Intelligent factory equipment safety management system and method

Abstract

The invention relates to the technical field of intelligent manufacturing and industrial Internet of Things, in particular to an intelligent factory equipment safety management system and method, and the system comprises an identity authentication module, a dynamic safety interaction module, an intelligent monitoring response module and a self-adaptive optimization module. An identity authentication module; through fusion of multi-dimensional information authentication, block chain decentralized storage, intelligent algorithm behavior analysis, encrypted communication and fine-grained authority control, multi-source data threat detection and reinforcement learning driven strategy optimization, a whole-process security system is constructed. The method comprises the steps of identity authentication, security interaction, monitoring response and adaptive optimization. The problems that in a traditional industrial scene, equipment identity authentication is fragile, data transmission is not safe, threat response lags and strategies are staticized are solved, high-safety authentication, dynamic encryption communication, real-time threat response and strategy self-optimization are achieved, and the equipment safety protection capacity and the system intelligence level are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent manufacturing and industrial Internet of Things, and specifically relates to an intelligent factory equipment safety management system and method. Background Art

[0002] With the rapid development of smart manufacturing, the number of Internet of Things (IoT) devices in smart factories has increased dramatically, and the interactions between these devices have become increasingly complex. As a crucial component of smart factories, the accuracy and efficiency of their identity authentication and secure interactions are directly related to the production efficiency and safety of the entire factory.

[0003] Artificial intelligence, with its powerful learning, reasoning, and decision-making capabilities, offers a new solution for device authentication and secure interaction in smart factories. By applying AI, the system can automatically identify and verify device identities, ensuring that only legitimate devices can access the network and participate in interactions. Furthermore, AI can monitor and analyze device behavior in real time, enabling timely identification and response to potential security threats.

[0004] Existing IoT device authentication and secure interaction solutions have several shortcomings in threat identification and intelligent response in complex interaction scenarios within industrial control systems:

[0005] 1. Simple and vulnerable authentication mechanisms: IoT device authentication in traditional smart factories often relies on single static passwords, keys, or pre-shared keys. These mechanisms are easily cracked or copied. Once the key is leaked, attackers can easily masquerade as legitimate devices to communicate, leading to data leakage or illegal system control. Furthermore, IoT device authentication in traditional smart factories is weak in security, making it difficult to defend against sophisticated attacks such as advanced persistent threats (APTs) and distributed denial of service (DDoS).

[0006] 2. Inefficient and inflexible interactions: Existing systems typically use a centralized authentication and authorization model. Every interaction between devices must be verified by a central server. In smart factory environments with numerous devices and frequent interactions, this can lead to network congestion and increased latency, impacting production efficiency and real-time performance. Furthermore, a single point of failure can paralyze the entire authentication system.

[0007] 3. Lack of intelligence and difficulty responding to dynamic changes: Traditional systems lack self-learning and adaptive capabilities, making it impossible to dynamically adjust security policies based on real-time data such as device behavior and network status. With the increasing variety of devices and complexity of environments in smart factories, these static security policies are becoming increasingly inadequate. They are unable to effectively prevent unknown threats and struggle to quickly respond and adjust to security incidents.

[0008] 4. Lack of a holistic perspective makes it difficult to achieve end-to-end security: Existing security solutions often focus only on security at a specific stage or level, such as identity authentication or data transmission security, while ignoring the interactions and impacts between components in the entire IoT ecosystem. This can easily create security blind spots, allowing attackers to exploit vulnerabilities between systems to infiltrate and damage.

[0009] In view of this, the present invention is proposed. Summary of the Invention

[0010] In order to solve the above-mentioned technical problems existing in the prior art, the present invention provides a smart factory equipment security management system and method, including an identity authentication module, a dynamic security interaction module, an intelligent monitoring response module and an adaptive optimization module; through the collaborative work of each module, the security of the identity authentication of Internet of Things devices in the smart factory, the interaction efficiency and the overall intelligence of the system are intelligently improved, the security protection problem in complex interaction scenarios is solved, and the healthy development of intelligent manufacturing is promoted.

