Connector automatic control system based on intelligent identification

Abstract

The application relates to the technical field of automatic connector control and discloses an automatic connector control system based on intelligent identification.The intelligent identification module of the system collects operation signals and environmental parameters, analyzes a running state change mode, calculates a connection frequency and a response delay, judges an abnormal behavior risk, and generates a state feature matrix; the risk assessment module positions an abnormal control node based on the state feature matrix, analyzes a matching relationship and a correlation degree, assesses a risk probability, predicts a security threat, and generates a risk feature vector; the control optimization module identifies a high-risk node, analyzes data distribution, calculates an adjustment priority, plans an optimized path, migrates control logic, and obtains a control parameter configuration set; the behavior monitoring module compares a response frequency, identifies abnormal activities, locates a threat source, and generates a monitoring result; and the adaptive response module judges an abnormal connector, adjusts permissions, allocates resources, updates a verification mechanism, and generates a response measure.

Description

Technical Field

[0001] This invention relates to the field of connector automatic control technology, specifically to an connector automatic control system based on intelligent recognition. Background Technology

[0002] In numerous fields such as industrial production, electronic communications, and smart devices, connectors serve as crucial components for signal, data, and energy transmission, and their operational stability directly impacts the normal operation of the entire system. With the rapid development of industrial automation and intelligence, the application scenarios for connectors are becoming increasingly complex, the number of connected devices is constantly increasing, and the operating environment is exhibiting diverse characteristics. This places higher demands on the control precision and reliability of connectors.

[0003] Traditional connector control systems often rely on manual inspections or sensor monitoring with fixed thresholds, making them ill-suited for dynamically changing operating environments. In terms of condition identification, existing technologies typically collect only single types of operational data, such as simple voltage and current parameters, lacking comprehensive analysis of multi-dimensional data including operating signals, ambient temperature and humidity, and vibration frequency. This results in an inability to accurately capture subtle changes in connector operating status. When potential anomalies such as poor contact or signal attenuation occur, traditional systems often fail to identify them early due to insufficient data dimensions, only becoming apparent after the fault has occurred through noticeable performance degradation, severely impacting continuous system operation.

[0004] In the risk assessment phase, existing technologies typically only perform isolated analyses of past failure events, lacking in-depth exploration of the correlation between control nodes and failure events. Most systems assess risk by using preset failure type matching, ignoring the correlation between dynamic factors such as operation frequency and response delay and failure events. This results in one-sided risk assessment results, making it difficult to accurately identify high-risk control nodes and thus hindering the implementation of preventative measures in advance.

[0005] In terms of control optimization, traditional control systems often rely on experience-based adjustment strategies, lacking quantitative analysis of operational data distribution and making it difficult to scientifically calculate adjustment priorities. When multiple connector nodes simultaneously pose potential risks, the system cannot rationally plan optimization paths, frequently resulting in issues such as unreasonable resource allocation and delayed control logic migration, causing some high-risk nodes to remain in an unstable state for extended periods.

[0006] Regarding behavior monitoring and response mechanisms, existing systems have limited monitoring scope, focusing primarily on the basic operating parameters of connectors. They lack comparative analysis of changes in operational response frequency before and after adjustments, making it difficult to identify hidden abnormal connection activities. Furthermore, response measures are mostly fixed patterns, unable to dynamically adjust operating permissions, resource allocation, and verification mechanisms based on real-time monitoring results. This results in insufficient adaptability to new security threats, easily leading to fault spread or system paralysis. Summary of the Invention

[0007] The purpose of this invention is to provide an automatic control system for connectors based on intelligent recognition, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides an automatic connector control system based on intelligent identification, the system comprising:

[0009] The intelligent identification module collects operation signals and environmental parameters from the connector status data stream, analyzes the change patterns of the connector's operating status, calculates the connection frequency and response delay, compares the change patterns of the operating status with the connection frequency, judges the risk of abnormal behavior of the connector, and generates a status feature matrix.

[0010] Based on the state feature matrix, the risk assessment module locates the control node of the abnormal connector, analyzes the matching relationship between the control node and the fault event, assesses the risk probability of the node, calculates the correlation between the operation frequency and the fault event, predicts the security threat to the control node, and generates a risk feature vector.

[0011] Based on the risk feature vector, the control optimization module identifies high-risk control nodes, analyzes the distribution of operational data, calculates adjustment priorities, plans optimization paths, migrates control logic to low-risk nodes, and obtains a set of control parameter configurations.

[0012] Based on the control parameter configuration set, the behavior monitoring module compares the operation response frequency of the connector before and after adjustment, identifies abnormal connection activities, judges the abnormal characteristics of the connection behavior pattern, locates the source of security threats, and generates behavior monitoring results.

[0013] Based on the behavior monitoring results, the adaptive response module identifies abnormal operation connectors, adjusts operation permission configurations, allocates response resource ratios, updates connection verification mechanisms, and generates adaptive response measures.

[0014] Preferably, the state feature matrix includes connection frequency differences, response latency fluctuations, and operation signal change rates; the risk feature vector includes node risk probability, operation and event matching degree, and threat identification results; the control parameter configuration set includes priority index adjustment, path optimization scheme, and permission update standard; the behavior monitoring results include behavior pattern offset index, response frequency comparison results, and abnormal activity identifiers; and the adaptive response measures include permission configuration update, response resource allocation, and post-update verification mechanism.

[0015] Preferably, the intelligent recognition module includes:

[0016] The signal acquisition submodule acquires the operation signals and environmental parameters in the connector status data stream, analyzes the timing changes of the operation signals, calculates the time interval between consecutive operations, statistically analyzes the fluctuation range of the operating status, compares the input and output signal ratios, identifies abnormal connectors, and obtains status fluctuation indicators.

[0017] Based on the state fluctuation index, the connection analysis submodule retrieves the connection data of abnormal state connectors, analyzes the distribution characteristics of the number of connections in a specific time period, calculates the degree of connection fluctuation of the connector in the short term, determines whether the connector has abnormal connection behavior, and obtains the connection fluctuation index.

[0018] Based on the connection fluctuation index, the parameter fusion submodule calls the connector's parameter configuration record, counts the number of parameter changes, and calculates the degree of abnormality of parameter changes by combining the connector's operating status and connection analysis data, generating a state feature matrix.

[0019] Preferably, the risk assessment module includes:

[0020] The abnormal operation identification submodule filters the operation data of abnormal connectors based on the state feature matrix, analyzes the correlation between operation time and intensity and control mode, calculates and classifies the distribution density of abnormal operations, identifies abnormal control nodes, and generates a set of abnormal control nodes.

[0021] The node matching submodule calls the set of abnormal control nodes, parses the characteristics of the operation behavior, compares the patterns of known fault events, calculates the degree of matching between the node and the fault event, assesses the risk level of the control node, and generates a risk matching index.

[0022] The threat prediction submodule analyzes the operation frequency of abnormal nodes based on the risk matching index, extracts the operation time interval, calculates the operation fluctuation range within a short period, predicts the probability of a security threat based on the risk matching degree of the node, and generates a risk feature vector.

[0023] Preferably, the control optimization module includes:

[0024] Based on the risk feature vector, the node identification submodule detects high-risk nodes in the control network, analyzes the connector data type and sensitivity level processed by the nodes, filters control nodes containing security threats, determines the range of control nodes that need to be optimized, and obtains a list of high-risk nodes.

