Concentrator data security monitoring system based on multi-core-one system

Abstract

The invention discloses a concentrator data security monitoring system based on a multi-core-one system, and relates to the technical field of data security, the concentrator data security monitoring system comprises a data security monitoring center, the data security monitoring center is in communication connection with a data uploading module, a data processing module, a data encryption module and a data verification module; data obtained by a concentrator are divided into a plurality of data fragments, corresponding hash values are obtained by using the plurality of data fragments, and cyclic redundancy codes for encrypting the hash values are obtained through cyclic redundancy check, so that the security of the hash values obtained from original data is ensured, and meanwhile, after the hash values are verified to be correct, the hash values are encrypted. According to the method, the Hash value is utilized to verify each data fragment, so that the correctness of the original data is ensured, the data volume of data encryption is greatly reduced when a large number of continuous periodic data packets are transmitted, and the data security monitoring efficiency is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data security, and more specifically, to a concentrator data security monitoring system based on a multi-core one-system. Background Art

[0002] In the current era of rapid digital development, data has become the core asset for the operation and decision-making of various industries, and ensuring data security is of utmost importance; concentrators widely used in many fields such as power and energy undertake a large number of tasks of data collection, storage, and transmission, and their data security directly affects the stable operation of the system and the normal development of business; in the face of increasingly complex network attacks and data leakage risks, the amount of data processed by concentrators has increased explosively, and the requirements for data processing capabilities and response speeds are also increasing day by day;

[0003] How to solve the problem of data security monitoring during the periodic large-scale data transmission process while improving the speed and efficiency of data security processing, so as to achieve efficient data security monitoring and management is the problem we need to solve. For this purpose, a concentrator data security monitoring system based on a multi-core one-system is provided. Summary of the Invention

[0004] The purpose of the present invention is to provide a concentrator data security monitoring system based on a multi-core one-system.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A concentrator data security monitoring system based on a multi-core one-system includes a data security monitoring center, and the data security monitoring center is communicatively connected to a data upload module, a data preprocessing module, a data processing module, a data encryption module, and a data verification module;

[0006] The data upload module is used to periodically upload the data obtained by the concentrators set in each area, and identify and empower the uploaded data;

[0007] The data preprocessing module is used to optimize the data periodically uploaded by the data upload module to obtain corresponding optimized data packets;

[0008] The data processing module is used to process the optimized data packets to obtain plaintext data to be encrypted;

[0009] The data encryption module is used to encrypt the plaintext data to be encrypted to obtain corresponding encrypted data and an encryption key, and upload the obtained encrypted data and encryption key to the data security monitoring center;

[0010] The data verification module is used to perform data security verification on the encrypted data uploaded to the data security monitoring center, and execute corresponding data security management policies according to the verification results.

[0011] Furthermore, the process of the data uploading module periodically uploading the data obtained by the concentrators set in each area and performing identification empowerment on the uploaded data includes:

[0012] Set a data upload period. The concentrator periodically uploads the obtained data according to the set data upload period and generates a time series corresponding to the data upload period;

[0013] The concentrator associates a number of data sources and sets a corresponding data source sequence for each data source;

[0014] Associate a corresponding identity recognition sequence with the concentrator and construct a sequence list corresponding to the identity recognition sequence;

[0015] Import the data source sequences corresponding to the data sources associated with the concentrator into the sequence list;

[0016] Back up the identity recognition sequence of the concentrator and the corresponding sequence list in the data security monitoring center;

[0017] Insert the data of each data source obtained by the concentrator into the corresponding data source sequence respectively;

[0018] After summarizing and packing all the data to obtain a corresponding data packet, bind the time series and the identity recognition sequence of the concentrator to the obtained data packet, thereby completing the identification empowerment of the uploaded data.