[0011] To achieve the above object, the technical solution of the present invention is as follows:

[0012] In a first aspect, a smart factory equipment safety management system includes:

[0013] The identity authentication module is used to authenticate device identities based on multi-dimensional information, output a list of authenticated legitimate devices, and trigger a secure interaction process;

[0014] A dynamic security interaction module, configured to connect to the identity authentication module, establish a secure communication channel based on the legal device list, and execute data interaction between devices according to a preset interaction strategy;

[0015] An intelligent monitoring and response module is used to collect the authentication process data of the identity authentication module and the interaction behavior data of the dynamic security interaction module in real time, identify anomalies through intelligent algorithms and send response instructions to each module;

[0016] The adaptive optimization module is used to receive the historical authentication data of the identity authentication module, the interaction log of the dynamic security interaction module and the abnormal record of the intelligent monitoring response module, and dynamically optimize the authentication rules and interaction strategies through a self-learning mechanism.

[0017] Furthermore, the identity authentication module includes:

[0018] Multi-factor authentication unit, used to integrate the device's static authentication information, dynamic authentication credentials, and biometric information for identity verification;

[0019] Behavior analysis unit, used to build a normal behavior model based on the device's historical behavior data and identify abnormal authentication requests through real-time behavior data comparison;

[0020] The blockchain management unit is used to manage the registration and identity information of devices on the blockchain.

[0021] Furthermore, the dynamic security interaction module includes:

[0022] An encryption communication unit, used to negotiate encryption keys through a security protocol and encrypt interactive data;

[0023] The policy execution unit verifies the legitimacy of device interaction requests based on smart contracts or preset rules, and controls data exchange and resource access;

[0024] The permission management unit is used to implement access control policies based on device roles and permission levels.

[0025] Furthermore, the intelligent monitoring response module includes:

[0026] Multi-source data acquisition unit, used to collect equipment operating status data, network traffic data and operation instruction data in real time;

[0027] Threat detection unit, which is used to analyze collected data through anomaly detection algorithms, identify potential security threats and generate risk levels;

[0028] The response scheduling unit is used to trigger corresponding response measures based on the threat level, including alarm prompts, access restrictions or authentication strength upgrades.

[0029] Furthermore, the adaptive optimization module includes:

[0030] Data fusion unit, used to integrate authentication logs, interaction records, and threat event data, build a multi-dimensional security analysis model, and extract useful information;

[0031] Policy optimization unit, used to optimize identity authentication strategies and interaction processes based on analysis results to improve system efficiency;

[0032] The adversarial verification unit is used to test the robustness of the optimized strategy through simulated attacks and improve the system's anti-attack capabilities.

[0033] In a second aspect, a method for managing smart factory equipment security includes:

[0034] Identity authentication: The device sends an authentication request, and the system integrates multi-factor authentication information, device behavior analysis results, and blockchain registration information to verify the identity and generate a list of legal devices;

[0035] Secure interaction: Establishing an encrypted communication channel based on a list of authorized devices, verifying interaction requests through smart contracts or permission rules, and executing data exchange or resource access operations;

[0036] Monitoring and response: Collect authentication and interaction data in real time, identify security threats through anomaly detection algorithms, and trigger response measures;

[0037] Adaptive optimization: Self-learning and optimizing authentication and interaction strategies based on historical data.

[0038] Furthermore, the multi-factor authentication includes combined verification of a unique device identifier, a dynamic token, and biometrics, wherein the dynamic token generates a one-time authentication credential based on time or event.

[0039] Furthermore, the device behavior analysis constructs a normal device behavior model through a machine learning algorithm, calculates an anomaly score based on the difference between real-time behavior data and the model, and determines the authentication result.

[0040] Furthermore, the blockchain registration information is registered through a decentralized ledger storage device and digitally signed.

[0041] Furthermore, the self-learning optimization authentication and interaction strategy includes: dynamically adjusting the number of authentication factors, encryption algorithm strength and access control permissions based on historical security event data to form an adaptive security strategy.