[0025] The optimization analysis submodule, based on the list of high-risk nodes, analyzes the data interaction among the affected nodes, calculates the degree of data correlation and interaction frequency between nodes, determines the scope and priority of the control optimization, and generates an adjustment priority index.

[0026] Based on the adjusted priority index, the control reconfiguration submodule analyzes the control logic paths between nodes, allocates control resources, plans the optimal optimization path, and adjusts the access permissions of security control nodes to obtain a set of control parameter configurations.

[0027] Preferably, the behavior monitoring module includes:

[0028] Based on the control parameter configuration set, the pattern analysis submodule calls the behavior log of the adjusted connector, compares the connector activity characteristics before and after the adjustment, analyzes the magnitude and frequency of behavior changes, calculates the degree of behavior pattern shift, and obtains the behavior pattern shift index.

[0029] The response comparison submodule compares the operation response frequency of the connector before and after adjustment based on the behavior pattern offset index, analyzes the changes in response time, response duration and response frequency, judges the fluctuation of response frequency, and generates a response frequency fluctuation index.

[0030] Based on the response frequency fluctuation index, the anomaly localization submodule identifies connection activities that deviate from the normal pattern, analyzes the characteristics of abnormal connector behavior patterns, matches the relationship between connection activity characteristics and known threats, locates the source of security threats, and generates behavior monitoring results.

[0031] Preferably, the adaptive response module includes:

[0032] Based on the behavior monitoring results, the permission adjustment submodule analyzes the frequency of risky operations of connectors, calculates the impact range of permission changes, identifies connectors with frequent abnormal operations, reconfigures operation permissions, implements operation restrictions on high-risk connectors, and generates permission configuration updates.

[0033] The resource allocation submodule calls the permission configuration update, calls the response record of the abnormal connector, calculates the fluctuation range of the response resources, analyzes the short-term trend of response resource changes, judges the degree of deviation of the response resources from the normal behavior of the connector, adjusts the upper limit of the response resources, allocates the proportion of response resources, and generates optimized response resources.

[0034] The verification update submodule, based on the optimized response resources, filters connectors with abnormal response frequencies, extracts the verification history of the connectors, analyzes the security level of the abnormal connector verification, determines whether the verification matches the risk level, optimizes the connection verification mechanism, and generates adaptive response measures.

[0035] Preferably, the connection analysis submodule includes:

[0036] Based on the state fluctuation index, the time distribution analysis unit segments the time intervals of the connection data, analyzes the peak number of connections in each interval, calculates the distance difference between the peaks, identifies abnormal connection time periods, and generates time distribution features.

[0037] The fluctuation calculation unit calls the time distribution characteristics to statistically analyze the change in the number of connections of the connector within a preset short window, calculates the standard deviation of the number of connections, determines whether the standard deviation exceeds the normal range, and generates a connection fluctuation index.

[0038] The behavior judgment unit analyzes the probability of abnormal connection behavior based on the connection fluctuation index and the operating signal characteristics of the connector, and generates a connection fluctuation index.

[0039] Preferably, the threat prediction submodule includes:

[0040] Based on the risk matching index, the frequency extraction unit scans the operation logs of abnormal nodes, extracts the operation timestamp sequence, calculates the time difference between adjacent operations, and generates an operation frequency sequence.

[0041] The fluctuation range calculation unit calls the operation frequency sequence, divides short-cycle time blocks, calculates the rate of change of the number of operations in each time block, and generates an operation fluctuation range index.

[0042] The threat probability prediction unit analyzes the potential probability of security threats based on the operational fluctuation range index and the risk matching degree data of the nodes, and generates a risk feature vector.

[0043] Preferably, the optimization analysis submodule includes:

[0044] Based on the list of high-risk nodes, the correlation calculation unit maps the data flow between nodes, analyzes the degree of dependence of data interaction, calculates the weight value of node interaction, and generates a data correlation index.

[0045] The frequency analysis unit calls the data correlation index to count the number of interactions between nodes within a unit of time, calculates the average and variance of the number of interactions, and generates an interaction frequency index.

[0046] The priority indexing unit integrates the scope of influence and priority order based on the data correlation index and interaction frequency index to generate an adjusted priority index.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] This intelligent recognition-based connector automatic control system effectively compensates for the shortcomings of traditional control systems through the collaborative work of multiple modules. The intelligent recognition module is no longer limited to single data acquisition but comprehensively acquires operational signals and environmental parameters from the connector status data stream. By analyzing operational status change patterns and calculating connection frequency and response delay, it achieves accurate judgment of abnormal behavior risks. The generated status feature matrix comprehensively reflects the connector's operational status, providing rich and reliable basic data for subsequent risk assessment.

[0049] The risk assessment module, based on the state feature matrix, accurately locates the control nodes of the abnormal connector, breaking through the traditional isolated fault analysis mode. By deeply analyzing the matching relationship between control nodes and fault events, and combining the calculation of the correlation between operation frequency and fault events, it realizes the scientific assessment of node risk probability and the effective prediction of safety threats. The generated risk feature vector can clearly present the risk status of each node, providing clear directional guidance for control optimization.

[0050] Based on risk feature vectors, the control optimization module accurately identifies high-risk control nodes. Through detailed analysis of the distribution of operational data, it scientifically calculates and adjusts priorities and plans optimization paths, migrating control logic to low-risk nodes. The resulting control parameter configuration set avoids the blindness of traditional experience-based adjustments, ensuring the rational allocation of resources and the efficient operation of control logic, and enhancing the system's proactiveness in responding to risks.

[0051] Based on the control parameter configuration set, the behavior monitoring module can keenly identify abnormal connection activities by comparing the operation response frequency of the connector before and after adjustment, deeply judge the abnormal characteristics of the connection behavior pattern and accurately locate the source of security threats. The generated behavior monitoring results realize the dynamic tracking of the connector's operating status, overcome the problems of limited monitoring range and delayed response of traditional monitoring, and provide timely and accurate basis for adaptive response.

[0052] Based on behavior monitoring results, the adaptive response module quickly identifies connectors exhibiting abnormal operations, dynamically adjusts operation permission configurations, rationally allocates response resource ratios, and promptly updates the connection verification mechanism. This adaptive response overcomes the limitations of traditional fixed response modes, flexibly adjusting response strategies according to actual risk conditions. This effectively curbs the spread of security threats and ensures the stability of connectors and the entire system. The synergistic effect of each module creates a closed loop in the system, encompassing state awareness, risk prediction, control adjustment, behavior monitoring, and response measures, significantly improving the intelligence level and operational reliability of connector control. Attached Figure Description

[0053] Figure 1 This is a timing diagram of the connector automatic control system based on intelligent recognition described in this invention;

[0054] Figure 2 A flowchart showing the relationship between system characteristics and output elements;

[0055] Figure 3 This is a flowchart of the intelligent recognition module's workflow.

