Industrial Internet of Things edge computing data processing system based on multi-modal large model

Abstract

The invention provides an industrial internet of things edge computing data processing system based on a multi-modal large model, which belongs to the technical field of data processing, and specifically comprises a node screening module, a group dividing module, an effective node group, a multi-modal large model, a multi-modal large model, a multi-modal large model, a multi-modal large model, a multi-modal large model and a multi-modal large model, and the time period evaluation module is responsible for determining the co-processing time period of the effective node group, and the data processing module is responsible for determining a strategy of performing co-processing on the large model data by using the co-processing types of different time periods to determine the optimal group and the edge node, so that the reliability of data processing is improved.

Description

Technical Field

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

[0002] Multimodal large models have great advantages in aspects such as fault diagnosis and data analysis of industrial equipment. Therefore, in order to combine industrial Internet of Things edge computing nodes to analyze and process large model data, in CN202411059341.6 "An Edge Computing Resource Scheduling Optimization Method and System Based on a Large Model", an evaluation function is introduced to obtain a scheduling index. When the index reaches a threshold, an instruction is issued and the scheduler is activated to bind the target POD to the target node, solving the technical problems of unreasonable resource allocation and poor scheduling effect in the prior art, and achieving the technical effect of ensuring the stable operation of the system and high-performance services.

[0003] However, in the process of data processing using edge nodes, the load data of the Internet of Things gateways between different edge nodes is ignored. When the load rate of the Internet of Things gateways between edge nodes is relatively high, using multiple edge nodes for collaborative computing analysis and processing will not only lead to slow data processing efficiency, but also cause the load rate of the Internet of Things gateways to be too high, thereby affecting the transmission reliability of the Internet of Things transmission system to a certain extent.

[0004] In view of the above technical problems, specifically, the present application provides an industrial Internet of Things edge computing data processing system based on a multimodal large model. Summary of the Invention

[0005] To achieve the object of the present invention, the present invention adopts the following technical solutions: Specifically, the present application provides an industrial Internet of Things edge computing data processing system based on a multimodal large model, which specifically includes: A node screening module, a group division module, a time period evaluation module, and a data processing module; The node screening module is responsible for determining the available edge nodes of the edge nodes based on the historical processing data of the multimodal large model of the edge nodes under different data processing types and the processing accuracy under different data processing types; The group division module is responsible for constructing multiple node groups using the available edge nodes, and obtaining the effective node groups in the node groups based on the recognition accuracy of the historical processing data of the edge nodes in different node groups; The time period evaluation module is responsible for determining the collaborative processing time period of the effective node groups using the historical load conditions of the Internet of Things gateways between the available edge nodes in the effective node groups and the edge nodes at different time periods; The data processing module is responsible for determining the preferred group in the effective node group and the collaborative processing types in different time periods based on the changes in the collaborative processing time periods of the effective node group on different dates, and determining the strategy for the preferred group and the edge nodes to perform collaborative processing of large model data by using the collaborative processing types in different time periods.

[0006] The beneficial effects of the present invention are as follows: Based on the recognition accuracy of the historical processing data of different node groups at the edge node, the effective node group in the node group is obtained, realizing the screening of low recognition accuracy, avoiding the influence on the accuracy of the data processing result of the large model data due to low recognition accuracy, and at the same time, through the screening of the effective node group, it also lays a foundation for further screening the preferred group in combination with the load condition of the physical network gateway, improving the efficiency of the screening process of the preferred group.

[0007] Determine the strategy for the preferred group and the edge nodes to perform collaborative processing of large model data by using the collaborative processing types in different time periods, so as to realize the determination of the collaborative processing strategy of large model data from the historical load conditions of the Internet of Things gateways in different time periods, avoiding the influence on the load of the Internet of Things gateway caused by performing collaborative processing during the period of excessive use load of the Internet of Things gateway, and at the same time, improving the reliability of the data processing result of the large model data.

[0008] A further technical solution is that the data processing types include voice, text, file, image, and video.