[0019] Furthermore, the process of the data preprocessing module preprocessing the uploaded data includes:

[0020] Set a standard reference data table, which includes a number of standard reference items and standard parameter items corresponding to each standard reference item;

[0021] Extract data feature items from the data uploaded by the concentrator to obtain reference feature items and parameter feature items;

[0022] Match the obtained reference feature items and parameter feature items with the standard reference items and standard parameter items in the standard reference data table respectively to obtain corresponding matching results;

[0023] If the match is successful, map the corresponding reference feature item to the corresponding standard reference item in the standard reference data table, and map the corresponding standard parameter item to the standard parameter item in the standard reference data table;

[0024] After completing the mapping of all reference feature items and parameter feature items, print a periodic data table corresponding to the upload period according to the mapping results;

[0025] Traversing the periodic data table to see if there is a blank entry;

[0026] If there are no blank items, it means that the obtained periodic data table has no missing values;

[0027] If there are blank entries, it means that the obtained periodic data table has missing values;

[0028] Mark the feature items with missing values ​​and obtain the historical data corresponding to the marked feature items;

[0029] If the marked feature item is a reference feature item, the missing value is directly filled in according to the corresponding reference feature item in the historical data.

[0030] Furthermore, if the marked feature item is a parameter feature item, sampling is performed according to the corresponding parameter feature item in the historical data to obtain the corresponding sample data and the value of the parameter feature item corresponding to the sample data;

[0031] Obtain the volatility coefficient Bx of the corresponding parameter characteristic item in the sample data;

[0032] Set the volatility threshold B0;

[0033] When Bx≤B0, it means that the sampled data has a low degree of discreteness, that is, the data reliability is high, then the mean of the parameter feature items sampled in the sample data is used as the simulation parameter, and the simulation parameter is used as the filling of the corresponding parameter feature item;

[0034] When Bx>B0, it means that the sample data has a high degree of dispersion, that is, the data reliability is low, so the maximum and minimum values ​​in the sample data are eliminated, and the volatility coefficient of the remaining sample data is re-obtained, and so on, until the obtained volatility coefficient satisfies Bx≤B0, the mean value of the parameter characteristic items sampled in the remaining sample data is used as the simulation parameter;

[0035] Set a sample quantity threshold m, where m < n. When the number of remaining sample data is less than m, the parameter feature item is marked as an uncertain item.

[0036] If there are uncertain items in the periodic data table, the periodic data table will be directly uploaded to the data security monitoring center, and the data security monitoring center will arrange technical personnel to make judgments on the periodic data table.

[0037] Furthermore, the data processing module processes the uploaded data to obtain the plaintext data to be encrypted, including:

[0038] Parse the obtained data packets to obtain the corresponding original data, and insert the time series and identity recognition sequence into the original data to obtain the corresponding data to be processed;

[0039] Convert the obtained data to be processed into a data stream, and divide the obtained data stream into several data stream segments;

[0040] Construct a data matrix composed of corresponding blank matrix items according to the number of data stream segments, and associate each data stream segment with a blank matrix item;

[0041] Obtain the hash values of each data stream segment, and import the hash value of each data stream segment into the corresponding blank matrix item to obtain the corresponding hash value matrix;

[0042] Associate the obtained hash value matrix with the obtained data stream segments, upload the obtained hash value matrix and data stream segments as the plaintext data to be encrypted to the data encryption module, and upload the hash value matrix to the data security management center for backup.

[0043] Further, the process of the data encryption module encrypting the plaintext data to be encrypted to obtain the corresponding encrypted data and encryption key includes:

[0044] Read the hash value matrix in the plaintext data to be encrypted, and combine the hash values in the obtained hash value matrix in sequence to obtain a hash value sequence;

[0045] Perform a cyclic redundancy check on the obtained hash value sequence to obtain the corresponding cyclic redundancy code;

[0046] Append the obtained cyclic redundancy code to the hash value matrix to complete the encryption of the hash value matrix;

[0047] Use the encrypted hash value matrix to encrypt each data stream segment to obtain the corresponding encrypted data;

[0048] Use the obtained cyclic redundancy code as the encryption key, and upload the encryption key and encrypted data to the data security monitoring center.

[0049] Further, the process of the data verification module performing data security verification on the encrypted data uploaded to the data security monitoring center and executing the corresponding data security management policy according to the verification result includes:

[0050] Read the received encryption key, and verify the hash value matrix in the encrypted data through the encryption key. If the verification passes, it means that the corresponding hash value matrix has not been abnormal during the transmission process. Otherwise, it means that the hash value matrix has been abnormal during the transmission process;

[0051] If an anomaly exists, generate a data security warning message, construct a temporary data storage space in the data security monitoring center according to the data security warning message, and import the corresponding encrypted data into the temporary data storage space for data isolation;

[0052] If no anomaly exists, parse the hash value matrix in the encrypted data to obtain a number of hash values;

[0053] Verify each data stream segment through the obtained hash values to determine whether the transmission of each data stream segment is normal. If not, mark the corresponding data stream segment and import the marked data stream segment into the temporary data storage space;

[0054] If normal, recombine each data stream segment to obtain the corresponding data stream, classify the data stream according to the identity recognition sequence of the concentrator and the corresponding sequence list backed up in the data security monitoring center, and store it in the data security monitoring center according to the classification result.

