Lightweight processing method and system for industrial data based on cloud-edge collaboration

Abstract

The present invention provides a method and system for lightweight processing of industrial data based on cloud-edge collaboration, wherein the method comprises: step S1: acquiring the multimodal features of the industrial data to be processed; step S2: deciding a lightweight processing scheme based on the multimodal features based on a scheme decision knowledge base; step S3: performing lightweight processing on the industrial data based on the lightweight processing scheme based on cloud-edge collaboration technology. The present invention is based on a scheme decision knowledge base and decides a lightweight processing scheme based on the multimodal features of the industrial data to be processed, and based on cloud-edge collaboration technology and the lightweight processing scheme, performs lightweight processing on the industrial data, overcoming the limitation of manually setting lightweight processing rules that makes the system unable to perform lightweight processing on industrial data to which the lightweight processing rules are not applicable, reducing labor costs, and greatly improving the efficiency and stability of lightweight processing of industrial data.

Description

Technical Field

[0001] The present invention relates to the field of computer data processing technology, and in particular to a method and system for lightweight processing of industrial data based on cloud-edge collaboration. Background Art

[0002] The industrial data generated in industrial environments is usually large and diverse, including sensor data, equipment operating status, etc., which need to be lightweight processed; in industrial production environments, the efficiency and stability of lightweight processing of industrial data directly affect the smoothness and cost-effectiveness of the production process.

[0003] However, when lightweight processing industrial data is performed, technicians often manually set lightweight processing rules, which are then executed by the system. However, the system cannot perform lightweight processing on industrial data to which the lightweight processing rules do not apply, resulting in significant limitations and an inability to guarantee the efficiency and stability of lightweight processing. Furthermore, the manual setting of lightweight processing rules by technicians incurs high labor costs.

[0004] Therefore, a solution is urgently needed. Summary of the Invention

[0005] The present invention provides a method and system for lightweight processing of industrial data based on cloud-edge collaboration. Based on a solution decision knowledge base and according to the multimodal characteristics of the industrial data to be processed, a lightweight processing solution is decided. Based on cloud-edge collaboration technology, the industrial data is lightweight processed according to the lightweight processing solution. This overcomes the limitation of manually setting lightweight processing rules that makes the system unable to lightweight process industrial data to which the lightweight processing rules are not applicable, reduces labor costs, and greatly improves the efficiency and stability of lightweight processing of industrial data.

[0006] The present invention provides a lightweight industrial data processing method based on cloud-edge collaboration, including:

[0007] Step S1: obtaining multimodal features of the industrial data to be processed;

[0008] Step S2: Based on the solution decision knowledge base and multimodal features, a lightweight processing solution is determined;

[0009] Step S3: Based on cloud-edge collaborative technology and according to the lightweight processing solution, perform lightweight processing on industrial data.

[0010] Preferably, the step S2: deciding a lightweight processing solution based on a solution decision knowledge base and multimodal features includes:

[0011] Preprocess the multimodal features to obtain preprocessed features;

[0012] Based on the preprocessing features, construct the knowledge application conditions;

[0013] Based on the knowledge applicability conditions, target knowledge is screened out from the solution decision knowledge base;

[0014] Determine lightweight processing solutions based on target knowledge;

[0015] The preprocessing of the multimodal features to obtain preprocessed features includes:

[0016] When there is a first sub-feature in the multimodal feature that matches the first trigger feature, supplementally screening out a second sub-feature from the multimodal feature based on the supplementary screening condition corresponding to the first trigger feature;

[0017] The first sub-feature and the second sub-feature are used as preprocessing features;

[0018] and / or,

[0019] Constructing a first target feature set; the first target feature set includes multiple third sub-features in the multimodal features, and the feature type distribution of each third sub-feature in the first target feature set is consistent with the feature type distribution of other preprocessing features based on which other knowledge application conditions were historically constructed;

[0020] The third sub-feature in the first target feature set is used as a preprocessing feature;

[0021] and / or,

[0022] Constructing a second target feature set; the second target feature set includes a plurality of fourth sub-features in the multimodal feature set, and each of the fourth sub-features in the second target feature set has a feature correlation relationship between each other;

[0023] The fourth sub-feature in the second target feature set is used as a preprocessing feature.

