Multi-source data self-adaptive compression storage method and system based on industrial database

Abstract

The invention relates to the technical field of data processing, and discloses a multi-source data self-adaptive compression storage method and system based on an industrial database. According to the method, multi-source data needing to be stored by a user is collected in real time, the collected multi-source data is processed, meanwhile, the obtained processed multi-source data is divided in a decision tree construction mode, and based on the obtained classified multi-source data, a data sensitivity system is analyzed and judged in a data transmission mode; the method comprises the steps of classifying various types of multi-source data, outputting sensitivity levels of the various types of multi-source data, setting different compression modes to compress the classified multi-source data based on the output sensitivity levels of the various types of multi-source data, storing the compressed multi-source data in a database, and finally setting different access permissions for control based on different compression modes of the multi-source data in the database. And the security of multi-source data self-adaptive compression storage access is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for adaptively compressing and storing multi-source data based on an industrial database. Background Art

[0002] With the continuous expansion of industrial data management systems, tens of thousands of industrial data files are stored in computers. Due to the particularity and diversity of industrial data, it is difficult for databases to provide enough storage space to store industrial data. Therefore, compressed storage of industrial data is the only choice at present.

[0003] The existing open patent application CN116561083A, this method obtains the file group to be compressed, calculates the compression time and writing time of the file group to be compressed, and sets the maximum delay T allowed for compressing the file group to be compressed and writing it to the disk; with the maximum delay T as a constraint, the most suitable compression algorithm is selected for each file in the file group to be compressed; each file in the file group to be compressed is compressed according to the selected compression algorithm and written to the disk through the file system. However, since it only focuses on maximizing the reduction of storage capacity, it ignores the type of file data and the loss of compression, so that the compressed data cannot be used accurately, which has certain limitations. Summary of the invention

[0004] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides a multi-source data adaptive compression storage method and system based on an industrial database, which has the advantages of security, high efficiency, and real-time, and solves the problem that it is difficult for the database to provide a large enough storage space to store industrial data.

[0005] (II) Technical solution In order to solve the technical problem that it is difficult for the above database to provide a large enough storage space to store industrial data, the present invention provides the following technical solutions: The present invention discloses a multi-source data adaptive compression storage method based on an industrial database, which specifically comprises the following steps: S1. Collect multi-source data that users need to store in real time, and process the collected multi-source data to obtain processed multi-source data; S2. Divide the processed multi-source data by constructing a decision tree to obtain classified multi-source data; S3. Based on the classified multi-source data, the data sensitivity system is determined by analyzing the data through data transmission, and the sensitivity level of each type of multi-source data is output; S4. Setting different compression methods based on the sensitivity levels of various types of output multi-source data to compress the classified multi-source data and save them to the database; S5. Different access permissions are set based on different compression methods of multi-source data in the database for control.

[0006] The present invention collects multi-source data that users need to store in real time, processes the collected multi-source data, divides the processed multi-source data by building a decision tree, and analyzes the classified multi-source data through data transmission to judge the data sensitivity system, and outputs the sensitivity level of each type of multi-source data. At the same time, different compression methods are set based on the sensitivity level of each type of multi-source data output to compress the classified multi-source data and save it to a database. Finally, different access rights are set based on different compression methods of the multi-source data in the database for control, thereby improving the security of adaptive compression storage access of multi-source data.

[0007] Preferably, the real-time collection of multi-source data that the user needs to store and processing the collected multi-source data to obtain the processed multi-source data includes the following steps: S11. Real-time collection of multi-source data that users need to store and formulation of a multi-source data standard structure; Multi-source data includes: data number, data source, data generation time and data content; The data generation time T is defined in a standard time format, accurate to milliseconds. The data source D includes the data sending end and the data receiving end. The data number A is the unique identifier of the multi-source data. The data content M represents the data carried in the current multi-source data. After the standard structure of multi-source data is defined, Save the collected multi-source data in the form of Among them, F represents a set of multi-source data, based on The importance of each part in the multi-source data is set in the form, with A being the most important and M being the least important; S12. Based on the established multi-source data standard structure, the collected multi-source data is processed to obtain processed multi-source data.

[0008] Preferably, the step of processing the collected multi-source data based on the formulation of a multi-source data standard structure to obtain the processed multi-source data comprises the following steps: S121, filtering the collected multi-source data; Performing a sequential detection on the collected multi-source data, and when it is detected during the sequential detection that there is multi-source data with missing content in the multi-source data, deleting the multi-source data with missing content; S122, deduplication of the collected multi-source data; Performing sequential detection on the collected multi-source data, and when duplicate multi-source data is detected in the multi-source data during the sequential detection process, deleting the duplicate multi-source data detected later; S123: Summarize the multi-source data after filtering and deduplication operations to obtain processed multi-source data.

