A new energy centralized control center intelligent anti-misoperation method and system capable of split-screen display

Abstract

This invention relates to the field of new energy centralized control technology, and discloses an intelligent anti-misoperation method and system for a new energy centralized control center with split-screen display. The method includes: collecting historical operating status data of target electrical equipment using new energy substations, and uploading the historical operating status data to the new energy centralized control center according to a transmission mechanism after completing safety verification; establishing an inversion scenario based on the historical operating status data and the target electrical equipment model to simulate and rehearse the operation process of the new energy substations; generating anti-misoperation verification basis data for the new energy substations, and outputting an operation ticket corresponding to the operation right when the new energy substation requests operation rights by comparing the real-time operating status data of the target electrical equipment with the anti-misoperation verification basis data; configuring the corresponding operation permissions of various operators when the operation ticket is executed, and sending the operation permissions to the visualization platform of each new energy substation. This invention can avoid misoperation at the source and eliminate unauthorized operations.

Description

Technical Field

[0001] This invention relates to the field of new energy centralized control technology, and more specifically, to an intelligent anti-misoperation method and system for a new energy centralized control center with split-screen display. Background Technology

[0002] The New Energy Central Control Center is the core hub for remote centralized monitoring and management of new energy power stations (photovoltaic, wind power, energy storage, etc.). It integrates all functions such as data acquisition, equipment monitoring, operation scheduling, intelligent error prevention, and operation and maintenance management, and realizes centralized control, unified scheduling and intelligent operation and maintenance of all subordinate new energy substations. It is the core infrastructure for the large-scale and intensive operation of new energy power stations.

[0003] Misoperation prevention management in the new energy control center is one of its core control functions. It is a key means to avoid misoperation of substation equipment and ensure the safe and stable operation of new energy power plants. It is also the core embodiment of the intensive and intelligent operation and maintenance of the control center.

[0004] However, the existing new energy centralized control center's error prevention management relies solely on simple interlocking during on-site operations to prevent errors. Without simulation and rehearsal stages, it cannot proactively identify potential operational errors in various maintenance stages. Error prevention management is reactive rather than preventative, resulting in low levels of intelligence and collaboration within the system. Furthermore, equipment models, operational data, error prevention rules, and operational permissions are independent of each other, failing to achieve deep integration and linkage. Consequently, it cannot form a full-chain error prevention management system, leading to decentralized management of each substation by the centralized control center. This results in inconsistent rules, asynchronous data, and a lack of refined management functions such as inter-station interlocking and unique operating rights. Collaboration loopholes easily occur during cross-substation operations, further increasing the probability of operational errors.

[0005] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0006] To address the problems in related technologies, this invention proposes an intelligent anti-misoperation method and system for a new energy centralized control center with split-screen display, in order to overcome the aforementioned technical problems existing in the existing related technologies.

[0007] Therefore, the specific technical solution adopted by the present invention is as follows:

[0008] In a first aspect, the present invention provides an intelligent error prevention method for a new energy centralized control center with split-screen display, the method comprising:

[0009] Based on the modeling elements of each target electrical equipment in the new energy center, construct a model of the target electrical equipment;

[0010] The historical operating status data of the target electrical equipment is collected by the new energy substation, and after the safety verification is completed, the historical operating status data is uploaded to the new energy central control center according to the transmission mechanism.

[0011] Based on historical operating status data and target electrical equipment models, an inversion scenario is established that includes full-process anti-misoperation management of the equipment, and the inversion scenario is run to simulate and rehearse the operation process of the new energy substation.

[0012] Based on the simulation results, the data for preventing misoperation of the new energy substation is generated. When the new energy substation applies for operation rights, the real-time operating status data of the target electrical equipment is compared with the data for preventing misoperation. Based on the comparison results, the operation ticket corresponding to the operation rights is output.

[0013] By configuring the corresponding operation permissions of various operators when the operation ticket is executed through hierarchical permission management rules, the operation permissions are sent to the visualization platform of each new energy substation to facilitate intelligent error prevention for various operators.

[0014] Preferably, the process of using a new energy substation to collect historical operating status data of the target electrical equipment and uploading the historical operating status data to the new energy central control center according to the transmission mechanism after completing security verification includes: the new energy substation collecting historical operating status data containing physical operating data and virtual remote signaling status of the target electrical equipment according to a preset collection cycle, and preprocessing the historical operating status data; performing rationality verification and timing verification on the preprocessed historical operating status data, and after passing the verification, detecting the communication transmission status between the new energy substation and the new energy central control center main station, and establishing a data transmission channel; the new energy substation transmitting the verified historical operating status data to the central control center main station from the data transmission channel according to the power information encryption transmission protocol; the new energy central control center receiving the historical operating status data and performing secondary verification; after the secondary verification of the historical operating status data, the new energy central control center sending a successful receipt to the new energy substation, and the new energy substation receiving the receipt and confirming that the historical operating status data upload is complete.

[0015] Preferably, the process involves performing rationality and timing checks on the preprocessed historical operating status data, and after passing the checks, detecting the communication transmission status between the new energy substation and the new energy central control center main station to establish a data transmission channel. This includes: verifying the numerical range, logical state, and temporal continuity of the preprocessed historical operating status data; removing unqualified historical operating status data based on the verification results to obtain preliminary admission data; using a change point detection algorithm to identify the non-steady-state nodes and change time point sets corresponding to the preliminary admission data; establishing a causal network graph to infer the local causal structure of the preliminary admission data to complete the timing check results; outputting the verified historical operating status data; analyzing the operating network scenario of the transmission channel between the new energy substation and the new energy central control center main station; describing node characteristic attributes using effective transmission rate and average transmission delay to construct malicious node attack rules; detecting the operating status of the operating network scenario based on the malicious node attack rules and constructing an observation sequence; analyzing the status of the observation sequence to verify the malicious obstruction degree of the operating network scenario; and establishing a data transmission channel.

[0016] Preferably, a change point detection algorithm is used to identify the non-steady-state nodes and change time point sets corresponding to the initial admission data, and a causal network graph is established to infer the local causal structure of the initial admission data to complete the time series verification results. The output of verified historical running state data includes: defining the minimum length sample number and hop count for the change point detection algorithm for the initial admission data, and selecting the Euclidean norm as the cost function to segment the initial admission data according to the definition results; judging the consistency cost and the penalty term of the number of segmentations of the initial admission data in each segmentation interval according to the segmentation results, screening the change point positions of the initial admission data, and minimizing the cost function based on the change point positions; dividing the initial admission data into several data segments of different lengths, and simultaneously setting the minimum length sample number and hop count as ... The transformed cost function is converted into a recursive form. The minimum cost of the short data segment is used to derive the minimum cost of the long data segment, thus locking in candidate change points. Change points are selected based on the mean and variance of the candidate change points, and the characteristic stability of each change point in all time periods is determined. Based on the characteristic stability results, change points that change over time are output as non-steady-state nodes. Using the non-steady-state nodes as dividing lines, the preliminary admission data is divided into multiple continuous and non-overlapping time intervals. Data points with no abrupt changes in statistical characteristics within each time interval are selected to form a set of approximately steady-state change time points. A time-series causal graph is established based on the set of change time points to infer the local causal structure of the preliminary admission data, determine the temporal state of the preliminary admission data, and output the verified historical operating state data.

[0017] Preferably, the process involves establishing a temporal causal graph based on the set of changing time points, inferring the local causal structure of the preliminary admission data, determining the temporal state of the preliminary admission data, and outputting verified historical operating state data. This includes: constructing an initial undirected complete graph based on the set of changing time points, performing a conditional independence test on the initial undirected complete graph to remove edges without conditional independence relationships, and obtaining an undirected causal skeleton based on the removal results; defining directions for the undirected causal skeleton, generating a static causal graph skeleton corresponding to the time interval of the set of changing time points, analyzing the conditional mutual information of each time change point in the static causal skeleton, and filtering temporal causal correlation variables based on the conditional mutual information; using the temporal causal correlation variables to temporally orient the edges of the static causal graph skeleton to obtain a temporal causal graph, repeatedly generating temporal causal graphs corresponding to each time interval, and merging them to generate a causal network graph; performing a consistency test on the causal network graph, removing temporal causal correlation variables with false causal relationships caused by fluctuations, and using the retained temporal causal correlation variables as preliminary admission data that meets the temporal state requirements to obtain verified historical operating state data.

[0018] Preferably, the analysis of the operational network scenario of the transmission channel between the new energy substation and the new energy central control center main station, and the construction of malicious node attack rules based on the effective transmission rate and average transmission delay to describe node characteristic attributes, includes: determining the local transmission channel composed of aggregation nodes based on the topology of the transmission channel between the new energy substation and the new energy central control center main station, and constructing a feature vector set based on the effective transmission rate and delay status of the aggregation nodes; sorting out the malicious node attack types in the transmission between the new energy substation and the new energy central control center main station based on the feature vector set, clarifying the behavioral characteristics of the attack types, and converting the behavioral characteristics into constraint relationships of feature attributes; and constructing malicious node attack rules by setting logical AND relationship judgment rules that include effective transmission rate and forwarding rate both being lower than the corresponding thresholds, so as to realize the quantitative judgment of malicious node attack behavior in the transmission channel.

[0019] Preferably, the process of establishing an inversion scenario based on historical operating status data and a target electrical equipment model, encompassing the entire process of preventing misoperation of the equipment, and running the inversion scenario to simulate and rehearse the operation of the new energy substation includes: establishing a set of rules for preventing misoperation of the target electrical equipment, including operation, interlocking, grounding, and locking management; converting these rules into logical judgment conditions to build an anti-misoperation rule library; associating the anti-misoperation rule library with the target electrical equipment model to form an anti-misoperation benchmark model; expanding the set of inversion scenarios based on historical operating status data to cover the simulation of equipment misoperation; inputting the inversion scenario set into the anti-misoperation benchmark model; setting the simulation time and speed based on the input results of the inversion scenario set; driving the anti-misoperation benchmark model to run according to the scenario settings; and outputting the status data of the target electrical equipment at each time point; matching the status data with the logical judgment conditions of the anti-misoperation rule library; identifying and marking the problem risk threshold points of the target electrical equipment during the inversion process; and completing the simulation and rehearsal of the new energy substation operation.

