Industrial control network data security sharing method and system based on blockchain

Abstract

The present invention relates to the field of data processing, and more particularly to a blockchain-based method and system for securely sharing industrial control network data. The method comprises the following steps: obtaining an industrial control network data sequence; segmenting the industrial control network data sequence, calculating the difference between each pair of segments, using the difference as an edge weight, constructing an undirected graph with each segment as a node, calculating the centrality of each node in the graph, selecting a data point from the node with the smallest centrality as an active replacement point, and selecting data points from the remaining nodes as passive replacement points, and replacing the active and passive replacement points; obtaining a final replacement segment in response to the regular destructibility of each replaced segment being no less than a preset destructibility threshold; and storing each final replacement segment in a shared blockchain space. This improves encryption effectiveness and efficiency.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for securely sharing industrial control network data based on blockchain. Background Art

[0002] Industrial control network data contains critical information such as real-time process data, control instructions, and diagnostic information. Its security directly impacts production continuity and reliability. Real-time process data reflects the operating status of equipment, while control instructions directly drive equipment actions. Leakage or tampering of this data can have catastrophic consequences for industrial production. Therefore, effective encryption and protection of industrial control network data is essential to ensure the secure and stable operation of industrial systems.

[0003] Scrambling encryption, a commonly used encryption method, offers advantages over traditional algorithms in terms of high real-time performance and strong compatibility, effectively meeting the high real-time requirements of industrial control network data. However, traditional scrambling encryption algorithms exhibit significant drawbacks when processing industrial control network data: when performing data permutation operations, these algorithms fail to deeply analyze the inherent patterns between data and instead simply permute a random portion of the data. Industrial control network data has distinct structural characteristics: data from different modules exhibits weak correlations, such as the relatively weak correlation between robot motion control data and environmental monitoring data, while data within the same module exhibits strong correlations, such as the tightly coupled and mutually influential operating status data of various devices in a production workshop. This results in traditional algorithms consuming significant computing resources during the encryption process on ineffective permutations of data within the same module, and also wastes computing power due to insufficient randomness in cross-module permutations. This ultimately results in low encryption efficiency and weak security, making it difficult to defend against targeted attacks against confidential industrial data. Therefore, improving the efficiency and security of permutation encryption is the focus of this research. Summary of the Invention

[0004] In order to solve the problem of how to improve the efficiency and security of permutation encryption, the present invention provides a blockchain-based industrial control network data security sharing method and system.

[0005] In the first aspect, the present invention provides a method for securely sharing industrial control network data based on blockchain, which adopts the following technical solutions:

[0006] The method for securely sharing industrial control network data based on blockchain includes the following steps:

[0007] Obtain industrial control network data sequence;

[0008] Segment the industrial control network data sequence according to the similarity of the rules, calculate the difference between each two segments, use the difference as the edge weight, and construct an undirected graph with each segment as a node. Calculate the centrality of each node in the undirected graph, select data points from the node with the smallest centrality as active replacement points, and select data points from other nodes as passive replacement points, and perform replacement processing on the active replacement points and the passive replacement points; in response to the existence of a regularity destruction degree of some replaced segments being less than a preset destruction degree threshold, use each replaced segment as a node, reconstruct the undirected graph, and perform data replacement processing based on the reconstructed undirected graph; in response to the regularity destruction degree of each replaced segment being not less than the preset destruction degree threshold, obtain the final replaced segment;

[0009] The final replaced segments are stored separately in the blockchain of the shared space.

[0010] The present invention performs segmentation processing on data, separates data with different regularities, and segments data with the same regularity together, thereby providing a basis for subsequent substitution encryption; further, by performing substitution processing between segments, the data in the segment is introduced into other segments, thereby mixing data with different regularities together, and destroying the regularity of each segment more quickly and more strongly; further, when performing substitution processing on data between segments, the differences between the segments are analyzed, thereby introducing data with large regularity differences into other segments, and destroying the regularity of the segments more quickly and more strongly.