[0042] Compared with the existing technology, the above-mentioned smart factory equipment security management system and method provided by the present invention ensures the high security and non-tamperability of equipment identity verification through multi-factor identity authentication; uses blockchain technology to realize decentralized storage and transparent and trusted verification of equipment registration information to ensure that data cannot be tampered with; uses intelligent algorithms such as LSTM and autoencoders to build a normal behavior model of the equipment, and compares and identifies abnormal authentication requests in real time to improve the real-time performance of threat detection; adopts TLS / SSL security protocol and AES encryption algorithm to establish a dynamic encrypted communication channel, and combines role-based access control model and smart contracts to realize the confidentiality and integrity of data transmission and automated compliance verification and fine-grained permission control of interaction requests; uses isolation forest anomaly detection algorithm to analyze multi-source data, and combines multi-level response mechanism to realize real-time identification and hierarchical disposal of security threats; constructs an adaptive optimization module based on reinforcement learning, evolutionary algorithm and fuzzy logic, and self-learns historical data to dynamically adjust authentication rules and interaction strategies to improve the system's adaptability and intelligent response capabilities to complex threats. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is an architectural diagram of the smart factory equipment safety management system provided in Example 1 of the present invention;

[0044] Figure 2 This is a flow chart of identity authentication provided in Example 2 of the present invention;

[0045] Figure 3This is a security interaction flow chart provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0047] It should be noted that, unless otherwise specifically stated, the relative arrangements of components and steps, and numerical expressions set forth in these embodiments should not be construed as limiting the scope of the present invention.

[0048] The following description of exemplary embodiments is merely illustrative and is not intended to limit the present invention, its application, or use in any sense. Technologies, methods, and apparatus known to those skilled in the art may not be discussed in detail herein, but to the extent applicable, such technologies, methods, and apparatuses should be considered part of this specification.

[0049] Example 1

[0050] See Figure 1 , Figure 1 This is the architecture diagram of the intelligent factory equipment security management system proposed in this invention. The system integrates multiple technical means such as multi-factor identity authentication, intelligent behavior analysis, security protocols and encryption technology to ensure secure communication and efficient interaction between devices. It also supports the access of various IoT devices and platforms and has good compatibility and scalability. Regardless of the device type, operating system or communication protocol, it can achieve fast and stable identity authentication and secure interaction. Specifically, it may include:

[0051] A1. Identity authentication module, used to authenticate device identities based on multi-dimensional information, output a list of authenticated legitimate devices, and trigger a secure interaction process. Specifically, it may include:

[0052] A11. Multi-factor authentication unit, used to integrate the device's static authentication information, dynamic authentication credentials, and biometric information for identity verification; wherein, the static authentication information is a password, the dynamic authentication credential is a dynamic token, and the biometric information is a fingerprint or iris.

[0053] A12, the behavior analysis unit, is used to build a normal behavior model based on historical device behavior data and identify abnormal authentication requests by comparing real-time behavior data. A deep learning algorithm (LSTM / autoencoder) is used to analyze historical device behavior data and build a dynamic behavior baseline. An LSTM network is used to process device operation sequences (such as continuous instruction streams), and an autoencoder is used for unsupervised learning of normal behavior patterns. The difference between the current behavior and the baseline is calculated in real time, and an abnormality is determined if the difference exceeds a threshold.

[0054] A13, the blockchain management unit, is used to manage device registration and identity information on the blockchain. This management uses blockchain technology to verify the immutability and transparency of device registration and identity data. Device identity information is stored on-chain, ensuring data immutability through the decentralized nature of blockchain. During authentication, the on-chain registration information is queried to verify the consistency of the device's requested data with the on-chain records.

[0055] A2. A dynamic security interaction module, configured to connect with the identity authentication module, establish a secure communication channel based on the legal device list, and perform data interaction between devices according to a preset interaction strategy; specifically, this module may include:

[0056] A21, encryption communication unit, is used to negotiate encryption keys through security protocols and encrypt interactive data; establish a secure communication channel between devices to ensure the confidentiality and integrity of interactive data.

[0057] Use security protocols such as TLS / SSL for key negotiation to complete two-way device identity authentication and encryption parameter exchange; use symmetric encryption algorithms such as AES to encrypt interactive data, transmit data through encrypted channels, support dynamic adjustment of encryption strength, and automatically select the appropriate encryption protocol and key length based on data sensitivity.