[0020] Preferably, the inversion scenario set includes primary equipment anti-misoperation simulation scenario, secondary equipment anti-misoperation simulation scenario, intelligent ground wire management simulation scenario, intelligent lock control management simulation scenario, and maintenance isolation interlocking management simulation scenario;

[0021] The simulation scenarios for preventing misoperation of primary equipment include the execution of anti-misoperation constraints during normal operation, fault handling, and condition switching of circuit breakers, disconnectors, grounding switches, high-voltage switchgear, and gate equipment; the simulation scenarios for preventing misoperation of secondary equipment include the remote control management of simulated pressure plate status collectors, pressure plate status sensors, and acquisition controllers, pressure plate operation invoicing, and simulation rehearsals; the simulation scenarios for intelligent grounding wire management include the installation and removal of intelligent grounding wires during maintenance work at new energy substations; the simulation scenarios for intelligent lock control management include the unlocking and locking operations, personnel access control, operation authorization management, and unlocking process monitoring of the intelligent locks corresponding to electrical equipment in new energy substations; and the simulation scenarios for maintenance isolation and interlocking management include the prevention of misoperation management during the entire process of power outage, voltage testing, grounding wire installation, and maintenance repair during electrical equipment maintenance, interval maintenance, and station-wide power outage maintenance in new energy substations.

[0022] Preferably, the operation permissions of various operators are configured according to hierarchical permission management rules when operation tickets are executed, and the operation permissions are sent to the visualization platform of each new energy substation. This facilitates intelligent error prevention for various operators, including: classifying operation permission levels according to operation positions based on the new energy substation operation and maintenance process; outputting permission rules for determining the operation permissions of different types of operators for operation tickets; establishing a binding relationship between permission rules and operation ticket execution process; configuring permission information for different types of operators according to the binding relationship; classifying and packaging permission information according to the new energy substation dimension; and sending the permission information to the new energy substation visualization platform through the communication link between the new energy center and the new energy substation. This allows various operators to execute operation ticket processing according to the permission information, thus completing intelligent error prevention.

[0023] Secondly, the present invention also provides an intelligent anti-misoperation system for a new energy centralized control center with split-screen display, the system comprising:

[0024] The electrical equipment model building module is used to build target electrical equipment models based on the modeling elements of each target electrical equipment in the new energy center;

[0025] The operation status data upload module is used to collect historical operation status data of target electrical equipment using the new energy substation, and upload the historical operation status data to the new energy central control center according to the transmission mechanism after completing the safety verification.

[0026] The substation operation simulation and pre-drill module is used to establish an inversion scenario that includes the whole process of equipment error prevention management based on historical operation status data and target electrical equipment model, and to run the inversion scenario to simulate and pre-drill the operation process of the new energy substation.

[0027] The operation ticket application output module is used to generate the anti-misoperation verification basis data of the new energy substation based on the simulation and pre-drill results, and when the new energy substation applies for operation rights, it compares the real-time operating status data of the target electrical equipment with the anti-misoperation verification basis data, and outputs the operation ticket corresponding to the operation rights based on the comparison results.

[0028] The operation permission configuration error prevention module is used to configure the corresponding operation permissions of various operators when the operation ticket is executed through the permission hierarchical management rules, and send the operation permissions to the visualization platform of each new energy substation to facilitate intelligent error prevention for various operators.

[0029] The beneficial effects of this invention are as follows:

[0030] 1. This invention constructs a full-process error prevention and inversion scenario by combining the target electrical equipment model with historical operating data and simulates and rehearses. Based on the rehearsal results, it generates verification criteria and then compares the real-time and benchmark data when applying for operation rights at the new energy substation before outputting the operation ticket. This avoids erroneous operations from the source, ensures operational compliance, and combines hierarchical management of operation permissions to achieve intelligent error prevention and verification of the operation process, eliminating unauthorized operations.

[0031] 2. This invention uses a change point detection algorithm to identify non-steady-state nodes and constructs a causal network graph to complete time series verification. It can accurately locate data time series anomalies, restore the causal logic of data, and output high-quality, high-reliability historical operation data, providing an accurate data source for the construction of inversion scenarios. Furthermore, for network scenarios analyzing transmission channels, it constructs malicious node attack rules based on effective transmission rate and average transmission latency, which can quantify and identify transmission risks, effectively avoid malicious node attacks, and ensure the security and stability of data transmission.

[0032] 3. This invention constructs a full-process error prevention inversion scenario and conducts simulation rehearsals, transforming error prevention requirements into quantifiable logical judgment conditions, eliminating control loopholes caused by ambiguous rules, and associating the rule base with the equipment model to form an error prevention benchmark model. Combined with historical data, it generates a set of inversion scenarios that fit the actual situation, allowing the rehearsal scenarios to match the on-site operation and maintenance conditions, accurately identifying and marking risk threshold points, and proactively investigating potential operational errors in each stage of operation and maintenance, thereby achieving advance discovery and prediction of error prevention risks. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart of an intelligent anti-misoperation method for a new energy centralized control center with split-screen display according to an embodiment of the present invention;

[0035] Figure 2 This is a schematic diagram of an intelligent anti-misoperation system for a new energy centralized control center with split-screen display according to an embodiment of the present invention;

[0036] Figure 3 This is a schematic diagram of the hardware operating environment involved in the embodiments of the present invention;

[0037] Figure 4 This is a flowchart of step S2 in an intelligent anti-misoperation method for a new energy centralized control center with split-screen display according to an embodiment of the present invention;

[0038] Figure 5 This is a flowchart of step S4 in an intelligent anti-misoperation method for a new energy centralized control center with split-screen display according to an embodiment of the present invention.

[0039] In the picture:

[0040] 1. Electrical equipment model building module; 2. Operation status data upload module; 3. Substation operation simulation and pre-play module; 4. Operation ticket application output module; 5. Operation permission configuration error prevention module. Detailed Implementation

[0041] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention.

[0042] According to an embodiment of the present invention, an intelligent anti-misoperation method and system for a new energy centralized control center with split-screen display is provided.

[0043] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 and Figures 4 to 5 As shown, according to an embodiment of the present invention, an intelligent anti-misoperation method for a new energy centralized control center with split-screen display includes:

[0044] Step S1: Construct target electrical equipment models based on the modeling elements of each target electrical equipment in the new energy center.

[0045] In one embodiment, the construction process of the target electrical equipment model needs to clarify the modeling scope, core objectives, and technical standards of the new energy substation booster station. The modeling elements are broken down into five core dimensions: physical attributes, characteristics, operating parameters, topology relationships, and control logic. Physical attribute elements include equipment model, dimensions, installation location, and material specifications; electrical characteristic elements include rated voltage, rated current, short-circuit impedance, turns ratio, insulation class, and power factor; operating parameter elements include load rate, temperature rise, voltage deviation, active power, reactive power, and harmonic content; topology relationship elements include connection methods between equipment, wiring groups, circuit numbers, and node relationships; and control logic elements include protection settings, interlocking conditions, start / stop logic, and control thresholds. The specific data acquisition process requires the fusion of multi-source information, including equipment manuals, on-site survey data, real-time SCADA system operating data, and historical fault data.

[0046] Furthermore, based on the collected modeling elements, a refined model of each piece of equipment is built using 3D modeling for full inspection. The various disassembled elements are converted into model parameters and accurately assigned values. At the same time, the equipment models are networked and spliced ​​according to the on-site topology to construct the overall electrical equipment model of the substation. The boundary conditions, simulation conditions, and data interfaces of the model are set simultaneously to realize the digital mapping of equipment characteristics. The model is verified using three methods: comparison of on-site measured data, verification of theoretical calculations, and review of operating conditions. The model simulation output data is compared with the actual operating data, short-circuit test data, and no-load test data of the substation to verify the accuracy and reliability of the model, eliminate modeling deviations, and finally import the verified model into the new energy centralized control center.

[0047] Step S2: Collect historical operating status data of the target electrical equipment using the new energy substation, and upload the historical operating status data to the new energy central control center according to the transmission mechanism after completing the safety verification.

[0048] In one embodiment, the process of collecting historical operating status data of target electrical equipment using a new energy substation and uploading the historical operating status data to the new energy central control center after completing security verification includes: the new energy substation collecting historical operating status data containing physical operating data and virtual remote signaling status of the target electrical equipment according to a preset collection cycle, and preprocessing the historical operating status data; performing rationality verification and timing verification on the preprocessed historical operating status data, and after passing the verification, detecting the communication transmission status between the new energy substation and the new energy central control center master station, and establishing a data transmission channel; the new energy substation transmitting the verified historical operating status data to the central control center master station from the data transmission channel according to the power information encryption transmission protocol; the new energy central control center receiving the historical operating status data and performing a second verification; after the second verification of the historical operating status data, the new energy central control center sending a successful receipt to the new energy substation, and the new energy substation receiving the receipt and confirming that the historical operating status data upload is complete.

[0049] In one embodiment, during the preprocessing of historical operational status data, the historical operational status data within the same collection period need to be uniformly collected according to timestamps to ensure that the data at the same moment forms a complete record. Data cleaning is also carried out to remove duplicate collection records, null records, invalid data that obviously exceeds the range of the communication protocol, and erroneous frame data. Missing data is also handled by interpolating, padding with zeros, or retaining the previous valid value to reasonably fill in missing data caused by communication interruption or collection delay, so as to ensure the continuity of historical operational status data.

[0050] The process involves several steps: First, the pre-processed historical operational status data undergoes rationality and timing verification. After successful verification, the communication transmission status between the new energy substation and the new energy central control center main station is checked. This includes verifying the numerical range (e.g., rated threshold range of electrical quantities), logical state (e.g., logical matching of virtual remote signaling's opening and closing states), and temporal continuity (e.g., no data gaps or timestamp errors within the acquisition period) of the pre-processed historical operational status data. Based on the verification results, unsatisfactory historical operational status data is removed to obtain preliminary admission data. A change point detection algorithm is used to identify the non-steady-state nodes and change time point sets corresponding to the preliminary admission data. A causal network graph is established to infer the local causal structure of the preliminary admission data, completing the timing verification results and outputting the verified historical operational status data. Second, the operational network scenario of the transmission channel between the new energy substation and the new energy central control center main station is analyzed. Node characteristic attributes are described using effective transmission rate and average transmission delay to construct malicious node attack rules. Based on these malicious node attack rules, the operational status of the operational network scenario is detected, and an observation sequence is constructed. The status of the observation sequence is analyzed to verify the malicious obstruction degree of the operational network scenario, and a data transmission channel is established.

