Industrial control network data security sharing method and system based on block chain

Abstract

The invention relates to the field of data processing, in particular to an industrial control network data security sharing method and system based on a block chain. The method comprises the following steps: acquiring an industrial control network data sequence; the method comprises the following steps: segmenting an industrial control network data sequence, calculating the difference between every two segments, taking the difference as an edge weight, taking each segment as a node, constructing an undirected graph, calculating the centrality of each node in the undirected graph, and selecting a data point from the node with the minimum centrality as an active replacement point. Data points are selected from other nodes to serve as passive replacement points, and the active replacement points and the passive replacement points are subjected to replacement processing; responding to the condition that the regular destructiveness of each post-replacement segment is not less than a preset damage degree threshold value, and obtaining a final post-replacement segment; and respectively storing the segments after final replacement into the block chain of the shared space. And the encryption effect and efficiency are improved.

Description

Technical Field

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

[0002] Industrial control network data contains key information such as real-time process data, control instructions, diagnostic information, etc. Its security directly affects the continuity and reliability of production. Real-time process data reflects the operating state of equipment, and control instructions directly drive equipment actions. Once these data are leaked or tampered with, it will cause catastrophic consequences to industrial production. Therefore, effective encryption protection of industrial control network data is a necessary means to ensure the safe and stable operation of industrial systems.

[0003] As a commonly used encryption method, scrambling encryption has the advantages of high real-time performance and strong compatibility compared with traditional algorithms, and can better meet the high real-time requirements of industrial control network data. However, traditional scrambling encryption algorithms have significant drawbacks when processing industrial control network data: when performing data permutation operations, this algorithm does not deeply analyze the internal laws between data, and only adopts a simple method of randomly extracting some data for permutation. Industrial control network data has distinct structural characteristics: the correlation between data of different modules is weak. For example, the correlation between robot motion control data and environmental monitoring data is relatively weak, while the data within the same module shows strong correlation characteristics. For example, the operating state data of various devices in the same production workshop are closely coupled and affect each other. This makes the traditional algorithm consume a large amount of computing resources on the ineffective permutation of data with the same theme during the encryption process, and waste computing power due to insufficient randomness in cross-module permutation. Eventually, it leads to low encryption efficiency and weak security, and it is difficult to resist targeted attacks on industrial confidential data. Therefore, how to improve the efficiency and security of permutation encryption has become the research focus of this solution. Summary of the Invention

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

[0005] In a first aspect, the present invention provides a method for secure sharing of industrial control network data based on blockchain, adopting the following technical solutions: The method for secure sharing of industrial control network data based on blockchain includes the steps of: Obtain an industrial control network data sequence; Segment the industrial control network data sequence according to the law similarity, calculate the difference between every two segments, use the difference as the edge weight value, use each segment as a node to construct an undirected graph, calculate the centrality of each node in the undirected graph, select data points as active replacement points among the nodes with the smallest centrality, select data points as passive replacement points among other nodes, and perform replacement processing on the active replacement points and the passive replacement points; in response to the fact that the degree of disruption of the regularity of some segments after replacement is less than the preset disruption degree threshold, use the segments after replacement as nodes to reconstruct the undirected graph, and perform data replacement processing according to the reconstructed undirected graph; in response to the fact that the degree of disruption of the regularity of each segment after replacement is not less than the preset disruption degree threshold, obtain the finally replaced segments. Store the finally replaced segments into the blockchain in the shared space respectively.

[0006] In the present invention, by segmenting the data, data with different laws are separated and data with the same law are grouped together, thereby providing a basis for subsequent replacement encryption; further, by performing replacement processing between segments, the data in the segments are introduced into other segments, so that data with different regularities are mixed together, and the regularity of each segment is destroyed faster and more strongly; further, when performing replacement processing on the data between segments, analyze the difference situation between segments, so that data with large law differences are introduced into other segments, and the regularity of the segments is destroyed faster and more strongly.

[0007] Preferably, the segmenting the industrial control network data sequence according to the law similarity includes: Slide a window with a preset size on the industrial control network data sequence, and calculate the law similarity degree of the window after each slide; arrange the law similarity degrees of the windows after all slides in the sliding order to obtain a law similarity degree sequence, obtain a preset range centered on the minimum value points in the law similarity degree sequence, and calculate the variance of the data within the preset range as the fluctuation degree of each minimum value point. Cluster all the minimum value points into two categories according to the fluctuation degree, calculate the average value of the fluctuation degrees of all the minimum value points in each category, and use the central data of the window after sliding corresponding to the minimum value points in the category with a larger average value of the fluctuation degree as the segmentation point; use the segmentation point to segment the industrial control network data sequence.