[0009] The present invention collects multi-source data that users need to store in real time and formulates a multi-source data standard structure by defining and combining various data in the multi-source data. At the same time, based on the formulated multi-source data standard structure, the collected multi-source data is filtered and deduplicated to obtain processed multi-source data, thereby improving the reliability of the collected multi-source data.

[0010] Preferably, the step of dividing the processed multi-source data by constructing a decision tree to obtain classified multi-source data comprises the following steps: S21, assuming that the processed multi-source data is a sample set, each sample in the sample set is a group of processed multi-source data, performing random sampling with replacement on the samples in the sample set, selecting N samples to train a decision tree, and using the selected N samples as root nodes; S22, starting from the root node, calculate the information gain of all features in the data set corresponding to the node, and select the feature with the largest information gain value as the partition feature of the data set; S23, dividing the data set corresponding to the feature into subtrees; S24, recursively call step S22 and step S23 on the subtree to construct a decision tree; S25. Until the information gain of the data set features reaches a certain condition or all samples belong to the same type, a final decision tree is obtained, and each leaf in the final decision tree is set to represent a classified multi-source data.

[0011] Preferably, the information gain of all features in the data set corresponding to the computing node comprises the following steps: The probability distribution of the processed multi-source data is set as: ; in, is the probability distribution function, For the The probability of the true value of a sample, For the The true value of the samples, is the sample set in the node, and N represents the number of samples; Then its information entropy calculation formula is: ; ; in, is the information entropy function, is the sample set in the node after partitioning, is the number of samples in the node after partitioning. samples; The information gain calculation formula is: ; in, is the information gain function, The attributes used in this partitioning.

[0012] The present invention divides the processed multi-source data by constructing a decision tree, and ensures the accuracy of the division of the processed multi-source data by calculating the information gain of the processed multi-source data, thereby improving the accuracy of multi-source data classification.

[0013] Preferably, the step of analyzing the obtained classified multi-source data through data transmission, determining the data sensitivity system, and outputting the sensitivity level of each type of multi-source data comprises the following steps: S31, setting a network transmission environment, and performing data transmission verification on the obtained classified multi-source data; Based on the obtained classified multi-source data, 10 groups of multi-source data are selected from each classified multi-source data; The selected 10 sets of multi-source data are transmitted according to the set network transmission environment, and are tested at the data receiving end to determine whether the transmitted multi-source data is complete; It is assumed that the transmission of complete multi-source data is represented by 1, and the transmission of missing multi-source data is represented by 0; S32. Based on the data transmission verification results, output the sensitivity level of various multi-source data; Summarize the data transmission verification results of various multi-source data and classify them through the sensitivity level calculation model; The sensitivity level calculation model is as follows: ; in, Indicates the sensitivity level of the output. Setting 1 indicates a high sensitivity level, and 0 indicates a low sensitivity level. represents the sensitivity level function, Indicates the data transmission verification results of various types of multi-source data input, Indicates the time of data transmission verification.

[0014] The present invention sets a network transmission environment, performs data transmission verification on the classified multi-source data, determines whether the transmitted multi-source data is complete through detection, and outputs the sensitivity level of each type of multi-source data based on the data transmission verification result, thereby improving the rationality of multi-source data compression.

[0015] Preferably, the step of setting different compression methods based on the sensitivity levels of the various types of multi-source data output to compress the classified multi-source data and save the compressed data to the database comprises the following steps: S41, setting the compression method of the output text data; Due to the particularity of text data, it is set that text data with high sensitivity level and low sensitivity level are not compressed; S42: Set the compression method of the output image data.

[0016] Preferably, the setting of the compression method of the output picture data includes the following steps: Compress image data with high sensitivity level in equal proportion; Compress image data with low sensitivity level; Setting the image data includes: resolution parameters and the grayscale value of each pixel; The compression steps include: S421, segmenting the image data into image blocks of the same size according to a resolution parameter of the image data; S422, traversing each pixel point in the image block, and calculating the pixel mean value of each pixel point in the image block; S423, compressing the segmented image blocks by using a DCT conversion function to obtain compressed image blocks; The DCT conversion function formula is as follows: ; in, represents the DCT transfer function, represents the row of the image block matrix, represents the columns of the image block matrix, Represents the grayscale matrix of the image block, represents the coordinates of the image block, and represents a real matrix; S424: Aggregate the compressed image blocks to obtain compressed picture data.