[0051] Specifically, a change point detection algorithm is used to identify the non-steady-state nodes and sets of changing time points corresponding to the initial admission data. A causal network graph is established to infer the local causal structure of the initial admission data to complete the time series verification results. The output of verified historical operating status data includes: defining the minimum length of the change point detection algorithm for the initial admission data, such as setting it to 10 collection cycles and the number of jumps, such as setting it to 3 consecutive data fluctuations. Based on the definition results, the Euclidean norm is selected as the cost function to segment the initial admission data, thereby setting a quantitative judgment standard for data segmentation. The Euclidean norm can accurately measure the distance difference between data points, and this segmentation process is based on the optimal partitioning OP algorithm. The quantification logic sets a calculable quantitative criterion for data segmentation, and the Euclidean norm can accurately measure the distance difference between data points, allowing data segmentation to be free from subjective judgment and have a clear mathematical basis. Based on the segmentation results, the consistency cost of the initial admission data within each segmentation interval is determined, which is the sum of the Euclidean norms of all data points and the interval mean within a single segmentation interval. This reflects the stability of the data within the interval and a penalty term for the number of segmentations to avoid over-segmentation. The optimal partitioning OP algorithm is used to screen the change points of the initial admission data, and the cost function is minimized based on the change point locations, thereby improving the accuracy and computational efficiency of data segmentation. Furthermore, the penalty term prevents misjudgment of change points due to over-segmentation. The input data is divided into several data segments of varying lengths. The minimized cost function is transformed into a recursive form, using the minimum cost of the short data segments to derive the minimum cost of the long data segments, thus identifying candidate change points. This recursive derivation process is implemented using the PELT algorithm. The PELT algorithm reduces time complexity by eliminating non-optimal segmentation paths through pruning strategies, aiming to decompose the complex long-sequence data segmentation problem into a short-sequence problem that can be iteratively computed. Change points are selected based on the mean and variance of the candidate change points. For example, a mean deviation exceeding 5% and a variance fluctuation exceeding 10% are set as thresholds for change point determination. The stability of each change point's characteristics across all time periods is also determined; for example, if the characteristics remain unchanged for three consecutive sampling periods, it is considered a change point. The data is defined as steady state, and the points of change over time based on the stable characteristics are output as non-steady-state nodes. Using the non-steady-state nodes as dividing lines, the initial admission data is divided into multiple continuous and non-overlapping time intervals. Data points with no abrupt changes in statistical characteristics (mean, variance, distribution characteristics) within each time interval are selected to form a set of approximately steady-state change time points. This allows the time series data to be divided into steady-state intervals, laying the foundation for subsequent causal analysis. This upgrades time series verification from single-point verification to interval verification, improving the completeness of the verification. Based on the set of change time points, a time series causal graph is established to infer the local causal structure of the initial admission data, determine the time series state of the initial admission data, and output the historical operating state data that has passed the verification.

[0052] It should be explained that both the Optimal Partitioning (OP) algorithm and the Pruned Exact Linear Time (PELT) algorithm are core algorithms for change point detection in time series data analysis. They are used to identify the time points where statistical characteristics (mean, variance, distribution, etc.) of time series data change abruptly, and to achieve optimal data segmentation. The core of the OP algorithm is to achieve optimal segmentation of time series data through global traversal and cost minimization. It uses a cost function to quantify the discreteness of a single data segment (such as Euclidean norm, mean square error), and uses a penalty term to balance segmentation accuracy and over-segmentation. The total cost is the sum of the discrete costs of each segment plus the product of the number of segmentations and the penalty coefficient. By traversing all possible segmentation points, it finds the segmentation scheme that minimizes the total cost and determines the location of the change point.

[0053] The PELT algorithm is an efficient change point detection algorithm optimized based on the OP algorithm. By using dynamic programming and pruning strategies, it reduces the time complexity to linear while retaining the global optimal accuracy of the OP algorithm. It inherits the core of cost minimization of the OP algorithm, transforms the total cost calculation into a recursive form, and eliminates non-optimal splitting paths through pruning conditions. If a candidate splitting point cannot become the optimal solution in subsequent calculations, it is directly discarded, which greatly reduces invalid calculations and achieves efficient processing of massive data.

[0054] Specifically, a time-series causal graph is established based on the set of changing time points to infer the local causal structure of the initial admission data, determine the temporal state of the initial admission data, and output the verified historical operating status data. This includes: constructing an initial undirected complete graph based on the set of changing time points, i.e., containing the relationships of all data dimensions; performing conditional independence tests on the initial undirected complete graph, such as Pearson correlation coefficient and mutual information values; removing edges without conditional independence relationships; and obtaining an undirected causal skeleton based on the removal results, thereby eliminating redundant dimensions without correlation and simplifying the complexity of causal analysis. The direction of the undirected causal skeleton is defined, a static causal graph skeleton corresponding to the time interval of the set of changing time points is generated, and the conditional mutual information of each time change point in the static causal skeleton is analyzed, such as assuming... A mutual information value greater than 0.8 indicates a strong association. Time-series causal association variables are selected based on conditional mutual information. These variables are then used to orient the edges of the static causal graph skeleton temporally, determining the causal direction based on chronological data to obtain the time-series causal graph. This process is repeated to generate time-series causal graphs for each time interval, which are then merged to create a causal network graph. This extends time-series verification from data state verification to logical association verification, ensuring the rationality of the data's temporal logic. A consistency check is performed on the causal network graph to verify the stability of causal relationships across multiple consecutive time intervals. Temporary causal association variables with false causal associations due to fluctuations are removed, and the remaining variables are used as preliminary admission data to meet the time-series state requirements, thus obtaining verified historical operational state data.

[0055] In one embodiment, analyzing the operational network scenario of the transmission channel between the new energy substation and the new energy central control center main station, and constructing malicious node attack rules by describing node characteristic attributes using effective transmission rate and average transmission delay, includes: based on the topology of the transmission channel between the new energy substation and the new energy central control center main station, such as a hierarchical transmission topology of new energy substation, edge aggregation node, regional gateway node, and central control center main station, determining the local transmission channel composed of aggregation nodes. The aggregation node can be an edge gateway node responsible for aggregating and forwarding data from multiple new energy substations. A feature vector set is constructed based on the effective transmission rate and delay status of the aggregation node. The effective transmission rate is the ratio of the number of data packets actually successfully sent by the node to the total number of data packets sent. The delay status is the average time taken for a data packet to travel from node input to output. These two core indicators are integrated into a feature vector set according to the time dimension (e.g., a 5-minute statistical period) and the spatial dimension (e.g., node number). This allows focusing on core nodes and key characteristics from a complex transmission network, transforming the abstract node operating status into a quantifiable one. The system uses vector data to identify malicious node attack types in the transmission between new energy substations and the new energy central control center, such as data tampering attacks and fake data injection attacks. It clarifies the behavioral characteristics of each attack type. For example, in a data tampering attack, malicious nodes intentionally reduce the effective transmission rate, resulting in data being discarded due to format errors, and the average transmission latency abnormally increases. Tampering with data increases processing time. In a fake data injection attack, malicious nodes send forged data; the effective transmission rate appears normal, but the forwarding rate is abnormal and latency fluctuates greatly. These behavioral characteristics are then converted into constraint relationships of feature attributes. For instance, the constraint relationship for a data tampering attack is an effective transmission rate less than a transmission threshold and an average transmission latency greater than a transmission threshold. This establishes a mapping relationship between attack behavior and node feature attributes, transforming qualitative attack behavior into quantitative constraint logic. Through these constraint relationships, logic and relationship judgment rules are set, including situations where both the effective transmission rate and forwarding rate are below the corresponding thresholds. This constructs malicious node attack rules to achieve quantitative judgment of malicious node attack behavior in the transmission channel.

[0056] Specifically, the thresholds for each feature are first determined using historical normal operation data and attack scenario test data. Assuming the effective transmission rate threshold is set to 95% (meaning a normal node's effective transmission rate ≥ 95%), the forwarding rate threshold is set to 90% (meaning a normal node's forwarding rate ≥ 90%), and the average transmission latency threshold is set to 50ms (meaning a normal node's transmission latency ≤ 50ms), the core judgment rule for malicious node attacks is constructed: if a certain aggregation node meets the following conditions—effective transmission rate < 95%, forwarding rate < 90%, or effective transmission rate < 95%, average transmission latency > 50ms—then the node is determined to be a malicious node and is capable of attacking. The system calculates the effective transmission rate deviation, forwarding rate deviation, and latency deviation. If any deviation is ≥20% and meets the above logic and rules, the attack level is determined to be high risk; a deviation of 10%-20% is determined to be medium risk; and a deviation <10% is determined to be low risk. This transforms the constraint relationship into directly executable judgment rules, enabling quantitative and graded judgment of attack behavior. Finally, the malicious node attack rules are embedded into the real-time monitor of the transmission channel, which collects the effective transmission rate, forwarding rate, and average transmission latency of each aggregation node in real time and substitutes them into the mechanism for dynamic judgment. Once a malicious node is identified, an alarm is immediately triggered and channel isolation is initiated.

[0057] The working principle or operation method of the present invention in actual process will be described in detail below.

[0058] Assuming a 110kV renewable energy substation is the implementing entity, the substation starts its data acquisition device and continuously collects historical operating status data of the target electrical equipment for the past 7 days according to a preset acquisition cycle of 5 minutes, collecting a total of 2016 raw records. After the collection is completed, it immediately enters the data preprocessing stage. Assuming that a total of 29 invalid raw data records are removed, 1987 valid data records remain, resulting in a preprocessed historical operating status dataset. Subsequently, the preprocessed data undergoes rationality and time sequence verification. It is set that 43 data records with excessive values, logical contradictions, and disordered timestamps are removed after verification, resulting in 1944 preliminary admission data records.