[0024] Preferably, the lightweight processing method for industrial data based on cloud-edge collaboration also includes:

[0025] Obtain information on the processing of lightweight industrial data;

[0026] Adaptively adjust the lightweight processing solution based on processing situation information;

[0027] Based on cloud-edge collaborative technology, according to the adjusted lightweight processing solution, the relay performs lightweight processing on industrial data.

[0028] Preferably, the adaptive adjustment of the lightweight processing solution based on the processing situation information includes:

[0029] Based on the standard evaluation system, the processing information is adjusted and evaluated to obtain multiple adjustment requirements and demand levels; the adjustment requirements and demand levels are one-to-one corresponding;

[0030] Sort the adjustment requirements from largest to smallest according to the degree of need to obtain the adjustment requirement sequence;

[0031] Traverse the adjustment requirements in the adjustment requirement sequence in sequence;

[0032] When traversing to the i-th adjustment requirement in the adjustment requirement sequence, a requirement screening condition is generated based on the i-th adjustment requirement;

[0033] Based on the demand screening conditions, select the target adjustment demand from the adjustment demand sequence;

[0034] When continuing to traverse the adjustment requirements in the adjustment requirements, the target adjustment requirements will no longer be traversed;

[0035] After traversing the adjustment requirements in the adjustment requirement sequence, the target adjustment requirement is removed from the adjustment requirement sequence;

[0036] Perform feature description processing on the adjustment demand sequence after removing the target adjustment demand to obtain a feature description vector;

[0037] Determine an adjustment scheme corresponding to the feature description vector from an adjustment scheme library;

[0038] Based on the adjustment plan, adaptively adjust the lightweight processing plan;

[0039] The requirements screening conditions include:

[0040] j>i; j is the order number of the target adjustment demand in the adjustment demand sequence;

[0041] and,

[0042] There is a demand association relationship between the target adjustment demand and the i-th adjustment demand.

[0043] Preferably, the lightweight processing method for industrial data based on cloud-edge collaboration also includes:

[0044] Based on the processing situation information, a situation visualization model is constructed;

[0045] Output visualization model.

[0046] The present invention provides an industrial data lightweight processing system based on cloud-edge collaboration, including:

[0047] An acquisition module, used to acquire multimodal features of the industrial data to be processed;

[0048] The decision module is used to decide on lightweight processing solutions based on the solution decision knowledge base and multimodal features;

[0049] The processing module is used to perform lightweight processing of industrial data based on cloud-edge collaborative technology and lightweight processing solutions.

[0050] Preferably, the decision module decides on a lightweight processing solution based on a solution decision knowledge base and multimodal features, including:

[0051] Preprocess the multimodal features to obtain preprocessed features;

[0052] Based on the preprocessing features, construct the knowledge application conditions;

[0053] Based on the knowledge applicability conditions, target knowledge is screened out from the solution decision knowledge base;

[0054] Determine lightweight processing solutions based on target knowledge;

[0055] The preprocessing of the multimodal features to obtain preprocessed features includes:

[0056] When there is a first sub-feature in the multimodal feature that matches the first trigger feature, supplementally screening out a second sub-feature from the multimodal feature based on the supplementary screening condition corresponding to the first trigger feature;

[0057] The first sub-feature and the second sub-feature are used as preprocessing features;

[0058] and / or,

[0059] Constructing a first target feature set; the first target feature set includes multiple third sub-features in the multimodal features, and the feature type distribution of each third sub-feature in the first target feature set is consistent with the feature type distribution of other preprocessing features based on which other knowledge application conditions were historically constructed;

[0060] The third sub-feature in the first target feature set is used as a preprocessing feature;

[0061] and / or,

[0062] Constructing a second target feature set; the second target feature set includes a plurality of fourth sub-features in the multimodal feature set, and each of the fourth sub-features in the second target feature set has a feature correlation relationship between each other;

[0063] The fourth sub-feature in the second target feature set is used as a preprocessing feature.