[0056] Figure 4 A flowchart for connecting the analysis submodules. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Please see Figure 1 This invention provides an automatic connector control system based on intelligent recognition, the system comprising:

[0059] This intelligent recognition-based connector automatic control system, through the collaborative work of multiple modules, achieves real-time monitoring, risk assessment, control optimization, behavior monitoring, and adaptive response of connector operating status. The system continuously collects operational signals and environmental parameters from the connector status data stream, analyzes operating status change patterns, calculates connection frequency and response delay, and compares these to determine abnormal behavior risks, generating a status feature matrix containing elements such as connection frequency differences, response delay fluctuations, and operational signal change rates. Based on this matrix, the system locates the control nodes of abnormal connectors, analyzes the matching relationship between node operations and fault events, assesses risk probability and the correlation between operational frequency and fault events, predicts security threats, and generates a risk feature vector containing node risk probability, operation-event matching degree, and threat identification results. Subsequently, the system identifies high-risk control nodes, analyzes operational data distribution, calculates adjustment priorities, plans optimized paths, and migrates control logic to low-risk nodes, forming a control parameter configuration set that includes an adjustment priority index, optimized path schemes, and permission update standards. Based on this configuration set, the system compares the operational response frequencies of connectors before and after adjustments, identifies abnormal connection activities, determines abnormal behavioral patterns, locates the source of security threats, and generates behavioral monitoring results including behavioral pattern offset indicators, response frequency comparison results, and abnormal activity identifiers. Finally, based on the monitoring results, the system identifies abnormally operating connectors, adjusts operational permission configurations, allocates response resource ratios, updates connection verification mechanisms, and generates adaptive response measures that include permission configuration updates, response resource allocation, and post-update verification mechanisms.

[0060] Example 1: See Figure 2This section demonstrates the specific composition and generation logic of the system's core data structure. The state feature matrix, as the core data structure for quantitatively representing the connector's operational status, relies on continuous monitoring and comprehensive analysis of multi-dimensional operating parameters during its construction. This matrix mainly includes three interrelated core dimensions with clear calculation logic: connection frequency difference, response delay fluctuation, and operation signal change rate. Obtaining the connection frequency difference first requires determining a baseline connection frequency. This baseline may be derived from the average historical statistical data of the connector under stable operating conditions, or a reasonable frequency range preset according to its application scenario. The system counts the actual number of connection operations performed by the connector within a specific time period in real time, directly comparing this actual value with the preset baseline frequency, and calculating the absolute difference or relative deviation percentage between the two. This difference or percentage value intuitively reflects abnormal changes in connector activity, such as unexpectedly frequent connection attempts or below-normal connection activity. Response delay fluctuation focuses on the timeliness and stability of the connector's operation execution. The system accurately records the time interval between the issuance of each operation command and the actual effective response from the connector, forming a series of time interval data points. Statistical analysis is performed on these time interval data, calculating their standard deviation or coefficient of variation to quantify the dispersion of response time. A high standard deviation or coefficient of variation indicates unstable response delay, fluctuating between fast and slow, which may be a sign of system overload, resource contention, or potential failure. The rate of change of the operating signal focuses on the dynamic characteristics of the connector's operating signals themselves. The system continuously acquires key signals generated by the connector during operation, such as voltage and current control signals. For these signals, the system analyzes their trajectory over time, calculating the rate of change of signal amplitude or key characteristic parameters per unit time. For example, for voltage signals, the rate of change of the slope of the rising or falling edge might be calculated; for periodic signals, the trend of their frequency or duty cycle might be analyzed. A rate of change value significantly higher or lower than historical norms is identified as an indicator of signal abrupt change or abnormal gradual change.

[0061] The risk feature vector is the core output of the system's assessment of the security threat level of control nodes. This vector consists of three closely related elements: node risk probability, operation-event matching degree, and threat identification result. Assessing the node risk probability is a comprehensive process. The system combines the control node's historical operational records, especially the associated data of past failures or security events, with real-time analysis of the current state characteristics, and uses a probabilistic model to calculate the probability that the node will trigger or be associated with a failure event in the current or future specific time period. This probability value is not static but dynamically updated as the operational state and external environment change. The operation-event matching degree focuses on the similarity analysis of behavioral patterns. The system extracts the current operation sequence features of the target control node, including operation type, sequence, parameters, and time distribution, forming a feature vector or sequence pattern. Simultaneously, the system maintains a known failure event pattern library, storing operation pattern features that have historically been proven to cause system failures or security events. By applying pattern recognition algorithms, such as dynamic time warping algorithms for sequence alignment or calculating cosine similarity and Euclidean distance between feature vectors, the system quantifies the similarity between the current node's operation pattern and each known failure event pattern, deriving a matching degree score. Threat identification results are the final output of risk assessment. The system integrates the calculated risk probability of a node with the score of the matching degree between operations and events, and performs a fusion analysis based on preset judgment rules or classification models. For example, thresholds are set for risk probability and matching degree; when both exceed the threshold, it is judged as a high threat. Alternatively, a machine learning classifier can be applied, using probability values ​​and matching degrees as feature inputs to output a qualitative threat level or a quantitative threat probability value. This result clearly identifies the nature and level of security threats faced by the control node.

[0062] The control parameter configuration set is a collection of specific schemes generated after the control optimization module performs optimization operations, guiding subsequent control logic migration and permission adjustments. It comprises three key components: adjustment priority index, optimization path scheme, and permission update criteria. The generation of the adjustment priority index is a multi-factor decision-making process. The system assesses the risk level of each control node to be optimized, typically derived directly from the node risk probability or threat identification results in the risk feature vector. Simultaneously, it analyzes the node's position in the control network and the importance of the data it processes, assessing the potential impact of its failure or attack on the overall system. Furthermore, factors such as data dependencies between nodes and current load status may also be considered. The system applies a sorting algorithm or weighted scoring model to calculate a priority score or ranking position for each high-risk node, forming an index list that clarifies which nodes require priority processing. The optimization path scheme details the execution of the control logic migration. It requires explicitly specifying the source and target nodes for migration. The selection strategy for the target node is crucial, potentially based on the target node's current load status, geographical distribution, security score, or resource redundancy. The solution also needs to describe the specific migration steps, such as how to replicate control logic state, how to switch control flows, how to synchronize data, and the resource allocation plan that may be needed during the migration process. The permission update standard defines the rules for permission adjustments. It specifies how the operational permissions of different control nodes should be dynamically adjusted based on their risk levels. For example, for extremely high-risk nodes, the standard may require immediately restricting their operational permissions to read-only mode, or requiring any operational instructions to undergo an additional security approval process; for medium-risk nodes, it may only require increasing the detail of operation logs or mandating two-factor authentication. These standards ensure the consistency and standardization of permission adjustments.

