Industrial internet security analysis system based on digital twinning

Abstract

This invention discloses an industrial internet security analysis system based on digital twins, belonging to the field of industrial internet security technology. It includes a platform and a user end. The system performs real-time monitoring of the industrial internet at the user's location to obtain monitoring data. Based on the industrial internet information, it identifies various potential security issues and labels each potential security issue with a corresponding simulation association tag. It analyzes the monitoring data based on these potential security issues to obtain various target security issues, identifies simulation association tags between these target security issues, and forms corresponding simulation problem combinations based on the simulation association tags. It sends these simulation problem combinations to the platform. The platform receives the simulation problem combinations from each user end in real time, generates a problem simulation model based on the industrial internet information, and simulates the problem simulation model using the problem simulation combination to obtain simulation results.

Description

Technical Field

[0001] This invention belongs to the field of industrial internet security technology, specifically an industrial internet security analysis system based on digital twins. Background Technology

[0002] With the rapid advancement of industrial informatization and digitalization, the Industrial Internet (IIoT) system has become a core support for modern industrial production, with its complexity and interconnectivity continuously increasing. However, this trend has also brought increasingly severe security challenges. Traditional security analysis techniques mainly focus on single-dimensional security protection, such as information security or physical security, making it difficult to comprehensively address the multi-dimensional security needs intertwined in modern industrial systems, including functional, informational, physical, and technological aspects. Particularly in fault prediction, traditional methods often lack foresight and accuracy, making it difficult to provide effective early warnings and interventions before faults occur. Furthermore, as the scale and complexity of IIoT systems expand, inter-system interactions become increasingly frequent, and the coupling relationships between security elements become more complex. Traditional security analysis techniques struggle to capture these potential risks and vulnerabilities, easily creating blind spots in analysis. Therefore, there is an urgent need for an innovative technology that can integrate multi-dimensional security elements and achieve dynamic threat prediction to comprehensively improve the security defense capabilities and overall operational efficiency of IIoT systems. Digital twin technology, as an innovative method that integrates physical entities and virtual models, provides a brand-new approach to solving the above problems. By constructing a dynamic mapping between physical systems and virtual models, it can achieve comprehensive and multi-stage security analysis of industrial internet systems, providing strong support for industrial internet security.

[0003] Based on this, the present invention provides an industrial internet security analysis system based on digital twins. Summary of the Invention

[0004] To address the problems of the aforementioned solutions, this invention provides an industrial internet security analysis system based on digital twins.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] An industrial internet security analysis system based on digital twins, comprising a platform and a user terminal;

[0007] The platform includes an industrial storage facility and a simulation module.

[0008] The industrial reserve is used to store various basic twin models.

[0009] Furthermore, the acquisition of the basic twin model includes:

[0010] The system acquires various industrial internet systems within the platform's service scope in real time, breaks down these systems into several sub-industrial systems, and then categorizes each sub-industrial system into several sub-categories.

[0011] Select at least one local industrial system from each local classification as a local representative system, and establish a basic twin model based on each local representative system.

[0012] Furthermore, the various local industrial systems are classified, including:

[0013] The platform sets adjustment requirements, combines two local industrial systems, and obtains several evaluation combinations; based on the adjustment requirements, features are extracted from the evaluation combinations to obtain the combination adjustment features of the evaluation combinations;

[0014] Based on the adjustment requirements, the combination adjustment characteristics of the corresponding evaluation combinations are analyzed to obtain the evaluation results of each evaluation combination. The evaluation results include qualified and unqualified combinations.

[0015] Individual industrial systems that are deemed compatible by mutual evaluation are grouped into one category, thus obtaining the classification of each individual system.

[0016] Furthermore, the adjustment characteristics of the corresponding assessment combinations are analyzed according to the adjustment requirements, including:

[0017] Establish an adjustment assessment model; integrate the adjustment requirements and the combined adjustment characteristics of the corresponding assessment combinations into input data and input them into the adjustment assessment model for analysis to obtain the adjustment assessment value of the corresponding assessment combination, where the adjustment assessment value is 1 or 0;

[0018] When the adjustment value is 1, the evaluation result is that the combination is qualified;

[0019] When the adjusted evaluation value is 0, the evaluation result is that the combination is unqualified.