[0059] For 1944 initial access time-series data points, the core parameters of the algorithm were defined. A minimum sample length was standardized at 10 collection cycles, meaning each data segment must be at least 50 minutes long and contain 50 data points to avoid statistical distortion in short segments. A jump threshold of 3 consecutive data fluctuations was set to eliminate single random disturbances. A penalty coefficient of 0.1 was fixed to balance segmentation accuracy with the risk of over-segmentation. The Euclidean norm was selected as the cost function for the OP algorithm. This cost function measures the dispersion of data within a segmented interval by calculating the sum of the straight-line distances between a single data point and the overall mean of its interval. Greater data fluctuations and higher dispersion result in a higher cost value. Based on the optimal segmentation logic of the OP algorithm, the total cost value was calculated by traversing all possible segmentation points. The total cost represents the discrete cost of each segmented interval. The sum of the costs and the product of the number of partitions and the penalty coefficient are added to avoid over-partitioning and increasing unnecessary computation. Taking the initial admission data of sequence numbers 1-100 as an example, the discrete cost of interval 1-50 is 12.6 and the discrete cost of interval 51-100 is 9.8. The total cost is the sum of the discrete costs of the two intervals plus the penalty value corresponding to the two partitions, resulting in 22.6. If we try to partition into three intervals: 1-30, 31-70, and 71-100, the total cost is the sum of the discrete costs of the three intervals plus the penalty value corresponding to the three partitions, resulting in 21.1. Although the cost is reduced, we need to continue to traverse all partition combinations until we find the partitioning method with the minimum total cost. The OP algorithm completes the initial partitioning of the entire data and the initial selection of the optimal change points, resulting in 126 candidate change point positions, as shown in Table 1.

[0060] Table 1 Initial Optimal Segmentation Execution Details

[0061] The PELT algorithm is used to recursively optimize and prune the initial selection results of the OP algorithm to speed up the process. The total cost calculation logic after minimizing the OP algorithm is transformed into a piecewise recursive form. First, the minimum cost of the first 50 short data segments is calculated to be 12.6. Then, the minimum cost of the first 100 data segments is derived based on this. The minimum cost of the entire dataset is recursively derived in this way, while candidate split points with costs higher than the current optimal value are eliminated. Finally, from the 126 variable points initially selected by the OP algorithm, 42 high-confidence variable point candidate points are locked, and a quantitative screening threshold is set. Calculate the mean and variance of the 10 data points before and after each candidate change point. The determination rule is that if the mean deviation between the early and late stages exceeds 5% and the variance fluctuation exceeds 10%, it is considered a true change point. Taking a candidate change point as an example, the mean of the main transformer voltage for the first 10 data points is 110.2kV and the variance is 0.8, while the mean of the second 10 data points is 115.8kV and the variance is 2.2. The mean deviation reaches 5.08% and the variance fluctuation reaches 175%, which meets the change point determination criteria. However, some candidate points only have a mean deviation of 3.2% and a variance fluctuation of 7%, so they are determined to be follow-up changes. The system identifies spurious fluctuation points and verifies the stability of each candidate point over three consecutive data collection cycles, eliminating temporary fluctuation points and ultimately determining 27 true non-steady-state nodes. Using these 27 nodes as dividing lines, the 1944 initial admission data points are divided into 38 continuous and non-overlapping steady-state time intervals. The mean, variance, and distribution characteristics of the data within each interval are verified to show no abrupt changes, and stable data points within these intervals are selected to form a set of approximately steady-state change time points. Subsequently, an initial undirected complete graph is constructed based on this set, and Pearson correlations are used to analyze the graphs. Conditional independence tests were performed on the numbers (absolute values ​​greater than 0.7 were considered strong associations). Eleven unrelated redundant edges were removed to obtain an undirected causal skeleton. The conditional mutual information of each node was calculated (mutual information values ​​greater than 0.8 were considered strong causal associations). Temporal causal association variables were screened, and causal edges were oriented in chronological order. A temporal causal graph was generated and merged into a global causal network graph. A consistency test was performed (verifying the stability of causal relationships over three consecutive intervals). Six sets of spurious causal association variables were removed, and finally, 1929 historical operational status data that passed the verification were output.

[0062] After successful verification, the communication transmission status between the substation and the central control center is immediately checked, and a secure data transmission channel is established, consisting of the new energy substation, the edge aggregation node (responsible for forwarding data from three new energy substations), the regional gateway node, and the central control center. A feature vector set is constructed focusing on the edge aggregation node, containing two core indicators: the node's effective transmission rate and the average transmission latency. Statistics for the aggregation node over the past 30 minutes are as follows: under normal operation, the average effective transmission rate is 96.2%, the average forwarding rate is 92.7%, and the average transmission latency is 32ms. Malicious attack types and behavioral characteristics are also analyzed, and thresholds are set: a normal threshold of 95% for effective transmission rate, 90% for forwarding rate, and 50ms for average transmission latency.

[0063] Malicious node attack rules and hierarchical judgment logic are constructed: Nodes with an effective transmission rate below 95% and a forwarding rate below 90%, or with an effective transmission rate below 95% and an average transmission latency above 50ms, are judged as malicious nodes. Simultaneously, the deviation of various indicators is calculated: deviation ≥20% is high risk, 10%-20% is medium risk, and <10% is low risk. The operating status of the aggregation node is monitored in real time. If the effective transmission rate is 95.8%, the forwarding rate is 91.3%, and the average transmission latency is 35ms, all indicators are normal, there is no malicious attack behavior, and the malicious obstruction level is 0, then an encrypted data transmission channel is established. The new energy substation adopts the IEC62351 power information encryption transmission protocol, encrypting 1929 verified historical operating status data using AES-256. The established secure transmission channel uploaded data to the main station of the New Energy Central Control Center in batches, with 100 data entries transmitted per batch. The total transmission time was 12 seconds, and the packet loss rate was 0%. After receiving the data, the main station of the Central Control Center conducted a secondary verification, including repeated rationality checks and timing logic verification. It was confirmed that all data was free of anomalies, tampering, and packet loss, with a 100% pass rate for the secondary verification. Subsequently, a standard successful reception receipt frame was sent to the New Energy substation. The New Energy substation received and parsed the receipt in real time, automatically marking the completion of the 7-day historical operating status data upload task, and simultaneously generating an upload log archive. The log recorded a total of 2016 data entries collected, 29 entries removed during preprocessing, 43 entries removed during rationality checks, 15 entries removed during timing checks, and a final upload of 1929 data entries. The average transmission latency was 35ms, and the effective transmission rate was 95.8%.

[0064] Step S3: Based on historical operating status data and target electrical equipment model, establish an inversion scenario that includes full-process error prevention management of the equipment, and run the inversion scenario to simulate and rehearse the operation process of the new energy substation.

[0065] In one embodiment, establishing an inversion scenario based on historical operating status data and a target electrical equipment model, encompassing the entire process of preventing equipment malfunctions, and running the inversion scenario to simulate and pre-run the operation of the new energy substation includes: establishing a set of rules for preventing malfunctions in target electrical equipment operation, interlocking, grounding management, and locking management; converting these rules into logical judgment conditions to build an anti-malfunction rule library; associating the anti-malfunction rule library with the target electrical equipment model to form an anti-malfunction benchmark model; expanding and generating an inversion scenario set covering equipment malfunction simulation based on historical operating status data; inputting the inversion scenario set into the anti-malfunction benchmark model; setting the simulation time and speed based on the input results of the inversion scenario set; driving the anti-malfunction benchmark model to run according to the scenario settings; and outputting the status data of the target electrical equipment at each time point; matching the status data with the logical judgment conditions of the anti-malfunction rule library; identifying and marking the problem risk threshold points of the target electrical equipment during the inversion process; and completing the simulation and pre-run of the new energy substation operation.

[0066] In one embodiment, the inversion scenario set includes primary equipment anti-misoperation simulation scenarios, secondary equipment anti-misoperation simulation scenarios, intelligent grounding management simulation scenarios, intelligent lock control management simulation scenarios, and maintenance isolation interlocking management simulation scenarios. The primary equipment anti-misoperation simulation scenarios include the execution of anti-misoperation constraints during normal operation, fault handling, and condition switching processes of circuit breakers, disconnectors, grounding switches, high-voltage switchgear, and gate equipment. The secondary equipment anti-misoperation simulation scenarios include remote control management of simulated pressure plate status collectors, pressure plate status sensors, and acquisition controllers, pressure plate operation invoicing, and simulation rehearsals. The intelligent grounding management simulation scenarios include the installation and removal of intelligent grounding wires during maintenance work at new energy substations. The intelligent lock control management simulation scenarios include unlocking operations, locking operations, personnel access control, operation authorization management, and unlocking process monitoring of intelligent locks corresponding to electrical equipment in new energy substations. The maintenance isolation interlocking management includes anti-misoperation management simulating the entire process of power outage, voltage testing, grounding wire installation, and maintenance repair during electrical equipment maintenance, interval maintenance, and station-wide power outage maintenance within new energy substations.

[0067] It should be explained that the central control center master station includes the following functions in practical applications:

[0068] 1. Centralized / Split-screen display: The main station displays the primary main wiring diagram of each new energy substation. It can be displayed centrally or in split-screen mode. The main wiring diagram is presented in a flexible manner. The main wiring diagram of each station can be selected through a menu. The equipment can be added or removed flexibly. It features a user-friendly interface, convenient operation and use, and high flexibility.

[0069] For example, in this embodiment, an implementation example of the centralized / split-screen display function is provided as follows:

[0070] Step A1: Using the basic identification information set of the devices to be displayed in the new energy substation, construct a unified physical object identification vector for each device, which includes substation number, bay number, voltage level number, device category number, device name number, installation location number, lock association number, and grounding location association number. Arrange the identification vectors of all devices in rows to form a unified physical object identification matrix.