[0017] The present invention improves the efficiency of multi-source data adaptive compression by setting the compression mode of output text data and setting the compression mode of output picture data, and by calculating the pixel value of the picture data and using the DCT conversion function to complete the compression of the picture data.

[0018] Preferably, the control based on different settings of access rights for different multi-source data compression modes in the database includes the following steps: Based on the sensitivity level of the output of various types of multi-source data, the access permission level of users in the database is set. For text data and image data with low sensitivity levels, level 1 is set, indicating normal access. For text data and image data with high sensitivity levels, level 2 is set, indicating verified access. When users need to access text data and image data with high sensitivity levels, they need to apply for it.

[0019] The present invention improves the security of multi-source data compression by setting the access permission level of users in the database based on the sensitivity level of outputting various types of multi-source data, and controlling the type and quantity of files accessed by users according to the set access permission level of users in the database.

[0020] This embodiment also discloses a multi-source data adaptive compression storage system based on an industrial database, which is used to implement a multi-source data adaptive compression storage method based on an industrial database, and the system includes: a data acquisition module, a data processing module, a decision tree classification module, a sensitivity level determination module, a data compression module, and an access restriction module; The data acquisition module is used to collect multi-source data that users need to store in real time; The data processing module is used to process multi-source data that needs to be stored; The decision tree classification module is used to classify the processed multi-source data; The sensitivity level determination module is used to perform sensitivity calculation on the classified multi-source data and determine the sensitivity level; The data compression module is used to set different compression methods according to the sensitivity levels of various types of multi-source data; The access restriction module is used to set access levels to restrict user access.

[0021] (III) Beneficial effects Compared with the prior art, the present invention provides a method and system for adaptive compression storage of multi-source data based on industrial database, which has the following beneficial effects: 1. The invention collects multi-source data that users need to store in real time, processes the collected multi-source data, and divides the processed multi-source data by building a decision tree. Based on the classified multi-source data, the invention analyzes the data sensitivity system through data transmission, and outputs the sensitivity level of each type of multi-source data. At the same time, based on the sensitivity level of each type of multi-source data output, different compression methods are set to compress the classified multi-source data and save it to a database. Finally, different access rights are set based on the different compression methods of the multi-source data in the database for control, thereby improving the security of adaptive compression storage access of multi-source data.

[0022] 2. The invention collects multi-source data that users need to store in real time and formulates a standard structure of multi-source data by defining and combining each data in the multi-source data. At the same time, based on the formulated standard structure of multi-source data, the collected multi-source data is filtered and deduplicated to obtain processed multi-source data, thereby improving the reliability of the collected multi-source data.

[0023] 3. The present invention divides the processed multi-source data by constructing a decision tree, and ensures the accuracy of the division of the processed multi-source data by calculating the information gain of the processed multi-source data, thereby improving the accuracy of multi-source data classification.

[0024] 4. The present invention performs data transmission verification on the classified multi-source data by setting a network transmission environment, determines whether the transmitted multi-source data is complete through detection, and outputs the sensitivity level of each type of multi-source data based on the data transmission verification result, thereby improving the rationality of multi-source data compression.

[0025] 5. The present invention improves the efficiency of adaptive compression of multi-source data by setting the compression method of output text data and the compression method of output image data, and by calculating the pixel value of the image data and processing it using the DCT conversion function.