[0020] Furthermore, the expression for the evaluation model is adjusted as follows:

[0021] ;

[0022] In the formula: (d, YQ) are the input data, d is the combined adjustment feature, YQ is the adjustment requirement; d→YQ means that the corresponding combined adjustment feature meets the adjustment requirement; the output data is the adjustment evaluation value PH(d, YQ).

[0023] Furthermore, at least one local industrial system is selected from each local classification as a local representative system, including:

[0024] Define a local simulation set, which is a set of local industrial systems whose evaluation results with the corresponding local industrial systems are qualified as a combination, including its own local simulation set; generate a classification set based on each local industrial system corresponding to the local classification.

[0025] Assuming the number of local representative systems is one, determine whether there exists a local industrial system whose local simulation set equals the classification set;

[0026] When it is determined that there is a corresponding local industrial system, the number of local representative systems in the local classification is determined to be one, and the local industrial system is marked as a local representative system.

[0027] When it is determined that there is no corresponding local industrial system, assuming that the number of local representative systems is two, determine whether the union of the local simulation sets with two local industrial systems is equal to the classification set.

[0028] When it is determined that there are two corresponding local industrial systems, the number of local representative systems in the local classification is determined to be two, and the two corresponding local industrial systems are marked as local representative systems.

[0029] When it is determined that there are no corresponding two local industrial systems, it is assumed that the number of local representative systems is three, and so on, until the union of the local simulation sets of the corresponding number of local industrial systems is equal to the classification set, and the corresponding number of local representative systems is determined.

[0030] The simulation module is used to perform platform simulation, receive simulation problem combinations from various user terminals in real time, obtain the corresponding user's industrial internet information, generate a problem simulation model for the corresponding simulation problem combination based on the industrial internet information, simulate the problem simulation model through the problem simulation combination, obtain the simulation results, and send the simulation results to the user terminal's security processing module.

[0031] The user terminal includes a monitoring module, a security analysis module, and a security processing module;

[0032] The monitoring module is used to monitor the industrial internet at the user's location in real time and obtain monitoring data.

[0033] The security analysis module is used to perform security analysis, obtain various potential security issues based on industrial internet information, and mark each potential security issue with a corresponding simulation association label; analyze the monitoring data based on various potential security issues to obtain the initial security analysis results for each potential security issue, the initial security analysis results including whether there is a security issue and whether there is no security issue;

[0034] The initial security results identify potential security issues as target security issues, and simulation association tags are identified between each target security issue. Based on these simulation association tags, corresponding simulation issue combinations are formed. Each simulation issue combination is then sent to the simulation module on the platform.

[0035] The security processing module is used to perform security processing based on the initial security analysis results and simulation results.

[0036] Furthermore, the platform establishes communication connections with each user terminal.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] The industrial internet security analysis system based on digital twins proposed in this invention effectively overcomes the limitations of traditional security analysis techniques that rely on single-dimensional protection by deeply integrating physical entities and virtual models to construct a dynamic mapping mechanism. This system achieves collaborative analysis of functional safety, information security, physical safety, and process safety, accurately capturing the complex coupling relationships between multi-dimensional security elements and eliminating security blind spots caused by the expansion of system scale and increased interaction frequency in traditional methods. Through the real-time data-driven and dynamic simulation capabilities of the digital twin, the system not only significantly improves the foresight and accuracy of fault prediction, enabling early warning and intervention at the fault initiation stage, but also optimizes the overall defense strategy through multi-stage security situation simulation. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a block diagram illustrating the principle of the present invention. Detailed Implementation

[0041] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0042] like Figure 1 As shown, an industrial internet security analysis system based on digital twins includes a platform and a user terminal.

[0043] The platform and each user terminal are generally connected via a communication connection.

[0044] The platform includes an industrial storage facility and a simulation module.

[0045] The industrial reserve is used to store local digital twin models of various industrial internet technologies, labeled as basic twin models.