[0071] Furthermore, step A1 specifically includes:

[0072] A11: For the section to be displayed For each piece of equipment, extract its substation number according to the equipment ledger and drawing numbering rules within the station. Interval numbering Voltage level number Equipment category number Equipment Name and Number Installation location number Lock association number Grounding location associated number This constitutes a unified physical object identifier vector: ;

[0073] All of these numbers are positive integers, used to uniquely identify the physical identity of the device, no longer relying on the name on the graphical interface.

[0074] A12: All The unified physical object identifier vectors of each device are arranged in rows to construct a unified physical object identifier matrix: ;

[0075] The matrix is Row 8, Column 1 The row corresponds to the first The unique physical identity of the device serves as the sole reference for device display and operation in all split-screen views.

[0076] Step A2: Based on the unified physical object identification matrix For each split-screen view, a view description vector containing physical identifier, display coordinates, display level, primitive category and display status is constructed for each device displayed in it. The description vectors of all devices in the same view are arranged in rows to form the object description matrix of the view. Finally, the object description matrix set of all split-screen views is output.

[0077] Furthermore, step A2 specifically includes:

[0078] A21: Regarding the first Physical objects in a split-screen view The matrix output from step A1 Extract its unified physical object identifier from it. Combined with the object's horizontal display coordinates in the current view Vertical coordinate display Display hierarchy number Element category number (Retrieve self-wiring elements, bay elements, maintenance elements, or interlocking elements), display status number. (Taken from normal, selected, locked, under maintenance, or inoperable), forming the view description vector:

[0079] It should be noted that this vector binds the device's physical identity to its display attributes in the current view.

[0080] A22: The first All in each split-screen view The view description vectors of each device are arranged by device number, forming the object description matrix of that view: ;

[0081] If a device is not displayed in this view, its coordinate items... , and status items Empty, but physical identifier It remains in use.

[0082] A23: Repeat A21 and A22, traversing all split-screen views to obtain the object description matrix corresponding to each view, forming a set of view object description matrices: ;

[0083] in The collection represents the total number of split-screen views. It injects a unified physical object identifier into the display object of each split-screen view, providing a unified data foundation for building cross-view binding relationships.

[0084] Step A3: Describe the set of objects in each split-screen view. For the same physical object in any two split-screen views, calculate its identity consistency item, hierarchy consistency item and state consistency item respectively, sum them to obtain the binding consistency value, and determine whether the cross-view binding is valid by using a preset threshold. Finally, summarize the binding results of all objects and all view pairs into a cross-view semantic binding matrix.

[0085] Furthermore, step A3 specifically includes:

[0086] A31: For any two different split-screen views and and physical objects From the view object description matrix and Extract the view description vectors of the object in the two views respectively. and and define binding consistency values The calculation method is based on identity consistency items. Hierarchical consistency items Consistent with state item sum: ;

[0087] A32: Calculate the three consistency items separately, including: identity consistency item. Hierarchical consistency items Consistent with state item ;

[0088] The identity consistency item Acquisition strategy: When and Uniform Physical Object Identifier The value is 1 if they are exactly the same, otherwise it is 0.

[0089] The hierarchical consistency item Based on display hierarchy number and The absolute difference is calculated using the following formula: ;

[0090] in, The maximum allowed level difference ranges from 1 to 10.

[0091] The state consistent item Based on the displayed status number and The absolute difference is calculated using the following formula: ;

[0092] in The maximum allowed state number difference ranges from 1 to 20.

[0093] A33: Set Binding Threshold The value ranges from 2.6 to 3.0. If the calculated binding consistency value satisfies: ;

[0094] Then determine the physical object In view With View Cross-view binding is established, meaning that the same physical device is displayed in both views.

[0095] A34: Traverse all physical objects ( ) and all views ( ), and calculate the binding consistency value each time. Write the cross-view semantic binding matrix:

[0096] It should be noted that this matrix is ​​a three-dimensional structure. The first dimension corresponds to the physical object number, and the second and third dimensions correspond to different split-screen view numbers, which are used to quantitatively describe the binding relationship of each device among all view pairs.

[0097] Step A4: Based on the cross-view semantic binding matrix When an operator selects a display object in any split-screen view, the system uses the unified physical object identifier corresponding to that object as the core, summarizes its binding consistency value with all other split-screen views, calculates the unique operation object resolution value, and determines whether the object is a valid unique operation object through a preset threshold. Finally, a unique operation object resolution table is generated to uniquely convert the selected object in the view into an executable physical object.

[0098] Furthermore, step A4 specifically includes:

[0099] A41: When the operator is in split-screen view When a display object is selected, the system extracts the Uniform Physical Object Identifier (UPI) corresponding to that object. and from the cross-view semantic binding matrix Read the object and all other split-screen views. ( The binding consistency value of ) Summing yields the unique operand's parsed value: ;

[0100] It should be noted that this resolution value is used to quantify the cross-view binding strength of the object across all split-screen views.

[0101] A42: Set a unique resolution threshold The value ranges from 7.8 to 12.0, and the specific value is determined by the total number of split-screen views.

[0102] If the calculated analytical value satisfies: If so, the object is determined to be a valid and unique operation object, and its parsing result flag is set. ;otherwise .

[0103] A43: Unify physical object identification Unique operation object resolution value Parsing result flag Combine them to form a unique operand resolution table: ;

[0104] It should be noted that this table records the parsing status of each device under the currently selected operation.

[0105] A44: After the parsing table is generated, the system will perform statistics on it. The number of entries, including:

[0106] If there are two or more objects that satisfy If this is not the case, confirmation of the target's landing point for this operation will be prohibited to prevent operational ambiguity.

[0107] If only one object satisfies If so, the object will be output as the sole operation object for use in subsequent operations.

[0108] It should be noted that, through this mechanism, click actions in any split-screen view no longer directly affect the primitives, but are first mapped to a unique physical object through a parsing table, thus suppressing cross-view semantic drift.

[0109] Step A5: Resolve the table based on the unique operation object For the unique physical object whose parsing result flag is 1, consistent display control is performed in all split-screen views. By calculating the consistent display flag value and constructing the consistent display matrix, the same device is visualized in all views with the same identity, the same landing point, and the same state, forming a cross-split-screen consistent display closed loop.

[0110] Furthermore, step A5 specifically includes:

[0111] A51: Regarding parsing tables Parsing result flag bit The only physical object Traverse the entire split view ( Defines the presence identifier of the object in the current view. If the object In view If a display element exists, the value is 1; otherwise, the value is 0. A consistent display flag value is then calculated. ;

[0112] It should be noted that this flag value is used to identify whether the unique physical object needs to be synchronized for display in each view.

[0113] A52: Combine the consistent display marker values ​​of all physical objects and all split-screen views by row and column to form a consistent display matrix: ;

[0114] in For physical object serial number, This matrix serves as the split-screen view sequence number, recording the synchronous display control identifier for each unique physical object across all views.

[0115] A53: Based on the consistent display matrix The values ​​of each element in the table are used to execute the corresponding display control strategies, including:

[0116] like In this view physical objects Simultaneously execute the same color highlighting, the same number prompting, and the same status locking;

[0117] like In this view The system displays a "Related objects not expanded" message, but retains the unified object number to ensure that the object's identity is not lost.

[0118] It should be noted that, through the above mechanism, the main wiring diagram, interval diagram, maintenance diagram, and lock control diagram always provide the same visual results for the same device, with the same identity, the same landing point, and the same status. The parsing results of the unique operation object are synchronized back to all split-screen views, eliminating the cross-view semantic drift problem where the screens appear to show the same device but the execution is not the same device.

[0119] 2. The main station has microcomputer anti-misoperation functions for each substation: It can realize centralized ticketing and decentralized operation. It can issue tickets and simulate the operation tasks of any substation in the central control center, perform five-prevention interlocking logic judgment to check whether the operation ticket is correct, and transmit the switching operation ticket to the computer key. Then, the operator takes the computer key to the corresponding unit control room to perform the switching operation on site. Alternatively, the operation ticket can be directly transmitted to the communication adapter in the unit control room and transmitted to the computer key through communication, and then the switching operation is performed on site.

[0120] 3. Supports inter-station interlocking function: The entire centralized control station can realize complete interlocking of equipment on the communication lines between stations, preventing serious accidents such as connecting the grounding wire / closing the grounding switch on the opposite side when there is a load on this side, or closing the disconnect switch on the opposite side when there is a grounding wire / grounding switch on this side.

[0121] 4. The main station has the function of receiving information feedback: After the operation is completed, whether the feedback is sent directly from the main station by the computer key or sent back to the substation via communication, the main station can receive the feedback information after the operation is completed in order to track the changes in the status of the field equipment.

[0122] 5. Alignment function between the main station and each substation: The main station and substations can be aligned online in real time and automatically display changes in equipment status to ensure consistency in equipment status between the main station and substations.

[0123] 6. Unique Operation Right Function: The system features a unique operation right function to ensure the exclusivity of operations. Before each operation, the substation must apply for operation right from the master station of the anti-misoperation system. Different substations can simultaneously apply for operation right from the master station to conduct simulations and operations in their respective unit's control room. However, once a substation has obtained operation right, the master station system cannot issue invoices for that substation. Similarly, when the master station obtains operation right for a substation, that substation cannot issue invoices for that substation. This ensures the uniqueness of operation right and improves the security of the centralized control system operation.

[0124] 7. Comprehensive communication functions: Through various communication methods, the main station equipment can communicate with each substation, the main station monitoring system, the main station operation ticket expert system, and other equipment to realize functions such as mutual remote signal transmission and interlocking remote control operation.

[0125] 8. Multi-task operation function: It has the function of multi-task parallel operation, which means that multiple operation tasks can be started at the same time on the five-proof system, and multiple operators can use different computer keys to perform different operation tasks at the same time.

[0126] 9. Monitoring error prevention verification function: The monitoring sends a verification message to the error prevention server. The error prevention server performs a logical judgment. If it passes, it sends an allow message to the monitoring backend; otherwise, it sends a disallow message.