[0055] Compared with the prior art, the beneficial effects of the present invention are:

[0056] By dividing the data obtained by the concentrator into multiple data segments, obtaining the corresponding hash values using the multiple data segments, and obtaining the cyclic redundancy code for encrypting the hash values through cyclic redundancy check, the security of the hash values obtained from the original data is guaranteed. At the same time, after the hash value verification is correct, the hash value is used to verify each data segment separately, thus ensuring the correctness of the original data. When facing the transmission of a large number of continuous periodic data packets, the amount of data encrypted is greatly reduced, thereby improving the data security monitoring efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0058] Figure 1 is the schematic diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] As Figure 1 shown, a concentrator data security monitoring system based on a multi-core one-system includes a data security monitoring center, and the data security monitoring center is communicatively connected to a data upload module, a data preprocessing module, a data processing module, a data encryption module, and a data verification module;

[0060] The data upload module is used to periodically upload the data obtained by the concentrators set in each area, and identify and empower the uploaded data;

[0061] The data preprocessing module is used to optimize the data periodically uploaded by the data upload module to obtain corresponding optimized data packets;

[0062] The data processing module is used to process the optimized data packets to obtain the plaintext data to be encrypted;

[0063] The data encryption module is used to encrypt the plaintext data to be encrypted to obtain corresponding encrypted data and encryption keys, and upload the obtained encrypted data and encryption keys to the data security monitoring center;

[0064] The data verification module is used to verify the data security of the encrypted data uploaded to the data security monitoring center, and execute corresponding data security management policies according to the verification results;

[0065] It should be further noted that in the specific implementation process, the process in which the data upload module is used to periodically upload the data obtained by the concentrators set in each area and identify and empower the uploaded data includes:

[0066] Set the data upload period. The concentrator periodically uploads the obtained data according to the set data upload period, and generates a time series corresponding to the data upload period;

[0067] The concentrator associates a number of data sources, and sets corresponding data source sequences for each data source;

[0068] Associate the concentrator with a corresponding identity recognition sequence, and construct a sequence list corresponding to the identity recognition sequence;

[0069] Import the data source sequences corresponding to the data sources associated with the concentrator into the sequence list;

[0070] Back up the identity recognition sequence of the concentrator and the corresponding sequence list in the data security monitoring center;

[0071] Insert the data of each data source obtained by the concentrator into the corresponding data source sequence respectively;

[0072] After summarizing and packing all the data, obtain the corresponding data packet, bind the time series and the identity recognition sequence of the concentrator to the obtained data packet, so as to complete the identification and empowerment of the uploaded data, and upload the data packet completed with identification and empowerment to the data preprocessing module.

[0073] It should be further noted that in the specific implementation process, the process of the data preprocessing module preprocessing the uploaded data includes:

[0074] Set a standard reference data table, which includes several standard reference items and standard parameter items corresponding to each standard reference item;

[0075] Extract data feature items from the data uploaded by the concentrator to obtain reference feature items and parameter feature items;

[0076] Match the obtained reference feature items and parameter feature items with the standard reference items and standard parameter items in the standard reference data table respectively to obtain corresponding matching results;

[0077] If the matching is successful, map the corresponding reference feature item to the corresponding standard reference item in the standard reference data table, and map the corresponding standard parameter item to the standard parameter item in the standard reference data table;

[0078] After completing the mapping of all reference feature items and parameter feature items, print a periodic data table corresponding to the upload period according to the mapping results;

[0079] Traverse the periodic data table to check if there are any blank items;

[0080] If there are no blank items, it means that the obtained periodic data table has no missing values;

[0081] If there are blank items, it means that the obtained periodic data table has missing values;

[0082] Mark the feature items with missing values and obtain the historical data corresponding to the marked feature items;