[0058] A22, a policy execution unit, is used to verify the legitimacy of device interaction requests based on smart contracts or preset rules, and to control data exchange and resource access;

[0059] Deploy smart contracts on the blockchain, pre-define authentication logic, permission verification rules, and interaction processes, including data exchange conditions, resource access scope, and other content. After receiving a device interaction request, automatically execute the smart contract code or call the preset rule engine to verify the compliance of the request, such as checking the validity of the request timestamp and the matching degree of operation permissions. For requests that pass verification, trigger the data exchange interface or resource access interface to achieve automated interaction; for requests that fail verification, reject and log them.

[0060] A23, the permissions management unit, is used to implement access control policies based on device roles and permission levels. Based on device roles, permission levels, and the policy rule base, device access requests are authorized or denied, achieving fine-grained access control.

[0061] The role-based access control (RBAC) model assigns preset roles to different types of devices such as sensors, actuators, and controllers. Each role corresponds to a specific set of permissions, such as data reading, command issuance, firmware upgrade, etc. When an access request is received, the role information of the requesting device is retrieved, and the permission entries in the policy rule base are matched to determine whether the requested operation is allowed to be executed, thereby achieving fine-grained access control.

[0062] A3, an intelligent monitoring and response module, is used to collect the authentication process data of the identity authentication module and the interaction behavior data of the dynamic security interaction module in real time, identify anomalies through intelligent algorithms, and send response instructions to each module. Specifically, it may include:

[0063] A31, multi-source data acquisition unit, used to collect equipment operating status data, network traffic data and operation instruction data in real time;

[0064] Equipment operation status data includes: real-time sensor parameters, equipment operating voltage and operating temperature, etc.; network traffic data includes communication protocol type, data transmission rate, source IP and destination IP addresses, etc.; operation instruction data includes equipment control instruction content, instruction sending frequency and instruction execution result feedback, etc.

[0065] A32, threat detection unit, used to analyze collected data using anomaly detection algorithms, identify potential security threats and generate risk levels;

[0066] Use anomaly detection algorithms to analyze the collected multi-source data; anomaly detection algorithms can use isolation forests, dynamic time warping, etc.; compare real-time data with preset normal behavior baselines to identify abnormal device behavior, signs of network attacks, or instruction operation violations; generate different levels of risk assessment results based on the type and severity of the threat, such as low risk, medium risk, and high risk.

[0067] A33, Response Dispatch Unit, is used to trigger corresponding response measures based on the threat level, including alarm prompts, access restrictions or authentication strength upgrades. Specific measures:

[0068] Low-risk threats: trigger alerts, record threat logs, and continuously monitor;

[0069] Medium-risk threats: Implement access restrictions, such as prohibiting some data interactions or limiting device operation permissions;

[0070] High-risk threats: Increase authentication strength, such as requiring secondary authentication or adding biometric verification, while blocking abnormal communication connections.

[0071] A4, an adaptive optimization module, is used to receive historical authentication data from the identity authentication module, interaction logs from the dynamic security interaction module, and exception records from the intelligent monitoring response module, and dynamically optimize authentication rules and interaction strategies through a self-learning mechanism. Based on the initialization model, the self-learning mechanism uses intelligent algorithms to adjust authentication rules and interaction strategies in real time. The core optimization process includes:

[0072] Reinforcement learning-driven policy optimization: The optimization problem is modeled as a Markov decision process (MDP), where the state space includes device risk level, network load, etc., and the action space corresponds to policy adjustments (such as upgrading encryption strength or restricting access). The reward function is designed to balance security and efficiency (for example, reducing threat events will receive positive rewards, while increasing interaction latency will receive negative rewards). Using Q-learning or policy gradient algorithms, the policy parameters are updated through interaction with the environment to achieve dynamic optimization. For example, when the threat detection unit identifies a medium risk, the algorithm automatically tightens the RBAC permission rules;

[0073] Evolutionary algorithm-assisted rule evolution: Genetic algorithms or particle swarm optimization (PSO) are introduced for complex nonlinear optimization problems (such as encryption protocol selection). The algorithm encodes authentication rules or interaction strategies into "chromosomes" and iteratively optimizes them through selection, crossover, and mutation operations. For example, when the optimization goal is to minimize the authentication failure rate, the algorithm searches the policy rule base for the optimal key length and protocol combination and verifies its robustness under new threat scenarios.