[0046] For example, the entire industrial internet system is divided into multiple local systems, each with relatively independent functions and security features. For each local system, a digital twin model is established, including virtual mappings of key elements such as physical devices, network architecture, control logic, and process flow. Standardized interfaces are designed for each local digital twin model to facilitate convenient connection and interaction between models in the future. The interface design should consider compatibility in terms of data format, communication protocol, and security mechanism to ensure seamless integration between models. The model is then labeled as the base twin model.

[0047] In one embodiment, the industrial reserve is established by the platform provider according to actual needs. For example, it may pre-establish various basic twin models in certain fields, specifically based on existing methods, or it may decompose existing digital twin models to form various basic twin models.

[0048] In one embodiment, obtaining the base twin model includes:

[0049] The system acquires various industrial internet systems within the platform's service scope in real time, breaks down these systems into several sub-industrial systems, and then categorizes each sub-industrial system into several sub-categories.

[0050] Select at least one local industrial system from each local classification as a local representative system, and establish a basic twin model based on each local representative system.

[0051] In one embodiment, to avoid redundant and invalid analysis, data from industrial internet systems that have already been established or analyzed may not be acquired.

[0052] In one embodiment, the industrial internet system can be split up in the following ways: it can be split up based on existing methods and identified and split up according to the above requirements; or it can be split up manually.

[0053] An example, an industrial manufacturing scenario: an automobile production line;

[0054] It can be broken down into local systems such as stamping workshop, welding workshop, painting workshop, final assembly workshop, and energy supply. Furthermore, it can be further broken down based on whether a basic twin model can be set up independently. For example, the painting workshop local system can be further broken down according to each work process, such as pretreatment, electrophoresis, painting, drying, and posttreatment.

[0055] Splitting has the following characteristics:

[0056] Functional independence: Each local system should have clear and independent functional objectives (such as temperature control, fluid transfer, surface treatment, etc.), and its input / output interaction with external systems can be defined through standardized interfaces.

[0057] Clear physical boundaries: Delineate physical boundaries based on equipment layout, process flow, or spatial scope to ensure that all relevant entities (equipment, sensors, actuators, etc.) are included within the local system, and that entities outside the boundary do not directly affect its core functions.

[0058] Data closed-loop: Local systems must have complete data acquisition, transmission, processing and feedback capabilities to form an independent data closed loop, supporting real-time status monitoring and dynamic optimization.

[0059] In one embodiment, various local industrial systems are classified. Each local industrial system within each local classification can be obtained by adjusting a local representative system, and the adjustment workload and cost meet preset adjustment requirements, i.e., the adjustment workload and cost are not greater than the specified values ​​corresponding to the adjustment requirements. To improve universality, adjustment requirements can be set using workload ratio, cost ratio, etc., which refers to calculating the adjustment workload ratio by comparing the workload of establishing the corresponding local industrial system with the adjustment workload. Based on the adjustment requirements standard, classification is performed according to existing classification methods, clustering algorithms, etc.

[0060] In one embodiment, the various local industrial systems are classified, including:

[0061] The platform sets adjustment requirements to combine two local industrial systems into several evaluation combinations. Systems with obvious differences in domain or function that do not belong to the same local category do not need to be combined. That is, combination conditions such as domain and function can be preset to improve analysis efficiency. Based on the adjustment requirements, feature extraction is performed on the evaluation combinations to obtain the combination adjustment features of the evaluation combinations. Feature extraction is performed according to the data corresponding to the adjustment requirements, such as workload ratio, cost ratio, etc.

[0062] Based on the adjustment requirements, the combination adjustment characteristics of the corresponding evaluation combinations are analyzed to obtain the evaluation results of each evaluation combination. The evaluation results include qualified and unqualified combinations.

[0063] Each local industrial system whose evaluation result is qualified is classified into a category, thus obtaining the local classification; for example, if A and B are qualified as a combination, they are classified into a category. If B and C are also qualified as a combination, C is added to the category corresponding to A and B. Even if the combination A and C is unqualified, they are still classified into a category.