[0083] If the marked feature item is a reference feature item, directly fill in the missing value according to the corresponding reference feature item in the historical data;

[0084] If the marked feature item is a parameter feature item, sample according to the corresponding parameter feature item in the historical data to obtain corresponding sample data;

[0085] Label the sampled sample data as i, where i = 1, 2,..., n, n is an integer, and n > 1;

[0086] Then record the value corresponding to the parameter feature item of the sample data labeled i as K i ; Then obtain the volatility coefficient of the corresponding parameter feature item in the sample data, denoted as Bx, where:

[0087]

[0088] Set a volatility threshold B0;

[0089] When Bx ≤ B0, it indicates that the degree of dispersion of the sampled sample data is low, that is, the data reliability is high. Then, the mean value of the parameter feature items sampled from the sample data is used as the simulation parameter, and the simulation parameter is used as the filling of the corresponding parameter feature item;

[0090] When Bx > B0, it indicates that the degree of dispersion of the sampled sample data is high, that is, the data reliability is low. Then, the maximum and minimum values in the sample data are removed, and the volatility coefficient of the remaining sample data is obtained again, and so on, until the obtained volatility coefficient satisfies Bx ≤ B0. At this time, the mean value of the parameter feature items sampled from the remaining sample data is used as the simulation parameter;

[0091] Set a sample quantity threshold m, where m < n. When the quantity of the remaining sample data is less than m, then this parameter feature item is marked as an uncertain item;

[0092] If there are uncertain items in the periodic data table, then the periodic data table is directly uploaded to the data security monitoring center, and the data security monitoring center arranges technical personnel to determine the periodic data table.

[0093] It should be further noted that in the specific implementation process, the process of the data processing module processing the uploaded data to obtain the plaintext data to be encrypted includes:

[0094] Parse the obtained data packet to obtain the corresponding original data, and insert the time series and the identity recognition sequence into the original data to obtain the corresponding data to be processed;

[0095] Convert the obtained data to be processed into a data stream, and record the data length of the obtained data stream as L;

[0096] Divide the obtained data stream into several data stream segments, and the data length of each data stream segment is k; It should be further noted that in the specific implementation process, when the data length of the divided data stream segment does not meet k, that is, L / k cannot be divided evenly, then padding is performed at the end of the data stream segment with a data length not equal to k, and the first unit code of the padding sequence is "1", and the rest are "0";

[0097] Illustrate with an example:

[0098] Set the data stream 10110111000111011011011001111010101110;

[0099] Divide the data stream into data stream segments with a data length k = 8, then the division result is:

[0100] 10110111, 00011101, 10110110, 01111010, 101110;

[0101] Among them, for the data stream segment 101110 with data length k = 6, 10 is filled at the end of this data stream segment;

[0102] The data stream segment after filling is 10111010;

[0103] Construct a data matrix composed of corresponding blank matrix items according to the number of data stream segments, and associate each data stream segment with a blank matrix item;

[0104] Obtain the hash values of each data stream segment, and import the hash value of each data stream segment into the corresponding blank matrix item to obtain the corresponding hash value matrix;

[0105] Associate the obtained hash value matrix with the obtained data stream segments, upload the obtained hash value matrix and the data stream segments as the plaintext data to be encrypted to the data encryption module, and upload the hash value matrix to the data security management center for backup.

[0106] It should be further noted that in the specific implementation process, the process by which the data encryption module encrypts the plaintext data to be encrypted to obtain the corresponding encrypted data and encryption key includes:

[0107] Read the hash value matrix in the plaintext data to be encrypted, and combine the hash values in the obtained hash value matrix in sequence to obtain a hash value sequence;

[0108] Perform a cyclic redundancy check on the obtained hash value sequence to obtain the corresponding cyclic redundancy code;

[0109] Append the obtained cyclic redundancy code to the hash value matrix to complete the encryption of the hash value matrix;

[0110] Use the encrypted hash value matrix to encrypt each data stream segment to obtain the corresponding encrypted data;

[0111] Use the obtained cyclic redundancy code as the encryption key, and upload the encryption key and the encrypted data to the data security monitoring center.