[0064] Preferably, the industrial data lightweight processing system based on cloud-edge collaboration also includes:

[0065] Adjustment modules to include:

[0066] Obtain information on the processing of lightweight industrial data;

[0067] Adaptively adjust the lightweight processing solution based on processing situation information;

[0068] Based on cloud-edge collaborative technology, according to the adjusted lightweight processing solution, the relay performs lightweight processing on industrial data.

[0069] Preferably, the adjustment module adaptively adjusts the lightweight processing solution based on the processing situation information, including:

[0070] Based on the standard evaluation system, the processing information is adjusted and evaluated to obtain multiple adjustment requirements and demand levels; the adjustment requirements and demand levels are one-to-one corresponding;

[0071] Sort the adjustment requirements from largest to smallest according to the degree of need to obtain the adjustment requirement sequence;

[0072] Traverse the adjustment requirements in the adjustment requirement sequence in sequence;

[0073] When traversing to the i-th adjustment requirement in the adjustment requirement sequence, a requirement screening condition is generated based on the i-th adjustment requirement;

[0074] Based on the demand screening conditions, select the target adjustment demand from the adjustment demand sequence;

[0075] When continuing to traverse the adjustment requirements in the adjustment requirements, the target adjustment requirements will no longer be traversed;

[0076] After traversing the adjustment requirements in the adjustment requirement sequence, the target adjustment requirement is removed from the adjustment requirement sequence;

[0077] Perform feature description processing on the adjustment demand sequence after removing the target adjustment demand to obtain a feature description vector;

[0078] Determine an adjustment scheme corresponding to the feature description vector from an adjustment scheme library;

[0079] Based on the adjustment plan, adaptively adjust the lightweight processing plan;

[0080] The requirements screening conditions include:

[0081] j>i; j is the order number of the target adjustment demand in the adjustment demand sequence;

[0082] and,

[0083] There is a demand association relationship between the target adjustment demand and the i-th adjustment demand.

[0084] Preferably, the industrial data lightweight processing system based on cloud-edge collaboration also includes:

[0085] Visualization modules include:

[0086] Based on the processing situation information, a situation visualization model is constructed;

[0087] Output visualization model.

[0088] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0089] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0091] Figure 1 This is a flowchart of a method for lightweight processing of industrial data based on cloud-edge collaboration in an embodiment of the present invention;

[0092] Figure 2 This is a schematic diagram of an industrial data lightweight processing system based on cloud-edge collaboration in an embodiment of the present invention. DETAILED DESCRIPTION

[0093] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0094] The present invention provides a lightweight processing method for industrial data based on cloud-edge collaboration, such as Figure 1 Shown, including:

[0095] Step S1: obtaining multimodal features of the industrial data to be processed;

[0096] Step S2: Based on the solution decision knowledge base and multimodal features, a lightweight processing solution is determined;

[0097] Step S3: Based on cloud-edge collaborative technology and according to the lightweight processing solution, perform lightweight processing on industrial data.

[0098] The industrial data to be processed refers to the industrial data to be lightweight processed; the multimodal characteristics include: the data type, data volume, data source, and data usage of the industrial data; there is a large amount of solution decision knowledge in the solution decision knowledge base, and the solution decision knowledge can be used to decide on a lightweight processing solution based on multimodal characteristics; the solution decision knowledge can be empirical information for deciding on a lightweight processing solution, etc.; the lightweight processing solution is a solution for lightweight processing of industrial data based on cloud-edge collaborative technology; cloud-edge collaborative technology refers to the technology of using cloud computing and edge computing to collaboratively process data, which belongs to the scope of existing technology and will not be elaborated on.