[0127] 10. It can receive virtual remote signaling equipment information (ground wire, network gate, etc.) sent by the central control substation and use it as a condition for error prevention analysis to avoid misoperation caused by information asymmetry between the central control center and the field. Error prevention verification is added to each link of the central control center's compilation, circulation, command issuance and specific operation to constrain the central control center's command issuance, remote control and switching operation behavior, and avoid the occurrence of erroneous remote control, misoperation and non-compliance with the control order.

[0128] 11. The anti-misoperation system of the central control center adopts a multi-layered model of server / client structure. An anti-misoperation central control server is set up at the central control center to centrally manage, statistically analyze, and query all data, and realize the status feedback and anti-misoperation interlocking functions of the anti-misoperation systems of all subordinate power stations.

[0129] 12. Supports one machine for multiple stations and one station for multiple diagrams. Users can select the anti-misoperation data of the corresponding stations within their jurisdiction to perform simulations and ticketing with anti-misoperation logic judgments, so as to verify the switching operation tickets.

[0130] 13. The first batch of 8 new energy substation anti-misoperation interlocking systems will be connected. The main station will reserve access interfaces for no less than 100 substations, and the access interfaces must be scalable.

[0131] 14. The central control center, operation and maintenance center, and each controlled station can simulate and rehearse, issue tickets, transmit tickets and receive tickets, realize hierarchical management, and support the interlocking of communication line equipment between controlled substations.

[0132] 15. It should have the uniqueness of the operating rights and be able to realize the transfer of operating rights and hierarchical management.

[0133] 16. Supports communication with the central control center monitoring system to receive equipment status and remote control operations from the interlock monitoring system.

[0134] 17. Supports data maintenance and software upgrades on any client, with the main and sub-sites updating synchronously after data changes.

[0135] 18. It has a strict and comprehensive user permission hierarchical management function, which can define the permissions that each operator has when using the device, and can specifically define the level at which the operator can operate the device.

[0136] 19. The system records the status of switching operations, including detailed information such as the executed items, unexecuted items, and operation time for each switching operation, and allows querying and accessing the operation content by operation time.

[0137] 20. Supports interface with production management system to realize operation ticket information exchange.

[0138] Meanwhile, it is assumed that 8 new energy substation anti-misoperation systems will be connected during application. The specific number of connections and the names of the substations will be determined by the construction unit based on the actual situation.

[0139] Assuming a comprehensive intelligent anti-misoperation upgrade is implemented for the booster stations of eight new energy substations, the system will incorporate potential operational risks at various stages of operation and maintenance into intelligent safety management. This includes: primary equipment anti-misoperation, secondary equipment anti-misoperation, intelligent grounding management, intelligent lock control management, and maintenance isolation interlocking management. It integrates control, monitoring, management, and anti-misoperation, with seamless connectivity between functions. Specific requirements are as follows: Independent microcomputer-based anti-misoperation function: Configured with anti-misoperation locks and accessories tailored to the actual conditions within the substation, ensuring anti-misoperation operations can be completed within the substation even without simulation by the central control center; and robust communication with the central control center: capable of uploading substation equipment status and receiving operation ticket information from the central control room's anti-misoperation master station, achieving the goal of central control center simulation and substation operation.

[0140] Step S3, based on verified historical operating status data and the target electrical equipment model, establishes an inversion scenario including full-process equipment error prevention management and conducts simulation exercises. Combining the 20 core functions of the central control center's main station and the comprehensive intelligent error prevention transformation requirements of the 8 new energy substations and booster stations, it needs to establish full-process equipment error prevention rules. This includes five major error prevention functions: primary equipment error prevention, secondary pressure plate error prevention, intelligent grounding management, intelligent lock control management, and maintenance isolation interlocking. Based on the Ministry-issued five-prevention requirements, substation error prevention transformation details, and rules regarding inter-station interlocking, unique operating rights, and monitoring error prevention verification at the main station, it identifies circuit breaker erroneous opening / closing interlocking, load-bearing opening / closing isolation switch interlocking, live grounding wire interlocking, and pressure plate illegal operation alarms. The system incorporates comprehensive anti-misoperation rules, including authorization control for ground wire access, access restrictions for smart locks, and arming / disarming for maintenance and isolation. Each rule is transformed into quantifiable logical judgment conditions. For example, determining whether a circuit breaker is closed requires that there be no load, no grounding switch closed, and no accidental entry into a live compartment. Determining whether a pressure plate operation is performed requires system invoicing simulation and permission verification. Determining whether a smart lock is unlocked requires that it be done within the authorized time and range and that personnel permissions match. Determining whether a ground wire is installed requires voltage verification and authorization. Determining whether maintenance and isolation is performed requires arming logic verification and hazard identification. This system builds a standardized anti-misoperation rule library, which can then integrate the anti-misoperation control requirements of the main and substations across all scenarios, transforming vague anti-misoperation requirements into executable, quantifiable judgment logic.

[0141] The established anti-misoperation rule base is linked and bound to the target electrical equipment model. The target electrical equipment model covers all equipment in the eight new energy substations and booster stations, including primary equipment (circuit breakers, disconnect switches, grounding switches, high-voltage switchgear, and grid gates), secondary equipment (pressure plates, data collectors, and sensors), smart locks, grounding devices, and maintenance interlocking devices. After association, an anti-misoperation benchmark model with anti-misoperation verification capabilities is formed. Simultaneously, 1,929 historical operating status data that have passed verification are retrieved. Based on the historical data, an inversion scenario set covering all scenarios is generated. The inversion scenario set includes primary equipment anti-misoperation simulation scenarios, secondary equipment anti-misoperation simulation scenarios, intelligent grounding management simulation scenarios, intelligent lock control management simulation scenarios, and maintenance isolation interlocking devices. The simulation scenarios include 12 types of operating conditions, such as normal closing of a 35kV circuit breaker, accidental closing of a disconnecting switch under load, and hanging a grounding switch while energized; 8 types of operating conditions, such as remote activation / deactivation of pressure plates, illegal operation of pressure plates, and pressure plate inspection; 5 types of operating conditions, such as removal and placement of 6 sets of ground wires, missing ground wires, and incorrect placement of ground wires; 7 types of operating conditions, such as unlocking of terminal box doors, unlocking without authorization, and unlocking after a timeout; and 4 types of operating conditions, such as single-bay maintenance, station-wide power outage maintenance, and arming / disarming of isolation systems. This allows for the connection between the anti-misoperation rules and the equipment model, generating inversion scenarios that fit actual operation and maintenance based on real historical data.

[0142] Based on the input results of the inversion scenario set, simulation parameters were set, with a total simulation duration of 120 minutes, a simulation speed twice the real-time speed, and a single-scenario simulation interval of 5 minutes. The anti-misoperation benchmark model was driven to run scenario by scenario according to the set process. The model collected and output the status data of the target electrical equipment at each time node in real time, including the opening and closing status of primary equipment, the activation and deactivation status of secondary pressure plates, the locking status of smart locks, the grounding status, the maintenance isolation and arming status, the operation permission status, and remote signaling alarm signals. The model tracked the status changes of the equipment throughout the entire process in real time, and finally compared the real-time status data output by the model with... The logical judgment conditions of the anti-misoperation rule base are matched and verified one by one. Based on the rules of main station monitoring anti-misoperation verification, unique operation rights, inter-station interlocking, and hierarchical access control, the problem risk threshold points in the inversion process are identified and marked. For example, risk points such as the opening and closing of disconnect switches under load, illegal operation of pressure plates, unauthorized unlocking, missing ground wires, and failure to disarm during maintenance isolation are marked. Risk level thresholds are set (Level 1 risk: violation of the five-prevention core rules, Level 2 risk: violation of access control rules, Level 3 risk: deviation in operating conditions) to complete the full-process simulation and rehearsal of the operation of 8 new energy substations.

[0143] Taking the No. 1 110kV step-up substation, one of the eight new energy substations connected to the central control station, as the object, a full-process anti-misoperation inversion simulation was conducted. The initial anti-misoperation rule library included 168 anti-misoperation logic judgment conditions, including 42 rules for primary equipment anti-misoperation, 36 rules for secondary pressure plates, 32 rules for intelligent lock control, 28 rules for intelligent grounding wires, and 30 rules for maintenance interlocking. This was linked to equipment models of the substation, including 12 circuit breakers, 24 sets of disconnect switches, 16 sets of grounding switches, 96 secondary pressure plates, 620 sets of intelligent locks, and 8 sets of intelligent grounding wires, forming an anti-misoperation benchmark model. 1929 historical operating status data points from the substation that had passed verification were retrieved, and 36 inversion scenarios were generated. The simulation duration was set to 120 minutes. The simulation speed is doubled, and after the driving model runs, it outputs the status data of each device in real time. After matching and verification with the anti-misoperation rule base, it identifies 2 level 3 risk points (simulated ground wire misplacement, pressure plate inspection timeout) and 0 level 2 and level 1 risk points. It marks the risk threshold points and generates a pre-drill report. At the same time, it verifies that the main station's centralized ticketing, split-screen display, inter-station interlocking, unique operation rights, data synchronization, and 100 reserved interfaces for the site are normal. The sub-station's independent anti-misoperation, remote signaling upload, and operation ticket receiving functions are running stably. The hardware parameters such as smart lock ID code (≥65000), unlocking life (≥10000 times), and ground wire head unlocking and locking operations (≥5000 times) meet the transformation requirements, making the whole simulation pre-drill fit the on-site operation and maintenance process.

[0144] Step S4: Generate the anti-misoperation verification basis data for the new energy substation based on the simulation and pre-drill results. When the new energy substation applies for operation rights, compare the real-time operating status data of the target electrical equipment with the anti-misoperation verification basis data, and output the operation ticket corresponding to the operation rights based on the comparison results.