[0011] Preferably, the segmenting of the industrial control network data sequence according to the similarity of the rules includes:

[0012] A window of preset size is used to slide on the industrial control network data sequence, and the regular similarity of the window after each sliding is calculated; the regular similarity of the windows after all sliding times is arranged in the sliding order to obtain a regular similarity sequence, the minimum point in the regular similarity sequence is taken as the center to obtain a preset range, and the variance of the data within the preset range is calculated and recorded as the fluctuation degree of each minimum point, all the minimum points are clustered into two categories according to the fluctuation degree, the mean fluctuation degree of all the minimum points in each category is calculated, and the central data of the sliding window corresponding to the minimum point in the category with a large mean fluctuation degree is used as the segmentation point; the industrial control network data sequence is segmented using the segmentation point.

[0013] The present invention accurately and quickly screens out segmentation points based on the changing characteristics of the degree of similarity of the rules, providing a basis for accurately and quickly segmenting data with different rules.

[0014] Preferably, the calculating of the regular similarity between the window after each sliding and the window after the previous sliding includes:

[0015] Obtain the frequency of occurrence of each type of data value in the window after each sliding, and record the sequence composed of the frequency of occurrence of all types of data values ​​in the window after each sliding as a frequency sequence; fit a polynomial to the data in the window after each sliding, and record the sequence composed of the coefficients of all terms in the polynomial as a coefficient sequence; calculate the Euclidean distance between the frequency sequence of the window after each sliding and the frequency sequence of the window after the previous sliding as the frequency difference, calculate the cosine similarity between the coefficient sequence of the window after each sliding and the coefficient sequence of the window after the previous sliding as the trend similarity; divide the trend similarity by the frequency difference to obtain the regular similarity between the window after each sliding and the window after the previous sliding.

[0016] When analyzing the similarity of patterns, the present invention not only introduces frequency information but also introduces trend information, thereby analyzing the similarity of patterns more comprehensively and accurately.

[0017] Preferably, the calculating the difference between every two segments includes:

[0018] Obtain the frequency sequence of each segment, calculate the Euclidean distance between the frequency sequences of every two segments and record it as the difference between the two segments.

[0019] The present invention reflects the difference of data between segments through frequency difference. This difference analysis method is relatively simple and has higher implementation efficiency.

[0020] Preferably, the calculating the centrality of each node in the undirected graph includes:

[0021] The closeness centrality calculation method in graph theory is used to calculate the centrality of each node in an undirected graph.

[0022] Preferably, selecting data points from the node with the smallest centrality as active replacement points and selecting data points from other nodes as passive replacement points includes:

[0023] Get the number of nodes , obtain the number L of data in the node with the smallest centrality, obtain the position key sequence, locate the data of S-1 positions in the segment corresponding to the node with the smallest centrality according to the data in the position key sequence, and record them as active replacement points, and locate the data of one position in the segment corresponding to each other node according to the data in the position key sequence, and record them as passive replacement points.

[0024] The present invention screens the replacement points by centrality, thereby screening out more data at segments with large differences, thereby introducing data with large differences into each segment, and destroying the regularity of the data faster and more strongly.

[0025] Preferably, the replacing of the active replacement point with the passive replacement point comprises:

[0026] The active displacement point and the passive displacement point are exchanged.

[0027] Preferably, the method for obtaining the degree of regularity destruction includes:

[0028] Obtain the frequency sequence and coefficient sequence in each replaced segment, calculate the Euclidean distance between the frequency sequence of the replaced segment and the corresponding segment without replacement, and record it as frequency destructiveness; calculate the Euclidean distance between the coefficient sequence of the replaced segment and the coefficient sequence of the corresponding segment without replacement, and record it as trend destructiveness; the product of frequency destructiveness and trend destructiveness is recorded as the degree of regularity destruction.

[0029] Preferably, the data replacement processing according to the reconstructed undirected graph includes:

[0030] Calculate the centrality of each node in the reconstructed undirected graph, select data points from the node with the smallest centrality as active replacement points, and select data points from other nodes as passive replacement points, and replace the active replacement points with the passive replacement points.

[0031] In a second aspect, the present invention provides a blockchain-based industrial control network data security sharing system, which adopts the following technical solutions:

[0032] The blockchain-based industrial control network data security sharing system includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned blockchain-based industrial control network data security sharing method is implemented.