[0063] Behavior monitoring results are the outcome of the system tracking, comparing, and analyzing the subsequent behavior of the connector after control optimization. It comprises three core components: behavior pattern shift index, response frequency comparison results, and abnormal activity identifiers. The behavior pattern shift index quantifies the degree of change in the overall connector behavior pattern before and after control optimization. The system first defines a set of features characterizing typical connector behavior, such as the distribution ratio of connection requests at different times of day, the average data throughput of a single connection, and the frequency distribution of specific protocol commands. Before control optimization, the system records the values ​​or distributions of these features in a stable state as a baseline. After optimization, the system recalculates the values ​​or distributions of the same features within the same time period. By calculating the distance metric between the baseline feature vector and the optimized feature vector, such as Euclidean distance, Manhattan distance, or cosine similarity, a quantified shift index value is obtained. The larger the index value, the greater the deviation of the optimized behavior pattern from the original pattern. The response frequency comparison results focus on analyzing changes in operational response frequency. The system selects a set of key operational commands, such as establishing a connection, disconnecting a connection, querying data, and updating configuration. The system counts the number of times the connector successfully responded to these operation commands per unit time before and after the control optimization. It compares the response frequency data between these two time periods, calculating the mean change, percentage change, or analyzing the distribution changes. Simultaneously, the system analyzes whether there are trends of delayed or advanced response times, and the statistical changes in the duration of a single response. These comparative results are presented in structured data or visual charts, clearly demonstrating the impact of optimization on response timeliness. Abnormal activity identification is based on preset rules or anomaly detection models to determine specific connection activities. The system continuously monitors all connection activities of the connector after optimization, applying detection mechanisms to identify activity instances that significantly deviate from expected behavior patterns or known normal patterns. Once abnormal activity is identified, the system marks it and records its characteristics, such as the source address of the anomaly, the target port, the protocol used, the packet size or content characteristics, and the time of occurrence.

[0064] Adaptive response measures are the final output of the system dynamically adjusting its security policies and resource configurations based on behavior monitoring results. It comprises three interoperable components: permission configuration updates, response resource allocation, and a post-update verification mechanism. Permission configuration updates detail the specific modifications required to the operational permissions of connectors flagged as abnormal by behavior monitoring. The system analyzes the frequency and type of risky operations performed by abnormal connectors, assessing the potential business scope impact of permission changes. Based on this assessment and pre-defined security policies, specific permission adjustment instructions are generated. For example, for high-risk connectors frequently attempting illegal operations, update instructions might include: restricting their access to non-critical network resources; prohibiting them from executing certain high-privilege commands; requiring all their operations to undergo real-time administrator approval; or, in extreme cases, temporarily freezing all their operational permissions. Response resource allocation specifies the resource quotas and allocation ratios available to the system's security response components. The system analyzes historical records of computing resources, memory resources, and network bandwidth resources consumed by security events triggered by abnormal connectors, calculates fluctuations in these resource consumption, and analyzes short-term demand trends. Considering the anticipated risk changes after permission configuration updates, the system dynamically adjusts the resource limits allocated to different security response functional modules. For example, increasing the proportion of resources used for deep packet inspection or intrusion prevention; allocating more bandwidth for traffic monitoring in areas where high-risk connectors are located; or reserving more computing instances for real-time alarm analysis tasks. The allocation scheme ensures that critical security response functions have sufficient resource support when needed. The updated verification mechanism describes the connection establishment or operation execution verification process optimized to adapt to new permission settings and resource allocation. The system filters connectors that show a consistently abnormal response frequency in behavior monitoring and checks their current verification mechanism and security level. It determines whether the existing mechanism is consistent with its risk level. For mismatches, the mechanism specifies specific optimization measures. For example, increasing the authentication security level of high-risk connectors, requiring the use of multi-factor authentication or certificate-based strong authentication; increasing the authentication frequency, such as requiring re-authentication before each important operation; introducing a dynamic challenge-response mechanism to trigger additional verification steps when suspicious behavior is detected; or adjusting the session key update strategy to shorten the key validity period to enhance communication security. These measures together constitute a dynamically adjusted security protection system.

[0065] Example 2, see Figure 3 and Figure 4This demonstrates the specific workflow and data processing logic of the intelligent identification module and its internal submodules. The signal acquisition submodule, serving as the data entry point for the entire system, is responsible for capturing two key types of information in real time from the continuously flowing connector status data stream: operational signals and environmental parameters. Operational signals typically refer to electrical or digital command signals generated by the connector during operation, such as switch signals indicating changes in connection or disconnection status, and analog signals reflecting real-time voltage and current values. Environmental parameters encompass monitoring data of the physical environment in which the connector is located, such as temperature sensor readings, humidity sensor readings, and electromagnetic interference intensity—external factors that may affect connector performance. This submodule performs in-depth time-series analysis on the acquired operational signals. This includes calculating the moving average of signal characteristic values ​​within a set sliding time window to observe whether the overall trend is upward, downward, or exhibits periodic fluctuations; simultaneously, it calculates the variance or standard deviation within the window to assess the dispersion and stability of the signal values. For consecutive operational events, the submodule accurately records the timestamp of each event and obtains the time interval sequence between consecutive operations by calculating the difference between adjacent operation timestamps. The analysis of operational fluctuation range focuses on the connector's key performance indicators. For example, within a set observation period, the maximum and minimum success rates of connection establishment are statistically analyzed, and their range is calculated; or the peak and trough values ​​of data transmission rates are recorded to determine their fluctuation range. Anomaly identification of the input-output signal ratio is a crucial step. The system statistically analyzes in real time the number of operation commands input to the connector and the number of commands that the connector actually successfully responds to and outputs valid results, calculating the ratio between the two. This real-time ratio is compared with a baseline ratio range established based on historical normal operation data, or with a pre-set reasonable threshold range. When the real-time ratio consistently falls below the lower limit (indicating a large number of commands not being responded to) or abnormally exceeds the upper limit (indicating abnormally active response), the connector is marked as having a potential abnormal state. Combining the above analysis results—including abnormal time interval sequences, parameter fluctuations exceeding the normal range, and input-output signal ratios deviating from the baseline—the signal acquisition submodule ultimately outputs a comprehensive state fluctuation index, which serves as the starting point for subsequent analysis.

[0066] The connectivity analysis submodule receives status fluctuation indicators from the signal acquisition submodule, which identify which connectors are in states requiring close monitoring. The core task of this submodule is to perform in-depth connectivity behavior analysis on these connectors marked as abnormal. It retrieves historical connection logs and real-time connection event data for these connectors. The time distribution analysis unit performs refined analysis of connection time characteristics. This unit divides the time axis into multiple analysis intervals, which can be of equal length or unequal length depending on business characteristics. Within each defined time interval, the unit counts the total number of connections occurring within that interval. A peak detection algorithm is used to identify the peak points of connection occurrences within each interval. The time distance (interval) between these identified peak points is calculated, and the distribution of these intervals is analyzed. If the interval between peak points is found to be abnormally short or long, or if the peak points occur in atypical time periods, that time period is marked as an abnormal connection period. These time period characteristics are summarized to form time distribution feature data. The fluctuation calculation unit focuses on changes in connection activity within a short-term window. This unit calls the time distribution feature data and selects a preset short-term time window length. Within this short window, the unit counts the number of connections occurring within the window and slides the window along the time axis to form a series of continuous connection count observations. The standard deviation of this series is calculated to measure the magnitude of change in connection counts over a short timescale. This calculated standard deviation is compared to a normal fluctuation range threshold derived from historical normal connection data. If the standard deviation significantly exceeds the upper limit of the normal range, it indicates that connection activity exhibits drastic and unstable fluctuations in the short term, and a high-value connection volatility index is generated. The behavior judgment unit is the final decision-making stage of the connection analysis. This unit receives the connection volatility index and combines it with specific operational signal characteristic information about the connector from the signal acquisition submodule. Using this information, the unit comprehensively analyzes the probability that the current connection behavior pattern is abnormal, through preset rule logic or probability calculation models.