[0009] A further technical solution is that the historical processing data includes the processed data volumes in different data processing types.

[0010] A further technical solution is that the method for determining the available edge nodes of the edge node is as follows: Based on the historical processing data of the multi-modal large model of the edge node under different data processing types, determine the processed data volume of the edge node under different data processing types, and use the proportion of the processed data volume in different data processing types in the processed data volume of the edge node to determine the weight coefficients of different data processing types; According to the processing accuracy of other edge nodes under different data processing types, and in combination with the weight coefficients of the edge node under different data processing types, determine the sum of the weight coefficients of the processing accuracy of other edge nodes, and use it as the corrected accuracy; Based on the corrected accuracy, determine whether the other edge nodes are available edge nodes.

[0011] A further technical solution is that when the correction accuracy rate of the other edge nodes is greater than a preset accuracy rate threshold, it is determined that the other edge nodes are available edge nodes.

[0012] A further technical solution is that the method for determining the preferred group in the effective node group is as follows: Based on the changes in the collaborative processing periods of the effective node group on different dates, determine the dates when different periods are recognized as collaborative processing periods, and use them as collaborative matching dates; According to the proportion of the number of collaborative matching dates of different periods within a preset time period, determine the collaborative matching coefficients of different periods; Based on the collaborative matching coefficients of different periods, determine the number of periods with collaborative matching coefficients greater than the preset matching coefficient, and use the number of periods with collaborative matching coefficients greater than the preset matching coefficient to determine the preferred group in the effective node group.

[0013] A further technical solution is that the preferred group is the effective node group with the largest number of periods having collaborative matching coefficients greater than the preset matching coefficient.

[0014] A further technical solution is that the collaborative processing types of the periods include a first type of collaborative type, a second type of collaborative type, and a third type of collaborative type.

[0015] A further technical solution is that the strategy for using the collaborative processing types of different periods to determine the preferred group and the edge nodes for collaborative processing of large model data specifically includes: When the collaborative processing type of the period is the first type of collaborative type, use the preferred group and the edge nodes to jointly perform collaborative processing of large model data; When the collaborative processing type of the period is the second type of collaborative type, it is also necessary to determine whether the average load rate of the Internet of Things gateways between the available edge nodes of the preferred group and the edge nodes within the nearest unit period is less than the preset load rate threshold. If so, use the preferred group and the edge nodes to jointly perform collaborative processing of large model data. If not, use the edge nodes to perform processing of large model data.

[0016] When the collaborative processing type of the period is the third type of collaborative type, use the edge nodes to perform processing of large model data.

[0017] Other features and advantages will be described in the subsequent description. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the description and the drawings.

[0018] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] By describing its exemplary embodiments in detail with reference to the accompanying drawings, the above and other features and advantages of the present invention will become more apparent.

[0020] Figure 1 is a flowchart of an industrial Internet of Things edge computing data processing system based on a multimodal large model; Figure 2 is a flowchart of a method for determining available edge nodes of an edge node; Figure 3 is a flowchart of a method for determining valid node groups in a node group; Figure 4 is a flowchart of a method for determining the collaborative processing period of a valid node group; Figure 5 is a flowchart of a method for determining a preferred group in a valid node group. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below 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 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.

[0022] In this application, a differentiated collaborative processing strategy is generated according to the idle situation of the Internet of Things gateway device between an edge node and other edge nodes, so as to reduce the load impact on the Internet of Things gateway device and at the same time improve the reliability of data processing of large model data by the edge node.

[0023] An available edge node is another edge node whose average processing accuracy rate under the data processing type of the edge node is greater than 0.7.

[0024] The average processing accuracy rates of the available edge nodes in a node group under different data processing types are used to determine the recognition accuracy rates under different data processing types. When the recognition accuracy rates under different data processing types are all greater than 0.8, the node group is determined to be a valid node group.

[0025] The collaborative processing period is a period when the historical load rates of the Internet of Things gateway at different times are all less than 0.5.