[0145] In one embodiment, step S4 first involves extracting qualified operating condition data from the simulation pre-run that has passed the anti-misoperation rule verification and is free of potential risks. This data covers core elements such as the permissible operation thresholds, operating boundaries, permission matching rules, inter-station interlocking conditions, and risk assessment criteria for the target electrical equipment. Abnormal risk points and non-compliant operating condition data marked during the pre-run are removed. The data is then categorized and organized according to the new energy substation number, equipment type, and operating scenario to form a standardized and reusable anti-misoperation verification database. This database is synchronized to the anti-misoperation server of the central control center and the terminals of each new energy substation to ensure real-time data alignment and a unified benchmark between the main and substations. Simultaneously, when a new energy substation initiates or accepts an operation right application, the substation maintenance personnel submit the operation right to the central control center through the substation's anti-misoperation terminal based on on-site maintenance needs. The application clearly specifies key information such as the substation number, target electrical equipment number, operation type, operation time period, and operator access ID. Upon receiving the application, the main station's anti-misoperation system immediately initiates a unique operation right verification, locking the operation rights of the corresponding substation and prohibiting simultaneous invoicing operations on the same equipment between the main station and substations, or between substations, ensuring the uniqueness of operation rights. The main station's anti-misoperation system issues instructions through the communication link, instructing the new energy substation to collect real-time operating status data of the target electrical equipment, including equipment opening and closing status, current and voltage parameters, grounding switch position, secondary pressure plate activation and deactivation status, smart lock locking status, grounding wire installation status, remote signaling alarm signals, and other full-dimensional real-time information. At the same time, it accurately retrieves the verification benchmark data for the corresponding substation, equipment, and operation scenario from the local anti-misoperation verification database.

[0146] The collected real-time operating status data of the target electrical equipment is precisely compared item by item with the retrieved anti-misoperation verification data. The key checks are whether the real-time status of the equipment is within the allowed operating range, whether the operator's permissions match, whether the operating time period is compliant, whether the inter-station interlocking logic is met, and whether there are any unresolved alarm risks. If the comparison finds violations such as real-time data exceeding the standard, permission mismatch, or interlocking conditions not being met, the operation right application is immediately rejected and the specific reason for the anomaly is pushed to the substation terminal. If all comparison results meet the verification requirements, the anti-misoperation verification is deemed to have passed. After the anti-misoperation verification is passed, the central control center automatically generates a standardized operation ticket for the corresponding operation right based on the qualified real-time status, the applied operation type, and the preset anti-misoperation rules. The operation ticket includes detailed operation steps, equipment number, anti-misoperation verification items, safety precautions, etc. After completing internal automatic review, the operation ticket is issued to the new energy substation terminal and the on-site computer key, and the corresponding substation operation right is officially authorized and the operation right is marked.

[0147] Step S5: Configure the corresponding operation permissions of various operators when the operation ticket is executed through the permission hierarchical management rules, and send the operation permissions to the visualization platform of each new energy substation to facilitate intelligent error prevention for various operators.

[0148] In one embodiment, the corresponding operation permissions for various operators are configured through hierarchical permission management rules when an operation ticket is executed, and the operation permissions are sent to the visualization platform of each new energy substation to facilitate intelligent error prevention for various operators, including:

[0149] Based on the operation and maintenance process of new energy substations, the operation permission levels are divided according to the operation positions. The operation permission rules for different types of operators are output to determine the operation permissions of operation tickets, and the binding relationship between the permission rules and the operation ticket execution process is established.

[0150] Configure permission information for different types of operators based on the binding relationship, and classify and package the permission information according to the dimension of new energy substation. Send the permission information to the new energy substation visualization platform through the communication link between the new energy center and the new energy substation, so that various operators can execute operation ticket processing according to the permission information and complete intelligent error prevention processing.

[0151] It needs to be explained that, based on the complete operation and maintenance process of the new energy substation, the operation permission levels are precisely divided according to different operational positions such as the central control center administrator, substation operation and maintenance manager, on-site operator, maintenance personnel, and monitoring personnel. For example, the central control center administrator has the authority to review, authorize, and cancel all operation tickets; the substation operation and maintenance manager has the authority to initiate and review operation tickets; the on-site operator only has the authority to execute authorized operation tickets; the maintenance personnel only has the authority to confirm maintenance operation tickets; and the monitoring personnel has the authority to supervise and suspend the execution process of operation tickets. At the same time, the specific operation permission boundaries of each level of operator are clearly defined, such as whether they can modify operation steps, whether they can skip the verification process, and whether they can cross... Standardized permission rules are output for equipment operation, whether timeouts are allowed, etc., and a binding relationship is established between the permission rules and the entire process of operation ticket execution (invoicing, simulation, review, issuance, execution, feedback, and archiving). It is stipulated that personnel with different permission levels can only perform compliant operations at corresponding process nodes, thereby transforming abstract permission management requirements into implementable hierarchical rules and deeply binding them to the entire process of operation ticket execution. Based on the established binding relationship between permission rules and operation ticket execution process, specific permission information for each type of operator is configured. The permission information includes quantitative content such as personnel ID, substation, operation position, permission level, scope of operable equipment, type of operation ticket that can be executed, operation time limit, and operation review requirements.

[0152] For example, configuring permission information for on-site operators: Personnel ID is CZ-001, Substation Affiliation is New Energy Substation No. 1, Permission Level is 3, Operable Equipment is 35kV Switchgear, Operable Operation Ticket Type is Closing / Opening, Operation Time Limit is 2 hours, and Supervision Personnel Verification is Required. After completing the permission information configuration, the permission information is categorized and packaged according to the dimensions of the 8 New Energy Substations to ensure that the permission information is accurately matched with the substation equipment and operation and maintenance scenarios. Relying on the encrypted communication link between the New Energy Central Control Center and each substation (following the IEC62351 Power Information Encryption Transmission Protocol), the categorized information is then further processed. The subsequent permission information is distributed in batches to the visualization platform of the corresponding new energy substation. The visualization platform needs to intuitively display information such as the operator's name, permission level, scope of operation, and operation restrictions. It should support operators to query their own permissions in real time and managers to dynamically adjust permissions. When various operators execute operation tickets, the visualization platform will first verify the matching of their permission information with the current operation ticket execution node. For example, if an on-site operator tries to modify the operation ticket steps, the platform will immediately trigger permission verification and prohibit the operation, only allowing them to execute authorized operation steps, thus completing intelligent error prevention.

[0153] like Figure 2 As shown, according to another embodiment of the present invention, an intelligent anti-misoperation system for a new energy centralized control center with split-screen display is also provided, the system comprising:

[0154] Electrical Equipment Model Building Module 1 is used to build target electrical equipment models based on the modeling elements of each target electrical equipment in the New Energy Center;

[0155] The operation status data upload module 2 is used to collect historical operation status data of the target electrical equipment using the new energy substation, and upload the historical operation status data to the new energy central control center according to the transmission mechanism after completing the safety verification.

[0156] The substation operation simulation and pre-play module 3 is used to establish an inversion scenario that includes the whole process of equipment error prevention management based on historical operation status data and target electrical equipment model, and to run the inversion scenario to simulate and pre-play the operation process of the new energy substation.

[0157] Operation ticket application output module 4 is used to generate anti-misoperation verification basis data for new energy substations based on simulation and pre-drill results, and when a new energy substation applies for operation rights, it compares the real-time operating status data of the target electrical equipment with the anti-misoperation verification basis data, and outputs the operation ticket corresponding to the operation rights based on the comparison results.

[0158] The operation permission configuration error prevention module 5 is used to configure the corresponding operation permissions of various operators when the operation ticket is executed through the permission hierarchical management rules, and send the operation permissions to the visualization platform of each new energy substation to facilitate intelligent error prevention for various operators.

[0159] Furthermore, the present invention also provides an electronic device. For example... Figure 3 The diagram illustrates the hardware operating environment of an electronic device, which may include: a processor (e.g., CPU), memory, a user interface, a network interface, and a communication bus. The communication bus is used to enable communication between components. The user interface may include a display screen and an input unit such as a keyboard; optionally, the user interface may also include a standard wired interface or a wireless interface. The network interface may optionally include a standard wired interface or a wireless interface. The memory may be high-speed RAM or stable memory, such as a disk storage device. Alternatively, the memory may be a storage device independent of the aforementioned processor.

[0160] Those skilled in the art will understand that Figure 3 The electronic devices shown do not constitute a limitation on electronic devices and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0161] like Figure 3 As shown, a memory, as a type of computer storage medium, may include an operating system, a network communication module, a user interface module, and device management programs. The operating system is a program that manages and controls the hardware and software resources of electronic devices, supporting the operation of electronic devices and other software or programs. Figure 3 In the electronic device shown, the user interface is mainly used to connect to the terminal and communicate with the terminal, such as receiving user signaling data sent by the terminal; the network interface is mainly used to communicate with the backend server; the processor can be used to call the program stored in the memory and execute the steps of the method or system described above.

[0162] Furthermore, the present invention also proposes a computer-readable storage medium storing a device management program, which, when executed by a processor, implements the steps of the method or system described above.

[0163] The specific embodiments of the computer-readable storage medium of the present invention are basically the same as those of the above-described methods or systems, and will not be repeated here. Furthermore, to achieve the above objectives, the present invention also provides a computer program product, comprising: a computer program, which, when executed by a processor, implements the steps of the methods or systems described above.

[0164] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0165] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for preventing errors in a new energy centralized control center with split-screen display, characterized in that, The method includes: Based on the modeling elements of each target electrical equipment in the new energy center, construct a model of the target electrical equipment; The historical operating status data of the target electrical equipment is collected by the new energy substation, and after the safety verification is completed, the historical operating status data is uploaded to the new energy central control center according to the transmission mechanism. Based on historical operating status data and target electrical equipment models, an inversion scenario is established that includes full-process anti-misoperation management of the equipment, and the inversion scenario is run to simulate and rehearse the operation process of the new energy substation. Based on the simulation results, the data for preventing misoperation of the new energy substation is generated. When the new energy substation applies for operation rights, the real-time operating status data of the target electrical equipment is compared with the data for preventing misoperation. Based on the comparison results, the operation ticket corresponding to the operation rights is output. By configuring the corresponding operation permissions of various operators when the operation ticket is executed through hierarchical permission management rules, the operation permissions are sent to the visualization platform of each new energy substation to facilitate intelligent error prevention for various operators.