[0099] This application is based on a solution decision knowledge base and decides on a lightweight processing solution based on the multimodal characteristics of the industrial data to be processed. Based on cloud-edge collaborative technology, the industrial data is lightweight processed according to the lightweight processing solution. This overcomes the limitation of manually setting lightweight processing rules that makes the system unable to perform lightweight processing on industrial data to which the lightweight processing rules are not applicable, reduces labor costs, and greatly improves the efficiency and stability of lightweight processing of industrial data.

[0100] In one embodiment, step S2: deciding a lightweight processing solution based on a solution decision knowledge base and multimodal features includes:

[0101] Preprocess the multimodal features to obtain preprocessed features;

[0102] Based on the preprocessing features, construct the knowledge application conditions;

[0103] Based on the knowledge applicability conditions, target knowledge is screened out from the solution decision knowledge base;

[0104] Determine lightweight processing solutions based on target knowledge;

[0105] The preprocessing of the multimodal features to obtain preprocessed features includes:

[0106] When there is a first sub-feature in the multimodal feature that matches the first trigger feature, supplementally screening out a second sub-feature from the multimodal feature based on the supplementary screening condition corresponding to the first trigger feature;

[0107] The first sub-feature and the second sub-feature are used as preprocessing features;

[0108] and / or,

[0109] Constructing a first target feature set; the first target feature set includes multiple third sub-features in the multimodal features, and the feature type distribution of each third sub-feature in the first target feature set is consistent with the feature type distribution of other preprocessing features based on which other knowledge application conditions were historically constructed;

[0110] The third sub-feature in the first target feature set is used as a preprocessing feature;

[0111] and / or,

[0112] Constructing a second target feature set; the second target feature set includes a plurality of fourth sub-features in the multimodal feature set, and each of the fourth sub-features in the second target feature set has a feature correlation relationship between each other;

[0113] The fourth sub-feature in the second target feature set is used as a preprocessing feature.

[0114] The preprocessing feature represents the situation of industrialized data that needs to be lightweight processed; the knowledge applicability condition is used to filter out the solution decision knowledge applicable to such situations, that is, the target knowledge; based on the target knowledge, the lightweight processing solution is determined; for example: the preprocessing feature is that the data type of the industrialized data is product quality inspection data, and the constructed knowledge applicability condition is that the solution decision knowledge must be applicable to the lightweight processing of product quality inspection data. There are three ways to preprocess multimodal features; in the first way, the first trigger feature represents the existence of other second sub-features that can be combined with the first sub-feature to represent the situation of industrialized data that needs to be lightweight processed. For example: the first sub-feature that matches the first trigger feature is that the data type of the industrialized data is product quality inspection data, and the second sub-feature is that the data purpose of the product quality inspection data is to report to the superior management department. Therefore, the first sub-feature and the second sub-feature can be combined to represent the situation of industrialized data that needs to be lightweight processed, and the supplementary screening condition is the condition for filtering out the second sub-feature; in the second way, the feature type distribution of the third sub-feature refers to the feature type of each third sub-feature; historically, when constructing other knowledge applicability conditions, it will also be based on other preprocessing features; The feature type distribution of other preprocessing features refers to the feature types of each of the other preprocessing features; when the feature type distribution of each third sub-feature in the first target feature set is consistent with the feature type distribution of other preprocessing features based on which other knowledge applicability conditions have been constructed in the past, it means that the third sub-feature in the first target feature set may also be used for the construction of knowledge applicability conditions, and the third sub-feature in the first target feature set is used as the preprocessing feature; in the third method, the second target feature set is constructed. When the fourth sub-features in the second target feature set have a feature association relationship between each other, it means that the fourth features in the second target feature set can be used together to determine the knowledge applicability conditions. For example, the feature association relationship is to jointly represent the main purpose of industrial data, etc. Technical personnel can set the feature association relationship in advance according to actual needs.