[0067] The parameter fusion submodule is the final output of the intelligent identification module. It integrates information from signal acquisition and connection analysis, and calls upon the connector's historical configuration records to generate a core state feature matrix. This submodule receives connection fluctuation indicators output by the connection analysis submodule. It actively accesses the connector's historical parameter configuration database to retrieve change records for key operating parameters. These parameters may include connection timeout settings, maximum number of retries after connection failure, data transmission buffer size, and the type and version of the encryption protocol used. The submodule counts the number of times these key parameters have been modified within a recent specific period. More importantly, it performs correlation analysis between these parameter change records and the connector's real-time operating status data and the analysis data provided by the connection analysis submodule. Through a fusion algorithm, it calculates the degree of anomaly in parameter changes. This calculation may consider multiple factors: whether the frequency of parameter changes is significantly higher than the historical average; whether the magnitude of parameter changes is excessive; whether the timing of parameter changes has a strong temporal correlation with the occurrence of abnormal connector operating status or connection behavior; and whether the changed parameter values ​​have been exploited by known failure or attack patterns. For example, frequent and significant modifications to the encryption protocol type, especially during periods of sharp drop in connection success rate, would be considered highly abnormal parameter changes. Ultimately, this submodule structurally integrates three core features: connection frequency difference (derived from connection analysis, reflecting abnormal activity), response delay fluctuation (derived from signal acquisition, reflecting abnormal timeliness), and operating signal change rate (derived from signal acquisition, reflecting abnormal signal dynamic characteristics). These three features together constitute a state feature matrix describing the current operating state of the connector, providing crucial input for the subsequent risk assessment module. The entire intelligent identification module completes the entire process from raw data acquisition to structured state feature extraction through the progressive processing of the three submodules: signal acquisition, connection analysis, and parameter fusion.

[0068] Example 3 illustrates the implementation details of the risk assessment module and its internal submodules, focusing on how to locate abnormal control nodes, assess their risk levels, predict security threat probabilities, and ultimately generate risk feature vectors, starting from the state feature matrix. The abnormal operation identification submodule is the starting point of the entire risk assessment process, receiving the state feature matrix from the intelligent identification module as input. This submodule first parses the state feature matrix, filtering out connector instances marked as having abnormal behavior risks. For these abnormal connectors, the submodule extracts their associated detailed operation data records. This operation data typically includes operation type, operation initiator identifier, operation target object, precise operation timestamp, and specific parameter values ​​associated with the operation. The submodule deeply analyzes the correlation between operation time, operation intensity, and control mode. Operation time analysis focuses on the temporal distribution characteristics of operation occurrences. Operation intensity analysis quantifies the resource consumption or impact of the operation, such as calculating the data throughput, CPU time occupied, and memory increment of a single operation, and determining whether its intensity value is within the acceptable range under the current control mode. Calculating the distribution density of abnormal operations involves statistical analysis of spatial and temporal dimensions. The submodule counts the number of abnormal operations occurring in specific network areas, specific types of connectors, or specific time periods, forming a frequency distribution. This distribution can be further categorized and statistically analyzed according to operation type, operation source, operation target, etc., to identify clusters or hotspots of abnormal operations. By setting density thresholds or applying clustering analysis algorithms to identify areas with significantly higher density than the background, the submodule can locate the control nodes behind clustered abnormal operations and generate a set containing the identifiers of these abnormal control nodes.

[0069] The node matching submodule is then invoked, taking as input the set of abnormal control nodes output by the abnormal operation identification submodule. The core task of this submodule is to evaluate the similarity between each control node in the set and known fault event patterns. The submodule first parses the operational behavior characteristics of the target node. This includes extracting the operation sequences (operation types and their order of occurrence) issued by the node over a period of time, the parameter range distribution of the operations, and the time interval patterns of the operations. Based on these characteristics, a feature vector or sequence model that can characterize the typical operation patterns of the node is constructed. Simultaneously, the submodule accesses a pre-built library of known fault event patterns. This library stores historical operational pattern feature data that has been definitively diagnosed as causing system failures or safety events. This feature data is also stored in the form of feature vectors or sequence models, labeled with the corresponding event type and severity. The submodule applies a pattern matching algorithm to calculate the similarity between the operational features of the current target node and each known fault event pattern in the library. For sequence data, a dynamic time warping algorithm may be used to align and calculate the distance between sequences; for feature vectors, cosine similarity, Euclidean distance, or other distance metrics may be calculated. This calculation process generates a similarity score or distance value, quantifying the degree to which the current node's behavior matches each known fault mode. The submodule assesses the current risk level of the control node based on the calculated matching score (or distance), combined with information on the severity and impact of the known fault events. For example, a similarity threshold can be set; nodes with a matching score exceeding the high threshold are rated as high-risk, and those exceeding the medium threshold are rated as medium-risk. Alternatively, a weighted score can be applied based on the matching score and event severity to classify risk levels. Finally, the submodule outputs a quantified risk matching index for each evaluated node, which comprehensively reflects the degree to which the node's behavioral pattern closely resembles a known hazardous mode and its potential harm.

[0070] The threat prediction submodule is the final stage of the risk assessment module, and its core input is the risk matching index generated by the node matching submodule. The goal of this submodule is to predict the probability of a security threat occurring to the target control node in the near future. The frequency extraction unit is responsible for acquiring the operational frequency data of the target node. This unit scans the detailed operation logs of the target node and extracts the precise timestamps of all operational events. These timestamps are arranged in chronological order, and the time interval (Δt) between two adjacent operational events is calculated. This series of time interval values ​​constitutes the sequence data reflecting the changes in the node's operational frequency. The fluctuation range calculation unit focuses on analyzing the volatility of operational frequency on a short time scale. This unit divides the time axis into continuous, fixed-length time blocks. For each time block, the unit counts the total number of operational events occurring within that block. Then, it calculates the rate of change of the number of operations in that block relative to the number of operations in the previous time block, or calculates the standard deviation or range (maximum value - minimum value) of all operational time intervals within that block. This process traverses all time blocks, ultimately generating an index reflecting the severity of fluctuations in node operations within a short period, namely the operational fluctuation range index. A higher value indicates greater operational instability, potentially leading to sudden high-frequency or abnormally low-frequency events. The threat probability prediction unit is the core decision-making unit of the submodule. This unit receives operational volatility index data and risk matching index data for the target node. The unit analyzes the correlation between operational volatility (suddenness, instability) and high-risk matching degree (indicating that its behavior pattern is close to a known harmful pattern). The unit applies a prediction model, based on the currently observed operational volatility index and risk matching index values, to calculate the probability of a security threat event occurring at the node within a specific future time period. This prediction model can be a predefined function mapping or a trained machine learning model. For example, a linear weighted model can be used to calculate the threat probability.

[0071] P threat =α·R match +β·O var

[0072] Where: P threat R represents the predicted probability of the threat occurring. match The risk matching index value output by the node matching submodule represents O. var This represents the operational volatility range index value generated by the volatility range calculation unit. α and β are pre-set weighting coefficients used to adjust the contribution ratio of the risk matching index and operational volatility range to the final threat probability prediction. These coefficients can be set and adjusted based on historical data analysis or expert experience. Finally, the threat probability prediction unit outputs the calculated threat probability value P. threatThe generation of the risk feature vector is the final output of the risk assessment module. This vector integrates three key elements: node risk probability (usually directly using the risk matching index R). match Alternatively, the value after normalization / transformation can be used as a quantification of the inherent risk level of a node; operation and event matching degree (i.e., the similarity score between the node matching submodule and a specific failure event pattern); threat identification result (i.e., the recent threat probability value P calculated by the threat probability prediction unit). threat This structured risk feature vector provides a comprehensive quantitative description of the security risk status of the control node for subsequent control optimization modules.