2. The intelligent anti-misoperation method for a new energy centralized control center with split-screen display according to claim 1, characterized in that, The process of collecting historical operating status data of target electrical equipment using new energy substations and uploading the historical operating status data to the new energy central control center according to the transmission mechanism after completing safety verification includes: The new energy substation collects historical operating status data, including physical operating data and virtual remote signaling status of the target electrical equipment, according to a preset collection cycle, and preprocesses the historical operating status data. The preprocessed historical operating status data is validated for rationality and timing. After the validation is passed, the communication transmission status between the new energy substation and the new energy central control center main station is checked, and a data transmission channel is established. The new energy substation transmits the verified historical operating status data to the central control center station from the data transmission channel in accordance with the power information encryption transmission protocol. The new energy central control center receives the historical operating status data and performs secondary verification. After the historical operating status data is verified twice, the New Energy Control Center sends a receipt to the New Energy Substation. The New Energy Substation receives the receipt and confirms that the historical operating status data upload is complete.

3. The intelligent anti-misoperation method for a new energy centralized control center with split-screen display according to claim 2, characterized in that, The process of performing rationality and timing checks on the preprocessed historical operating status data, and then, after passing the checks, detecting the communication transmission status between the new energy substation and the new energy central control center main station to establish a data transmission channel includes: The numerical range, logical state, and temporal continuity of the preprocessed historical operating status data are verified. Based on the verification results, historical operating status data that does not meet the requirements are removed to obtain preliminary admission data. The change point detection algorithm is used to identify the non-steady-state nodes and change time point sets corresponding to the preliminary admission data, and a causal network diagram is established to infer the local causal structure of the preliminary admission data in order to complete the time series verification results and output the historical running status data that has passed the verification. The operation network scenario of the transmission channel between the new energy substation and the new energy central control center main station is analyzed, and malicious node attack rules are constructed by describing the node characteristic attributes with effective transmission rate and average transmission delay. Based on malicious node attack rules, the operational status of the running network scenario is detected and an observation sequence is constructed. The status of the observation sequence is analyzed to verify the degree of malicious obstruction of the running network scenario and establish a data transmission channel.

4. The intelligent anti-misoperation method for a new energy centralized control center with split-screen display according to claim 3, characterized in that, The algorithm for detecting change points is used to identify the set of non-steady-state nodes and change time points corresponding to the preliminary admission data. A causal network graph is then established to infer the local causal structure of the preliminary admission data, thereby completing the time series verification results. The output of historical operating status data that has passed the verification includes: The minimum length of the sample and the number of hops for the change point detection algorithm are defined for the initial admission data, and the Euclidean norm is selected as the cost function to segment the initial admission data based on the definition results. Based on the segmentation results, determine the consistency cost and the penalty term for the number of segmentations of the initial admission data within each segmentation interval, filter the change points of the initial admission data, and minimize the cost function based on the change points. The initial admission data is divided into several data segments of different lengths. At the same time, the minimized cost function is transformed into a recursive form. The minimum cost of the short data segment is used to derive the minimum cost of the long data segment, thus locking in the candidate points of change. Variable points are selected based on the mean and variance of candidate variable points, and the characteristic stability of each variable point is determined over all time periods. Based on the characteristic stability results, the variable points that change over time are output as non-steady-state nodes. Using non-steady-state nodes as dividing lines, the initial admission data is divided into multiple continuous and non-overlapping time intervals. Data points with no abrupt changes in statistical characteristics within each time interval are selected to form a set of approximately steady-state change time points. A time-series causal graph is established based on the set of changing time points. The local causal structure of the preliminary admission data is inferred, the temporal state of the preliminary admission data is determined, and the historical operation status data that has passed the verification is output.

5. The intelligent anti-misoperation method for a new energy centralized control center with split-screen display according to claim 4, characterized in that, The process of establishing a time-series causal graph based on the set of changing time points, inferring the local causal structure of the preliminary admission data, determining the temporal state of the preliminary admission data, and outputting historical operational status data that has passed verification includes: An initial undirected complete graph is constructed based on the set of changing time points, and the conditional independence of the initial undirected complete graph is checked to remove edges that do not have conditional independence relationships. Based on the removal results, an undirected causal skeleton is obtained. Define directions for the undirected causal skeleton, generate a static causal graph skeleton corresponding to the time interval of the set of changing time points, analyze the conditional mutual information of each time point in the static causal skeleton, and filter time-series causal correlation variables based on the conditional mutual information. Temporal orientation of the edges of the static causal graph skeleton is achieved by using temporal causal correlation variables to obtain a temporal causal graph. The temporal causal graphs corresponding to each time interval are repeatedly generated and then merged to generate a causal network graph. A consistency check is performed on the causal network diagram to remove time-series causal variables that have false causal relationships due to fluctuations. The remaining time-series causal variables are used as preliminary admission data that meet the time-series state requirements in order to obtain historical operating state data that has passed the verification.

6. The intelligent anti-misoperation method for a new energy centralized control center with split-screen display according to claim 5, characterized in that, The analysis of the operational network scenario of the transmission channel between the new energy substation and the new energy central control center main station, and the construction of malicious node attack rules based on the node characteristic attributes described by the effective transmission rate and the average transmission delay, includes: Based on the topology of the transmission channel between the new energy substation and the new energy central control center main station, a local transmission channel composed of aggregation nodes is determined, and a feature vector set is constructed according to the effective transmission rate and delay status of the aggregation nodes. Based on the feature vector set, the types of malicious node attacks transmitted between the new energy substation and the new energy central control center main station are sorted out, the behavioral characteristics of the attack types are identified, and the behavioral characteristics are converted into the constraint relationship of feature attributes. By setting logical and relational judgment rules that include effective sending rate and forwarding rate both being lower than corresponding thresholds, malicious node attack rules are constructed to achieve quantitative judgment of malicious node attack behavior in the transmission channel.

7. The intelligent anti-misoperation method for a new energy centralized control center with split-screen display according to claim 1, characterized in that, The process of establishing an inversion scenario based on historical operating status data and target electrical equipment models, which includes full-process error prevention management of the equipment, and running the inversion scenario to simulate and rehearse the operation of the new energy substation includes: Establish a set of rules to prevent operational errors of target electrical equipment, prevention of interlocking errors, prevention of ground wire management errors, and prevention of interlocking management errors, and transform these rules into logical judgment conditions to build an error prevention rule library; The anti-misoperation rule base is associated with the target electrical equipment model to form an anti-misoperation benchmark model. Based on historical operating status data, an inversion scenario set covering the equipment anti-misoperation simulation is generated and the inversion scenario set is input into the anti-misoperation benchmark model. Based on the input results of the inversion scenario set, the simulation time and simulation speed are set, and the anti-misoperation benchmark model is driven to run according to the scenario setting process, and the status data of the target electrical equipment at each time node is output. The status data is matched with the logical judgment conditions of the anti-misoperation rule base to identify and mark the problem risk threshold points of the target electrical equipment in the inversion process, and complete the simulation and pre-running of the new energy substation operation.

8. The intelligent anti-misoperation method for a new energy centralized control center with split-screen display according to claim 7, characterized in that, The inversion scenario set includes primary equipment anti-misoperation simulation scenario, secondary equipment anti-misoperation simulation scenario, intelligent ground wire management simulation scenario, intelligent lock control management simulation scenario, and maintenance isolation interlocking management simulation scenario. The primary equipment anti-misoperation simulation scenario includes the execution of anti-misoperation constraints during normal operation, fault handling, and working condition switching of circuit breakers, disconnectors, grounding switches, high-voltage switch cabinets, and gate equipment. The simulation scenario for preventing errors in secondary equipment includes remote control and management of the simulated pressure plate status collector, pressure plate status sensor and acquisition controller, pressure plate operation invoicing and simulation rehearsal; The intelligent ground wire management simulation scenario includes the installation and removal of intelligent ground wires during the maintenance of new energy substations. The intelligent lock control management simulation scenario includes unlocking operations, locking operations, personnel access control, operation authorization control, and unlocking process monitoring of the intelligent locks corresponding to electrical equipment in the new energy substation. The maintenance isolation and interlocking management includes error prevention management for the entire process of simulating power outage, voltage testing, grounding, and repair work during electrical equipment maintenance, interval maintenance, and station-wide power outage maintenance in the new energy substation.

9. The intelligent anti-misoperation method for a new energy centralized control center with split-screen display according to claim 1, characterized in that, The process of configuring the corresponding operation permissions for various operators when executing operation tickets through hierarchical permission management rules, and sending these permissions to the visualization platform of each new energy substation to facilitate intelligent error prevention for various operators includes: Based on the operation and maintenance process of new energy substations, the operation permission levels are divided according to the operation positions. The operation permission rules for different types of operators are output to determine the operation permissions of operation tickets, and the binding relationship between the permission rules and the operation ticket execution process is established. Configure permission information for different types of operators based on the binding relationship, and classify and package the permission information according to the dimension of new energy substation. Send the permission information to the new energy substation visualization platform through the communication link between the new energy center and the new energy substation, so that various operators can execute operation ticket processing according to the permission information and complete intelligent error prevention processing.

10. An intelligent anti-misoperation system for a new energy centralized control center with split-screen display, used to implement the intelligent anti-misoperation method for a new energy centralized control center with split-screen display as described in any one of claims 1-9, characterized in that, The system includes: The electrical equipment model building module is used to build target electrical equipment models based on the modeling elements of each target electrical equipment in the new energy center; The operation status data upload module is used to collect historical operation status data of target electrical equipment using the new energy substation, and upload the historical operation status data to the new energy central control center according to the transmission mechanism after completing the safety verification. The substation operation simulation and pre-drill module is used to establish an inversion scenario that includes the whole process of equipment error prevention management based on historical operation status data and target electrical equipment model, and to run the inversion scenario to simulate and pre-drill the operation process of the new energy substation. The operation ticket application output module is used to generate the anti-misoperation verification basis data of the new energy substation based on the simulation and pre-drill results, and when the new energy substation applies for operation rights, it compares the real-time operating status data of the target electrical equipment with the anti-misoperation verification basis data, and outputs the operation ticket corresponding to the operation rights based on the comparison results. The operation permission configuration error prevention module is used to configure the corresponding operation permissions of various operators when the operation ticket is executed through the permission hierarchical management rules, and send the operation permissions to the visualization platform of each new energy substation to facilitate intelligent error prevention for various operators.

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