Energy storage control system information transmission and index evaluation method and system

Abstract

The embodiment of the invention discloses an energy storage control system information transmission and index evaluation method and system. The method comprises the following steps: acquiring signal data of a lower computer of a hydropower station unit and transmitting the signal data to a remote centralized control target center; obtaining the integrity of unit lower computer signal data received by the target center; obtaining the availability of unit lower computer signal data received by the target center; acquiring the transmission safety of the acquired hydropower station unit lower computer signal data to a target center; and based on integrity, availability and transmission security, determining evaluation information of the acquired hydropower station unit lower computer signal data. According to the method, by comprehensively considering integrity, usability and safety, powerful support can be provided for a remote centralized control operation on-duty mode, an operator can accurately monitor the operation state of a unit in a remote centralized control center in real time and perform remote operation and control, centralized management and optimal scheduling of a hydropower station are achieved, and the production efficiency and the management level are improved.

Description

Technical Field

[0001] This disclosure relates to the field of hydropower station data analysis technology, and in particular to a method and system for information transmission and index evaluation of energy storage control systems. Background Technology

[0002] With the rapid development of smart grids and digital hydropower station construction, remote centralized control and intelligent operation and maintenance of hydropower station units have become an important development direction for the power industry. Traditional hydropower station operation and maintenance models mainly rely on on-site manual inspections and local monitoring systems, which makes it difficult to achieve large-scale, cross-regional centralized control and predictive maintenance.

[0003] Currently, there are numerous technical challenges in the field of data transmission and management for hydropower station units. First, existing data acquisition systems lack unified quality evaluation standards, making it impossible to accurately assess the integrity and reliability of transmitted data, leading to significant risks in decisions based on incomplete or erroneous data. Second, traditional data transmission methods lack sufficient security protection capabilities, making them vulnerable to network attacks and data tampering, particularly lacking effective end-to-end security mechanisms during cross-network transmission. Furthermore, existing technologies lack the ability to intelligently assess data availability, failing to automatically identify abnormal data and sensor malfunctions, thus affecting the accuracy and timeliness of operation and maintenance decisions. Summary of the Invention

[0004] In view of this, the present disclosure provides a method and system for information transmission and index evaluation of energy storage control systems, which can solve problems such as difficulty in centralized management and control of traditional operation and maintenance, inaccurate analysis results due to one-sided data analysis, and inability to achieve accurate remote monitoring and control.

[0005] In a first aspect, embodiments of this disclosure provide a method for information transmission and performance evaluation of an energy storage control system, including: Collect signal data from the lower-level machines of the hydropower station units and transmit it to the target center for remote centralized control; To ensure the integrity of the unit's lower-level machine signal data received by the target center; The availability of the unit's lower-level machine signal data received by the target center; The security of acquiring and transmitting the collected signal data from the lower-level machines of the hydropower station units to the target center; Based on the aforementioned integrity, availability, and transmission security, evaluation information for the collected lower-level machine signal data of the hydropower station units is determined.

[0006] Secondly, this disclosure also provides an information transmission and performance evaluation system for an energy storage control system, comprising: The data acquisition module is used to acquire signal data from the lower-level machines of the hydropower station units; The transmission module is used to transmit the collected signal data from the lower-level machines of the hydropower station units to the target center of the remote centralized control system. The analysis module is used to obtain the integrity of the unit lower-level machine signal data received by the target center, obtain the availability of the unit lower-level machine signal data received by the target center, obtain the transmission security of the collected hydropower station unit lower-level machine signal data transmitted to the target center, and determine the evaluation information of the collected hydropower station unit lower-level machine signal data based on the integrity, availability, and transmission security.

[0007] The information transmission and index evaluation method for energy storage control systems disclosed in this application collects lower-level machine signal data from hydropower station units and transmits it to a remote centralized control target center. By assessing the integrity, availability, and transmission security of the received lower-level machine signal data, the method determines the evaluation information of the collected hydropower station unit lower-level machine signal data. By comprehensively considering integrity, availability, and security, this method provides strong support for remote centralized control operation and duty modes. Operators can monitor the unit's operating status in real time and accurately from the remote control center, perform remote operation and control, realize centralized management and optimized scheduling of hydropower stations, and improve production efficiency and management level. Simultaneously, the evaluation information of the collected lower-level machine signal data from hydropower station units, based on integrity, availability, and transmission security, provides comprehensive and objective information for operators and managers, helping them make more scientific and rational decisions. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating the information transmission and performance evaluation method for an energy storage control system provided in this embodiment.