[0115] Due to the large amount of industrial data, the multimodal features of industrial data are also numerous and complex. As a result, the steps for constructing knowledge applicability conditions may be cumbersome, reducing the efficiency of subsequent lightweight processing of industrial data. The embodiment of the present invention can solve this problem. Before using multimodal features to construct knowledge applicability conditions, the multimodal features are preprocessed and the preprocessed multimodal features are used to construct the knowledge applicability conditions. In addition, when preprocessing the multimodal features, three preprocessing methods are introduced, which greatly improves the rationality, comprehensiveness and adaptability of preprocessing multimodal data.

[0116] In one embodiment, the method for lightweight processing of industrial data based on cloud-edge collaboration further includes:

[0117] Obtain information on the processing of lightweight industrial data;

[0118] Adaptively adjust the lightweight processing solution based on processing situation information;

[0119] Based on cloud-edge collaborative technology, according to the adjusted lightweight processing solution, the relay performs lightweight processing on industrial data.

[0120] Processing status information includes processing progress, processing duration, and estimated remaining processing time. Based on this processing status information, the lightweight processing solution is adaptively adjusted. Leveraging cloud-edge collaboration technology, lightweight processing of industrial data is then relayed based on the adjusted lightweight processing solution. This ensures that the optimal lightweight processing solution is always used for lightweight processing of industrial data, significantly improving the efficiency of lightweight processing of industrial data.

[0121] In one embodiment, adaptively adjusting the lightweight processing solution based on the processing situation information includes:

[0122] Based on the standard evaluation system, the processing status information is adjusted and evaluated to obtain multiple adjustment requirements and demand levels. The adjustment requirements and demand levels correspond one to one. The standard evaluation system is a system for adjusting and evaluating the processing status information. The adjustment requirements and corresponding demand levels can be determined through evaluation. For example, the standard evaluation system determines whether the processing progress in the processing status information is too slow. If so, the adjustment requirement is output as speeding up the processing progress and the corresponding demand level is output as 8. The larger the demand level, the higher the priority of the adjustment requirement.

[0123] Sort the adjustment requirements from largest to smallest according to the degree of need to obtain the adjustment requirement sequence;

[0124] Traverse the adjustment requirements in the adjustment requirement sequence in sequence;

[0125] When traversing to the i-th adjustment requirement in the adjustment requirement sequence, a requirement screening condition is generated based on the i-th adjustment requirement;

[0126] Based on the demand screening conditions, select the target adjustment demand from the adjustment demand sequence;

[0127] When continuing to traverse the adjustment requirements in the adjustment requirements, the target adjustment requirements will no longer be traversed;

[0128] After traversing the adjustment requirements in the adjustment requirement sequence, the target adjustment requirement is removed from the adjustment requirement sequence;

[0129] Perform feature description processing on the adjustment demand sequence after removing the target adjustment demand to obtain a feature description vector;

[0130] Determine the adjustment scheme corresponding to the feature description vector from the adjustment scheme library; the adjustment scheme library contains adjustment schemes corresponding to different feature description vectors; technicians can set them in advance;

[0131] Based on the adjustment plan, adaptively adjust the lightweight processing plan;

[0132] The requirements screening conditions include:

[0133] j>i; j is the order number of the target adjustment demand in the adjustment demand sequence; the order number is the order in the sequence; setting this demand screening condition can make the selected target adjustment demand come after the i-th adjustment demand;

[0134] and,

[0135] There is a demand association relationship between the target adjustment demand and the i-th adjustment demand. A demand association relationship refers to belonging to the same demand type. If they belong to the same demand type, and the i-th adjustment demand has a greater demand degree than the target adjustment demand, only the i-th adjustment demand is retained, and the target adjustment demand can be eliminated.

[0136] The embodiment of the present invention determines adjustment requirements when adaptively adjusting the lightweight processing solution based on processing situation information, and performs elimination screening on the adjustment requirements, thereby improving the efficiency of adaptive adjustment of the lightweight processing solution.

[0137] In one embodiment, the method for lightweight processing of industrial data based on cloud-edge collaboration further includes:

[0138] Based on the processing situation information, a situation visualization model is constructed;

[0139] Output visualization model.

[0140] It is also possible to build a situation visualization model based on the processing status information and output the situation visualization model to facilitate managers to visualize the lightweight processing status of industrial data.