[0074] Fuzzy logic and adaptive threshold adjustment: Fuzzy rules are combined to handle uncertain data (such as the fuzzy definition of "high risk" threats). Based on real-time feedback, anomaly detection thresholds or authentication strength rules are dynamically adjusted. For example, if data fusion shows that a certain device type frequently triggers false alarms, the threshold for its behavioral deviation is automatically lowered to reduce false positives.

[0075] A41, Data Fusion Unit, is used to integrate authentication logs, interaction records, and threat event data, build a multi-dimensional security analysis model, and extract useful information; specifically, it includes:

[0076] Clean, aggregate, and analyze multi-source data such as authentication logs, device interaction records, and threat detection results; mine behavioral patterns, risk trends, and optimization targets in the data, such as high-frequency abnormal authentication request periods and vulnerable device types; and generate data visualization reports to provide decision-making basis for strategy optimization.

[0077] A42, policy optimization unit, is used to optimize identity authentication strategies and interaction processes based on analysis results to improve system efficiency. It models policy optimization as a Markov decision process (MDP) and updates strategies online through Q-learning. For nonlinear problems (such as the selection of encryption protocol combinations), it encodes strategies as chromosomes and searches for Pareto optimal solutions in the policy rule base through crossover and mutation operations of genetic algorithms to verify its robustness in new attack scenarios. It uses a fuzzy rule base to process uncertain inputs (such as the semantic ambiguity of "high risk"), dynamically adjusts anomaly detection thresholds (such as the behavioral deviation threshold adaptively decreases with the false alarm rate), and authentication strength rules (for example, the probability of triggering biometric verification increases nonlinearly with the threat level). It calibrates the fuzzy membership function based on a real-time feedback closed loop to reduce the false positive rate.

[0078] A43, the adversarial verification unit, tests the robustness of optimization strategies by simulating high-level attack chains, quantifies attack resistance, and provides feedback for strategy iteration. It integrates the MITREATT&CK industry framework to generate targeted test cases: identity forgery (static credential theft + dynamic token replay), protocol downgrade attacks (forcing negotiation of weak encryption protocols), and privilege escalation (RBAC rule bypass). It also generates adversarial examples for policy rules using a genetic algorithm (for example, mutating smart contract parameters to trigger permission verification vulnerabilities).

[0079] Example 2

[0080] The present invention provides a flow chart of a method for managing the safety of smart factory equipment. The method may include the following steps:

[0081] S1. Identity Authentication: See Figure 2 This is the identity authentication flow chart. The device sends an authentication request. The system integrates multi-factor authentication information, device behavior analysis results, and blockchain registration information to verify the identity and generate a list of legal devices.

[0082] Multi-factor authentication involves verifying a device's unique identifier, a dynamic token, and biometrics. The dynamic token generates a one-time authentication credential based on time or events. Device behavior analysis uses machine learning algorithms to build a model of normal device behavior. Based on the discrepancy between real-time behavior data and the model, an anomaly score is calculated and used to determine the authentication result. Blockchain registration information is stored and digitally signed in a decentralized ledger. This can include:

[0083] S11. Multi-factor authentication information

[0084] S111. The device sends an authentication request to the authentication server, including basic information such as the device ID and request timestamp.

[0085] S112. The device enters a static password, and the system hashes the password using a hash algorithm (such as SHA-256) and then compares it with the stored hash value.

[0086] Algorithm formula example (hash formula): H(password) = SHA-256(password)

[0087] S113. If the system requires a dynamic token, the device needs to display or input a currently valid dynamic token; the time-based one-time password TOTP, the specific algorithm formula is:

[0088] TOTP=HMAC-SHA-1(K,T / (30×60))mod10 6

[0089] Where K is the key, T is the current time (seconds), and 30 is the time step (minutes).