[0026] Based on the changes in the collaborative processing periods of the effective node groups on different dates, determine the dates of the periods recognized as collaborative processing periods, and use them as collaborative matching dates. According to the proportion of the number of collaborative matching dates of different periods within a preset time period, determine the collaborative matching coefficients of different periods, and select the effective node group with the largest number of periods having a collaborative matching coefficient greater than the preset matching coefficient as the preferred group.

[0027] The collaborative processing types of the periods include type I collaborative type, type II collaborative type, and type III collaborative type.

[0028] Embodiment 1 As Figure 1 shown, the present application provides an industrial Internet of Things edge computing data processing system based on a multimodal large model, specifically including: A node screening module, a group division module, a period evaluation module, and a data processing module; The node screening module is responsible for determining the available edge nodes of the edge nodes based on the historical processing data of the multimodal large model of the edge nodes under different data processing types and the processing accuracy rates under different data processing types; The group division module is responsible for using the available edge nodes to construct multiple node groups, and obtaining the effective node groups in the node groups based on the recognition accuracy rates of the historical processing data of the edge nodes of different node groups; The period evaluation module is responsible for determining the collaborative processing periods of the effective node groups by using the historical load conditions of the Internet of Things gateways between the available edge nodes in the effective node groups and the edge nodes at different periods; The data processing module is responsible for determining the preferred group in the effective node groups and the collaborative processing types of different periods based on the changes in the collaborative processing periods of the effective node groups on different dates, and using the collaborative processing types of different periods to determine the strategy for the preferred group and the edge nodes to perform collaborative processing of large model data.

[0029] Further, the data processing types include voice, text, file, image, and video.

[0030] Specifically, the historical processing data includes the amounts of processing data of different data processing types.

[0031] Specifically, as Figure 2 shown, the method for determining the available edge nodes of the edge nodes is: Based on the historical processed data of the multimodal large model of the edge node under different data processing types, determine the amount of processed data of the edge node under different data processing types, and use the proportion of the amount of processed data under different data processing types in the amount of processed data of the edge node to determine the weight coefficients of different data processing types; According to the processing accuracy of other edge nodes under different data processing types, and combining the weight coefficients of the edge node under different data processing types, determine the sum of the weight coefficients of the processing accuracy of other edge nodes, and use it as the corrected accuracy; Based on the corrected accuracy, determine whether the other edge nodes are available edge nodes.

[0032] Further, when the corrected accuracy of the other edge nodes is greater than the preset accuracy threshold, determine that the other edge nodes are available edge nodes.

[0033] In another possible embodiment, the method for determining available edge nodes of the edge node is: Based on the historical processed data of the multimodal large model of the edge node under different data processing types, determine the amount of processed data of the edge node under different data processing types, and use the data processing types with the amount of processed data greater than the preset data volume threshold among the amounts of processed data under different data processing types, and use it as the matching data type; According to the processing accuracy of other edge nodes under different matching data processing types, determine the average value of the processing accuracy of the other edge nodes under different processing accuracies, and use it as the average accuracy; Based on the average accuracy, determine whether the other edge nodes are available edge nodes.

[0034] Further, when the average accuracy of the other edge nodes is greater than the preset accuracy threshold, determine that the other edge nodes are available edge nodes.

[0035] In addition, it should be noted that using the available edge nodes to construct multiple node groups specifically includes: Freely combine different available edge nodes to obtain multiple node groups.

[0036] In one possible embodiment, as Figure 3 shown, the method for determining the effective node groups in the node groups is: Based on the constituent data of the available edge nodes in the node group, determine the matching edge nodes in the node group; Determine the group processing accuracy rate of the node group under different data processing types and different data volume ranges based on the average value of the processing accuracy rates of different matching edge nodes under different data processing types and different data volume ranges; Determine the weight coefficients in different data volume ranges based on the proportion of the historical processing times of the edge nodes under different data processing types and different data volume ranges, and combine the group processing accuracy rates of the node group under different data processing types and different data volume ranges to determine the recognition accuracy rate of the node group, and use the recognition accuracy rate to determine whether the node group is a valid node group.