[0033] By adopting the above technical solution, the above-mentioned blockchain-based industrial control network data security sharing method is generated into a computer program and stored in a memory to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0034] The present invention has the following technical effects:

[0035] The present invention processes data in segments, separates data with different patterns, and groups data with the same pattern together, thereby providing a basis for subsequent permutation encryption.

[0036] Furthermore, by performing permutation processing between segments, the data in a segment is introduced into other segments, thereby mixing data with different regularities together, destroying the regularity of each segment faster and more strongly;

[0037] Furthermore, when performing inter-segment data replacement processing, the differences between the segments are analyzed, so that data with large regular differences are introduced into other segments, destroying the regularity of the segments faster and more effectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flowchart of a method for securely sharing industrial control network data based on blockchain in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The embodiment of the present invention discloses a method for securely sharing industrial control network data based on blockchain. Figure 1 , including steps S1 to S4:

[0040] S1: Obtain industrial control network data sequence.

[0041] Specifically, an industrial control network data sequence is obtained on the industrial control network platform. The types of data in the industrial control network data sequence include but are not limited to the following aspects: real-time process data, control instruction data, diagnostic information data, and process parameter data.

[0042] S2: Segment the industrial control network data sequence according to the similarity of regularity, calculate the difference between each two segments, use the difference as the edge weight, and construct an undirected graph with each segment as a node. Calculate the centrality of each node in the undirected graph, select data points from the nodes with the smallest centrality as active replacement points, and select data points from other nodes as passive replacement points, and replace the active replacement points with the passive replacement points; in response to the fact that the degree of regularity destruction of some replaced segments is less than a preset destruction degree threshold, use each replaced segment as a node, reconstruct the undirected graph, and perform data replacement processing based on the reconstructed undirected graph; in response to the fact that the regularity destruction of each replaced segment is not less than the preset destruction degree threshold, obtain the final replaced segment.

[0043] It should be noted that due to the high correlation between the data in the same module, the encryption effect achieved by replacing the data within the module is poor. In order to improve the encryption efficiency and effect, it is necessary to replace the data between different modules. First, the data of different modules need to be separated.

[0044] S20: Segment the industrial control network data sequence according to regular similarity.

[0045] It should be noted that the data of the same module are highly correlated, that is, the patterns of the data of the same module are relatively similar. Therefore, the modules can be segmented according to the similarity of the patterns, thereby separating the data of different modules.

[0046] Preferably, as an example, segmenting the industrial control network data sequence according to regular similarity includes:

[0047] A window of preset size is used to slide on the industrial control network data sequence, and the similarity between the regularity of the window after each sliding and the window after the previous sliding is calculated and recorded as the regularity similarity degree of the window after each sliding;

[0048] The regular similarity of all sliding windows is arranged in sliding order to obtain a regular similarity sequence, and the minimum value points in the regular similarity sequence are obtained. The preset range is obtained with each minimum value point as the center, and the variance of the data within the preset range is calculated and recorded as the fluctuation degree of each minimum value point. All minimum points are clustered into two categories according to the fluctuation degree, and the mean fluctuation degree of all minimum points in each category is calculated. The center data of the sliding window corresponding to the minimum point in the category with a large mean fluctuation degree is used as the segmentation point; the industrial control network data sequence is divided into several segments using the segmentation point.

[0049] It is understandable that, under normal circumstances, when the window slides within the same module, the regular similarity between the windows is relatively large. In the process of the window sliding from one module to the next module, the window will contain part of the data of the two modules. Therefore, the regularity of the window data will gradually decrease and then increase, so there will be a regular minimum value at the data between the modules. This embodiment uses the minimum value to filter out possible segmentation points between modules. Since the regular similarity of the same module is not constant, the regular similarity of the same module will also have minimum points, and some minimum points are not segmentation points. The fluctuation of the regular similarity between modules is relatively large, so some interfering minimum points are filtered out by the fluctuation situation to obtain accurate segmentation points, thereby achieving accurate segmentation.

[0050] The above embodiments involve the degree of similarity of rules. The method for determining the degree of similarity of rules will be described below.