[0073] Example 4: This example demonstrates the implementation process of the control optimization module and its sub-modules, showcasing its operational logic through a specific application scenario. Assume that in a connector management network of an industrial control system, the risk assessment module identifies several high-risk control nodes, and the control optimization module will perform optimization operations accordingly. The node identification sub-module receives risk feature vectors from the risk assessment module. This sub-module scans all nodes in the entire control network, detecting node instances marked as high-risk by the risk feature vectors. For example, the risk feature vector might show that node "C-101" has a node risk probability as high as 0.92, and the operation and event matching degree indicates that its behavior is highly similar to the known "illegal configuration tampering" event pattern (match degree 0.85). The threat identification result predicts that it has a 0.78 probability of causing a security threat within the next hour. The sub-module analyzes the connector data type processed by this node and finds that it is responsible for processing real-time control command data for critical equipment on the production line, with the data sensitivity level marked as "confidential." Combining the high-threat information in the risk feature vector, the sub-module confirms that node "C-101" poses a clear security threat and includes it in the target scope for optimization. Similarly, node "C-102" was identified as processing device status monitoring data (sensitive level "internal"), but with a risk probability of 0.65, a match rate of 0.45 (close to the "resource exhaustion attack" mode), and a threat probability of 0.55, it was also judged to contain a security threat. After scanning and analyzing all nodes, the submodule finally generated a list of high-risk nodes, as shown in Table 1.

[0074] Table 1: List of High-Risk Nodes.

[0075]

[0076] The optimization analysis submodule uses this list of high-risk nodes as input to analyze the impact and priority of optimization operations. The correlation calculation unit first analyzes the data flow dependencies between nodes in the list and other nodes. For node "C-101", parsing its data flow graph reveals that it receives raw data from sensor nodes "S-001" and "S-002", and outputs processed control commands to actuator nodes "A-100" and "A-101". Simultaneously, the status information of "C-101" is synchronized to monitoring node "M-050". The unit analyzes the degree of dependency of these interactions: actuator nodes "A-100" and "A-101" are highly dependent on the output commands of "C-101", with no alternative path, and the dependency level is rated as "high"; monitoring node "M-050" relies on its status information for display, but this is not critical, and the dependency level is rated as "medium". The unit calculates the weight values ​​of node interactions. For example, the weight for "A-100" and "A-101" is set to 0.8 (high dependency), and the weight for "M-050" is set to 0.3 (medium dependency). This ultimately generates a data association degree index for "C-101," reflecting the range and extent of nodes that its failure may affect. The frequency analysis unit calls this data association information to count the number of interactions between "C-101" and its associated nodes per unit time. Statistics show that the average interaction frequency between "C-101" and "S-001" and "S-002" is 120 times per minute, with a small variance (approximately 5), indicating stable data input; the average interaction frequency with "A-100" and "A-101" is 60 times per minute, with a slightly larger variance (approximately 15); and the interaction frequency with "M-050" is low, averaging 2 times per minute. These statistical results form the interaction frequency index. The priority index unit, considering both data correlation (wide impact, many critical dependent nodes) and interaction frequency (high-frequency interaction, high stability requirements), determined that optimizing node "C-101" has a large impact and high urgency. In contrast, node "C-102" has lower data correlation (mainly affecting status monitoring display), moderate interaction frequency, and low variance. Node "C-203" has moderate data correlation (affecting some report generation) and low interaction frequency. Therefore, the unit calculates the adjustment priority ranking: C-101 > C-203 > C-102, generating an adjustment priority index.

[0077] The control reconfiguration submodule, based on the priority index, begins planning and executing control logic migration and permission adjustments for the highest-priority node "C-101". This submodule analyzes the control logic paths of node "C-101" and other nodes. Currently, "C-101" is the core node of the control chain. The submodule plans an optimized path: migrating the control logic of "C-101" to node "S-205" (a backup safety node), which currently has lower load, higher safety score, and redundancy capabilities. The migration plan details the steps as follows: First, initialize the same control logic environment on "S-205"; second, copy the current state machine snapshot of "C-101" to "S-205"; then, gradually switch the data input streams of sensors "S-001" and "S-002" to "S-205"; next, point the control command receiving endpoints of actuators "A-100" and "A-101" to "S-205"; finally, disconnect "C-101" and place it in a pending inspection state. During the migration process, additional computing resources need to be allocated to "S-205" to handle the increased load, and network bandwidth needs to be reserved to ensure smooth command transmission during the switchover. Simultaneously, according to the permission update standard, the submodule adjusts the permissions of "C-101" before the migration: its configuration modification permission is downgraded to "read-only," prohibiting it from initiating new control commands and only allowing it to respond to status queries. For nodes "C-203" and "C-102," planning is also done based on their priority indexes and respective optimized path schemes (and permission update standards). Finally, the control refactoring submodule outputs a control parameter configuration set, which contains specific optimization measures for each high-risk node: for "C-101," it includes adjusting the priority index (highest), optimizing the path scheme (detailed steps and resource allocation for migration to S-205), and setting the permission update standard (downgrading to read-only); for "C-203" and "C-102," it similarly includes their corresponding priority indexes, optimization schemes, and permission adjustment rules. This configuration set provides a clear set of instructions for subsequent behavior monitoring and actual execution.

[0078] Example 5 illustrates the implementation of the behavior monitoring module and adaptive response module, focusing on the continuous tracking of connector behavior, anomaly identification, and dynamic adjustment of the system's response strategy after the implementation of control optimization measures. The pattern analysis submodule of the behavior monitoring module first receives a set of control parameter configurations from the control optimization module. This submodule then retrieves detailed behavior log data of the target connector after the control optimization measures take effect. To evaluate the optimization effect and monitor subsequent behavior, the submodule systematically compares the connector activity characteristics over a period after optimization with the baseline activity characteristics recorded in a stable state before optimization. These compared activity characteristics aim to characterize typical connector behavior patterns, including but not limited to: the time distribution pattern of connection request initiation, the duration distribution of a single connection session, the statistical characteristics of data transmission volume, and the frequency of use of specific application layer protocol commands. The submodule analyzes the magnitude of these characteristics' changes in the two time periods before and after optimization, such as calculating the change in the average value or median shift of a certain characteristic; it also analyzes the frequency or trend of changes, such as observing whether the changes occur instantaneously or evolve gradually. By calculating the difference measure between the baseline feature vector (representing behavior before optimization) and the observed feature vector after optimization, the submodule outputs a quantified behavior pattern shift index. The magnitude of this index directly reflects the degree to which control optimization alters the overall behavior of the connector; a larger value indicates a more significant deviation from the original pattern.