[0064] In one embodiment, the combined adjustment characteristics of the corresponding evaluation combination are analyzed according to the adjustment requirements. The evaluation can be based on existing evaluation methods to determine whether the combined adjustment characteristics meet the adjustment requirements. For example, the evaluation can be based on machine learning, deep learning algorithms, etc.

[0065] In one embodiment, the combination adjustment characteristics of the corresponding evaluation combination are analyzed according to the adjustment requirements, including:

[0066] Establish an adjustment evaluation model, the expression of which is:

[0067] ;

[0068] In the formula: (d, YQ) are the input data, d is the combined adjustment feature, and YQ is the adjustment requirement; d→YQ means that the corresponding combined adjustment feature meets the adjustment requirement; the output data is the adjustment evaluation value PH(d, YQ), and the adjustment evaluation value is 1 or 0; the training set is labeled with the corresponding historical data for training.

[0069] The adjustment requirements and the combined adjustment characteristics of the corresponding assessment combinations are integrated into the input data and fed into the adjustment assessment model for analysis to obtain the adjustment assessment value of the corresponding assessment combination.

[0070] When the adjustment value is 1, the evaluation result is that the combination is qualified;

[0071] When the adjusted evaluation value is 0, the evaluation result is that the combination is unqualified.

[0072] In one embodiment, selecting at least one local industrial system from each local classification as a local representative system includes:

[0073] The evaluation results of each local industrial system in the local classification are identified. The number of local representative systems is determined based on each evaluation result. Since the evaluation results between AB and BC are both acceptable combinations, but the evaluation results between AC are not necessarily acceptable combinations, if the local classification only has AB and BC, selecting B as the local representative system can satisfy all the adjustment requirements of A and C. If there is also a D, and the combination between D and B is unacceptable, then two local representative systems are needed. Therefore, the minimum number of local representative systems can be determined based on each evaluation result and existing mathematical methods. Each local representative system is determined based on the number of local representative systems and the above analysis process.

[0074] For example, a local simulation set is defined, which is the set of local industrial systems whose evaluation results with the corresponding local industrial system are qualified combinations. For example, if a local classification has five local industrial systems A, B, C, D, and E, and the evaluation results between A and B, C, D are all qualified combinations, then the local simulation set of A is {ABCD}, which includes itself; a classification set is generated based on each local industrial system corresponding to the local classification, that is, the classification set includes each local industrial system corresponding to the local classification.

[0075] Assuming the number of local representative systems is one, determine whether there exists a local industrial system whose local simulation set equals the classification set;

[0076] When it is determined that there is a corresponding local industrial system, the number of local representative systems in this local classification is one, and the corresponding local industrial system is marked as a local representative system;

[0077] When it is determined that there is no corresponding local industrial system, assuming that the number of local representative systems is two, determine whether the union of the local simulation sets with two local industrial systems is equal to the classification set.

[0078] When it is determined that there are two corresponding local industrial systems, the number of local representative systems in this local classification is two, and the two corresponding local industrial systems are marked as local representative systems;

[0079] When it is determined that there are no corresponding two local industrial systems, it is assumed that the number of local representative systems is three, and so on, until the union of the local simulation sets of the corresponding number of local industrial systems is equal to the classification set, and the corresponding number of local representative systems is determined.

[0080] The simulation module is used to perform platform simulation, receive simulation problem combinations from various user terminals in real time, obtain the corresponding user's industrial internet information, generate a problem simulation model for the corresponding simulation problem combination based on the industrial internet information, simulate the problem simulation model through the problem simulation combination, and obtain the corresponding simulation results. That is, simulation is performed based on the corresponding monitoring data and problem simulation combination to obtain the corresponding simulation results, and the simulation results are sent to the user terminal's security processing module.

[0081] In one embodiment, a problem simulation model is generated based on industrial internet information to create a combination of simulation problems, including:

[0082] Identify the corresponding local industrial system information based on the combination of industrial internet information and simulation problems, that is, which local industrial systems(s) correspond to the target security problem of the simulation problem combination, and obtain the corresponding local industrial system information from the industrial internet information; obtain the problem simulation model of the simulation problem combination based on the local industrial system information and the industrial reserve.

