A cross-domain data access trusted control system and method

Abstract

The present invention discloses a cross-domain data access trusted control system and method, which relates to the field of data security technology. The method collects energy dynamic coupling data in real time by setting up collection points and implementing edge gateway devices in the power grid system. The method can effectively monitor the energy exchange of the power grid system and perform preprocessing in real time. By standardizing the energy dynamic coupling data, an accurate standard energy dynamic coupling data set can be generated to provide reliable data support for the subsequent regional energy dynamic coupling intensity index ECI and data access disturbance response score DSR. This process combines multi-dimensional data of energy fluctuations, dispatch response offsets and disturbance spectra, which can help the power grid dispatch system accurately identify potential risks, thereby reducing dispatch errors and system crashes caused by data access errors, and effectively improving the security and stability of the power grid system.

Description

Technical Field

[0001] The present invention relates to the field of data security technology, and in particular to a cross-domain data access trusted control system and method. Background Art

[0002] Cross-domain data access trusted control methods belong to information security and power system management. In current power systems, with the widespread application of smart grids and regional power dispatching systems, the demand for cross-domain data exchange and access is increasing. Specifically, the data exchange involved in the power system not only covers real-time data such as dispatch instructions, load forecasts, and wind power forecasts, but also includes information at multiple levels such as equipment status monitoring and energy trading information. In order to ensure the accuracy of power dispatch and the stability of the system, the credibility of data access behavior must be ensured. Therefore, cross-domain data access trusted control methods have emerged, aiming to strictly control and manage data exchange across regions and between different domains in the power dispatching system, ensuring the security of system operation and the reliability of data exchange.

[0003] The current status of cross-domain data access mainly faces the problems of data contamination and decision conflicts. Especially in the field of power dispatching, the process of inter-regional energy exchange often leads to cross-contamination of data between different regions. For example, when the energy supply and demand in a region fluctuate, the dispatching system must rely on real-time data from other regions to balance it. However, due to the different control logic and decision-making standards of the dispatching systems in different regions, conflicts may arise during the data exchange process, resulting in data distortion or scheduling decision conflicts. Existing access control methods generally do not fully consider the impact of the actual physical state of the power grid energy flow on data exchange and collaborative decision-making, resulting in imprecise data access control and an inability to effectively avoid potential risks.

[0004] In power systems, energy exchange interference is not fully considered in cross-domain data access, which often leads to inconsistent decisions or uncoordinated scheduling during energy exchange, triggering a series of abnormal consequences. For example, in the data exchange process of wind power and load scheduling, because the scheduling system in a certain area does not take into account the complexity of energy fluctuations, this may cause the energy exchange data in that area to change frequently, while the control system in another area fails to respond to this change in a timely manner, resulting in delayed or invalid decision-making. In addition, data cross-contamination may affect the accuracy of model predictions and even cause the erroneous execution of scheduling instructions, resulting in reduced security of the power grid or decreased energy utilization efficiency. Therefore, it is necessary to enhance cross-domain access control methods through innovative energy exchange interference analysis to ensure the stable operation of the scheduling system and reduce the potential risks of the system caused by data exchange. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides a cross-domain data access trusted control system and method, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: comprising the following steps:

[0007] S1. Set up collection points in the power grid system and set up collection tools at the collection points to collect energy dynamic coupling data in real time. Set up edge gateway devices at the collection points to pre-process the energy dynamic coupling data and obtain a standard energy dynamic coupling data set.

[0008] S2. Based on the standard energy dynamic coupling data set, calculate and output the regional energy dynamic coupling intensity index ECI, and conduct preliminary comparative assessment and regional access risk level classification based on the output results;

[0009] S3. Trigger access intervention based on preliminary comparative assessment, start the collection point replenishment mechanism, collect and obtain the standard access disturbance data set, and calculate the output data access disturbance response score DSR;

[0010] S4. Perform comprehensive calculation based on the data access disturbance response score DSR and the energy dynamic coupling intensity index ECI, output the access risk level judgment score Vpr, and then perform a secondary comparative evaluation based on the output result, and execute the access control policy based on the secondary comparative evaluation result.

[0011] Preferably, said S1 includes S11;

[0012] S11. Initially set up three collection points in the control system of the power grid system, and embed a collection tool in the three collection points. When bidirectional energy is supplied between the power grid systems, start the collection tool to collect energy dynamic coupling data in real time.

[0013] The collection points include A1 collection point, A2 collection point and A3 collection point;

[0014] The collection tools include PQ analyzer, high-frequency electric energy meter, EMS control system log collector, and frequency disturbance identification instrument;

[0015] The energy dynamic coupling data includes the power fluctuation value Pfluc(t) at time t, the scheduling parameter response offset Odes, and the disturbance spectrum intermediate frequency value Wper.

[0016] Preferably, said S1 further includes S12;

[0017] S12. Setting an edge gateway device, and integrating and connecting the edge gateway device with the collection point through the built-in data collection interface module and network communication module of the edge gateway device, using the collection protocol and the 5G communication network, and transmitting the energy dynamic coupling data to the edge gateway device;

[0018] Preprocess the energy dynamic coupling data in the edge gateway device to obtain a standard energy dynamic coupling data set;

[0019] The preprocessing includes denoising and outlier elimination, scale unification and normalization, and timestamp alignment;

[0020] The denoising and outlier elimination may use bandpass filtering to identify and eliminate drift data and noise points in the energy dynamic coupling data;

[0021] The unified scale and normalization are performed by performing standard normalization processing on the energy dynamic coupling data by using Z-score normalization to eliminate the dimensional influence between all parameters in the energy dynamic coupling data;

[0022] The timestamp alignment is performed by using a synchronization timestamp correction to align the timestamps of the scheduling instruction and the response.