[0037] Furthermore, the valid node group is a node group whose recognition accuracy rate meets the requirements.

[0038] Optionally, the method for determining the valid node group in the node group is as follows: Determine the matching edge nodes in the node group based on the constituent data of the available edge nodes in the node group; Determine the group processing accuracy rate of the node group under different data processing types and different data volume ranges based on the average value of the processing accuracy rates of different matching edge nodes under different data processing types and different data volume ranges; Determine the recognition accuracy rate of the node group based on the average value of the group processing accuracy rates of the node group under different data processing types and different data volume ranges, and use the recognition accuracy rate to determine whether the node group is a valid node group.

[0039] Optionally, the method for determining the valid node group in the node group is as follows: Determine the matching edge nodes in the node group based on the constituent data of the available edge nodes in the node group; Determine the group processing accuracy rate of the node group under different data processing types and different data volume ranges based on the average value of the processing accuracy rates of different matching edge nodes under different data processing types and different data volume ranges. When the group processing accuracy rates of the node group under different data processing types and different data volume ranges all meet the requirements, then determine that the node group is a valid node group; When there is a data volume range in the node group where the group recognition accuracy rate does not meet the requirements: Obtain the number of data volume ranges in which the group recognition accuracy rate of the node group does not meet the requirements under different data processing types. When the number of data volume ranges in which the group recognition accuracy rate of the node group does not meet the requirements does not meet the requirements for different data processing types, then determine that the node does not belong to the valid node group; When there is no data processing type in the node group where the number of data volume intervals with unsatisfactory group recognition accuracy does not meet the requirement: Use the number of data volume intervals with unsatisfactory group recognition accuracy under different data processing types to calculate the total number of data volume intervals with unsatisfactory group recognition accuracy. When the total number of data volume intervals with unsatisfactory group recognition accuracy is greater than the preset interval number, it is determined that the node does not belong to the valid node group; When the total number of data volume intervals with unsatisfactory group recognition accuracy is not greater than the preset interval number: Use the number of data volume intervals with unsatisfactory group recognition accuracy under different data processing types and the group recognition accuracy under different data volume intervals to determine the recognition reliability coefficient under different data processing types. When there is a data processing type with an unsatisfactory recognition reliability coefficient, it is determined that the node does not belong to the valid node group; When there is no data processing type with an unsatisfactory recognition reliability coefficient: Use the average value of the recognition reliability coefficients of the node group under different data processing types to determine the recognition accuracy of the node group, and use the recognition accuracy to determine whether the node group is a valid node group.

[0040] Specifically, as Figure 4 shown, the method for determining the collaborative processing period of the valid node group is: Regard the Internet of Things gateway between the available edge nodes and the edge nodes as the node connection gateway; According to the average value of the load rates of the node connection gateways at different moments in the period for different available edge nodes, determine the average gateway load rate at different moments; Based on the average gateway load rate, determine the high-load moments in the period, and use the proportion of the number of high-load moments in the period to determine whether the period is the collaborative processing period of the valid node group.

[0041] Furthermore, the high-load moment is the moment when the average gateway load rate does not meet the requirement.

[0042] In addition, it should be noted that when the proportion of the number of high-load moments in the period is greater than the preset proportion of the number of load moments, it is determined that the period is the collaborative processing period of the valid node group.

[0043] It can be understood that, as Figure 5 shown, the method for determining the preferred group in the valid node group is: Based on the changes in the collaborative processing periods of the effective node groups on different dates, determine the dates when different periods are recognized as collaborative processing periods, and use them as collaborative matching dates; According to the proportion of the number of collaborative matching dates of different periods within a preset time period, determine the collaborative matching coefficients of different periods; Based on the collaborative matching coefficients of different periods, determine the number of periods with collaborative matching coefficients greater than the preset matching coefficient, and use the number of periods with collaborative matching coefficients greater than the preset matching coefficient to determine the preferred group in the effective node groups.

