time periods to the data volume of the target period is used as a data volume ratio. When the average value of the data volume ratios of the other target terminals in different time periods is less than a preset data volume ratio threshold, it is determined that the other target terminals are not deviation data terminals of the target terminal.

[0120] When the average value of the data volume ratio of the other target terminals in different time periods is not less than the preset data volume ratio threshold:

[0121] Determining, based on the processing deviation situation, deviation data amounts of different types of model interaction data between the target terminal and other target terminals in different time periods; determining, based on the deviation data amounts of different types of model interaction data in different time periods, time periods in which the deviation data amount is not within a preset deviation data amount interval; and using the time periods as data deviation periods; and determining, when a proportion of the number of data deviation time periods between the other target terminals and the target terminal is greater than a proportion of the preset deviation time periods, that the other target terminals are deviation data terminals of the target terminal;

[0122] When the proportion of the data deviation periods between the other target terminals and the target terminal is not greater than the proportion of the preset deviation periods:

[0123] Determining the amount of deviation data in different types of model interaction data between the target terminal and other target terminals in different time periods based on the processing deviation situation, and determining the interaction demand coefficients in different time periods based on the proportion of the number of data deviation periods in different time periods. When the number of time periods in which the interaction demand coefficients are greater than a preset demand coefficient threshold meets the requirement, determining that the other target terminals are deviation data terminals of the target terminal;

[0124] When the number of time periods in which the interaction demand coefficient is greater than the preset demand coefficient threshold does not meet the requirement:

[0125] The interaction demand coefficients in different time periods are used to determine the interaction demand between the other target terminals and the target terminal, and the interaction demand is used to determine whether the other target terminals are deviation data terminals of the target terminal.

[0126] Example 3

[0127] Optionally, the above step S21 includes the following contents:

[0128] S211: obtaining the number of deviation data terminals associated with the association gateway. When the number of deviation data terminals associated with the association gateway is greater than a preset number of deviation terminals, determining that the association degree of the deviation terminals of the association gateway does not meet the requirement. When the number of deviation data terminals associated with the association gateway is not greater than the preset number of deviation terminals, proceeding to step S212.

[0129] S212 determines a data deviation period in which the deviation data volume between the associated deviation data terminal and the target terminal is not within a preset deviation data volume range, and determines data deviation coefficients of different associated deviation data terminals based on the proportion of the number of data deviation periods of different associated deviation data terminals. When there is a deviation data terminal with a data deviation coefficient greater than a set deviation coefficient threshold, the process proceeds to step S213; when there is no deviation data terminal with a data deviation coefficient greater than the set deviation coefficient threshold, the process proceeds to step S214;

[0130] S213: When the number of deviation data terminals whose data deviation coefficients are 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 association gateway does not meet the requirement; when the number of deviation data terminals whose data deviation coefficients are greater than the set deviation coefficient threshold meets the requirement, the process proceeds to step S214;

[0131] S214 determines a basic deviation coefficient based on the number of the 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 terminal of the association gateway does not meet the requirement. When the basic deviation coefficient meets the requirement, the process proceeds to step S215.

[0132] S215: When the basic deviation coefficient is within the preset deviation coefficient interval, the process proceeds to step S22; when the basic deviation coefficient is not within the preset deviation coefficient interval, it is determined that the association degree of the deviation terminal of the associated gateway meets the requirement.

[0133] Optionally, the above step S22 includes the following contents:

[0134] S221 determines the deviation coefficients of different deviation data terminals in different time periods based on the proportion of the number of data deviation periods of different deviation data terminals in different time periods, and determines the deviation data distribution clustering coefficients in different time periods based on the deviation coefficients of different deviation data terminals in different time periods. If there is a time period in which the deviation data distribution clustering coefficient does not meet the requirements, the process proceeds to step S222; if there is no time period in which the deviation data distribution clustering coefficient does not meet the requirements, the process proceeds to step S223;

[0135] S222: When the number of time periods in which the deviation data distribution clustering coefficient does not meet the requirement does not meet the requirement, it is determined that the association degree of the deviation terminal of the association gateway does not meet the requirement; when the number of time periods in which the deviation data distribution clustering coefficient does not meet the requirement meets the requirement, the process proceeds to step S223;

[0136] S223 determines the distribution clustering coefficient of the deviation period with the deviation coefficients of different deviation data terminals in different time periods. When the distribution clustering coefficient of the deviation period does not meet the requirements, it is determined that the association degree of the deviation terminal of the associated gateway does not meet the requirements. When the distribution clustering coefficient of the deviation period meets the requirements, proceed to step S23.

[0137] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0138] The foregoing description of this specification describes specific embodiments. 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 an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0139] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be 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 different types of model interaction data between the target terminal and other target terminals is determined. If it is determined that the target terminal has a deviation data terminal based on the processing deviation, the next step is performed; An 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 requirements based on the deviation of the processed data of the associated deviation data terminal, determining 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. The elastic control method of the associated gateway is determined based on 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, wherein: The different types of model interaction data include text, voice, image and video.

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

4. The data processing method according to claim 1, wherein: The method for determining the deviation data terminal of the target terminal is: Determining the deviation data volume 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, wherein: When the proportion of the number of data deviation periods is greater than the proportion of the number of deviation periods, the other target terminals are determined to be deviation data terminals of the target terminal.

6. The data processing method according to claim 4, wherein: 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, wherein: 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, wherein: 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 in the distribution of idle periods of the associated gateway and the IoT gateway device in the deviation communication link on different dates; Determine the idle correlation coefficients of different idle periods based on 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 day idle correlation coefficients of different days based on the average of the idle correlation coefficients of different idle periods on different days; According to the average values of the idle association coefficients of different dates, the average association coefficient of the association gateway is determined, and the elastic control method of the association gateway is determined by using the average association coefficient.

9. The data processing method according to claim 8, wherein: The elastic control method for determining the association gateway using the association coefficient mean value specifically includes: When the association coefficient mean is greater than a preset association coefficient threshold, there is no need to perform elastic optimization control of the association gateway; When the mean value of the correlation coefficient is not greater than a preset correlation coefficient threshold value, or when the mean value of the correlation coefficient is less than a preset mean threshold value, a control number of idle ports of the association gateway is determined by using a preset number of spare ports, 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; When the mean value of the association coefficient is not less than a preset mean value threshold, the product of the mean value of the association coefficient and a preset proportional factor is used to determine the control number of the idle ports of the association 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. A flexible 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.

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