[0023] Preferably, said S2 includes S21;

[0024] S21. By extracting the standard energy dynamic coupling data set, triggering an integral window operation every 5 minutes, and outputting the regional energy dynamic coupling intensity index ECI, it measures the data access interference intensity of the current control system area per unit time.

[0025] Preferably, said S2 further includes S22;

[0026] S22. Based on the output of the regional energy dynamic coupling intensity index (ECI), conduct a preliminary comparative assessment to determine the risk of energy and control coupling during access interactions between current power grid systems. Based on the preliminary comparative assessment results, classify regional access risk levels, and then trigger access intervention based on the regional access risk classification results. The specific assessment contents are as follows;

[0027] When the regional energy dynamic coupling intensity index ECI is less than 0.33, it means that the current control system is normally affected by energy disturbances. At this time, there is no interference risk in cross-regional data access. At this time, the current disturbance risk level is classified as Level I, and access is open without restriction.

[0028] When 0.33≤Regional Energy Dynamic Coupling Intensity Index ECI<1, it indicates that the current control system is affected by energy disturbances. In this case, the current disturbance risk level is classified as Level II. A rate limit instruction is sent to the power grid system through the edge gateway device. The upper limit of the access frequency is limited to 80% of the current access frequency. If the impact persists for five consecutive evaluation cycles, the system is automatically upgraded to Level III.

[0029] When the regional energy dynamic coupling intensity index ECI ≥ 1, it means that the current control system is abnormally disturbed by energy. At this time, the current disturbance risk level is classified as level III, access isolation is immediately performed, and access intervention is triggered.

[0030] Preferably, said S3 includes S31;

[0031] S31. After the preliminary comparative assessment triggers access intervention, a collection point supplement mechanism is initiated. The collection point supplement mechanism collects access disturbance data in real time by setting up additional collection points and setting up collection devices at the collection points. The collection devices are integrated with edge gateway devices to transmit the access disturbance data to the edge gateway devices and perform preprocessing to obtain a standard access disturbance data set.

[0032] The supplement includes A4 collection point, A5 collection point and A6 collection point;

[0033] The collection device includes an API access log collector, a topology perception module, a data access link tracking module, and a parameter scheduling analysis engine;

[0034] The standard access perturbation dataset includes the cross-domain request intensity vector Ocall, the topological cascade propagation depth Ttopo, and the parameter scheduling kurtosis Aconf.

[0035] Preferably, the S3 further includes S32;

[0036] S32. Based on the standard access disturbance data set, a joint disturbance factor is formed by multiplication, and the joint disturbance factor is integrated to output the data access disturbance response score DSR, which reflects the degree of influence of each cross-domain access on the control link disturbance of the control system.

[0037] Preferably, the S4 includes S41;

[0038] S41. Based on the product of the data access disturbance response score DSR and the energy dynamic coupling intensity index ECI, and then calculating the ratio with the access recovery capability of the control system, the access risk level judgment score Vpr is output to measure the comprehensive disturbance risk score of the access request under the current regional control system status.

[0039] Preferably, the S4 further includes S42;

[0040] S42. Based on the output of the access risk level determination score Vpr, a secondary comparative evaluation is performed to determine the risk level of the access behavior. Based on the secondary comparative evaluation result, a response access trust control policy is executed. The specific evaluation contents are as follows:

[0041] When the access risk level score Vpr is less than 0.2, the L1 policy is executed. At this time, all cross-domain requests are fully allowed to access the control system data and core resources without any restrictions, and logging and monitoring of all access are enabled;

[0042] When 0.2≤Access Risk Level Determination Score Vpr<0.6, the L2 strategy is executed, which limits the access frequency of the current control system to 50% and the number of accesses to a maximum of 5 requests per 10 minutes;

[0043] When the access risk level score Vpr ≥ 0.6, the L3 policy is executed, and the data isolation layer isolation and access credibility fallback mechanism are implemented;

[0044] The data isolation layer divides the data into core prediction data, neutral scheduling information and public auxiliary information by labeling the data in a hierarchical manner, and implements an isolation method on the isolation layer, which includes field masking, middleware forwarding terminal and prediction vector precision reduction to the decimal point;

[0045] The access credibility fallback mechanism prohibits access if the L3 policy is evaluated to be executed for three consecutive cycles.

[0046] A cross-domain data access trusted control system includes an energy coupling data acquisition module, an energy coupling intensity analysis module, an access intervention analysis module, and an access disturbance control module;

[0047] The energy coupling data acquisition module collects energy dynamic coupling data in real time by setting up collection points in the power grid system and setting up collection tools in the collection points. It also sets up edge gateway devices at the collection points to pre-process the energy dynamic coupling data and obtain a standard energy dynamic coupling data set.

[0048] The energy coupling intensity analysis module calculates and outputs the regional energy dynamic coupling intensity index ECI based on the standard energy dynamic coupling data set, and performs preliminary comparative evaluation and regional access risk level classification based on the output results;

[0049] The access intervention analysis module triggers access intervention based on preliminary comparative evaluation, starts the collection point replenishment mechanism, collects and obtains the standard access disturbance data set, and calculates and outputs the data access disturbance response score DSR;

[0050] The access disturbance control module performs comprehensive calculation based on the data access disturbance response score DSR and the energy dynamic coupling intensity index ECI, outputs the access risk level judgment score Vpr, then performs a secondary comparative evaluation based on the output result, and executes the access control strategy based on the secondary comparative evaluation result.

[0051] The present invention provides a cross-domain data access trusted control system and method. It has the following beneficial effects:

[0052] (1) This method collects energy dynamic coupling data in real time by setting up collection points and implementing edge gateway devices in the power grid system. This method can effectively monitor the energy exchange of the power grid system and perform preprocessing in real time. By standardizing the energy dynamic coupling data, an accurate standard energy dynamic coupling data set can be generated, providing reliable data support for the subsequent regional energy dynamic coupling intensity index (ECI) and data access disturbance response score (DSR). This process combines multi-dimensional data of energy fluctuations, dispatch response offsets, and disturbance spectra, which can help the power grid dispatch system accurately identify potential risks, thereby reducing dispatch errors and system crashes caused by data access errors, and effectively improving the security and stability of the power grid system.