[0090] S12. Device Behavior Analysis

[0091] S121. Collect historical behavior data of the device, such as login time, location, operation sequence, etc.

[0092] S122: Use a machine learning algorithm to train the device's historical behavior data to build a normal behavior model. The learning algorithm may use LSTM or RNN.

[0093] Model training: Use historical data to train the model and identify normal behavior patterns.

[0094] S123. Calculate the anomaly score between the device's current behavior and the normal behavior model. The conceptual formula is: AnomalyScore = f(CurrentBahavior, NormalBehaviorModel)

[0095] S124. If the anomaly score exceeds the threshold, it is determined to be abnormal behavior, triggering a security alert and rejecting the authentication request.

[0096] S13. Blockchain identity information verification

[0097] S131. Query the device registration information on the blockchain, including device ID, manufacturer information, registration timestamp, etc.

[0098] S132. Verify that the information in the device request is consistent with the information recorded on the blockchain, including using a digital signature to verify the integrity and source of the information. The digital signature formula based on the RSA algorithm is:

[0099] Signature=Sign(m,PrivateKey)

[0100] Verification=Verify(m,Signature,PublicKey)

[0101] S133. Check the timestamp on the blockchain to confirm the timeliness of the information.

[0102] S134. If all verifications pass, the device identity is confirmed to be legitimate and is allowed to enter the smart factory network; otherwise, access is denied and the exception is recorded.

[0103] S2. Secure Interaction: See Figure 3 For secure interaction flow charts, an encrypted communication channel is established based on a list of legal devices, interaction requests are verified through smart contracts or permission rules, and data exchange or resource access operations are performed;

[0104] S21: Data encryption communication

[0105] S211: Negotiate encryption protocols and keys between devices (such as the TLS handshake process).

[0106] S212: Encrypt the data using the negotiated encryption algorithm (such as AES) and key. The AES encryption algorithm formula is:

[0107] Ciphertext=AES_Encrypt(Plaintext,Key)

[0108] S213. Send the encrypted data through an encrypted channel to ensure security during data transmission.

[0109] S22. Smart Contract Execution

[0110] S221. The device initiates an interaction request, along with request details and required operations.

[0111] S222. The smart contract execution unit verifies the validity of the request according to the predefined contract logic (such as a smart contract written in Solidity).

[0112] Smart contract verification process: The contract logic will include a series of conditional judgments and function calls to ensure that the request complies with the preset rules.

[0113] S223. If the request is valid, execute the interaction logic defined in the contract, such as data exchange, resource allocation, etc.

[0114] S224. Record the contract execution results and notify relevant devices.

[0115] S23. Access Control

[0116] S231 : Check the access rights of the requesting device according to the rules in the policy rule base (such as role-based access control (RBAC)).

[0117] S232: Verify whether the requested operation complies with the device's role, permission level, and access scope.

[0118] S233. If the permission verification is passed, the request is allowed to execute; otherwise, the access is denied and a log is recorded.

[0119] S3, Monitoring and Response: Collect authentication and interaction data in real time, identify security threats through anomaly detection algorithms, and trigger response measures;

[0120] S31. Data Analysis and Feature Modeling

[0121] S311. Multi-source data fusion collection, specifically including:

[0122] Establish data fusion matrix:

[0123]

[0124] in, It represents the characteristic value of the jth data source (such as sensor readings, network traffic packet characteristics, etc.) of the i-th device at time t. n represents the number of devices, and m represents the dimension of the data source (such as time series data, operation instruction sequence, etc.).

[0125] S312, dynamic feature extraction, specifically including:

[0126] The improved IsolationForest algorithm is used for abnormal behavior detection, and the abnormality scoring formula is:

[0127]

[0128] Where h(x) is the path length of sample x in the isolation tree (the number of edges from the root node to the leaf nodes). E(h(x)) is the mean of the path lengths of multiple isolation trees. c(n) is the normalization factor.