[0010] Figure 2 This is a flowchart illustrating a method for collecting signal data from the lower-level machines of a hydropower station unit and transmitting it to a remote centralized control target center, as provided in an embodiment of this disclosure.

[0011] Figure 3 This is a flowchart illustrating a method for obtaining the integrity of signal data from a target center received by a lower-level machine, as provided in an embodiment of this disclosure.

[0012] Figure 4This is a flowchart illustrating a method for obtaining the availability of unit lower-level machine signal data received by a target center, as provided in an embodiment of this disclosure.

[0013] Figure 5 This is a flowchart illustrating the method for ensuring the transmission security of data collected from the lower-level machine of a hydropower station unit to the target center, as provided in this embodiment of the disclosure.

[0014] Figure 6 This is a flowchart illustrating a method for acquiring evaluation information from collected lower-level machine signal data of hydropower station units, as provided in an embodiment of this disclosure. Detailed Implementation

[0015] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0016] Reference Figure 1 This application discloses a method for information transmission and performance evaluation of an energy storage control system, including: S100 collects signal data from the lower-level machines of the hydropower station units and transmits it to the target center for remote centralized control. S200, obtain the integrity of the unit's lower-level machine signal data received by the target center; S300, obtain the availability of unit lower-level machine signal data received by the target center; S400 ensures the security of data transmission from the collected lower-level signal data of the hydropower station unit to the target center. S500 determines the evaluation information of the collected lower-level machine signal data of hydropower station units based on integrity, availability, and transmission security.

[0017] The information transmission and index evaluation method for energy storage control systems disclosed in this application ensures the real-time performance and security of signal data acquisition and transmission from hydropower station units through a distributed sensor network and encrypted transmission channels. It significantly improves the accuracy and reliability of data quality monitoring by employing a multi-dimensional integrity evaluation model and time-series continuity analysis. Based on machine learning algorithms, it intelligently identifies data availability, effectively recognizes abnormal data, and provides high-quality decision support information. The constructed comprehensive evaluation system for transmission security meets the requirements of graded protection, comprehensively safeguarding the information security of critical infrastructure. The overall technical solution realizes intelligent control of the entire process of data acquisition, transmission, and evaluation, providing a solid foundation for data quality assurance for remote centralized control and intelligent operation and maintenance of hydropower stations.

[0018] Regarding the integrity aspect, by collecting signal data from the lower-level machines of hydropower station units and transmitting it to the remote centralized control target center, it is ensured that operators receive complete unit operation information. For example, various parameter signals such as unit temperature, pressure, speed, and power can be collected and transmitted, enabling operators to comprehensively monitor the unit's operating status, promptly identify potential problems and anomalies, and provide accurate basis for decision-making. A signal acquisition integrity rate index is defined to evaluate the status of acquired signals, quantifying the quality of signal acquisition. This helps to promptly identify missing or abnormal signals during the acquisition process, allowing for rapid repair or improvement measures, ensuring the stable operation of the signal acquisition system, and improving monitoring reliability.

[0019] From an availability perspective, analyzing various control processes of hydropower station units and studying signal redundancy configuration schemes that reflect the important states of unit equipment (important I / O points) can effectively improve the success rate of each process execution. When a signal fails, the redundant signal can take over in time, ensuring that the control process is not affected, guaranteeing the normal operation of the unit, and reducing the probability of downtime or accidents caused by signal failures. By improving the success rate of control process execution, the availability of the lower-level machines of the monitoring system is improved. Operators can obtain unit information more stably, perform remote centralized control operations, reduce the risk of monitoring interruption caused by system failures, and ensure the continuous and stable operation of the hydropower station.