[0026] 6. The invention improves the security of multi-source data compression by setting the access permission level of users in the database based on the sensitivity level of the output various types of multi-source data, and controlling the type and quantity of files accessed by users according to the set access permission level of users in the database. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the structure of the multi-source data adaptive compression storage process of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Example 1 See also Figure 1 This embodiment discloses a multi-source data adaptive compression storage method based on an industrial database, which specifically includes the following steps: S1. Collect multi-source data that users need to store in real time, and process the collected multi-source data to obtain processed multi-source data; S2. Divide the processed multi-source data by constructing a decision tree to obtain classified multi-source data; S3. Based on the classified multi-source data, the data sensitivity system is determined by analyzing the data through data transmission, and the sensitivity level of each type of multi-source data is output; S4. Setting different compression methods based on the sensitivity levels of various types of output multi-source data to compress the classified multi-source data and save them to the database; S5. Setting different access rights for control based on different compression methods of multi-source data in the database; For further information, see Figure 1 , real-time collection of multi-source data that users need to store, and processing of the collected multi-source data, to obtain the processed multi-source data includes the following steps: S11. Real-time collection of multi-source data that users need to store and formulation of a multi-source data standard structure; Multi-source data includes: data number, data source, data generation time and data content; The data generation time T is defined in a standard time format, accurate to milliseconds. The data source D includes the data sending end and the data receiving end. The data number A is the unique identifier of the multi-source data. The data content M represents the data carried in the current multi-source data. After the standard structure of multi-source data is defined, Save the collected multi-source data in the form of Among them, F represents a set of multi-source data, based on The importance of each part in the multi-source data is set in the form, with A being the most important and M being the least important; S12, based on the established multi-source data standard structure, processing the collected multi-source data to obtain processed multi-source data; S121, filtering the collected multi-source data; Performing a sequential detection on the collected multi-source data, and when it is detected during the sequential detection that there is multi-source data with missing content in the multi-source data, deleting the multi-source data with missing content; S122, deduplication of the collected multi-source data; Performing sequential detection on the collected multi-source data, and when duplicate multi-source data is detected in the multi-source data during the sequential detection process, deleting the duplicate multi-source data detected later; S123, aggregating the multi-source data after filtering and deduplication operations to obtain processed multi-source data; For further information, see Figure 1 , the processed multi-source data is divided by constructing a decision tree, and the classified multi-source data includes the following steps: S21, assuming that the processed multi-source data is a sample set, each sample in the sample set is a group of processed multi-source data, performing random sampling with replacement on the samples in the sample set, selecting N samples to train a decision tree, and using the selected N samples as root nodes; S22, starting from the root node, calculate the information gain of all features in the data set corresponding to the node, and select the feature with the largest information gain value as the partition feature of the data set; The information gain of all features in the data set corresponding to the calculation node is as follows: The probability distribution of the processed multi-source data is set as: ; in, is the probability distribution function, For the The probability of the true value of a sample, For the The true value of the samples, is the sample set in the node, and N represents the number of samples; Then its information entropy calculation formula is: ; ; in, is the information entropy function, is the sample set in the node after partitioning, is the number of samples in the node after partitioning. samples; The information gain calculation formula is: ; in, is the information gain function, The attributes used for this division are; S23, dividing the data set corresponding to the feature into subtrees; S24, recursively call step S22 and step S23 on the subtree to construct a decision tree; S25, until the information gain of the data set feature reaches a certain condition or all samples belong to the same type, a final decision tree is obtained, and each leaf in the final decision tree is set to represent a classified multi-source data; For further information, see Figure 1 ,Based on the classified multi-source data, the data sensitivity system is judged through data transmission, and the sensitivity level of each type of multi-source data is output, including the following steps: S31, setting a network transmission environment, and performing data transmission verification on the obtained classified multi-source data; Based on the obtained classified multi-source data, 10 groups of multi-source data are selected from each classified multi-source data; Furthermore, the selected 10 sets of multi-source data are transmitted according to the set network transmission environment, and are tested at the data receiving end to determine whether the transmitted multi-source data is complete; It is assumed that the transmission of complete multi-source data is represented by 1, and the transmission of missing multi-source data is represented by 0; S32. Based on the data transmission verification results, output the sensitivity level of various multi-source data; Summarize the data transmission verification results of various multi-source data and classify them through the sensitivity level calculation model; The sensitivity level calculation model is as follows: ; in, Indicates the sensitivity level of the output. Setting 1 indicates a high sensitivity level, and 0 indicates a low sensitivity level. represents the sensitivity level function, Indicates the data transmission verification results of various types of multi-source data input, Indicates the time of data transmission verification; For further information, see Figure 1 , different compression methods are set based on the sensitivity levels of various types of multi-source data output to compress the classified multi-source data, and save them to the database, including the following steps: Set various types of multi-source data including: text data and image data; S41, setting the compression method of the output text data; Due to the particularity of text data, it is set that text data with high sensitivity level and low sensitivity level are not compressed; S42, setting the compression method of the output image data; Compress image data with high sensitivity level in equal proportion; Compress image data with low sensitivity level; Setting the image data includes: resolution parameters and the grayscale value of each pixel; The compression steps include: S421, segmenting the image data into image blocks of the same size according to a resolution parameter of the image data; S422, traversing each pixel point in the image block, and calculating the pixel mean value of each pixel point in the image block; S423, compressing the segmented image blocks by using a DCT conversion function to obtain compressed image blocks; The DCT conversion function formula is as follows: ; in, represents the DCT transfer function, represents the row of the image block matrix, represents the columns of the image block matrix, Represents the grayscale matrix of the image block, represents the coordinates of the image block, and represents a real matrix; S424, summarizing the compressed image blocks to obtain compressed picture data; For further information, see Figure 1 , setting different access rights based on different compression methods of multi-source data in the database to control includes the following steps: Based on the sensitivity level of the output of various types of multi-source data, the access permission level of users in the database is set. For text data and image data with low sensitivity levels, level 1 is set, indicating normal access. For text data and image data with high sensitivity levels, level 2 is set, indicating verified access. When users need to access text data and image data with high sensitivity levels, they need to apply for it.