[0129] Operation mode clustering (using incremental K-means++ algorithm) dynamically updates the cluster center. The specific formula is:

[0130]

[0131] Among them, μ t is the cluster center at the current moment, η is the decay learning rate, which decreases with time to avoid excessive impact of new data on historical patterns. j The distance is less than the dynamic threshold θ = α·σ jTime-updated temporal pattern recognition (LSTM-Attention model):

[0132] α t =softmax(υ t tanh(W h h t +W s s t-1 ))

[0133] Among them, h t is the LSTM hidden state, s t-1 is the attention state at the previous moment, α t is the attention weight, which is used to weight the key time step features.

[0134] S313, Threat Assessment Modeling

[0135] Construct a threat level assessment function. The specific formula is:

[0136]

[0137] Among them, f i (x i ) is the standardized value of the i-th threat indicator (such as anomaly score, attack frequency). i For dynamic weights, update via Bayesian network:

[0138] S32, Adaptive Strategy Optimization

[0139] S321, Strategy Evaluation Framework

[0140] Build a Markov Decision Process (MDP) model:<S,A,P,R,γ>

[0141] Where S is the system security state space, A is the executable security action (such as enhanced authentication, restricted access, etc.), P is the state transition probability matrix, R is the immediate reward function, and γ is the discount factor.

[0142] S322, deep reinforcement learning optimization, using the DDPG (Deep Deterministic Policy Gradient) algorithm. Dynamic parameter adjustment can include:

[0143] Authentication strength adjustment (fuzzy logic control): Establish a fuzzy rule base, for example: IF the anomaly score is high AND the threat level is high THEN the authentication factors are increased to 3.

[0144] Encryption strength optimization (based on flexible parameter selection of elliptic curve cryptography) and dynamic selection of ECC curve parameters:

[0145] Safety factor: λ = α·T threat +β·S data , curve parameter: ρ = f(λ)

[0146] Among them, T threat is the normalized threat level (0-1), S data is the data sensitivity weight (0.1 to 1.0), and f(λ) is a lookup table function.

[0147] S323, Adversarial Verification Test

[0148] Generate adversarial sample test strategy robustness: Use conditional generative adversarial network (CGAN), the specific formula / expression is:

[0149]

[0150] Where c is the threat context vector (such as the attack type encoding), G(z|c) is the generator synthesizing adversarial samples based on noise z and condition c, and D(·|c) is the discriminator evaluating the authenticity of the sample under condition c.

[0151] S4. Adaptive optimization: Self-learning optimization of authentication and interaction strategies based on historical data. Specifically, it can include: dynamically adjusting the number of authentication factors, encryption algorithm strength, and access control permissions based on historical security event data to form an adaptive security strategy.

[0152] S41. Logging

[0153] S411. Record detailed information of each authentication attempt, including time, device information, authentication result, etc.

[0154] S412. Record the log of the interaction between devices, including the request content, execution result, timestamp, etc.

[0155] S413. Store the behavior analysis results and security decision logs.

[0156] S42. Audit and Tracking

[0157] S421. Provide a log query interface that supports filtering by time, device ID, operation type, etc.

[0158] S422. When a security incident occurs, quickly locate the relevant logs and conduct security audits and tracking.

[0159] S423. Generate security audit reports regularly, analyze security situation, and make improvement suggestions.

[0160] In summary, the present invention has the following advantages:

[0161] 1. By introducing an intelligent multi-factor identity authentication mechanism, combining multiple factors such as device physical characteristics, behavior patterns, timestamps, etc. for identity authentication, the complexity and difficulty of authentication are enhanced, effectively resisting various attacks and improving the security of identity authentication.

[0162] 2. Adopt a distributed authentication and authorization model, combined with edge computing and blockchain technology, to achieve autonomous authentication and decentralized interaction between devices, reduce the burden on the central server, improve the response speed and scalability of the overall system, and improve interaction efficiency and flexibility.

[0163] 3. Use intelligent algorithms to monitor and analyze device behavior, network traffic and other data in real time, automatically detect abnormal behavior, dynamically adjust security policies, achieve self-learning and self-adaptation of security policies, improve the system's intelligence level and ability to respond to complex threats, and enhance the system's intelligence and adaptability.