[0053] (2) This method can flexibly adjust cross-domain data access strategies under different risk levels through risk assessment based on the regional energy dynamic coupling intensity index ECI and the data access disturbance response score DSR. In low-risk conditions, full access is achieved, while in medium- and high-risk conditions, the access frequency is limited by speed limits, number limits, and other methods. In particular, when the regional energy dynamic coupling intensity index ECI reaches a high-risk level, access intervention is performed based on the data access disturbance response score DSR to prevent unnecessary access requests from putting pressure on the power grid dispatching system. Through intelligent evaluation and dynamic regulation, this strategy maximizes resource utilization efficiency, avoids excessive data exchange load, and enhances the rapid response and adjustment capabilities of the power grid system, ensuring stable operation even in high-risk situations.

[0054] (3) In the power grid system, energy exchange between regions may lead to data cross-contamination and decision conflicts, thus affecting the accuracy of scheduling decisions. By introducing the access risk level judgment score Vpr, this method can determine whether access isolation is needed based on the exchange frequency and critical threshold analysis, thereby effectively reducing the possibility of decision conflicts and system instability during cross-domain data access. By reversely controlling data flow permissions through energy flow data, access intervention will be automatically triggered when data exchange conflicts or inconsistencies occur, ensuring system coordination and consistency, avoiding potential over-dependence and erroneous scheduling, and effectively improving the decision reliability and operational stability of the power grid scheduling system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a schematic diagram of the steps of a cross-domain data access trusted control method of the present invention;

[0056] Figure 2 This is a flow chart of a cross-domain data access trusted control system of the present invention;

[0057] Figure 3 Processing diagram for edge gateway devices. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0059] Example 1: Please refer to Figure 1 and Figure 3 The present invention provides a cross-domain data access trusted control method. To achieve the above purpose, the present invention is implemented through the following technical solutions: comprising the following steps:

[0060] S1. Set up collection points in the power grid system and set up collection tools at the collection points to collect energy dynamic coupling data in real time. Set up edge gateway devices at the collection points to pre-process the energy dynamic coupling data and obtain a standard energy dynamic coupling data set.

[0061] S2. Based on the standard energy dynamic coupling data set, calculate and output the regional energy dynamic coupling intensity index ECI, and conduct preliminary comparative assessment and regional access risk level classification based on the output results;

[0062] S3. Trigger access intervention based on preliminary comparative assessment, start the collection point replenishment mechanism, collect and obtain the standard access disturbance data set, and calculate the output data access disturbance response score DSR;

[0063] S4. Perform comprehensive calculation based on the data access disturbance response score DSR and the energy dynamic coupling intensity index ECI, output the access risk level judgment score Vpr, and then perform a secondary comparative evaluation based on the output result, and execute the access control policy based on the secondary comparative evaluation result.

[0064] In this embodiment, in a cross-regional energy supply scenario, this method establishes multiple collection points and deploys collection tools and edge gateway devices to collect raw energy dynamic coupling data reflecting energy disturbances at high frequency. Preprocessing methods such as denoising, normalization, and time synchronization are then used to construct a unified, standardized energy dynamic coupling dataset. Based on this, the constructed regional energy dynamic coupling intensity index (ECI) is used to quantitatively assess the current degree of coupling disturbance in the power grid and initially determine the access risk level. When the assessment indicates a medium or high risk, an access intervention mechanism is automatically triggered, introducing access disturbance collection points and forming a standardized access disturbance dataset. A data access disturbance response score (DSR) is then calculated to reflect the potential interference risk of a specific access behavior on the control system. By integrating the DSR with the ECI and combining it with historical access recovery capability indicators, an access risk level determination score (Vpr) is calculated. This score, used as the core criterion, is used to implement a multi-level access control policy, enabling dynamic responses to cross-domain access behavior, including precise restriction, isolation control, or full access. In practical applications, this method can effectively address common cross-domain access issues such as control instability, data contamination, and decision conflicts. It not only improves the credibility and selectivity of data access, but also strengthens the control system's risk identification and responsiveness in multi-region data interaction. Ultimately, this method achieves a closed-loop control mechanism where "data access behavior is constrained by the physical energy state," enabling the power control system to achieve greater operational stability, security robustness, and strategic adaptability in complex dynamic environments. This provides a solid technical foundation for trusted cross-domain data collaboration in future smart grids.

[0065] Example 2: Please refer to Figure 1 and Figure 3 , specifically: S1 includes S11;

[0066] S11. Initially set up three collection points in the control system of the power grid system, and embed a collection tool in the three collection points. When bidirectional energy is supplied between the power grid systems, start the collection tool to collect energy dynamic coupling data in real time.

[0067] The collection points include A1 collection point, A2 collection point and A3 collection point;

[0068] Collection tools include PQ analyzers, high-frequency energy meters, EMS control system log collectors, and frequency disturbance identifiers;

[0069] Energy dynamic coupling data includes the power fluctuation value Pfluc(t) at time t, the scheduling parameter response offset Odes, and the disturbance spectrum intermediate frequency value Wper;

[0070] The power fluctuation value Pfluc(t) at time t is collected in real time using an A1 collection point, which is set on the busbar or outgoing line side of a 110 kV or higher trunk substation at the regional intersection. This is collected using a PQ analyzer in conjunction with a high-frequency energy meter. This reflects the time-varying rate of inter-regional exchange power and captures energy disturbance behavior.