[0030] Example 2 See also Figure 1 , the present embodiment also discloses a multi-source data adaptive compression storage system based on an industrial database, which is used to implement a multi-source data adaptive compression storage method based on an industrial database, the system comprising: a data acquisition module, a data processing module, a decision tree classification module, a sensitivity level determination module, a data compression module and an access restriction module; The data acquisition module is used to collect multi-source data that users need to store in real time; The data processing module is used to process multi-source data that needs to be stored; The decision tree classification module is used to classify the processed multi-source data; The sensitivity level determination module is used to perform sensitivity calculation on the classified multi-source data and determine the sensitivity level; The data compression module is used to set different compression methods according to the sensitivity levels of various types of multi-source data; The access restriction module is used to set access levels to restrict user access.

[0031] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-source data adaptive compression storage method based on industrial database, characterized in that: The following steps are involved: S1. Collect multi-source data that users need to store in real time, and process the collected multi-source data to obtain processed multi-source data; S2. Divide the processed multi-source data by constructing a decision tree to obtain classified multi-source data; S3. Based on the classified multi-source data, the data sensitivity system is determined by analyzing the data through data transmission, and the sensitivity level of each type of multi-source data is output; S4. Setting different compression methods based on the sensitivity levels of various types of output multi-source data to compress the classified multi-source data and save them to the database; S5. Different access permissions are set based on different compression methods of multi-source data in the database for control.

2. According to claim 1, a method for adaptive compression storage of multi-source data based on industrial database is characterized in that: The real-time collection of multi-source data that the user needs to store and processing the collected multi-source data to obtain the processed multi-source data include the following steps: S11. Real-time collection of multi-source data that users need to store and formulation of a multi-source data standard structure; Multi-source data includes: data number, data source, data generation time and data content; The data generation time T is defined in a standard time format, accurate to milliseconds. The data source D includes the data sending end and the data receiving end. The data number A is the unique identifier of the multi-source data. The data content M represents the data carried in the current multi-source data. After the standard structure of multi-source data is defined, Save the collected multi-source data in the form of Among them, F represents a set of multi-source data, based on The importance of each part in the multi-source data is set in the form, with A being the most important and M being the least important; S12. Based on the established multi-source data standard structure, the collected multi-source data is processed to obtain processed multi-source data.

3. The method for adaptively compressing and storing multi-source data based on an industrial database according to claim 2 is characterized in that: The method of processing the collected multi-source data based on the formulation of the multi-source data standard structure to obtain the processed multi-source data includes the following steps: S121, filtering the collected multi-source data; Performing a sequential detection on the collected multi-source data, and when it is detected during the sequential detection that there is multi-source data with missing content in the multi-source data, deleting the multi-source data with missing content; S122, deduplication of the collected multi-source data; Performing sequential detection on the collected multi-source data, and when duplicate multi-source data is detected in the multi-source data during the sequential detection process, deleting the duplicate multi-source data detected later; S123: Summarize the multi-source data after filtering and deduplication operations to obtain processed multi-source data.

4. The method for adaptively compressing and storing multi-source data based on an industrial database according to claim 1 is characterized in that: The method of dividing the processed multi-source data by constructing a decision tree to obtain the classified multi-source data includes the following steps: S21, assuming that the processed multi-source data is a sample set, each sample in the sample set is a group of processed multi-source data, performing random sampling with replacement on the samples in the sample set, selecting N samples to train a decision tree, and using the selected N samples as root nodes; S22, starting from the root node, calculate the information gain of all features in the data set corresponding to the node, and select the feature with the largest information gain value as the partition feature of the data set; S23, dividing the data set corresponding to the feature into subtrees; S24, recursively call step S22 and step S23 on the subtree to construct a decision tree; S25. Until the information gain of the data set features reaches a certain condition or all samples belong to the same type, a final decision tree is obtained, and each leaf in the final decision tree is set to represent a classified multi-source data.