[0008] In the present invention, according to the variation characteristics of the law similarity degree, the segmentation points are accurately and quickly screened out, providing a basis for accurately and quickly separating data with different laws.

[0009] Preferably, the calculating the law similarity between the window after each slide and the window after the previous slide includes: Obtain the occurrence frequency of each value within the window after each slide. Denote the sequence composed of the occurrence frequencies of all values within the window after each slide as the frequency sequence. Perform polynomial fitting on the data within the window after each slide, and denote the sequence composed of the coefficients of all terms in the polynomial as the coefficient sequence. Calculate the Euclidean distance between the frequency sequence of the window after each slide and the frequency sequence of the window after the previous slide, which is denoted as the frequency difference. Calculate the cosine similarity between the coefficient sequence of the window after each slide and the coefficient sequence of the window after the previous slide, which is denoted as the trend similarity. Divide the trend similarity by the frequency difference to obtain the pattern similarity between the window after each slide and the window after the previous slide.

[0010] When analyzing the pattern similarity, the present invention not only introduces frequency information but also trend information, thereby more comprehensively and accurately analyzing the pattern similarity situation.

[0011] Preferably, the calculation of the difference between every two segments includes: Obtain the frequency sequence of each segment, and calculate the Euclidean distance between the frequency sequences of every two segments, which is denoted as the difference between every two segments.

[0012] The present invention reflects the difference between the data of segments through the frequency difference. This way of difference analysis is relatively simple and has higher implementation efficiency.

[0013] Preferably, the calculation of the centrality of each node in the undirected graph includes: Use the calculation method of closeness centrality in graph theory to calculate the centrality of each node in the undirected graph.

[0014] Preferably, the selection of data points as active replacement points among the nodes with the smallest centrality and the selection of data points as passive replacement points among other nodes include: Obtain the number of nodes , obtain the number L of data in the nodes with the smallest centrality, obtain the position key sequence, locate the data at S - 1 positions in the segments corresponding to the nodes with the smallest centrality according to the data in the position key sequence, which are denoted as active replacement points, and locate the data at one position in the segments corresponding to each of the other nodes according to the data in the position key sequence, which are denoted as passive replacement points.

[0015] The present invention screens replacement points through centrality, thereby screening out more data at the segments with large differences, and introducing the data with large differences into each segment, so as to more quickly and strongly destroy the regularity of the data.

[0016] Preferably, the replacement process of the active replacement points and the passive replacement points includes: Exchange the positions of the active replacement points and the passive replacement points.

[0017] Preferably, the method for obtaining the degree of regularity destruction includes: Obtain the frequency sequence and coefficient sequence in each segmented part after permutation, calculate the Euclidean distance between the frequency sequence of the segmented part after permutation and the corresponding segmented part without permutation, which is denoted as the frequency destructiveness, calculate the Euclidean distance between the coefficient sequence of the segmented part after permutation and the corresponding coefficient sequence of the segmented part without permutation, which is denoted as the trend destructiveness, and denote the product of the frequency destructiveness and the trend destructiveness as the degree of regularity destruction.

[0018] Preferably, the data permutation processing based on the reconstructed undirected graph includes: Calculate the centrality of each node in the reconstructed undirected graph, select data points as active permutation points among the nodes with the smallest centrality, select data points as passive permutation points among other nodes, and perform permutation processing on the active permutation points and the passive permutation points.

[0019] In a second aspect, the present invention provides a blockchain-based industrial control network data security sharing system, adopting the following technical solution: The blockchain-based industrial control network data security sharing system includes: a processor and a memory, and 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.

[0020] 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 the memory to be loaded and executed by the processor, so as to manufacture a terminal device according to the memory and the processor, which is convenient to use.