[0141] The present invention provides an industrial data lightweight processing system based on cloud-edge collaboration, such as Figure 2 Shown, including:

[0142] Acquisition module 1, used to acquire multimodal features of industrial data to be processed;

[0143] Decision module 2 is used to decide on a lightweight processing solution based on the solution decision knowledge base and multimodal features;

[0144] Processing module 3 is used to perform lightweight processing on industrial data based on cloud-edge collaborative technology and a lightweight processing solution.

[0145] The decision module 2 decides a lightweight processing solution based on the solution decision knowledge base and multimodal features, including:

[0146] Preprocess the multimodal features to obtain preprocessed features;

[0147] Based on the preprocessing features, construct the knowledge application conditions;

[0148] Based on the knowledge applicability conditions, target knowledge is screened out from the solution decision knowledge base;

[0149] Determine lightweight processing solutions based on target knowledge;

[0150] The preprocessing of the multimodal features to obtain preprocessed features includes:

[0151] When there is a first sub-feature in the multimodal feature that matches the first trigger feature, supplementally screening out a second sub-feature from the multimodal feature based on the supplementary screening condition corresponding to the first trigger feature;

[0152] The first sub-feature and the second sub-feature are used as preprocessing features;

[0153] and / or,

[0154] Constructing a first target feature set; the first target feature set includes multiple third sub-features in the multimodal features, and the feature type distribution of each third sub-feature in the first target feature set is consistent with the feature type distribution of other preprocessing features based on which other knowledge application conditions were historically constructed;

[0155] The third sub-feature in the first target feature set is used as a preprocessing feature;

[0156] and / or,

[0157] Constructing a second target feature set; the second target feature set includes a plurality of fourth sub-features in the multimodal feature set, and each of the fourth sub-features in the second target feature set has a feature correlation relationship between each other;

[0158] The fourth sub-feature in the second target feature set is used as a preprocessing feature.

[0159] The lightweight industrial data processing system based on cloud-edge collaboration also includes:

[0160] Adjustment modules to include:

[0161] Obtain information on the processing of lightweight industrial data;

[0162] Adaptively adjust the lightweight processing solution based on processing situation information;

[0163] Based on cloud-edge collaborative technology, according to the adjusted lightweight processing solution, the relay performs lightweight processing on industrial data.

[0164] The adjustment module adaptively adjusts the lightweight processing solution based on the processing situation information, including:

[0165] Based on the standard evaluation system, the processing information is adjusted and evaluated to obtain multiple adjustment requirements and demand levels; the adjustment requirements and demand levels are one-to-one corresponding;

[0166] Sort the adjustment requirements from largest to smallest according to the degree of need to obtain the adjustment requirement sequence;

[0167] Traverse the adjustment requirements in the adjustment requirement sequence in sequence;

[0168] When traversing to the i-th adjustment requirement in the adjustment requirement sequence, a requirement screening condition is generated based on the i-th adjustment requirement;

[0169] Based on the demand screening conditions, select the target adjustment demand from the adjustment demand sequence;

[0170] When continuing to traverse the adjustment requirements in the adjustment requirements, the target adjustment requirements will no longer be traversed;

[0171] After traversing the adjustment requirements in the adjustment requirement sequence, the target adjustment requirement is removed from the adjustment requirement sequence;

[0172] Perform feature description processing on the adjustment demand sequence after removing the target adjustment demand to obtain a feature description vector;

[0173] Determine an adjustment scheme corresponding to the feature description vector from an adjustment scheme library;

[0174] Based on the adjustment plan, adaptively adjust the lightweight processing plan;

[0175] The requirements screening conditions include:

[0176] j>i; j is the order number of the target adjustment demand in the adjustment demand sequence;

[0177] and,

[0178] There is a demand association relationship between the target adjustment demand and the i-th adjustment demand.

[0179] The lightweight industrial data processing system based on cloud-edge collaboration also includes:

[0180] Visualization modules include:

[0181] Based on the processing situation information, a situation visualization model is constructed;

[0182] Output visualization model.