[0083] In one embodiment, a problem simulation model is obtained based on local industrial system information and an industrial reserve, including:

[0084] Based on Industrial Internet information, various combinations of simulation problems that users may have are pre-determined. Corresponding basic twin models are matched from the industrial database based on these combinations. The basic twin models are then adjusted according to the Industrial Internet information to meet the user's simulation requirements. The adjusted digital twin models of the local industrial system are marked as basic twin models and stored in the industrial database. The adjusted basic twin models corresponding to the simulation problem combinations are then combined and debugged to form problem simulation models. Subsequently, problem simulation models can be directly retrieved based on the simulation problem groups. The platform provides the necessary settings. If intelligent generation of problem simulation models based on AI or other intelligent technologies is possible, then intelligent generation will be implemented using these technologies.

[0085] The user terminal includes a monitoring module, a security analysis module, and a security processing module;

[0086] The monitoring module is used to monitor the industrial internet at the user's location in real time and obtain corresponding monitoring data.

[0087] The security analysis module is used to perform security analysis. It identifies various potential security issues based on industrial internet information, including internal faults, external attacks, and other security problems requiring user analysis. These are marked as potential security issues, and each potential security issue is assigned a corresponding simulation association label. The labeling is determined based on whether the potential security issue needs to be simulated simultaneously during the simulation process. This determination is generally based on existing historical data. For example, historical data can be used to identify combinations of problems that may occur simultaneously. The actual verification and impact data of each problem combination can then be used to determine if it is a potential problem combination. Intelligent models can be built based on machine learning and deep learning algorithms for intelligent analysis. Potential security issues belonging to the same potential problem combination are marked with simulation association labels to indicate that they will be jointly simulated when the corresponding problem occurs. For example, if ABCD belongs to the same potential problem combination, and analysis shows that the current system may have problems AB, then AB will need to be jointly simulated.

[0088] The monitoring data is analyzed based on various potential security issues to obtain the initial security analysis results for each potential security issue. The initial security analysis results include those with security issues and those without security issues.

[0089] The initial security result identifies potential security issues as target security issues. Simulation association tags are identified between target security issues, and corresponding simulation problem combinations are formed based on these tags. Specifically, target security issues with interconnected simulation association tags are grouped into a single combination, i.e., AB in the example above. The remaining individual target security issues are also grouped into a single simulation problem combination. All simulation problem combinations are then sent to the simulation module on the platform.

[0090] In one embodiment, monitoring data is analyzed based on various potential security issues, and the analysis is performed using existing methods for analyzing corresponding security issues to determine the security outcome.

[0091] The security processing module is used to perform security processing based on the initial security analysis results and simulation results.

[0092] In one embodiment, security processing is performed based on the initial security analysis results and simulation results, and can be carried out in accordance with existing security processing methods.

[0093] For example, for urgent issues, emergency measures are taken first based on the initial security analysis results, and then the analysis method for the corresponding potential security issues is optimized based on the simulation results.

[0094] The initial security analysis results and simulation results are presented to the user for comparison.