[0021] The present invention has the following technical effects: By segmenting the data, the present invention separates data with different regularities and groups data with the same regularity together, thus providing a basis for subsequent permutation encryption; Furthermore, by performing permutation processing between the segmented parts, the data in the segmented parts is introduced into other segmented parts, so as to mix data with different regularities together and more quickly and strongly destroy the regularity of each segmented part; Furthermore, when performing permutation processing on the data between segmented parts, analyze the difference situation between segmented parts, so as to introduce data with large regularity differences into other segmented parts and more quickly and strongly destroy the regularity of the segmented parts. Description of the Drawings

[0022] Figure 1 is a flowchart of the method in the blockchain-based industrial control network data security sharing method according to an embodiment of the present invention. Detailed Embodiments

[0023] The embodiments of the present invention disclose a method for secure sharing of industrial control network data based on blockchain, referring to Figure 1 , including steps S1 - S4: S1: Obtain the industrial control network data sequence.

[0024] Specifically, obtain the industrial control network data sequence 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, process parameter data.

[0025] S2: Segment the industrial control network data sequence according to the law similarity, calculate the difference between every two segments, use the difference as the edge weight value, use each segment as a node to construct an undirected graph, calculate the centrality of each node in the undirected graph, select a data point as the active replacement point from the nodes with the smallest centrality, 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 fact that the degree of law destruction of some segments after replacement is less than the preset destruction degree threshold, use the segments after replacement as nodes, reconstruct the undirected graph, and perform data replacement processing according to the reconstructed undirected graph; in response to the fact that the law destruction degrees of all segments after replacement are not less than the preset destruction degree threshold, obtain the finally replaced segments.

[0026] It should be noted that since the data within the same module has a large correlation, the encryption effect achieved by performing replacement processing on the data within the module is poor. In order to improve the encryption efficiency and effect, replacement processing needs to be performed between the data of different modules. First, the data of different modules needs to be separated.

[0027] S20: Segment the industrial control network data sequence according to the law similarity.

[0028] It should be noted that the data within the same module has a large correlation, that is, the laws of the data within the same module are relatively similar. Therefore, the module can be segmented according to the law similarity, so as to separate the data of different modules.

[0029] Preferably, as an example, segmenting the industrial control network data sequence according to the law similarity includes: Slide a window with a preset size on the industrial control network data sequence, calculate the law similarity between the window after each slide and the window after the previous slide, and record it as the law similarity degree of the window after each slide; Arrange the similarity degrees of the patterns of all post-sliding windows in the sliding order to obtain a pattern similarity degree sequence. Obtain the minimum value points in the pattern similarity degree sequence. Take each minimum value point as the center to obtain a preset range, and calculate the variance of the data within the preset range, which is denoted as the fluctuation degree of each minimum value point. Cluster all the minimum value points into two categories according to the fluctuation degree, calculate the average value of the fluctuation degrees of all the minimum value points in each category, and take the central data of the post-sliding window corresponding to the minimum value points in the category with the larger average value of the fluctuation degree as the segmentation point; use the segmentation point to divide the industrial control network data sequence into several segments.

[0030] It can be understood that under normal circumstances, when the window slides within the same module, the similarity degrees of the patterns between the windows are relatively large. When the window slides out of one module and enters the next module, the window will contain partial data of the two modules, so the regularity of the window data will gradually decrease and then increase. Therefore, a minimum value of the regularity will appear at the data between the modules. In this embodiment, possible segmentation points between the modules are screened out through the minimum values. Since the similarity degree of the patterns within the same module is not constant, there will also be minimum value points in the pattern similarity of the same module, and some minimum value points are not segmentation points. However, the fluctuation of the pattern similarity between the modules is relatively large, so some interfering minimum value points are screened out through the fluctuation situation to obtain accurate segmentation points, thus achieving accurate segmentation.

[0031] The above embodiments involve the pattern similarity degree. Next, the method for determining the pattern similarity degree needs to be described.

[0032] Preferably, as an example, the method for obtaining the pattern similarity degree includes: Obtain the occurrence frequency of each value of the data within the window after each slide, and denote the sequence composed of the occurrence frequencies of all values of the data within the window after each slide as the frequency sequence; perform polynomial fitting processing on the data within the window after each slide, and denote the sequence composed of the coefficients of all terms in the polynomial as the coefficient sequence; Calculate the Euclidean distance between the frequency sequence of the window after each slide and the frequency sequence of the window after the previous slide, which is denoted as the frequency difference, and calculate the cosine similarity between the coefficient sequence of the window after each slide and the coefficient sequence of the window after the previous slide, which is denoted as the trend similarity; divide the trend similarity by the frequency difference to obtain the pattern similarity between the window after each slide and the window after the previous slide.