[0183] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A lightweight industrial data processing method based on cloud-edge collaboration, characterized by: include: Obtain multimodal features of industrial data to be processed; Preprocess the multimodal features to obtain preprocessed features; Based on the preprocessing features, construct the knowledge application conditions; Based on the knowledge applicability conditions, target knowledge is screened out from the solution decision knowledge base; Determine lightweight processing solutions based on target knowledge; Based on cloud-edge collaborative technology and lightweight processing solutions, industrial data is lightweight processed; The preprocessing of the multimodal features to obtain preprocessed features includes: When a first sub-feature matching the first trigger feature exists in the multimodal feature, a second sub-feature is additionally selected from the multimodal feature based on the supplementary screening condition corresponding to the first trigger feature; the first trigger feature represents a situation where the second sub-feature exists in combination with the first sub-feature to represent industrial data that requires lightweight processing; The first sub-feature and the second sub-feature are used as preprocessing features; and / or, Constructing a first target feature set; the first target feature set includes multiple third sub-features in the multimodal features, and the feature type distribution of each third sub-feature in the first target feature set is consistent with the feature type distribution of other pre-processing features based on which other knowledge applicable conditions were historically constructed; the feature type distribution of the third sub-features refers to the feature type of each third sub-feature; historically, when constructing other knowledge applicable conditions, they were also based on other pre-processing features; the feature type distribution of other pre-processing features refers to the feature type of each other pre-processing features; The third sub-feature in the first target feature set is used as a preprocessing feature; and / or, Constructing a second target feature set; the second target feature set includes multiple fourth sub-features in the multimodal feature set, and each fourth sub-feature in the second target feature set has a feature association relationship between each other; when each fourth sub-feature in the second target feature set has a feature association relationship between each other, it indicates that the fourth features in the second target feature set are jointly used to determine the knowledge applicability condition; The fourth sub-feature in the second target feature set is used as a preprocessing feature.

2. The method for lightweight processing of industrial data based on cloud-edge collaboration according to claim 1 is characterized in that: Also includes: Obtain information on the processing of lightweight industrial data; Adaptively adjust the lightweight processing solution based on processing situation information; Based on cloud-edge collaborative technology, according to the adjusted lightweight processing solution, the relay performs lightweight processing on industrial data.

3. The method for lightweight processing of industrial data based on cloud-edge collaboration according to claim 2 is characterized in that: The adaptive adjustment of the lightweight processing solution based on the processing situation information includes: Based on the standard evaluation system, the processing information is adjusted and evaluated to obtain multiple adjustment requirements and demand levels; the adjustment requirements and demand levels are one-to-one corresponding; Sort the adjustment requirements from largest to smallest according to the degree of need to obtain the adjustment requirement sequence; Traverse the adjustment requirements in the adjustment requirement sequence in sequence; When traversing to the i-th adjustment requirement in the adjustment requirement sequence, a requirement screening condition is generated based on the i-th adjustment requirement; Based on the demand screening conditions, select the target adjustment demand from the adjustment demand sequence; When continuing to traverse the adjustment requirements in the adjustment requirements, the target adjustment requirements will no longer be traversed; After traversing the adjustment requirements in the adjustment requirement sequence, the target adjustment requirement is removed from the adjustment requirement sequence; Perform feature description processing on the adjustment demand sequence after removing the target adjustment demand to obtain a feature description vector; Determine an adjustment scheme corresponding to the feature description vector from an adjustment scheme library; Based on the adjustment plan, adaptively adjust the lightweight processing plan; The requirements screening conditions include: j>i; j is the order number of the target adjustment demand in the adjustment demand sequence; and, There is a demand association relationship between the target adjustment demand and the i-th adjustment demand.

4. The method for lightweight processing of industrial data based on cloud-edge collaboration according to claim 2 is characterized in that: Also includes: Based on the processing situation information, a situation visualization model is constructed; Output visualization model.