[0071] The dispatch parameter response offset Odes is set up in the regional control center or power grid control room through the A2 collection point and deployed in the middleware layer between SCADA and AGC. The EMS control system log collector is used to intercept operation instructions and response data in real time, analyze the difference between the current operation instructions and response data, and use it to analyze the difference between the dispatch instructions and execution data within the control system.

[0072] The intermediate frequency value Wper of the disturbance spectrum is set at the low-voltage side of the main transformer, the capacitor switching circuit and the power plant busbar measurement point through the A3 acquisition point, and is acquired in real time through the frequency disturbance identification instrument.

[0073] S1 also includes S12;

[0074] S12. Setting an edge gateway device, and integrating and connecting the edge gateway device with the collection point through the built-in data collection interface module and network communication module of the edge gateway device, using the collection protocol and the 5G communication network, and transmitting the energy dynamic coupling data to the edge gateway device;

[0075] Preprocess the energy dynamic coupling data in the edge gateway device to obtain a standard energy dynamic coupling data set;

[0076] Preprocessing includes denoising and outlier removal, scaling and normalization, and timestamp alignment;

[0077] Denoising and outlier elimination: using bandpass filtering to identify and eliminate drifting data and noise points in energy dynamic coupling data.

[0078] Unified scale and normalization: By using Z-score normalization on the energy dynamic coupling data, standard normalization is performed to eliminate the dimensional effects between all parameters in the energy dynamic coupling data;

[0079] Timestamp alignment aligns the timestamps of scheduling instructions and responses by using synchronized timestamp correction.

[0080] In this embodiment, this method pre-defines collection points A1, A2, and A3 in the power grid control system, deploying a PQ analyzer, a high-frequency energy meter, an EMS control system log collector, and a frequency disturbance identifier. This enables real-time collection of multidimensional energy dynamic coupling data reflecting energy exchange disturbances during inter-regional bidirectional energy supply. This includes the power fluctuation value Pfluc(t) at time t, the dispatch parameter response offset Odes, and the intermediate frequency value Wper of the disturbance spectrum. Subsequently, an edge gateway device is deployed, utilizing its built-in multi-protocol acquisition module and 5G communication module to rapidly transmit the collected data to an edge-side processing module. Within the edge gateway device, the energy dynamic coupling data undergoes systematic preprocessing, including bandpass filtering for denoising and outlier elimination, Z-score normalization for uniform scaling, and timestamp alignment. This constructs a highly consistent and available standard energy dynamic coupling dataset. This step transforms the raw multi-point disturbance feature data into a structured standard dataset, providing a unified and reliable data foundation for subsequent cross-domain access risk identification, interference factor modeling, and trusted control strategy execution. The resulting benefits are reflected in the following three aspects: First, it significantly improves the grid control system's dynamic perception of energy fluctuations and scheduling offsets; second, leveraging the rapid preprocessing capabilities of edge gateways, it enables on-site data screening, reduces central processing load, and accelerates response times; and third, through time alignment and unified normalization, it ensures the comparability and temporal consistency of multi-source heterogeneous data, providing standard support for subsequent indicator modeling. Ultimately, this solution effectively improves data consistency and access trustworthiness in inter-regional energy exchange scenarios, providing a solid technical foundation for the operational security and system adaptability of smart grids in cross-regional collaboration.

[0081] Example 3: Please refer to Figure 1 , specifically: S2 includes S21;

[0082] S21. By extracting the standard energy dynamic coupling data set, triggering an integral window operation every 5 minutes, and outputting the regional energy dynamic coupling intensity index ECI, which measures the data access interference intensity of the current control system area per unit time;

[0083] The regional energy dynamic coupling intensity index ECI is calculated and output by the following algorithm formula;

[0084] ;

[0085] In the formula, d represents the integral function, sin represents the cosine function, t1 represents the integral end time, t0 represents the integral start time, and dt represents the time integral function;

[0086] The derivation logic of the formula: Indicates the nonlinear response degree of power change per unit time. It is processed with 1.5 powers to amplify short-term sharp changes and weaken slow ramp changes. The purpose is to highlight the potential impact of sudden changes in renewable energy output, load shedding, and spikes on system structure disturbances.

[0087] It represents the square of the response offset of the dispatch parameter, which is the difference in the response of the control system after executing the dispatch instruction. The larger the offset, the worse the anti-disturbance ability of the control system. Using the square to enhance the discreteness will punish large offset behaviors more severely, which represents the degree of decline of "feedback elasticity" in the dispatch execution chain.

[0088] It indicates that when the control system enters the resonance or resonant state, the influence factor of the amplified energy disturbance on the scheduling chain is amplified. When the control system has medium-frequency disturbances, such as 5-15Hz resonance or low-order oscillation, this item has a frequency modulation enhancement effect within the integration period. 2 The positive and negative disturbances are unified into positive energy values, ensuring that the integral value is non-negative and continuously enhanced. The engineering meaning is: when the control system enters the resonance or resonant state, the impact factor of the energy disturbance on the scheduling chain is amplified;

[0089] The denominator t1-t0 represents the integration period and is used for normalization, so that the indicator has a time-domain average characteristic and prevents the calculation results of different time windows from being incomparable;

[0090] The physical meaning of the formula: In actual power system operation, the high volatility of renewable energy output, the coupling complexity of the control structure, and the frequency domain characteristics of disturbance propagation will jointly affect the stability of the control system and data reliability. The regional energy dynamic coupling intensity index (ECI) can be understood as the product integral average of the physical disturbance behavior, the control offset response, and the resonance modulation factor. It is a quantitative intensity indicator that characterizes whether the power disturbance has formed a control layer coupling risk. If the regional energy dynamic coupling intensity index (ECI) is too high, it means that the current region is in a period of high interference in data access, and cross-domain access behavior must be immediately restricted.