[0033] It can be understood that the regularity of the data is mainly reflected in the data occurrence frequency and the change rule. Therefore, the pattern similarity between segments can be analyzed through the occurrence frequency and the change rule.

[0034] S21: Calculate the difference between every two segments, use the difference as the edge weight, take each segment as a node to construct an undirected graph, calculate the centrality of each node in the undirected graph, select data points as active replacement points from the nodes with the minimum centrality, select data points as passive replacement points from other nodes, and perform replacement processing on the active replacement points and the passive replacement points.

[0035] It should be noted that in order to better destroy the readability of information, the readability of data can be destroyed faster and stronger by introducing data in the difference information.

[0036] Preferably, as an example, calculate the difference between every two segments, use the difference as the edge weight, take each segment as a node to construct an undirected graph, calculate the centrality of each node in the undirected graph, select data points as active replacement points from the nodes with the minimum centrality, select data points as passive replacement points from other nodes, and perform replacement processing on the active replacement points and the passive replacement points, including: Obtain the frequency sequence of each segment, and calculate the Euclidean distance between the frequency sequences of every two segments, which is recorded as the difference between every two segments.

[0037] Use the difference as the edge weight, take each segment as a node, and connect every two nodes to obtain an undirected graph.

[0038] Use the calculation method of closeness centrality in graph theory to calculate the centrality of each node in the undirected graph.

[0039] Obtain the number of nodes , obtain the number L of data in the nodes with the minimum centrality, obtain the position key sequence, locate the data at S - 1 positions in the segment corresponding to the nodes with the minimum centrality according to the data in the position key sequence, which is recorded as the active replacement points, and locate the data at one position in the segments corresponding to other nodes respectively according to the data in the position key sequence, which is recorded as the passive replacement points. Exchange the positions of the active replacement points and the passive replacement points.

[0040] It can be understood that the smaller the centrality, the greater the information difference between this segment and other segments. By placing the information in the segment with a large information difference into other segments, the information of other segments can be better destroyed.

[0041] It should be added that the method of locating the data at S - 1 positions in the segment corresponding to the nodes with the minimum centrality according to the data in the position key sequence includes: Generate a chaotic sequence using a chaotic model, perform modulo operation on the data in the chaotic sequence with the number L of data in the nodes with the minimum centrality, and normalize the data to between 1 and L. Record the normalized chaotic sequence as the position key sequence.

[0042] Assume the current permutation is C + 1, and the number of position keys required for each permutation round is 2S - 2. Then the position keys required for the current permutation round are at the to in the key sequence. Locate the data at S - 1 positions in the segment corresponding to the node with the minimum centrality according to the position keys required for the current permutation round.

[0043] Assume the first position key required for the current permutation round is 3. Then locate the data at the 3rd position in the segment corresponding to the node with the minimum centrality.

[0044] It should be noted that the parameters of the chaos model are constants agreed upon in advance by the encryptor and the decryptor, and there is no need to exchange parameters.

[0045] S22: In response to the degree of regularity disruption of some segments after permutation being less than the preset disruption degree threshold, use the permuted segments as nodes to reconstruct an undirected graph, and perform data permutation processing according to the reconstructed undirected graph.

[0046] S220: Calculate the degree of regularity disruption of the segments after permutation.

[0047] It should be noted that in order to prevent information leakage caused by insufficient permutation encryption or unnecessary time waste caused by excessive scrambling encryption, the scrambling encryption effect needs to be evaluated after each permutation.

[0048] Preferably, as an example, calculating the degree of regularity disruption of the segments after permutation includes: Obtain the frequency sequence and coefficient sequence in each segment after permutation, calculate the Euclidean distance between the frequency sequence of the segment after permutation and the corresponding segment before permutation, which is denoted as frequency disruption, calculate the Euclidean distance between the coefficient sequence of the segment after permutation and the corresponding segment before permutation, which is denoted as trend disruption, and denote the product of the frequency disruption and the trend disruption as the degree of regularity disruption.

[0049] It can be understood that by analyzing the difference in the regular information of the segments before and after permutation, the degree of disruption of the scrambling encryption to the information is judged, providing a basis for subsequent encryption cut-off control.