5. The method for adaptively compressing and storing multi-source data based on an industrial database according to claim 4 is characterized in that: The information gain of all features in the data set corresponding to the computing node comprises the following steps: The probability distribution of the processed multi-source data is set as: ； in, is the probability distribution function, For the The probability of the true value of a sample, For the The true value of the samples, is the sample set in the node, and N represents the number of samples; Then its information entropy calculation formula is: ； ； in, is the information entropy function, is the sample set in the node after partitioning, is the number of samples in the node after partitioning. samples; The information gain calculation formula is: ； in, is the information gain function, The attributes used in this partitioning.

6. The method for adaptively compressing and storing multi-source data based on an industrial database according to claim 1 is characterized in that: The method of analyzing the classified multi-source data through data transmission, determining the data sensitivity system, and outputting the sensitivity level of each type of multi-source data includes the following steps: S31, setting a network transmission environment, and performing data transmission verification on the obtained classified multi-source data; Based on the obtained classified multi-source data, 10 groups of multi-source data are selected from each classified multi-source data; The selected 10 sets of multi-source data are transmitted according to the set network transmission environment, and are tested at the data receiving end to determine whether the transmitted multi-source data is complete; It is assumed that the transmission of complete multi-source data is represented by 1, and the transmission of missing multi-source data is represented by 0; S32. Based on the data transmission verification results, output the sensitivity level of various multi-source data; Summarize the data transmission verification results of various multi-source data and classify them through the sensitivity level calculation model; The sensitivity level calculation model is as follows: ； in, Indicates the sensitivity level of the output. Setting 1 indicates a high sensitivity level, and 0 indicates a low sensitivity level. represents the sensitivity level function, Indicates the data transmission verification results of various types of multi-source data input, Indicates the time of data transmission verification.

7. The method for adaptively compressing and storing multi-source data based on an industrial database according to claim 1 is characterized in that: The method of setting different compression methods based on the sensitivity levels of various types of output multi-source data to compress the classified multi-source data and save the compressed data to the database includes the following steps: S41, setting the compression method of the output text data; Due to the particularity of text data, it is set that text data with high sensitivity level and low sensitivity level are not compressed; S42, setting the compression method of the output image data.

8. The method for adaptively compressing and storing multi-source data based on an industrial database according to claim 7, characterized in that: The compression method of the image data to be outputted is set to include the following steps: Compress image data with high sensitivity level in equal proportion; Compress image data with low sensitivity level; Setting the image data includes: resolution parameters and the grayscale value of each pixel; The compression steps include: S421, segmenting the image data into image blocks of the same size according to a resolution parameter of the image data; S422, traversing each pixel point in the image block, and calculating the pixel mean value of each pixel point in the image block; S423, compressing the segmented image blocks by using a DCT conversion function to obtain compressed image blocks; The DCT conversion function formula is as follows: ； in, represents the DCT transfer function, represents the row of the image block matrix, represents the columns of the image block matrix, Represents the grayscale matrix of the image block, represents the coordinates of the image block, and represents a real matrix; S424: Aggregate the compressed image blocks to obtain compressed picture data.

9. The method for adaptively compressing and storing multi-source data based on an industrial database according to claim 1 is characterized in that: The control based on different settings of access rights for different multi-source data compression methods in the database includes the following steps: Based on the sensitivity level of the output of various types of multi-source data, the access permission level of users in the database is set. For text data and image data with low sensitivity levels, level 1 is set, indicating normal access. For text data and image data with high sensitivity levels, level 2 is set, indicating verified access. When users need to access text data and image data with high sensitivity levels, they need to apply for it.

10. A system for implementing the method for adaptively compressing and storing multi-source data based on an industrial database according to any one of claims 1 to 9, characterized in that: It includes a data acquisition module, a data processing module, a decision tree classification module, a sensitivity level determination module, a data compression module and an access restriction module; The data acquisition module is used to collect multi-source data that users need to store in real time; The data processing module is used to process multi-source data that needs to be stored; The decision tree classification module is used to classify the processed multi-source data; The sensitivity level determination module is used to perform sensitivity calculation on the classified multi-source data and determine the sensitivity level; The data compression module is used to set different compression methods according to the sensitivity levels of various types of multi-source data; The access restriction module is used to set access levels to restrict user access.

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