[0112] It should be further noted that in the specific implementation process, the process by which the data verification module performs data security verification on the encrypted data uploaded to the data security monitoring center and executes the corresponding data security management strategy according to the verification result includes:

[0113] Read the received encryption key and verify the hash value matrix in the encrypted data using the encryption key. If the verification passes, it indicates that the corresponding hash value matrix has not been abnormal during transmission; otherwise, it indicates that the hash value matrix is abnormal during transmission.

[0114] If there is an abnormality, generate a data security warning message, construct a temporary data storage space in the data security monitoring center according to the data security warning message, and import the corresponding encrypted data into the temporary data storage space for data isolation.

[0115] If there is no abnormality, parse the hash value matrix in the encrypted data to obtain a number of hash values.

[0116] Verify each data stream segment through the obtained hash values to determine whether the transmission of each data stream segment is normal. If it is not normal, mark the corresponding data stream segment and import the marked data stream segment into the temporary data storage space.

[0117] If it is normal, recombine each data stream segment to obtain the corresponding data stream, classify the data stream according to the identity recognition sequence of the concentrator backed up in the data security monitoring center and the corresponding sequence list, and store it in the data security monitoring center according to the classification result.

[0118] The above is only a preferred embodiment of the present invention and does not impose any form of limitation on the present invention. Although the present invention has been disclosed above with a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to it as equivalent embodiments with equivalent changes within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any modification or equivalent replacement made to the above embodiments according to the technical essence of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A concentrator data security monitoring system based on a multi-core one-system, including a data security monitoring center, characterized in that, The data security monitoring center is communicatively connected to a data upload module, a data preprocessing module, a data processing module, a data encryption module, and a data verification module; The data upload module is used to periodically upload the data obtained by the concentrators set in each area, and identify and empower the uploaded data; The data preprocessing module is used to optimize the data periodically uploaded by the data upload module to obtain corresponding optimized data packets; The data processing module is used to process the optimized data packets to obtain plaintext data to be encrypted; The data encryption module is used to encrypt the plaintext data to be encrypted to obtain corresponding encrypted data and an encryption key, and upload the obtained encrypted data and encryption key to the data security monitoring center; The data verification module is used to perform data security verification on the encrypted data uploaded to the data security monitoring center, and execute corresponding data security management policies according to the verification results.

2. The concentrator data security monitoring system based on the multi-core one-system according to claim 1, characterized in that, The process in which the data upload module periodically uploads the data obtained by the concentrators set in each area and identifies and empowers the uploaded data includes: Set a data upload period, and the concentrator periodically uploads the obtained data according to the set data upload period, and generates a time series corresponding to the data upload period; The concentrator associates a number of data sources, and sets a corresponding data source sequence for each data source; Associate a corresponding identity recognition sequence with the concentrator, and construct a sequence list corresponding to the identity recognition sequence; Import the data source sequences corresponding to the data sources associated with the concentrator into the sequence list; Back up the identity recognition sequence of the concentrator and the corresponding sequence list in the data security monitoring center; Insert the data of each data source obtained by the concentrator into the corresponding data source sequence; After summarizing and packing all the data to obtain a corresponding data packet, bind the time series and the identity recognition sequence of the concentrator to the obtained data packet, thereby completing the identification and empowerment of the uploaded data.

3. The concentrator data security monitoring system based on the multi-core one-system according to claim 2, wherein The process in which the data preprocessing module preprocesses the uploaded data includes: Set a standard reference data table, which includes a number of standard reference items and standard parameter items corresponding to each standard reference item; Extract data feature items from the data uploaded by the concentrator to obtain reference feature items and parameter feature items; Match the obtained reference feature items and parameter feature items with the standard reference items and standard parameter items in the standard reference data table respectively to obtain corresponding matching results; If the match is successful, map the corresponding reference feature item to the corresponding standard reference item in the standard reference data table, and map the corresponding standard parameter item to the standard parameter item in the standard reference data table; After completing the mapping of all reference feature items and parameter feature items, print a periodic data table corresponding to the upload period according to the mapping results; Traverse the periodic data table to check if there are any blank items; If there are no blank items, it means that the obtained periodic data table has no missing values; If there are blank items, it means that the obtained periodic data table has missing values; Mark the feature items with missing values ​​and obtain the historical data corresponding to the marked feature items; If the marked feature item is a reference feature item, the missing value is directly filled in according to the corresponding reference feature item in the historical data.