[0050] S221: In response to the degree of regularity disruption of some segments after permutation being less than the preset disruption degree threshold, use the permuted segments as nodes to reconstruct an undirected graph, and perform data permutation processing according to the reconstructed undirected graph.

[0051] Preferably, as an example, in response to the degree of regularity disruption of some segments after permutation being less than the preset disruption degree threshold, using the permuted segments as nodes to reconstruct an undirected graph and performing data permutation processing according to the reconstructed undirected graph includes: In response to the degree of disruption of the regularity of the segmented parts after partial replacement being less than the preset disruption degree threshold, each segmented part after replacement is used as a node to reconstruct an undirected graph. Calculate the centrality of each node in the reconstructed undirected graph. Select data points as active replacement points among the nodes with the smallest centrality, and select data points as passive replacement points among other nodes. Perform replacement processing on the active replacement points and the passive replacement points.

[0052] It can be understood that the degree of disruption of the regularity being less than the disruption degree threshold indicates that the degree of disruption of the information does not yet meet the encryption requirements, and further replacement processing of the information is required to destroy the readability of the information.

[0053] It should be added that the method for obtaining the active replacement points and the passive replacement points is the same as the method in step S21.

[0054] S23: In response to the degree of disruption of the regularity of each segmented part after replacement being not less than the preset disruption degree threshold, obtain the finally replaced segmented parts.

[0055] It can be understood that the degree of disruption of the regularity being less than the disruption degree threshold indicates that the degree of disruption of the information in the segmented part meets the encryption requirements, and at this time, the scrambling encryption can be stopped.

[0056] S3: Store each finally replaced segmented part in the blockchain in the shared space.

[0057] It can be understood that due to the characteristics of the blockchain such as decentralization and autonomy, it is very difficult to obtain all the data in all blockchains without permission. By storing different finally replaced segmented parts in different blockchains, people without permission cannot obtain all the data, and thus cannot restore the data information before scrambling.

[0058] S4: Perform decryption processing on the encrypted data sequence.

[0059] Preferably, as an example, performing decryption processing on the encrypted data sequence includes: Obtain the centrality of each node in each undirected graph, and record the sequence formed by the centrality of all nodes in all undirected graphs as the information key sequence. Store the information key sequence in the private blockchain.

[0060] The decryptor obtains the information key sequence in the private blockchain and obtains the centrality of each node in each undirected graph in the information key sequence. Generate a chaotic sequence, and normalize the data in the chaotic sequence according to the content in step S21 to obtain the position key sequence.

[0061] The decryptor obtains all the permuted segments in the blockchain of the shared space, and based on the centrality of each node in the last undirected graph, obtains the segment corresponding to the node with the minimum centrality. According to the content in step S21, the active permutation points are located in the segment corresponding to the node with the minimum centrality according to the data in the position key sequence, and the passive permutation points are located in other segments. The active permutation points and the passive permutation points are subjected to permutation processing. According to this method, using the centrality of each node in each undirected graph, permutation processing is sequentially performed until the permutation is completed according to the centrality of each node in the first undirected graph, and the industrial control network data sequence is obtained.

[0062] An embodiment of the present invention also discloses an industrial control network data security sharing system based on a blockchain, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the industrial control network data security sharing method according to the present invention is implemented.

[0063] The above system further includes other components well-known to those skilled in the art, such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be described in detail here.

[0064] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory, a dynamic random access memory, a static random access memory, an enhanced dynamic random access memory, a high-bandwidth memory, a hybrid storage cube, etc., or any other medium that can be used to store the required information and can be accessed by an application program, a module, or both. Any such computer storage medium can be a part of the device or accessible or connectable to the device.

Claims

1. A method for secure data sharing in an industrial control network based on blockchain, characterized in that, Including the steps: Obtain the industrial control network data sequence; Perform segmentation processing on the industrial control network data sequence according to the law similarity, calculate the difference between every two segments, use the difference as the edge weight value, use each segment as a node, construct an undirected graph, calculate the centrality of each node in the undirected graph, select data points as active replacement points from the nodes with the smallest centrality, select data points as passive replacement points from other nodes, and perform replacement processing on the active replacement points and the passive replacement points; in response to the fact that the degree of disruption of the regularity of some segments after replacement is less than the preset disruption degree threshold, use the segments after replacement as nodes, reconstruct the undirected graph, and perform data replacement processing according to the reconstructed undirected graph; in response to the fact that the degree of disruption of the regularity of each segment after replacement is not less than the preset disruption degree threshold, obtain the finally replaced segments; Store the finally replaced segments into the blockchain in the shared space respectively.