[0020] Regarding security, ensuring the secure transmission of collected signal data from the hydropower station units to the target center prevents data theft, tampering, or interference during transmission. Employing encryption technology and authentication, among other security measures, ensures data confidentiality, integrity, and availability, guaranteeing the safe and stable operation of the remote control system. By guaranteeing secure signal transmission, the system can more reliably obtain the actual operating status information of the units, enabling operators to make reasonable controls and operations based on accurate information, avoiding misoperations caused by erroneous information, and ensuring the safe operation of the units.

[0021] Reference Figure 2 The method for S100 "collecting signal data from the lower-level machines of hydropower station units and transmitting it to the target center of remote centralized control" specifically includes: S110 obtains raw signal data by real-time acquisition of operating parameters of hydropower station units based on distributed sensor networks; S120: Preprocess the raw signal data to generate structured data packets that conform to the transmission protocol; S130, based on structured data packets, uses a timestamp synchronization mechanism and a data integrity verification algorithm to construct a secure data frame with a unique identifier; S140 sends secure data frames to the remote centralized control target center through an encrypted transmission channel.

[0022] For S110, various sensors are installed at different key locations within the hydropower station unit. For example, a speed sensor is installed on the generator shaft to measure the shaft's rotational frequency and thus the unit's speed; a power sensor is installed at the generator's output to measure the unit's output power; temperature sensors are installed at the unit's bearings to monitor bearing temperature in real time; vibration sensors are installed on the unit's casing to detect vibrations during operation; and water level sensors are installed at the reservoir and turbine's inlet and outlet to measure water level. These sensors form a distributed sensor network that collects operating parameters in real time at a preset sampling frequency (e.g., once per second), generating raw signal data. This process comprehensively covers the key operating parameters of the hydropower station unit, accurately reflecting its operating status in real time. Monitoring these parameters allows for timely detection of abnormalities during unit operation, providing a basis for subsequent fault diagnosis and maintenance. The distributed sensor network layout improves the reliability and accuracy of data acquisition. Even if one sensor malfunctions, other sensors continue to operate normally, ensuring continuous data acquisition.

[0023] Specifically, for S120, digital filtering algorithms, such as moving average filtering and Kalman filtering, are used to process the raw signal data. For example, the moving average filtering algorithm calculates the average value of the data within a certain time window and uses this average value to replace each data point within the window, thereby smoothing the data and removing noise interference. For outlier detection: a normal range for each operating parameter is set. When the collected data exceeds this range, it is judged as an outlier. For example, if the temperature data collected by the temperature sensor exceeds the highest temperature threshold during normal operation of the unit, it is considered an outlier. Outliers can be processed using interpolation or deletion methods. For data format standardization: according to the requirements of the transmission protocol, the processed data is converted into a unified format. For example, analog signals collected by different sensors are converted into digital signals and stored according to a fixed byte length and encoding method to generate structured data packets. In this step, noise filtering can improve data quality, remove interference signals, and make subsequent analysis and processing more accurate. Outlier detection can promptly identify errors or anomalies in the data, preventing these abnormal data from misleading subsequent analysis and decision-making. Data format standardization enables the data to be correctly parsed by the target center during transmission, improving the efficiency and reliability of data transmission.

[0024] Specifically, for S130, timestamp information is added to each structured data packet, with timestamps accurate to the millisecond level; the clocks of all sensors and data acquisition devices are synchronized through the Global Positioning System (GPS) or Network Time Protocol (NTP) to ensure that the timestamp of each data packet is accurate. In this way, after receiving the data frame, the target center can sort and analyze the data according to the timestamp.

[0025] For data integrity verification algorithms: Cyclic Redundancy Check (CRC) or hash algorithms (such as MD5, SHA-1, etc.) are used to process structured data packets to generate checksums. These checksums are added to the data packets to form secure data frames. Upon receiving a data frame, the target center recalculates the checksum and compares it with the received checksum to verify data integrity. For unique identifiers: Each secure data frame is assigned a unique identifier, which can be a randomly generated number or string. This identifier is used to track and manage data frames, ensuring that they are not lost or duplicated during transmission. The timestamp synchronization mechanism guarantees the temporal order of data, facilitating data analysis and processing at the target center. For example, analyzing operating parameters at different times can reveal the operating trends and patterns of the unit. Data integrity verification algorithms ensure that data is not tampered with or lost during transmission, improving data reliability and security. Unique identifiers facilitate data frame management and tracking, preventing data frame corruption and loss, and improving data transmission efficiency.