4. The concentrator data security monitoring system based on the multi-core one-system according to claim 3, characterized in that, If the marked feature item is a parameter feature item, sampling is performed according to the corresponding parameter feature item in the historical data to obtain the corresponding sample data and the value of the parameter feature item corresponding to the sample data; Obtain the volatility coefficient Bx of the corresponding parameter characteristic item in the sample data; Set the volatility threshold B0; When Bx≤B0, it means that the sampled data has a low degree of discreteness, that is, the data reliability is high, then the mean of the parameter feature items sampled in the sample data is used as the simulation parameter, and the simulation parameter is used as the filling of the corresponding parameter feature item; When Bx>B0, it means that the sample data has a high degree of dispersion, that is, the data reliability is low, so the maximum and minimum values ​​in the sample data are eliminated, and the volatility coefficient of the remaining sample data is re-obtained, and so on, until the obtained volatility coefficient satisfies Bx≤B0, the mean value of the parameter characteristic items sampled in the remaining sample data is used as the simulation parameter; Set a sample quantity threshold m, where m < n. When the number of remaining sample data is less than m, the parameter feature item is marked as an uncertain item. If there are uncertain items in the periodic data table, the periodic data table will be directly uploaded to the data security monitoring center, and the data security monitoring center will arrange technical personnel to make judgments on the periodic data table.

5. The concentrator data security monitoring system based on the multi-core one-system according to claim 4, wherein The data processing module processes the uploaded data to obtain the plaintext data to be encrypted, including: Parse the obtained data packets to obtain the corresponding original data, and insert the time series and identity recognition sequence into the original data to obtain the corresponding data to be processed; Converting the obtained data to be processed into a data stream, and dividing the obtained data stream into a plurality of data stream segments; constructing a data matrix consisting of a corresponding number of blank matrix entries according to the number of data stream segments, and associating each data stream segment with a blank matrix entry; Obtain the hash value of each data stream segment, and import the hash value of each data stream segment into the corresponding blank matrix item to obtain the corresponding hash value matrix; The obtained hash value matrix is ​​associated with the obtained data stream fragment, the obtained hash value matrix and the data stream fragment are uploaded to the data encryption module as plaintext data to be encrypted, and the hash value matrix is ​​uploaded to the data security management center for backup.

6. The concentrator data security monitoring system based on the multi-core one-system according to claim 5, wherein The data encryption module encrypts the plaintext data to be encrypted, and the process of obtaining the corresponding encrypted data and encryption key includes: Read the hash value matrix in the plaintext data to be encrypted, and combine the hash values ​​in the obtained hash value matrix in sequence to obtain a hash value sequence; Perform a cyclic redundancy check on the obtained hash value sequence to obtain a corresponding cyclic redundancy code; After the obtained cyclic redundancy code is attached to the hash value matrix, the encryption of the hash value matrix is ​​completed; Each data stream segment is encrypted using the encrypted hash value matrix to obtain the corresponding encrypted data; Use the obtained cyclic redundancy code as the encryption key, and upload the encryption key and the encrypted data to the data security monitoring center.

7. The concentrator data security monitoring system based on the multi-core one-system according to claim 6, characterized in that, The process in which the data verification module performs data security verification on the encrypted data uploaded to the data security monitoring center and executes the corresponding data security management strategy includes: Read the received encryption key, and verify the hash value matrix in the encrypted data through the encryption key. If the verification passes, it indicates that the corresponding hash value matrix has not been abnormal during the transmission process; otherwise, it indicates that the hash value matrix has been abnormal during the transmission process. If there is an abnormality, generate a data security warning message, construct a temporary data storage space in the data security monitoring center according to the data security warning message, and import the corresponding encrypted data into the temporary data storage space for data isolation. If there is no abnormality, parse the hash value matrix in the encrypted data to obtain a number of hash values. Verify each data stream segment through the obtained hash values to determine whether the transmission of each data stream segment is normal. If it is not normal, mark the corresponding data stream segment and import the marked data stream segment into the temporary data storage space. If it is normal, recombine each data stream segment to obtain the corresponding data stream, classify the data stream according to the identity recognition sequence of the concentrator backed up in the data security monitoring center and the corresponding sequence list, and store it in the data security monitoring center according to the classification result.

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