2. The method for secure sharing of industrial control network data based on blockchain according to claim 1, wherein The performing segmentation processing on the industrial control network data sequence according to the law similarity includes: Slide a window with a preset size on the industrial control network data sequence, and calculate the law similarity degree of the window after each slide; arrange the law similarity degrees of the windows after all slides in the sliding order to obtain a law similarity degree sequence, obtain a preset range centered on the minimum value points in the law similarity degree sequence, and calculate the variance of the data within the preset range and record it as the fluctuation degree of each minimum value point, cluster all the minimum value points into two categories according to the fluctuation degree, calculate the average value of the fluctuation degrees of all the minimum value points in each category, and use the central data of the window after sliding corresponding to the minimum value points in the category with the larger average value of the fluctuation degree as the segmentation point; segment the industrial control network data sequence by using the segmentation point.

3. The method for secure sharing of industrial control network data based on blockchain according to claim 2, wherein The calculating the law similarity between the window after each slide and the window after the previous slide includes: Obtain the occurrence frequency of each value of the data within the window after each slide, and record the sequence composed of the occurrence frequencies of all values of the data within the window after each slide as the frequency sequence; perform fitting polynomial processing on the data within the window after each slide, and record the sequence composed of the coefficients of all terms in the polynomial as the coefficient sequence; calculate the Euclidean distance between the frequency sequence of the window after each slide and the frequency sequence of the window after the previous slide and record it as the frequency difference, calculate the cosine similarity between the coefficient sequence of the window after each slide and the coefficient sequence of the window after the previous slide and record it as the trend similarity; divide the trend similarity by the frequency difference to obtain the law similarity between the window after each slide and the window after the previous slide.

4. The method for secure sharing of industrial control network data based on blockchain according to claim 1, wherein, The calculating the difference between every two segments includes: Obtain the frequency sequence of each segment, and calculate the Euclidean distance between the frequency sequences of every two segments and record it as the difference between every two segments.

5. The method for secure sharing of industrial control network data based on blockchain according to claim 1, characterized in that, The calculating the centrality of each node in the undirected graph includes: Use the calculation method of closeness centrality in graph theory to calculate the centrality of each node in the undirected graph.

6. The method for secure sharing of industrial control network data based on blockchain according to claim 1, characterized in that The selecting data points as active replacement points from the nodes with the smallest centrality and selecting data points as passive replacement points from other nodes includes: Obtain the number of nodes , obtain the number L of data in the node with the minimum centrality, obtain the position key sequence, locate the data at S - 1 positions in the segment corresponding to the node with the minimum centrality according to the data in the position key sequence and record them as active replacement points, and locate the data at one position in the segments corresponding to each of the other nodes according to the data in the position key sequence and record them as passive replacement points.

7. The method for secure sharing of industrial control network data based on blockchain according to claim 1, characterized in that The performing replacement processing on the active replacement points and the passive replacement points includes: Exchange the positions of the active replacement points and the passive replacement points.

8. The method for secure sharing of industrial control network data based on blockchain according to claim 1, characterized in that, The method for obtaining the degree of regular destruction includes: Obtain the frequency sequence and coefficient sequence in each segmented section after permutation, calculate the Euclidean distance between the frequency sequence of the segmented section after permutation and the corresponding segmented section without permutation, which is denoted as frequency destructiveness, calculate the Euclidean distance between the coefficient sequence of the segmented section after permutation and the corresponding coefficient sequence of the segmented section without permutation, which is denoted as trend destructiveness, and denote the product of the frequency destructiveness and the trend destructiveness as the degree of regular destruction.

9. The method for secure sharing of industrial control network data based on blockchain according to claim 1, characterized in that, The data permutation process according to the re-constructed undirected graph includes: Calculate the centrality of each node in the re-constructed undirected graph, select a data point as the active permutation point from the nodes with the minimum centrality, select data points from other nodes as passive permutation points, and perform permutation processing on the active permutation point and the passive permutation points.

10. A blockchain-based industrial control network data security sharing system, characterized in that, It includes: A processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the blockchain-based industrial control network data security sharing method according to any one of claims 1-9 is implemented.

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