[0044] Furthermore, the preferred group is the effective node group with the largest number of periods having collaborative matching coefficients greater than the preset matching coefficient.

[0045] In addition, it should be noted that the collaborative processing types of the periods include type I collaborative type, type II collaborative type, and type III collaborative type.

[0046] It should be noted that the method for determining the collaborative processing types of the periods is as follows: When the collaborative matching coefficient of the period is greater than the preset matching coefficient threshold, determine that the collaborative processing type of the period is type I collaborative type; When the collaborative matching coefficient of the period is not greater than the preset matching coefficient threshold, it is also necessary to further determine whether the collaborative matching coefficient of the period is within the preset collaborative matching coefficient range. If so, determine that the collaborative processing type of the period is type II collaborative type; if not, determine that the collaborative processing type of the period is type III collaborative type.

[0047] Optionally, the method for determining the preferred group in the effective node groups is as follows: S41 Determine the gateway idle coefficient of the effective node group based on the number of collaborative processing periods of the effective node group on different dates within a preset time period; S42 Based on the changes in the collaborative processing periods of the effective node group on different dates, determine the dates when different periods are recognized as collaborative processing periods, and use them as collaborative matching dates. According to the proportion of the number of collaborative matching dates of different periods within a preset time period, determine the collaborative matching coefficients of different periods, and determine the idle period change coefficient of the effective node group based on the collaborative matching coefficients of different periods; S43 Based on the idle period change coefficient and the gateway idle coefficient, determine the gateway availability coefficient of the effective node group, and use the gateway availability coefficient to determine the preferred group in the effective node group.

[0048] Furthermore, the preferred group is the effective node group with the largest gateway availability coefficient.

[0049] Specifically, a strategy for collaborative processing of large model data by determining the preferred group and edge nodes using collaborative processing types in different time periods is as follows: When the collaborative processing type in the time period is a type I collaborative type, the preferred group and edge nodes are used to jointly perform collaborative processing of large model data; When the collaborative processing type in the time period is a type II collaborative type, it is also necessary to determine whether the average load rate of the Internet of Things gateways between the available edge nodes of the preferred group and the edge nodes in the most recent unit time period is less than a preset load rate threshold. If so, the preferred group and edge nodes are used to jointly perform collaborative processing of large model data. If not, the edge nodes are used to process the large model data.

[0050] When the collaborative processing type in the time period is a type III collaborative type, the edge nodes are used to process the large model data.

[0051] Embodiment 2 Optionally, the above step S41 includes the following content: S411 determines whether the number of collaborative processing time periods of the effective node group on different dates within a preset time period is less than the preset number of processing time periods. If so, it is determined that the effective node group does not belong to the preferred group. When there is a date when the number of collaborative processing time periods of the effective node group is not less than the preset number of processing time periods, proceed to step S412; S412 obtains the proportion of the number of dates when the number of collaborative processing time periods of the effective node group is not less than the preset number of processing time periods. When the proportion of the number of dates does not meet the requirements, it is determined that the effective node group does not belong to the preferred group. When the proportion of the number of dates meets the requirements, proceed to step S413; S413 determines the gateway idle coefficient of the effective node group based on the number of collaborative processing time periods of the effective node group on different dates within a preset time period. When the gateway idle coefficient of the effective node group is less than the preset idle coefficient threshold, it is determined that the effective node group does not belong to the preferred group. When the gateway idle coefficient of the effective node group is less than the preset idle coefficient threshold, proceed to step S42.