[0091] S2 also includes S22;

[0092] S22. Based on the output of the regional energy dynamic coupling intensity index (ECI), conduct a preliminary comparative assessment to determine the risk of energy and control coupling during access interactions between current power grid systems. Based on the preliminary comparative assessment results, classify regional access risk levels, and then trigger access intervention based on the regional access risk classification results. The specific assessment contents are as follows;

[0093] When the regional energy dynamic coupling intensity index ECI is less than 0.33, it means that the current control system is normally affected by energy disturbances. At this time, there is no interference risk in cross-regional data access. At this time, the current disturbance risk level is classified as Level I, and access is open without restriction.

[0094] When 0.33≤Regional Energy Dynamic Coupling Intensity Index ECI<1, it indicates that the current control system is affected by energy disturbances. In this case, the current disturbance risk level is classified as Level II. A rate limit instruction is sent to the power grid system through the edge gateway device. The upper limit of the access frequency is limited to 80% of the current access frequency. If the impact persists for five consecutive evaluation cycles, the system is automatically upgraded to Level III.

[0095] When the regional energy dynamic coupling intensity index ECI ≥ 1, it means that the current control system is abnormally disturbed by energy. At this time, the current disturbance risk level is classified as level III, access isolation is immediately performed, and access intervention is triggered.

[0096] In this embodiment, this method extracts a standard energy dynamic coupling dataset and triggers an integration window operation every 5 minutes to construct a regional energy dynamic coupling intensity index (ECI). This index dynamically quantifies the intensity of data access interference within the current power grid control system. This index comprehensively considers the nonlinear response of power variations per unit time, the response offset strength of dispatch parameters, and the resonant modulation effect of frequency domain disturbances, resulting in high sensitivity and engineering relevance. The formula uses a 1.5-power amplification of short-term, severe fluctuations, uses the square of Odes to enhance the rigidity of dispatch feedback, and introduces a medium-frequency sin² function to capture potential system resonances. Normalization enhances data comparability across different cycles. Based on the output of the regional energy dynamic coupling intensity index (ECI), a preliminary comparative assessment and risk grading mechanism are further performed. The current control status is classified into three levels: Level I (low disturbance, fully open); Level II (medium disturbance, speed and frequency limited); and Level III (high disturbance, mandatory isolation). Thresholds are set to accurately determine whether the current control system energy disturbance poses a cross-domain access risk. Once the assessment determines that the risk level is elevated, the access intervention process is automatically triggered. This implementation achieves the following objectives and beneficial effects: it implements a "quasi-real-time, continuous, and quantitative" assessment of the operating status of the control system, effectively identifying potential interference risks in cross-domain access; it constructs a risk level system with ECI as the core, enabling the control system to have adaptive access regulation capabilities; and through dynamic speed limiting and access isolation mechanisms, it reduces the probability of system-level anomalies caused by data access, thereby significantly improving the access reliability, operational stability, and dispatch response flexibility of the power grid system in highly volatile energy scenarios.

[0097] Example 4: Please refer to Figure 1 , specifically: S3 includes S31;

[0098] S31. After the preliminary comparative assessment triggers access intervention, the collection point supplement mechanism is activated. The collection point supplement mechanism collects access disturbance data in real time by setting up supplementary collection points and setting up collection devices at the collection points. The collection devices are integrated with the edge gateway device to transmit the access disturbance data to the edge gateway device and perform preprocessing to obtain a standard access disturbance data set.

[0099] Supplementary collection points include A4, A5 and A6;

[0100] The collection equipment includes API access log collector, topology perception module, data access link tracking module and parameter scheduling analysis engine;

[0101] The standard access perturbation dataset includes the cross-domain request intensity vector Ocall, the topological cascade propagation depth Ttopo, and the parameter scheduling kurtosis Aconf;

[0102] The cross-domain request intensity vector Ocall is obtained by setting the A4 collection point at the data control service export gateway and cloud platform API service bus of each region, using the API access log collector to collect the call frequency and parameter load characteristics of each type of API per unit time. The product of the number of calls C initiated by the regional control system for the remote interface and its average parameter transmission load and the ratio of the statistical period reflects the request density per unit time of this type of interface, and obtains the cross-domain request intensity vector Ocall. The physical meaning of the parameter is that the parameter is obtained by analyzing the call frequency and request volume of each type of interface in the API access log, and can be used to construct the cross-domain request intensity vector Ocall as a feature input for evaluating the remote call intensity and model dependency characteristics.

[0103] The topological cascade propagation depth Ttopo is achieved by setting the A5 collection point in the control system middleware layer, using the topology structure perception module and the data access link tracking module to collect the actual data propagation path set in real time, and extracting the depth value of the maximum path level in the actual data propagation path set. The physical meaning of the parameter is that each propagation path corresponds to the number of jump levels from a starting service node to the terminal device. It is calculated in real time through topology graph analysis and link tracking technology. This parameter is used to reflect the interference intensity of the access request on the overall cascade structure of the system;

[0104] The parameter scheduling kurtosis Aconf is obtained by setting the A6 collection point in the EMS parameter usage log and using the parameter scheduling analysis engine to collect the parameter call frequency sequence in its original domain control cycle in real time, and calculate the distribution peak index. The physical meaning of the parameter is: it is used to measure the concentration of its call behavior in the time dimension. The higher the kurtosis value, the more concentrated the parameter is in a specific time period or specific scenario, and has a stronger control dependence characteristic.

[0105] S3 also includes S32;

[0106] S32. Based on the standard access disturbance data set, a joint disturbance factor is formed by multiplication, and the joint disturbance factor is integrated to output a data access disturbance response score DSR, which reflects the degree of influence of each cross-domain access on the control link disturbance of the control system;

[0107] The data access disturbance response score DSR is calculated and output by the following algorithm formula;

[0108] ;

[0109] In the formula, e represents the exponential function, ln represents the natural logarithm function, dx represents the independent variable of the integrated function, and the function value of the variable is accumulated and summed.