[0079] The response comparison submodule, based on the behavior pattern shift index output by the pattern analysis submodule, further focuses its analysis on the key indicator of operation response frequency. This submodule selects a set of core operation commands crucial to system functionality or security as its analysis object, such as connection establishment request commands, connection termination commands, specific data query commands, and critical configuration update commands. The submodule statistically analyzes the number of times the target connector successfully responds to these core operation commands within a baseline time period defined before the implementation of control optimization measures, and within a time period of the same length after the implementation of optimization measures; this is the response frequency. By comparing the response frequency data within these two time periods, the submodule calculates the change in its mean, percentage change, or analyzes the changes in its overall distribution. In addition to frequency comparison, the submodule also analyzes changes in the temporal characteristics of the response: checking whether there is an overall trend of delayed or advanced response times; analyzing whether the statistical characteristics of the duration of a single response operation have changed; and observing whether the distribution of response events on the time axis has become more concentrated or dispersed. These comparative results, whether statistical differences in response frequency or changes in response temporal characteristics, are comprehensively organized and structured into a response frequency fluctuation index, clearly demonstrating the impact of control optimization on the connector's response timeliness and behavioral rhythm.

[0080] The anomaly localization submodule is the final stage of the behavior monitoring module. Its inputs include response frequency fluctuation indicators and broader behavioral pattern characteristic change data provided by the pattern analysis submodule. The core task of this submodule is to identify and locate specific abnormal connection activities. It continuously scans all connection activity records of the optimized connector using pre-defined rule-based detection logic or more complex anomaly detection algorithms. Once a connection activity instance is identified as significantly deviating from the normal behavioral pattern defined based on the initial stable data after optimization or pre-defined rules in key characteristics, the activity is marked as anomaly. The submodule analyzes in detail the specific pattern characteristics exhibited by these marked abnormal activity instances, such as the source network address of the anomaly, the target service port of the anomaly, the abnormal communication protocol characteristics used, the abnormal packet size or content characteristics, and the specific time point of the activity. The submodule attempts to match and correlate these abnormal activity characteristics with a known threat signature database maintained by the system. The threat signature database may contain signature patterns of known attacks, precursor patterns of known failures, or malware communication characteristics. The matching process may use string matching, regular expression matching, or machine learning classifiers. Through feature matching and context analysis, the submodule infers the type of security threat that the abnormal activity may be associated with and attempts to locate the possible source of the threat. Finally, the submodule outputs behavior monitoring results, which include quantitative data on behavior pattern deviation index and response frequency fluctuation index, and most importantly: a list of specifically identified abnormal activity instances and their characteristic descriptions, inferences on associated threat types, and source location information.

[0081] The adaptive response module's permission adjustment submodule receives behavior monitoring results as trigger input. This submodule first analyzes the list of connector instances explicitly identified as abnormal in the results, calculating the frequency and type of risky operations performed by each abnormal connector during the monitoring period. Risky operations may include frequent connection failure attempts, attempts to access unauthorized resources, and execution of sensitive configuration modification commands. The submodule assesses the potential business scope and user impact of implementing permission change operations on these abnormal connectors. Based on this impact assessment, preset security policy rules, and the identified risk level of the connector, the submodule generates specific permission configuration update commands. For example, for a high-risk connector identified as frequently attempting unauthorized access with a threat type inferred as "unauthorized data access," the permission update command might include: strictly restricting its network access scope, allowing access only to necessary, non-critical business servers; prohibiting it from executing any configuration modification or data write commands; and requiring all operations initiated by it to be approved by the administrator in real-time online before execution. For a connector identified as having occasional abnormal connections but a medium threat level, the command might only require increasing the detail of its operation logs or mandating additional two-factor authentication before performing specific sensitive operations. The permission adjustment submodule outputs these specific permission configuration update instruction sets.

[0082] The resource allocation submodule is then invoked, its input being the permission configuration update information generated by the permission adjustment submodule. This submodule retrieves system resource data stored in the history logs, detailing the system resources consumed by security response events triggered by these anomalous connectors in the past. This resource data includes CPU computation time, memory usage, and network bandwidth resources consumed in processing the events. The submodule calculates the fluctuation range of these resource consumptions over the historical observation period, such as maximum, minimum, and standard deviation, to understand the volatility of resource demand. Simultaneously, it analyzes recent trends in resource consumption, for example, using linear regression analysis to determine whether resource demand is increasing, decreasing, or stable. The submodule determines whether the current resource quota limit allocated to the security response function components matches the connector's needs under normal behavior and the expected risk changes after permission adjustments. For example, if analysis shows that deep packet inspection tasks triggered by a certain type of anomalous connector have frequently caused CPU resource overload in the past week, and the monitoring intensity of this type of connector is expected to increase after permission adjustments, the submodule will increase the CPU core quota limit allocated to deep packet inspection tasks. The submodule dynamically adjusts the resource limits allocated to different security response functional modules based on the analysis results, and clearly defines the proportion of response resources that should be used by connectors with different security event types or risk levels. For example, it may stipulate that events from extremely high-risk connectors receive the highest priority resource allocation to ensure that their events are processed immediately; while medium- and low-risk events share a resource pool. Finally, the submodule outputs the optimized response resource allocation strategy, specifying resource quotas and allocation ratios.

[0083] The verification update submodule is the final stage of the adaptive response module, and its input is the optimized response resource allocation strategy output by the resource allocation submodule. This submodule first filters out connector instances whose response frequency consistently shows abnormalities in the behavior monitoring results. For these filtered connectors, the submodule extracts their historical connection verification records, including the authentication method used, the success rate of authentication attempts, the use of multi-factor authentication, and the distribution of reasons for authentication failures. The submodule analyzes the security level of the verification mechanism currently configured for these abnormal connectors to determine whether this security level matches the current risk level determined in the behavior monitoring results. For example, a connector determined to be high-risk is considered mismatched if it is still using simple static password authentication. For identified mismatches, the submodule generates specific verification mechanism optimization instructions. Optimization measures may include: increasing the authentication security level, such as upgrading from static password to two-factor authentication (digital certificate plus one-time password); increasing the authentication frequency, such as forcing re-authentication before each important operation; introducing a dynamic challenge-response mechanism to trigger additional verification steps when suspicious behavior patterns are detected; or adjusting the session security policy, such as shortening the validity period of the session key and increasing the key update frequency to reduce the risk of session hijacking. Finally, the verification update submodule outputs the updated connection verification mechanism rule set. The adaptive response module integrates the permission configuration update instruction set from the permission adjustment submodule, the optimized response resource allocation strategy from the resource allocation submodule, and the updated verification mechanism rule set from the verification update submodule, together forming a complete adaptive response measure set to dynamically adjust the system security policy to respond to monitored abnormal behavior.