[0052] Optionally, the above step S42 includes the following content: S421 determines the dates of the collaborative processing periods recognized in different periods based on the changes in the collaborative processing periods of the effective node group on different dates, and takes them as the collaborative matching dates. According to the proportion of the number of collaborative matching dates of different periods within a preset time period, the collaborative matching coefficients of different periods are determined. When the collaborative matching coefficients of different periods are all less than the preset collaborative matching coefficient threshold, it is determined that the effective node group does not belong to the preferred group. When there are periods with collaborative matching coefficients not less than the preset collaborative matching coefficient threshold, it proceeds to step S413; S422 obtains the number of periods with collaborative matching coefficients not less than the preset collaborative matching coefficient threshold. When the number of periods with collaborative matching coefficients not less than the preset collaborative matching coefficient threshold is less than the preset number of collaborative periods, it proceeds to step S423. When the number of periods with collaborative matching coefficients not less than the preset collaborative matching coefficient threshold is not less than the preset number of collaborative periods, it proceeds to step S424; S423 When the gateway idle coefficient of the effective node group is within the preset idle coefficient range, it is determined that the effective node group does not belong to the preferred group. When the gateway idle coefficient of the effective node group is not within the preset idle coefficient range, it proceeds to step S424; S424 determines the idle period variation coefficient of the effective node group according to the collaborative matching coefficients of different periods. When the idle period variation coefficient of the effective node group does not meet the requirements, it is determined that the effective node group does not belong to the preferred group. When the idle period variation coefficient of the effective node group meets the requirements, it proceeds to step S43.

[0053] Embodiment 3 Optionally, the method for determining the available edge nodes of the edge nodes is as follows: Based on the historical processing data of the multi-modal large model of the edge node under different data processing types, determine the data processing types for which the edge node has data processing, and take them as the node processing types. When the average processing accuracy of other edge nodes under different node processing types does not meet the requirements, it is determined that the other edge nodes do not belong to the available edge nodes; When the average processing accuracy of other edge nodes under different node processing types meets the requirements: Based on the historical processing data of the multi-modal large model of the edge node under different data processing types, determine the processing data volumes of the edge node under different data processing types, and use the processing data volumes under different data processing types to perform data processing types with processing data volumes greater than the preset data volume threshold, and take them as the matching data types. When there are matching data types with processing accuracies that do not meet the requirements for other edge nodes, it is determined that the other edge nodes do not belong to the available edge nodes; When there is no matching data type with a processing accuracy rate that does not meet the requirements in the other edge nodes: According to the processing accuracy rates of the other edge nodes under different matching data processing types, determine the average value of the processing accuracy rates of the other edge nodes under different processing accuracy rates, and use it as the average accuracy rate. When the average accuracy rate does not meet the requirements, it is determined that the other edge nodes do not belong to the available edge nodes; When the average accuracy rate meets the requirements: Use the proportion of the processed data volume under different data processing types in the processed data volume of the edge node to determine the weight coefficients of different data processing types; According to the processing accuracy rates of the other edge nodes under different data processing types, and in combination with the weight coefficients of the edge node under different data processing types, determine the sum of the weight coefficients of the processing accuracy rates of the other edge nodes, and use it as the corrected accuracy rate. Based on the corrected accuracy rate, determine whether the other edge nodes are available edge nodes.

[0054] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0055] 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 results. Additionally, the processes depicted in the figures do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0056] The above is only one or more embodiments of this specification and is not used to limit this specification. For those skilled in the art, there can be various changes and modifications to one or more embodiments of this specification. Any modifications, equivalent replacements, improvements, 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 application.

Claims

1. An industrial Internet of Things edge computing data processing system based on a multimodal large model, characterized in that: Specifically include: Node screening module, group division module, time period evaluation module, data processing module; The node screening module is responsible for determining the available edge nodes of the edge nodes based on the historical processing data of the multimodal large model of the edge nodes under different data processing types and the processing accuracy under different data processing types; The group division module is responsible for constructing a plurality of node groups using the available edge nodes, and obtaining valid node groups in the node groups based on the recognition accuracy of different node groups in the historical processing data of the edge nodes; The time period evaluation module is responsible for determining the collaborative processing time period of the effective node group by using the historical load conditions of the IoT gateway between the available edge nodes in the effective node group and the edge nodes at different time periods; The data processing module is responsible for determining the preferred group in the valid node group and the collaborative processing type in different time periods based on the changes in the collaborative processing time periods of the valid node group on different dates, and using the collaborative processing type in different time periods to determine the strategy for the collaborative processing of large model data by the preferred group and edge nodes.