[0095] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0096] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An industrial internet security analysis system based on digital twins, characterized in that, Including both the platform side and the user side; The platform includes an industrial reserve and a simulation module; the user terminal includes a monitoring module, a security analysis module, and a security processing module. The industrial reserve warehouse is used to store various basic twin models; The simulation module is used to perform platform simulation, receive simulation problem combinations from various user terminals in real time, obtain the corresponding user's industrial internet information, generate a problem simulation model for the corresponding simulation problem combination based on the industrial internet information, simulate the problem simulation model through the problem simulation combination, obtain simulation results, and send the simulation results to the user terminal's security processing module. The monitoring module is used to monitor the industrial internet at the user's location in real time and obtain monitoring data; The security analysis module is used to perform security analysis, obtain various potential security issues based on industrial internet information, and mark each potential security issue with a corresponding simulation association label. It determines whether to mark the simulation association label based on whether the simulation needs to be performed simultaneously with the corresponding potential security issue. The monitoring data is analyzed based on various potential security issues to obtain initial security analysis results for each potential security issue. The initial security analysis results include those with security issues and those without security issues. The initial security results identify potential security issues as target security issues, and simulation association tags are identified between these target security issues. Based on these simulation association tags, corresponding simulation issue combinations are formed. These simulation issue combinations are then sent to the simulation module on the platform. The security processing module is used to perform security processing based on the initial security analysis results and simulation results; Based on industrial internet information, a problem simulation model is generated to create a combination of simulation problems, including: Based on the combination of industrial internet information and simulation problems, identify the corresponding local industrial system information, and based on the local industrial system information and industrial reserve, obtain the problem simulation model of the simulation problem combination; A simulation model for a combination of simulation problems is obtained based on local industrial system information and industrial reserve data, including: Based on industrial internet information, various combinations of simulation problems for the user are determined in advance. According to the combination of simulation problems, the corresponding basic twin model is matched from the industrial reserve. The basic twin model is adjusted according to the industrial internet information until it meets the user's simulation requirements. The adjusted digital twin model of the local industrial system is marked as the basic twin model and stored in the industrial reserve. The adjusted basic twin model corresponding to the combination of simulation problems is combined and debugged to form the problem simulation model. The acquisition of the basic twin model includes: The system acquires various industrial internet systems within the platform's service scope in real time, breaks down these systems into several local industrial systems, and then categorizes each local industrial system into several local categories. Select at least one local industrial system from each local classification as a local representative system, and establish a basic twin model based on each local representative system; The various local industrial systems are classified, including: The platform sets adjustment requirements, combines two local industrial systems, and obtains several evaluation combinations; based on the adjustment requirements, features are extracted from the evaluation combinations to obtain the combination adjustment features of the evaluation combinations; Based on the adjustment requirements, the combination adjustment characteristics of the corresponding evaluation combinations are analyzed to obtain the evaluation results of each evaluation combination. The evaluation results include qualified and unqualified combinations. Each local industrial system whose evaluation results are deemed acceptable is grouped into one category to obtain the local classification. Select at least one local industrial system from each local category as a local representative system, including: Define a local simulation set, which includes one of the local industrial systems and each of the local industrial systems whose evaluation results with the local industrial system are qualified as a combination; generate a classification set according to each local industrial system corresponding to the local classification; Assuming the number of local representative systems is one, determine whether there exists a local industrial system whose local simulation set equals the classification set; When it is determined that there is a corresponding local industrial system, the number of local representative systems in the local classification is determined to be one, and the local industrial system is marked as a local representative system. When it is determined that there is no corresponding local industrial system, assuming that the number of local representative systems is two, determine whether the union of the local simulation sets with two local industrial systems is equal to the classification set. When it is determined that there are two corresponding local industrial systems, the number of local representative systems in the local classification is determined to be two, and the two corresponding local industrial systems are marked as local representative systems. When it is determined that there are no corresponding two local industrial systems, it is assumed that the number of local representative systems is three, and so on, until the union of the local simulation sets of the corresponding number of local industrial systems is equal to the classification set, and the corresponding number of local representative systems is determined.

2. The industrial internet security analysis system based on digital twins according to claim 1, characterized in that, The adjustment characteristics of the corresponding assessment portfolios are analyzed according to the adjustment requirements, including: Establish an adjustment assessment model; integrate the adjustment requirements and the combined adjustment characteristics of the corresponding assessment combinations into input data and input them into the adjustment assessment model for analysis to obtain the adjustment assessment value of the corresponding assessment combination, where the adjustment assessment value is 1 or 0; When the adjustment value is 1, the evaluation result is that the combination is qualified; When the adjusted evaluation value is 0, the evaluation result is that the combination is unqualified.

3. The industrial internet security analysis system based on digital twins according to claim 2, characterized in that, The expression for the adjusted evaluation model is as follows: ; In the formula: (d, YQ) are the input data, d is the combined adjustment feature, YQ is the adjustment requirement; d→YQ means that the corresponding combined adjustment feature meets the adjustment requirement; the output data is the adjustment evaluation value PH(d, YQ).

4. The industrial internet security analysis system based on digital twins according to claim 1, characterized in that, The platform establishes communication connections with each user terminal.

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