[0051] Preferably, as an example, the method for obtaining the degree of regular similarity includes:

[0052] Obtain the frequency of occurrence of each value in the window after each sliding, and record the sequence consisting of the frequency of occurrence of all values ​​in the window after each sliding as the frequency sequence; fit the data in the window after each sliding with a polynomial, and record the sequence consisting of the coefficients of all items in the polynomial as the coefficient sequence;

[0053] The Euclidean distance between the frequency sequence of each sliding window and the frequency sequence of the previous sliding window is calculated and recorded as the frequency difference. The cosine similarity between the coefficient sequence of each sliding window and the coefficient sequence of the previous sliding window is calculated and recorded as the trend similarity. The trend similarity is divided by the frequency difference to obtain the regular similarity between each sliding window and the previous sliding window.

[0054] It is understandable that the regularity of data is mainly reflected in the frequency of occurrence and the law of change of data. Therefore, the similarity of the laws between segments can be analyzed through the frequency of occurrence and the law of change.

[0055] S21: Calculate the difference between every two segments, use the difference as the edge weight, and use each segment as a node to construct an undirected graph. Calculate the centrality of each node in the undirected graph, select data points from the node with the smallest centrality as active replacement points, and select data points from other nodes as passive replacement points, and replace the active replacement points with the passive replacement points.

[0056] It should be noted that, in order to better destroy the readability of information, the readability of data can be destroyed faster and more strongly by introducing data in the difference information.

[0057] Preferably, as an example, the difference between each two segments is calculated, the difference is used as the edge weight, each segment is used as a node, an undirected graph is constructed, the centrality of each node in the undirected graph is calculated, a data point is selected from the node with the smallest centrality as the active replacement point, and a data point is selected from other nodes as the passive replacement point, and the active replacement point and the passive replacement point are replaced, including:

[0058] Obtain the frequency sequence of each segment, calculate the Euclidean distance between the frequency sequences of every two segments and record it as the difference between the two segments.

[0059] The difference is used as the edge weight, each segment is used as a node, and every two nodes are connected to obtain an undirected graph.

[0060] The closeness centrality calculation method in graph theory is used to calculate the centrality of each node in an undirected graph.

[0061] Get the number of nodes , obtain the number of data points L in the node with the smallest centrality, obtain the position key sequence, and locate S-1 data points in the segment corresponding to the node with the smallest centrality based on the data in the position key sequence. These are recorded as active replacement points. Also, locate one data point in the segment corresponding to each of the other nodes based on the data in the position key sequence. These data points are recorded as passive replacement points. The active and passive replacement points are swapped.

[0062] It can be understood that the smaller the centrality, the greater the information difference between the segment and other segments. By placing the information in the segment with large information difference into other segments, the information of other segments can be better destroyed.

[0063] It should be added that the method for locating the data at S-1 positions in the segment corresponding to the node with the smallest centrality according to the data in the position key sequence includes:

[0064] A chaotic sequence is generated using a chaotic model. The data in the chaotic sequence is modulo the number of data in the node with the smallest centrality, L, and the data is normalized to between 1 and L. The normalized chaotic sequence is recorded as the position key sequence.

[0065] Assuming that the current permutation is C+1, the number of position keys required for each permutation round is 2S-2, then the position key required for the current permutation round is in the first position in the key sequence. arrive , according to the position key required for the current permutation round, locate the data of S-1 positions in the segment corresponding to the node with the smallest centrality.

[0066] Assuming that the first position key required for the current permutation round is 3, the data at the third position is located in the segment corresponding to the node with the smallest centrality.

[0067] It should be noted that the parameters of the chaos model are constants agreed upon in advance by the encryptor and decryptor, and there is no need to exchange parameters.

[0068] S22: In response to the fact that the degree of regularity destruction of some replaced segments is less than a preset destruction degree threshold, each replaced segment is used as a node to reconstruct an undirected graph, and data replacement processing is performed according to the reconstructed undirected graph.

[0069] S220: Calculate the degree of regularity destruction of the replaced segments.

[0070] It should be noted that in order to prevent information leakage due to insufficient permutation encryption or unnecessary time waste due to excessive scrambling encryption, the scrambling encryption effect needs to be evaluated after each permutation.