[0084] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0085] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An automatic connector control system based on intelligent recognition, characterized in that, The system includes: The intelligent identification module collects operation signals and environmental parameters from the connector status data stream, analyzes the change patterns of the connector's operating status, calculates the connection frequency and response delay, compares the change patterns of the operating status with the connection frequency, judges the risk of abnormal behavior of the connector, and generates a status feature matrix. Based on the state feature matrix, the risk assessment module locates the control node of the abnormal connector, analyzes the matching relationship between the control node and the fault event, assesses the risk probability of the node, calculates the correlation between the operation frequency and the fault event, predicts the security threat to the control node, and generates a risk feature vector. Based on the risk feature vector, the control optimization module identifies high-risk control nodes, analyzes the distribution of operational data, calculates adjustment priorities, plans optimization paths, migrates control logic to low-risk nodes, and obtains a set of control parameter configurations. Based on the control parameter configuration set, the behavior monitoring module compares the operation response frequency of the connector before and after adjustment, identifies abnormal connection activities, judges the abnormal characteristics of the connection behavior pattern, locates the source of security threats, and generates behavior monitoring results. Based on the behavior monitoring results, the adaptive response module identifies abnormal operation connectors, adjusts operation permission configurations, allocates response resource ratios, updates connection verification mechanisms, and generates adaptive response measures. The risk assessment module includes: The abnormal operation identification submodule filters the operation data of abnormal connectors based on the state feature matrix, analyzes the correlation between operation time and intensity and control mode, calculates and classifies the distribution density of abnormal operations, identifies abnormal control nodes, and generates a set of abnormal control nodes. The node matching submodule calls the set of abnormal control nodes, parses the characteristics of the operation behavior, compares the patterns of known fault events, calculates the degree of matching between the node and the fault event, assesses the risk level of the control node, and generates a risk matching index. The threat prediction submodule analyzes the operation frequency of abnormal nodes based on the risk matching index, extracts the operation time interval, calculates the operation fluctuation range within a short period, predicts the probability of a security threat based on the risk matching degree of the node, and generates a risk feature vector.

2. The connector automatic control system based on intelligent recognition according to claim 1, characterized in that, The state feature matrix includes connection frequency differences, response delay fluctuations, and operation signal change rates; the risk feature vector includes node risk probability, operation and event matching degree, and threat identification results; the control parameter configuration set includes priority index adjustment, path optimization scheme, and permission update standard; the behavior monitoring results include behavior pattern offset index, response frequency comparison results, and abnormal activity identifiers; and the adaptive response measures include permission configuration update, response resource allocation, and post-update verification mechanism.

3. The connector automatic control system based on intelligent recognition according to claim 1, characterized in that, The intelligent recognition module includes: The signal acquisition submodule acquires the operation signals and environmental parameters in the connector status data stream, analyzes the timing changes of the operation signals, calculates the time interval between consecutive operations, statistically analyzes the fluctuation range of the operating status, compares the input and output signal ratios, identifies abnormal connectors, and obtains status fluctuation indicators. Based on the state fluctuation index, the connection analysis submodule retrieves the connection data of abnormal state connectors, analyzes the distribution characteristics of the number of connections in a specific time period, calculates the degree of connection fluctuation of the connector in the short term, determines whether the connector has abnormal connection behavior, and obtains the connection fluctuation index. Based on the connection fluctuation index, the parameter fusion submodule calls the connector's parameter configuration record, counts the number of parameter changes, and calculates the degree of abnormality of parameter changes by combining the connector's operating status and connection analysis data, generating a status feature matrix.

4. The connector automatic control system based on intelligent recognition according to claim 1, characterized in that, The control optimization module includes: Based on the risk feature vector, the node identification submodule detects high-risk nodes in the control network, analyzes the connector data type and sensitivity level processed by the nodes, filters control nodes containing security threats, determines the range of control nodes that need to be optimized, and obtains a list of high-risk nodes. Based on the list of high-risk nodes, the optimization analysis submodule analyzes the data interaction among the affected nodes, calculates the degree of data correlation and interaction frequency between nodes, determines the scope and priority of the control optimization, and generates an adjustment priority index. Based on the adjusted priority index, the control reconfiguration submodule analyzes the control logic paths between nodes, allocates control resources, plans the optimal optimization path, and adjusts the access permissions of security control nodes to obtain a set of control parameter configurations.

5. The connector automatic control system based on intelligent recognition according to claim 1, characterized in that, The behavior monitoring module includes: Based on the control parameter configuration set, the pattern analysis submodule calls the behavior log of the adjusted connector, compares the connector activity characteristics before and after the adjustment, analyzes the magnitude and frequency of behavior changes, calculates the degree of behavior pattern shift, and obtains the behavior pattern shift index. The response comparison submodule compares the operation response frequency of the connector before and after adjustment based on the behavior pattern offset index, analyzes the changes in response time, response duration and response frequency, judges the fluctuation of response frequency, and generates a response frequency fluctuation index. Based on the response frequency fluctuation index, the anomaly localization submodule identifies connection activities that deviate from the normal pattern, analyzes the characteristics of abnormal connector behavior patterns, matches the relationship between connection activity characteristics and known threats, locates the source of security threats, and generates behavior monitoring results.

6. The connector automatic control system based on intelligent recognition according to claim 1, characterized in that, The adaptive response module includes: Based on the behavior monitoring results, the permission adjustment submodule analyzes the frequency of risky operations of connectors, calculates the impact range of permission changes, identifies connectors with frequent abnormal operations, reconfigures operation permissions, implements operation restrictions on high-risk connectors, and generates permission configuration updates. The resource allocation submodule calls the permission configuration update, calls the response record of the abnormal connector, calculates the fluctuation range of the response resources, analyzes the short-term trend of response resource changes, judges the degree of deviation of the response resources from the normal behavior of the connector, adjusts the upper limit of the response resources, allocates the proportion of response resources, and generates optimized response resources. The verification update submodule, based on the optimized response resources, filters connectors with abnormal response frequencies, extracts the verification history of the connectors, analyzes the security level of the abnormal connector verification, determines whether the verification matches the risk level, optimizes the connection verification mechanism, and generates adaptive response measures.

7. The connector automatic control system based on intelligent recognition according to claim 3, characterized in that, The connection analysis submodule includes: Based on the state fluctuation index, the time distribution analysis unit segments the time intervals of the connection data, analyzes the peak number of connections in each interval, calculates the distance difference between the peaks, identifies abnormal connection time periods, and generates time distribution features. The fluctuation calculation unit calls the time distribution characteristics to statistically analyze the change in the number of connections of the connector within a preset short window, calculates the standard deviation of the number of connections, determines whether the standard deviation exceeds the normal range, and generates a connection fluctuation index. The behavior judgment unit analyzes the probability of abnormal connection behavior based on the connection fluctuation index and the operating signal characteristics of the connector, and generates a connection fluctuation index.

8. The connector automatic control system based on intelligent recognition according to claim 1, characterized in that, The threat prediction submodule includes: Based on the risk matching index, the frequency extraction unit scans the operation logs of abnormal nodes, extracts the operation timestamp sequence, calculates the time difference between adjacent operations, and generates an operation frequency sequence. The fluctuation range calculation unit calls the operation frequency sequence, divides short-cycle time blocks, calculates the rate of change of the number of operations in each time block, and generates an operation fluctuation range index. The threat probability prediction unit analyzes the potential probability of security threats based on the operational fluctuation range index and the risk matching degree data of the nodes, and generates a risk feature vector.

9. The connector automatic control system based on intelligent recognition according to claim 4, characterized in that, The optimization analysis submodule includes: Based on the list of high-risk nodes, the correlation calculation unit maps the data flow between nodes, analyzes the degree of dependence of data interaction, calculates the weight value of node interaction, and generates a data correlation index. The frequency analysis unit calls the data correlation index to count the number of interactions between nodes within a unit of time, calculates the average and variance of the number of interactions, and generates an interaction frequency index. The priority indexing unit integrates the scope of influence and priority order based on the data correlation index and interaction frequency index to generate an adjusted priority index.

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