[0110] Represents a nonlinear amplification term for access intensity. An exponent of 1.2 enhances high-frequency, high-volume access requests, weakens the impact of low-frequency accesses, and amplifies the response to high-frequency, large-scale accesses, ensuring the model's high sensitivity to abnormal batch call behavior.

[0111] It represents the exponential amplification term of topological propagation. The depth of topological propagation increases exponentially. That is, for each additional propagation level, the risk of coupling disturbance to the control system is not linearly accumulated, but exponentially increased. For example, the impact of level 3 propagation is 7.39, that is, e 2 , while level 5 is as high as 148.4, which is e 5 ;

[0112] It represents parameter kurtosis modulation, which modulates the kurtosis in logarithmic form after square enhancement. It has the following features: enhancing the response to extreme parameter concentration calls, making weak responses to the parameter call behavior with flat distribution, and using logarithmic processing to suppress explosive growth of abnormally high peak values;

[0113] Integral calculation is used to normalize the joint disturbance factor to the range of 0-1 to prevent visits of different scales from dominating the score and maintain comparability. The integral reflects the cumulative disturbance value of the visit behavior in the concentrated interval.

[0114] The physical meaning of the formula: It is an indicator used to measure the risk of a single cross-domain data access behavior causing disturbances to the source domain control system. In the energy-control-model coupling system, it determines whether it may cause the diffusion of chain control responses, whether it has potential resonance amplification risks, and whether it triggers excessive data-dependent feedback.

[0115] In this embodiment, after a preliminary comparative assessment triggers access intervention, the method deploys a supplementary collection mechanism, dynamically setting collection points A4, A5, and A6. It also integrates an API access log collector, a topology perception module, a data access link tracking module, and a parameter scheduling analysis engine. This allows for the precise collection of core perturbation parameters, such as the cross-domain request intensity vector Ocall, the topology cascade propagation depth Ttopo, and the parameter scheduling kurtosis Aconf, to construct a standard access perturbation dataset. After uniform preprocessing by edge gateway devices, this dataset enters the joint perturbation factor calculation process. Relying on a nonlinear enhancement mechanism and a normalized integral method, it ultimately outputs a data access perturbation response score (DSR), quantifying the risk of a single cross-domain access causing perturbation to the control link. This scoring model fully integrates the high-frequency characteristics of request behavior. Through exponential amplification of Ocall, the progressive nature of structural propagation, the exponential gain of Ttopo, and the volatility of parameter call time series, and the squared and logarithmic modulation of Aconf, it ensures the model's responsiveness and generalization capabilities in multi-source, high-dimensional access scenarios. At the same time, integral normalization eliminates the dominant bias in scoring due to access scale, making the scoring results more comparable and stable. In summary, this implementation effectively achieves a detailed characterization and dynamic identification of access perturbation risks, achieving the goal of improving system-level risk perception capabilities during cross-domain data access. Specific benefits include significantly enhancing the early warning capability for chained interference behaviors and reducing the probability of cascading impacts on the original regulatory chain triggered by access.

[0116] Example 5: Please refer to Figure 1 , specifically: S4 includes S41;

[0117] S41. Based on the product of the data access disturbance response score DSR and the energy dynamic coupling intensity index ECI, the ratio is calculated with the access recovery capability of the control system to output the access risk level judgment score Vpr, which measures the comprehensive disturbance risk score of the access request under the current regional control system state;

[0118] The access risk level determination score Vpr is calculated and output by the following algorithm formula;

[0119] ;

[0120] Where Urec represents the historical access recovery response index, which is set by the user based on the recovery capability of the control system after the access;

[0121] ECI 0.8The nonlinear amplification term representing the regional coupling strength. An exponent of 0.8 is used to weakly amplify the energy dynamic coupling strength index (ECI) value in the access risk level determination score (Vpr). It generally represents the impact of energy disturbances on the control system. A higher value indicates a greater risk and is more likely to lead to a decrease in access credibility.

[0122] DSR 1.2 It represents a nonlinear enhancement item for the data access disturbance response score. It indicates that the stronger the system disturbance caused by cross-domain data access, the more control it needs to achieve through rate limiting or isolation. Using an exponential amplification of 1.2 can make the access risk level judgment score Vpr more sensitive to the data access disturbance response score DSR. In particular, in medium and high risk situations, the system will respond more strongly.

[0123] 1+Urec represents the adjustment term of the historical recovery response index, indicating that the system recovers slower the longer the system disturbance lasts during the access process, so this term is weighted to reduce the risk when the system recoverability is poor.

[0124] S4 also includes S42;

[0125] S42. Based on the output of the access risk level determination score Vpr, a secondary comparative evaluation is performed to determine the risk level of the access behavior. Based on the secondary comparative evaluation result, a response access trust control policy is executed. The specific evaluation contents are as follows:

[0126] When the access risk level score Vpr is less than 0.2, the L1 policy is executed. At this time, all cross-domain requests are fully allowed to access the control system data and core resources without any restrictions, and logging and monitoring of all access are enabled. This indicates that the system risk is low at this time, and cross-domain access will not cause significant disturbances to the control system, so access can be fully opened;

[0127] When 0.2≤Access Risk Level Determination Score Vpr<0.6, the L2 strategy is executed. At this time, the access frequency of the current control system is limited to 50%, and the number of access requests is limited to a maximum of 5 requests every 10 minutes. This indicates that the risk has increased and it is necessary to control access behavior and implement strategies such as local parameter isolation, speed limit and number of times limit.

[0128] When the access risk level score Vpr ≥ 0.6, the L3 policy is executed, and the data isolation layer isolation and access credibility fallback mechanism are implemented;

[0129] The data isolation layer divides the data into core forecast data, neutral dispatch information, and public auxiliary information by labeling the data in different levels. The isolation layer implements isolation methods, including field masking, middleware forwarding terminals, and reducing the accuracy of forecast vectors to the decimal point. Core forecast data includes wind power forecast parameters, neutral dispatch information includes compliance with transfer recommendations, and public auxiliary information includes regional weather forecasts.