5. The lightweight industrial data processing system based on cloud-edge collaboration is characterized by: include: An acquisition module, used to acquire multimodal features of the industrial data to be processed; The decision module is used to preprocess the multimodal features to obtain preprocessed features; Based on the preprocessing features, construct the knowledge application conditions; Based on the knowledge applicability conditions, target knowledge is screened out from the solution decision knowledge base; Determine lightweight processing solutions based on target knowledge; The processing module is used to perform lightweight processing on industrial data based on cloud-edge collaborative technology and lightweight processing solutions; The decision module preprocesses the multimodal features to obtain preprocessed features, including: When a first sub-feature matching the first trigger feature exists in the multimodal feature, a second sub-feature is additionally selected from the multimodal feature based on the supplementary screening condition corresponding to the first trigger feature; the first trigger feature represents a situation where the second sub-feature exists in combination with the first sub-feature to represent industrial data that requires lightweight processing; The first sub-feature and the second sub-feature are used as preprocessing features; and / or, Constructing a first target feature set; the first target feature set includes multiple third sub-features in the multimodal features, and the feature type distribution of each third sub-feature in the first target feature set is consistent with the feature type distribution of other pre-processing features based on which other knowledge applicable conditions were historically constructed; the feature type distribution of the third sub-features refers to the feature type of each third sub-feature; historically, when constructing other knowledge applicable conditions, they were also based on other pre-processing features; the feature type distribution of other pre-processing features refers to the feature type of each other pre-processing features; The third sub-feature in the first target feature set is used as a preprocessing feature; and / or, Constructing a second target feature set; the second target feature set includes multiple fourth sub-features in the multimodal feature set, and each fourth sub-feature in the second target feature set has a feature association relationship between each other; when each fourth sub-feature in the second target feature set has a feature association relationship between each other, it indicates that the fourth features in the second target feature set are jointly used to determine the knowledge applicability condition; The fourth sub-feature in the second target feature set is used as a preprocessing feature.

6. The industrial data lightweight processing system based on cloud-edge collaboration according to claim 5 is characterized in that: Also includes: Adjustment modules to include: Obtain information on the processing of lightweight industrial data; Adaptively adjust the lightweight processing solution based on processing situation information; Based on cloud-edge collaborative technology, according to the adjusted lightweight processing solution, the relay performs lightweight processing on industrial data.

7. The industrial data lightweight processing system based on cloud-edge collaboration according to claim 6 is characterized in that: The adjustment module adaptively adjusts the lightweight processing solution based on the processing situation information, including: Based on the standard evaluation system, the processing information is adjusted and evaluated to obtain multiple adjustment requirements and demand levels; the adjustment requirements and demand levels are one-to-one corresponding; Sort the adjustment requirements from largest to smallest according to the degree of need to obtain the adjustment requirement sequence; Traverse the adjustment requirements in the adjustment requirement sequence in sequence; When traversing to the i-th adjustment requirement in the adjustment requirement sequence, a requirement screening condition is generated based on the i-th adjustment requirement; Based on the demand screening conditions, select the target adjustment demand from the adjustment demand sequence; When continuing to traverse the adjustment requirements in the adjustment requirements, the target adjustment requirements will no longer be traversed; After traversing the adjustment requirements in the adjustment requirement sequence, the target adjustment requirement is removed from the adjustment requirement sequence; Perform feature description processing on the adjustment demand sequence after removing the target adjustment demand to obtain a feature description vector; Determine an adjustment scheme corresponding to the feature description vector from an adjustment scheme library; Based on the adjustment plan, adaptively adjust the lightweight processing plan; The requirements screening conditions include: j>i; j is the order number of the target adjustment demand in the adjustment demand sequence; and, There is a demand association relationship between the target adjustment demand and the i-th adjustment demand.

8. The industrial data lightweight processing system based on cloud-edge collaboration according to claim 6 is characterized in that: Also includes: Visualization modules include: Based on the processing situation information, a situation visualization model is constructed; Output visualization model.

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