[0071] Preferably, as an example, calculating the degree of regularity destruction of the replaced segments includes:

[0072] Obtain the frequency sequence and coefficient sequence in each replaced segment, calculate the Euclidean distance between the frequency sequence of the replaced segment and the corresponding segment without replacement, and record it as frequency destructiveness; calculate the Euclidean distance between the coefficient sequence of the replaced segment and the coefficient sequence of the corresponding segment without replacement, and record it as trend destructiveness; the product of frequency destructiveness and trend destructiveness is recorded as the degree of regularity destruction.

[0073] It can be understood that by analyzing the difference in regular information of the segments before and after the replacement, the degree of damage to the information caused by scrambling encryption can be determined, thereby providing a basis for subsequent encryption cutoff control.

[0074] S221: In response to the fact that the degree of regularity destruction of some replaced segments is less than a preset destruction degree threshold, each replaced segment is used as a node to reconstruct an undirected graph, and data replacement processing is performed according to the reconstructed undirected graph.

[0075] Preferably, as an example, in response to the fact that the degree of regularity destruction of some replaced segments is less than a preset destruction degree threshold, each replaced segment is used as a node, an undirected graph is reconstructed, and data replacement processing is performed according to the reconstructed undirected graph, including:

[0076] In response to the fact that the degree of regularity destruction of some replaced segments is less than a preset destruction degree threshold, the replaced segments are used as nodes to reconstruct the undirected graph, the centrality of each node in the reconstructed undirected graph is calculated, data points are selected from the nodes with the smallest centrality as active replacement points, and data points are selected from other nodes as passive replacement points, and the active replacement points and the passive replacement points are replaced.

[0077] It is understandable that if the degree of regular destruction is less than the destruction threshold, it means that the degree of information destruction cannot meet the encryption requirements and further replacement processing of the information is required to destroy the readability of the information.

[0078] It should be added that the method for obtaining the active replacement point and the passive replacement point is the same as that in step S21.

[0079] S23: In response to the regular destructiveness of each replaced segment being no less than a preset destructiveness threshold, a final replaced segment is obtained.

[0080] It can be understood that if the regular destructiveness is less than the destruction threshold, it means that the destruction level of the information in the segment meets the encryption requirements, and the scrambling encryption can be stopped at this time.

[0081] S3: Store the final replaced segments separately in the blockchain of the shared space.

[0082] It is understandable that due to the characteristics of blockchain such as decentralization and autonomy, it is difficult to obtain all the data in all blockchains without permission. Different final replaced segments are stored in different blockchains. People without permission cannot obtain all the data and thus cannot restore the data information before the scrambling.

[0083] S4: Decrypt the encrypted data sequence.

[0084] Preferably, as an example, decrypting the encrypted data sequence includes:

[0085] Obtain the centrality of each node in each undirected graph, record the sequence of centralities of all nodes in all undirected graphs as the information key sequence, and store the information key sequence in the private blockchain.

[0086] The decryptor obtains the information key sequence from the private blockchain and obtains the centrality of each node in each undirected graph from the information key sequence. A chaotic sequence is generated and the data in the chaotic sequence is normalized according to the content of step S21 to obtain the position key sequence.

[0087] The decryptor obtains all replaced segments in the blockchain of the shared space, obtains the segment corresponding to the node with the smallest centrality according to the centrality of each node of the last undirected graph, locates the active replacement point in the segment corresponding to the node with the smallest centrality according to the data in the position key sequence in accordance with the content in step S21, locates the passive replacement point in other segments, replaces the active replacement point and the passive replacement point, and performs replacement processing in sequence according to the centrality of each node of each undirected graph using this method until the replacement is completed according to the centrality of each node of the first undirected graph, and obtains the industrial control network data sequence.

[0088] An embodiment of the present invention also discloses a blockchain-based industrial control network data security sharing system, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, the blockchain-based industrial control network data security sharing method according to the present invention is implemented.

[0089] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0090] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory, dynamic random access memory, static random access memory, enhanced dynamic random access memory, high bandwidth memory, hybrid memory cube, etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium may be part of, accessible to, or connectable to the device.