2. The industrial Internet of Things edge computing data processing system based on a multimodal large model as claimed in claim 1, characterized in that: The data processing types include voice, text, file, image and video.

3. The industrial Internet of Things edge computing data processing system based on a multimodal large model as claimed in claim 1, characterized in that: The historical processing data includes processing data volumes in different data processing types.

4. The industrial Internet of Things edge computing data processing system based on a multimodal large model as claimed in claim 1, characterized in that: The method for determining the available edge nodes of the edge nodes is: Based on the historical processing data of the multimodal large model of the edge node under different data processing types, the processing data volume of the edge node under different data processing types is determined, and the weight coefficients of different data processing types are determined by using the proportion of the processing data volume under different data processing types to the processing data volume of the edge node; According to the processing accuracy of other edge nodes under different data processing types, and in combination with the weight coefficients of the edge nodes under different data processing types, the sum of the weight coefficients of the processing accuracy of other edge nodes is determined, and the sum is used as the corrected accuracy; Based on the correction accuracy, it is determined whether the other edge nodes are available edge nodes.

5. The industrial Internet of Things edge computing data processing system based on a multimodal large model as claimed in claim 4, characterized in that: When the correction accuracy of the other edge nodes is greater than a preset accuracy threshold, the other edge nodes are determined to be available edge nodes.

6. The industrial Internet of Things edge computing data processing system based on a multimodal large model as claimed in claim 1, characterized in that: Using the available edge nodes to construct multiple node groups specifically includes: Different available edge nodes are freely combined to obtain multiple node groups.

7. The industrial Internet of Things edge computing data processing system based on a multimodal large model as claimed in claim 1, characterized in that: The method for determining the preferred group in the valid node group is: Based on the changes in the collaborative processing time periods of the valid node group on different dates, the dates on which different time periods are identified as collaborative processing time periods are determined, and used as collaborative matching dates; Determine the synergistic matching coefficients for different time periods according to the proportion of the number of synergistic matching dates in different time periods within the preset time period; Based on the collaborative matching coefficients of different time periods, the number of time periods in which the collaborative matching coefficient is greater than the preset matching coefficient is determined, and the preferred group in the valid node group is determined using the number of time periods in which the collaborative matching coefficient is greater than the preset matching coefficient.

8. The industrial Internet of Things edge computing data processing system based on a multimodal large model as claimed in claim 7, characterized in that: The preferred group is a valid node group having the largest number of time periods in which the collaborative matching coefficient is greater than the preset matching coefficient.

9. The industrial Internet of Things edge computing data processing system based on a multimodal large model as claimed in claim 1, characterized in that: The method for determining the collaborative processing type of the time period is: When the collaborative matching coefficient of the time period is greater than a preset matching coefficient threshold, determining the collaborative processing type of the time period as a type of collaborative type; When the collaborative matching coefficient of the time period is not greater than the preset matching coefficient threshold, it is necessary to further determine whether the collaborative matching coefficient of the time period is within the preset collaborative matching coefficient range. If so, the collaborative processing type of the time period is determined to be the second type of collaborative type; if not, the collaborative processing type of the time period is determined to be the third type of collaborative type.

10. The industrial Internet of Things edge computing data processing system based on a multimodal large model according to claim 1, characterized in that: The strategy of collaborative processing of large model data by the preferred group and edge nodes is determined by using the collaborative processing types in different time periods, specifically including: When the collaborative processing type of the time period is a type of collaborative type, the preferred group and the edge node are used to jointly perform collaborative processing of the large model data; When the collaborative processing type of the time period is the second type of collaborative processing, it is also necessary to determine whether the average value of the load rate of the available edge nodes of the preferred group and the IoT gateway between the edge nodes in the most recent unit time period is less than a preset load rate threshold. If so, the preferred group and the edge node are used to jointly perform collaborative processing of the large model data. If not, the edge node is used to process the large model data. When the collaborative processing type of the time period is the third collaborative type, the edge node is used to process the large model data.

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