[0130] Access credibility fallback mechanism: if the L3 policy is evaluated for execution for three consecutive cycles, access is prohibited.

[0131] In this embodiment, this method constructs a product calculation model centered on the data access perturbation response score (DSR) and the energy dynamic coupling intensity index (ECI). It also introduces the control system's historical access recovery response index (Urec) as a modulating ratio term to form an access risk level determination score (Vpr), achieving comprehensive quantification of cross-domain access perturbation risk. This scoring mechanism employs a nonlinear enhancement approach to differentially amplify energy perturbation factors and access perturbation intensity, resulting in a more sensitive response to system perturbations within medium- and high-risk ranges. Furthermore, the "1+Urec" adjustment term design proactively tightens the scoring results in situations where recovery capabilities are weak, mitigating system vulnerability caused by potential perturbation accumulation. Based on the output of the access risk level determination score (Vpr), a hierarchical access trust control strategy is further implemented. Through three policy levels, L1 to L3, a dynamic, adjustable, and proactive risk response system is established, evolving from open access to refined speed and frequency limits, and finally to strong isolation and access fallback mechanisms. Among them, the L2 strategy limits the frequency and number of accesses, and cooperates with local parameter isolation technology to effectively avoid the impact of large-scale data calls on key models or scheduling links; and the data classification labeling and field shielding mechanism introduced by the L3 strategy completely cuts off the high-risk access path to core prediction resources from the physical implementation level, ensuring the stable operation of the system under high-voltage interference.

[0132] Example 6: Please refer to Figure 1 and Figure 2 ,A cross-domain data access trusted control system includes an energy coupling data acquisition module, an energy coupling intensity analysis module, an access intervention analysis module, and an access disturbance control module;

[0133] The energy coupling data acquisition module collects energy dynamic coupling data in real time by setting up collection points in the power grid system and setting up collection tools at the collection points. It also sets up edge gateway devices at the collection points to pre-process the energy dynamic coupling data and obtain a standard energy dynamic coupling data set.

[0134] The energy coupling intensity analysis module calculates and outputs the regional energy dynamic coupling intensity index (ECI) based on the standard energy dynamic coupling data set, and conducts preliminary comparative assessment and regional access risk level classification based on the output results;

[0135] The access intervention analysis module triggers access intervention based on preliminary comparative assessment, starts the collection point replenishment mechanism, collects and obtains the standard access disturbance data set, and calculates the output data access disturbance response score DSR;

[0136] The access disturbance control module performs comprehensive calculations based on the data access disturbance response score DSR and the energy dynamic coupling intensity index ECI, outputs the access risk level judgment score Vpr, then performs a secondary comparative evaluation based on the output result, and executes the access control strategy based on the secondary comparative evaluation result.

[0137] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A cross-domain data access trusted control method, characterized by: The following steps are involved: S1. Set up collection points in the power grid system and set up collection tools at the collection points to collect energy dynamic coupling data in real time. Set up edge gateway devices at the collection points to pre-process the energy dynamic coupling data and obtain a standard energy dynamic coupling data set. The energy dynamic coupling data includes the power fluctuation value Pfluc(t) at time t, the scheduling parameter response offset Odes and the disturbance spectrum intermediate frequency value Wper; S2. Based on the standard energy dynamic coupling data set, calculate and output the regional energy dynamic coupling intensity index ECI, and conduct preliminary comparative assessment and regional access risk level classification based on the output results; The regional energy dynamic coupling intensity index ECI is calculated and output by the following algorithm formula; ； In the formula, d represents the integral function, sin represents the cosine function, t1 represents the integral end time, t0 represents the integral start time, and dt represents the time integral function; S3. Trigger access intervention based on preliminary comparative assessment, start the collection point replenishment mechanism, collect and obtain the standard access disturbance data set, and calculate the output data access disturbance response score DSR; The standard access perturbation dataset includes the cross-domain request intensity vector Ocall, the topological cascade propagation depth Ttopo, and the parameter scheduling kurtosis Aconf; The data access disturbance response score DSR is calculated and output by the following algorithm formula; ； In the formula, e represents the exponential function, ln represents the natural logarithm function, and dx represents the independent variable of the integral function; S4. Perform a comprehensive calculation based on the data access disturbance response score DSR and the energy dynamic coupling intensity index ECI to output the access risk level judgment score Vpr. Then, perform a secondary comparative evaluation based on the output result, and execute the access control policy based on the secondary comparative evaluation result. The access risk level determination score Vpr is calculated and output by the following algorithm formula; ； Where Urec represents the historical access recovery response index, which is set by the user based on the recovery capability of the control system after the access.

2. A cross-domain data access trusted control method according to claim 1, characterized in that: Said S1 includes S11; S11. Initially set up three collection points in the control system of the power grid system, and embed a collection tool in the three collection points. When bidirectional energy is supplied between the power grid systems, start the collection tool to collect energy dynamic coupling data in real time. The collection points include A1 collection point, A2 collection point and A3 collection point; The collection tools include a PQ analyzer, a high-frequency electric energy meter, an EMS control system log collector, and a frequency disturbance identifier.

3. A cross-domain data access trusted control method according to claim 2, characterized in that: Said S1 also includes S12; S12. Setting an edge gateway device, and integrating and connecting the edge gateway device with the collection point through the built-in data collection interface module and network communication module of the edge gateway device, using the collection protocol and the 5G communication network, and transmitting the energy dynamic coupling data to the edge gateway device; Preprocess the energy dynamic coupling data in the edge gateway device to obtain a standard energy dynamic coupling data set; The preprocessing includes denoising and outlier elimination, scale unification and normalization, and timestamp alignment; The denoising and outlier elimination may use bandpass filtering to identify and eliminate drift data and noise points in the energy dynamic coupling data; The unified scale and normalization are performed by performing standard normalization processing on the energy dynamic coupling data by using Z-score normalization to eliminate the dimensional influence between all parameters in the energy dynamic coupling data; The timestamp alignment is performed by using a synchronization timestamp correction to align the timestamps of the scheduling instruction and the response.