[0164] The above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the scope of the technical solutions of the present invention, and all of these should be included in the scope of the claims of the present invention.

Claims

1. A smart factory equipment safety management system, characterized in that: include: The identity authentication module is used to authenticate device identities based on multi-dimensional information, output a list of authenticated legitimate devices, and trigger a secure interaction process; A dynamic security interaction module, configured to connect to the identity authentication module, establish a secure communication channel based on the legal device list, and execute data interaction between devices according to a preset interaction strategy; An intelligent monitoring and response module is used to collect the authentication process data of the identity authentication module and the interaction behavior data of the dynamic security interaction module in real time, identify anomalies through intelligent algorithms and send response instructions to each module; The adaptive optimization module is used to receive the historical authentication data of the identity authentication module, the interaction log of the dynamic security interaction module and the abnormal record of the intelligent monitoring response module, and dynamically optimize the authentication rules and interaction strategies through a self-learning mechanism.

2. The intelligent factory equipment safety management system according to claim 1, characterized in that: The identity authentication module includes: Multi-factor authentication unit, used to integrate the device's static authentication information, dynamic authentication credentials, and biometric information for identity verification; Behavior analysis unit, used to build a normal behavior model based on the device's historical behavior data and identify abnormal authentication requests through real-time behavior data comparison; The blockchain management unit is used to manage the registration and identity information of devices on the blockchain.

3. The intelligent factory equipment safety management system according to claim 1, characterized in that: The dynamic security interaction module includes: An encryption communication unit, used to negotiate encryption keys through a security protocol and encrypt interactive data; The policy execution unit verifies the legitimacy of device interaction requests based on smart contracts or preset rules, and controls data exchange and resource access; The permission management unit is used to implement access control policies based on device roles and permission levels.

4. The intelligent factory equipment safety management system according to claim 1, characterized in that: The intelligent monitoring response module includes: Multi-source data acquisition unit, used to collect equipment operating status data, network traffic data and operation instruction data in real time; Threat detection unit, which is used to analyze collected data through anomaly detection algorithms, identify potential security threats and generate risk levels; The response dispatch unit is used to trigger corresponding response measures according to the threat level, including alarm prompts, access restrictions or authentication strength upgrades.

5. The intelligent factory equipment safety management system according to claim 1, characterized in that: The adaptive optimization module includes: Data fusion unit, used to integrate authentication logs, interaction records, and threat event data, build a multi-dimensional security analysis model, and extract useful information; Policy optimization unit, used to optimize identity authentication strategies and interaction processes based on analysis results to improve system efficiency; The adversarial verification unit is used to test the robustness of the optimized strategy through simulated attacks and improve the system's anti-attack capabilities.

6. A method for safety management of smart factory equipment, characterized in that: include: Identity authentication: The device sends an authentication request, and the system integrates multi-factor authentication information, device behavior analysis results, and blockchain registration information to verify the identity and generate a list of legal devices; Secure interaction: Establishing an encrypted communication channel based on a list of authorized devices, verifying interaction requests through smart contracts or permission rules, and executing data exchange or resource access operations; Monitoring and response: Collect authentication and interaction data in real time, identify security threats through anomaly detection algorithms, and trigger response measures; Adaptive optimization: Self-learning and optimizing authentication and interaction strategies based on historical data.

7. The smart factory equipment safety management method according to claim 6, characterized in that: The multi-factor authentication includes combined verification of a device's unique identifier, a dynamic token, and biometrics, wherein the dynamic token generates a one-time authentication credential based on time or event.

8. The smart factory equipment safety management method according to claim 6, characterized in that: The device behavior analysis constructs a normal device behavior model through a machine learning algorithm, calculates anomaly scores based on the difference between real-time behavior data and the model, and determines the authentication result.

9. The smart factory equipment safety management method according to claim 6, characterized in that: The blockchain registration information is stored in a decentralized ledger and digitally signed.

10. The smart factory equipment safety management method according to claim 6, characterized in that: The self-learning optimization authentication and interaction strategy includes: dynamically adjusting the number of authentication factors, encryption algorithm strength and access control permissions based on historical security event data to form an adaptive security strategy.

Images