Claims

1. A blockchain-based industrial control network data security sharing method, characterized in that: Including steps: Obtain industrial control network data sequence; Segment the industrial control network data sequence according to the similarity of the rules, calculate the difference between each two segments, use the difference as the edge weight, and use each segment as a node to construct an undirected graph, and calculate the centrality of each node in the undirected graph, including: using the calculation method of proximity centrality in graph theory to calculate the centrality of each node in the undirected graph; Select data points from the node with the smallest centrality as active replacement points, and select data points from other nodes as passive replacement points, including: obtaining the number of nodes , obtain the number L of data in the node with the smallest centrality, obtain the position key sequence, locate the data of S-1 positions in the segment corresponding to the node with the smallest centrality according to the data in the position key sequence, and record them as active replacement points; locate the data of one position in the segment corresponding to each other node according to the data in the position key sequence, and record them as passive replacement points; Performing a replacement process on the active replacement points and the passive replacement points; in response to the regularity destruction degree of some replaced segments being less than a preset destruction degree threshold, using each replaced segment as a node, reconstructing an undirected graph, and performing data replacement processing based on the reconstructed undirected graph; in response to the regularity destruction degree of each replaced segment being not less than the preset destruction degree threshold, obtaining a final replaced segment; The final replaced segments are stored separately in the blockchain of the shared space.

2. The method for securely sharing industrial control network data based on blockchain according to claim 1 is characterized in that: The segmentation processing of the industrial control network data sequence according to the similarity of the rules includes: A window of preset size is used to slide on the industrial control network data sequence, and the regular similarity of the window after each sliding is calculated; the regular similarity of the windows after all sliding times is arranged in the sliding order to obtain a regular similarity sequence, the minimum point in the regular similarity sequence is taken as the center to obtain a preset range, and the variance of the data within the preset range is calculated and recorded as the fluctuation degree of each minimum point, all the minimum points are clustered into two categories according to the fluctuation degree, the mean fluctuation degree of all the minimum points in each category is calculated, and the central data of the sliding window corresponding to the minimum point in the category with a large mean fluctuation degree is used as the segmentation point; the industrial control network data sequence is segmented using the segmentation point.

3. The method for securely sharing industrial control network data based on blockchain according to claim 2 is characterized in that: The calculation of the regularity similarity between each sliding window and the previous sliding window includes: Obtain the frequency of occurrence of each type of data value in the window after each sliding, and record the sequence composed of the frequency of occurrence of all types of data values ​​in the window after each sliding as a frequency sequence; fit a polynomial to the data in the window after each sliding, and record the sequence composed of the coefficients of all terms in the polynomial as a coefficient sequence; calculate the Euclidean distance between the frequency sequence of the window after each sliding and the frequency sequence of the window after the previous sliding as the frequency difference, calculate the cosine similarity between the coefficient sequence of the window after each sliding and the coefficient sequence of the window after the previous sliding as the trend similarity; divide the trend similarity by the frequency difference to obtain the regular similarity between the window after each sliding and the window after the previous sliding.

4. The method for securely sharing industrial control network data based on blockchain according to claim 3 is characterized in that: The calculation of the difference between each two segments includes: Obtain the frequency sequence of each segment, calculate the Euclidean distance between the frequency sequences of every two segments and record it as the difference between the two segments.

5. The method for securely sharing industrial control network data based on blockchain according to claim 1 is characterized in that: The replacing process of the active replacement point and the passive replacement point includes: The active displacement point and the passive displacement point are exchanged.

6. The method for securely sharing industrial control network data based on blockchain according to claim 3 is characterized in that: The method for obtaining the degree of regularity destruction includes: Obtain the frequency sequence and coefficient sequence in each replaced segment, calculate the Euclidean distance between the frequency sequence of the replaced segment and the corresponding segment without replacement, and record it as frequency destructiveness; calculate the Euclidean distance between the coefficient sequence of the replaced segment and the coefficient sequence of the corresponding segment without replacement, and record it as trend destructiveness; the product of frequency destructiveness and trend destructiveness is recorded as the degree of regularity destruction.

7. The method for securely sharing industrial control network data based on blockchain according to claim 1 is characterized in that: The data replacement processing according to the reconstructed undirected graph includes: Calculate the centrality of each node in the reconstructed undirected graph, select data points from the node with the smallest centrality as active replacement points, and select data points from other nodes as passive replacement points, and replace the active replacement points with the passive replacement points.

8. The industrial control network data security sharing system based on blockchain is characterized by: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a blockchain-based industrial control network data security sharing method according to any one of claims 1 to 7 is implemented.

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