4. A cross-domain data access trusted control method according to claim 3, characterized in that: Said S2 includes S21; S21. By extracting the standard energy dynamic coupling data set, triggering an integral window operation every 5 minutes, and outputting the regional energy dynamic coupling intensity index ECI, it measures the data access interference intensity of the current control system area per unit time.

5. A cross-domain data access trusted control method according to claim 4, characterized in that: Said S2 also includes S22; S22. Based on the output of the regional energy dynamic coupling intensity index (ECI), conduct a preliminary comparative assessment to determine the risk of energy and control coupling during access interactions between current power grid systems. Based on the preliminary comparative assessment results, classify regional access risk levels, and then trigger access intervention based on the regional access risk classification results. The specific assessment contents are as follows; When the regional energy dynamic coupling intensity index ECI is less than 0.33, it means that the current control system is normally affected by energy disturbances. At this time, there is no interference risk in cross-regional data access. At this time, the current disturbance risk level is classified as Level I, and access is open without restriction. When 0.33≤Regional Energy Dynamic Coupling Intensity Index ECI<1, it indicates that the current control system is affected by energy disturbances. In this case, the current disturbance risk level is classified as Level II. A rate limit instruction is sent to the power grid system through the edge gateway device. The upper limit of the access frequency is limited to 80% of the current access frequency. If the impact persists for five consecutive evaluation cycles, the system is automatically upgraded to Level III. When the regional energy dynamic coupling intensity index ECI ≥ 1, it means that the current control system is abnormally disturbed by energy. At this time, the current disturbance risk level is classified as level III, access isolation is immediately performed, and access intervention is triggered.

6. A cross-domain data access trusted control method according to claim 5, characterized in that: Said S3 includes S31; S31. After the preliminary comparative assessment triggers access intervention, a collection point supplement mechanism is initiated. The collection point supplement mechanism collects access disturbance data in real time by setting up additional collection points and setting up collection devices at the collection points. The collection devices are integrated with edge gateway devices to transmit the access disturbance data to the edge gateway devices and perform preprocessing to obtain a standard access disturbance data set. The supplement includes A4 collection point, A5 collection point and A6 collection point; The collection device includes an API access log collector, a topology perception module, a data access link tracking module and a parameter scheduling analysis engine.

7. A cross-domain data access trusted control method according to claim 6, characterized in that: Said S3 also includes S32; S32. Based on the standard access disturbance data set, a joint disturbance factor is formed by multiplication, and the joint disturbance factor is integrated to output the data access disturbance response score DSR, which reflects the degree of influence of each cross-domain access on the control link disturbance of the control system.

8. A cross-domain data access trusted control method according to claim 6, characterized in that: Said S4 includes S41; S41. Based on the product of the data access disturbance response score DSR and the energy dynamic coupling intensity index ECI, and then calculating the ratio with the access recovery capability of the control system, the access risk level judgment score Vpr is output to measure the comprehensive disturbance risk score of the access request under the current regional control system status.

9. A cross-domain data access trusted control method according to claim 8, characterized in that: Said S4 also includes S42; S42. Based on the output of the access risk level determination score Vpr, a secondary comparative evaluation is performed to determine the risk level of the access behavior. Based on the secondary comparative evaluation result, a response access trust control policy is executed. The specific evaluation contents are as follows: When the access risk level score Vpr is less than 0.2, the L1 policy is executed. At this time, all cross-domain requests are fully allowed to access the control system data and core resources without any restrictions, and logging and monitoring of all access are enabled; When 0.2≤Access Risk Level Determination Score Vpr<0.6, the L2 strategy is executed, which limits the access frequency of the current control system to 50% and the number of accesses to a maximum of 5 requests per 10 minutes; When the access risk level score Vpr ≥ 0.6, the L3 policy is executed, and the data isolation layer isolation and access credibility fallback mechanism are implemented; The data isolation layer divides the data into core prediction data, neutral scheduling information and public auxiliary information by labeling the data in a hierarchical manner, and implements an isolation method on the isolation layer, which includes field masking, middleware forwarding terminal and prediction vector precision reduction to the decimal point; The access credibility fallback mechanism prohibits access if the L3 policy is evaluated to be executed for three consecutive cycles.

10. A cross-domain data access trusted control system, applied to a cross-domain data access trusted control method according to any one of claims 1 to 9, characterized in that: It includes energy coupling data acquisition module, energy coupling intensity analysis module, access intervention analysis module and access disturbance control module; The energy coupling data acquisition module collects energy dynamic coupling data in real time by setting up collection points in the power grid system and setting up collection tools in the collection points. It also sets up edge gateway devices at the collection points to pre-process the energy dynamic coupling data and obtain a standard energy dynamic coupling data set. The energy coupling intensity analysis module calculates and outputs the regional energy dynamic coupling intensity index ECI based on the standard energy dynamic coupling data set, and performs preliminary comparative evaluation and regional access risk level classification based on the output results; The access intervention analysis module triggers access intervention based on preliminary comparative evaluation, starts the collection point replenishment mechanism, collects and obtains the standard access disturbance data set, and calculates and outputs the data access disturbance response score DSR; The access disturbance control module performs comprehensive calculation based on the data access disturbance response score DSR and the energy dynamic coupling intensity index ECI, outputs the access risk level judgment score Vpr, then performs a secondary comparative evaluation based on the output result, and executes the access control strategy based on the secondary comparative evaluation result.

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