[0026] For S140, a Virtual Private Network (VPN) technology or Secure Sockets Layer (SSL) / Transport Layer Security (TLS) protocol is used to establish an encrypted transmission channel. For example, a VPN connection is established between the data acquisition end and the target center, and all secure data frames are transmitted through this encrypted channel. During transmission, the data is encrypted into ciphertext, and only the target center with the correct key can decrypt it and obtain the information. Encrypted transmission channels protect data security and privacy, preventing data theft or tampering during transmission. This is especially crucial for critical infrastructure like hydropower stations, where data security is paramount. Encrypted transmission effectively avoids security risks caused by data leaks and significantly improves data transmission reliability. Encrypted transmission channels typically have higher stability and anti-interference capabilities, reducing packet loss and errors during data transmission.

[0027] The methods disclosed in S110-S140, through comprehensive data acquisition, preprocessing, and verification mechanisms, ensure the accuracy, reliability, and integrity of data transmitted to the target center, providing high-quality foundational data for subsequent data analysis and decision-making. The use of encrypted transmission channels and data integrity verification algorithms protects data security during transmission, preventing data leakage and tampering, and ensuring the information security of the hydropower station. Real-time data acquisition and transmission enable the remote centralized control target center to promptly understand the operating status of the hydropower station units, facilitating unified scheduling and management, reducing the workload of manual inspections, and improving management efficiency. Accurate and real-time data helps technicians promptly identify abnormalities in unit operation, enabling fault diagnosis and predictive maintenance, reducing equipment failure rates, extending equipment lifespan, and improving the economic benefits of the hydropower station.

[0028] Reference Figure 3 The method for "obtaining the integrity of the unit's lower-level machine signal data received by the target center" in S200 specifically includes: S210, perform packet counting verification on the data frames received by the target center, identify data loss or duplicate transmission by comparing the sequence number of the sending end with the sequence number of the receiving end, and generate a preliminary report on packet integrity. S220, based on the preliminary report of data packet integrity, performs CRC cyclic redundancy check and MD5 hash value verification on the check code in each data frame, detects whether the data has been damaged or tampered with during transmission, and outputs the data content integrity verification result; S230, based on the data content integrity verification results, the time window sliding algorithm is used to analyze the temporal continuity of the data, identify timestamp breaks and abnormal data sampling intervals, and form a temporal integrity evaluation matrix; S240, based on the timing integrity assessment matrix, constructs a multi-dimensional integrity evaluation model, comprehensively considering data packet integrity rate, content verification pass rate and timing continuity index, and calculates the comprehensive integrity score of the unit's lower-level machine signal data; S250 compares and analyzes the comprehensive integrity score with the preset integrity threshold, generates hierarchical integrity status indicators and abnormal alarm information, and forms the final data integrity evaluation report.

[0029] For S210, specifically at the sending end, each data frame is assigned a unique sequence number, for example, starting from 1 and incrementing sequentially. When sending a data frame, the sending end records the sequence numbers of the sent data frames; similarly, when receiving data frames at the target center, it records the sequence numbers of the received data frames. The target center sorts the received data frames according to the sequence numbers and then compares them with the sequence numbers recorded by the sending end. If a missing sequence number is found in the receiving end's sequence numbers, for example, if the sending end's sequence numbers are 1-100, but the receiving end only receives 1-98 and 100, then it can be determined that the data frame corresponding to sequence number 99 is lost. If duplicate sequence numbers are found, such as the receiving end receiving two data frames with sequence number 50, it can be determined that there is a duplicate transmission. Based on the comparison results, a preliminary data packet integrity report is generated, which will list the sequence numbers of lost or duplicated data frames in detail.

[0030] By comparing the sequence numbers of the sender and receiver, data loss or duplicate transmissions can be quickly identified. Data loss may indicate problems such as transmission link failure or network congestion, while duplicate transmissions may be caused by abnormal retransmission mechanisms or signal interference. Timely detection of these problems helps maintenance personnel quickly locate and resolve transmission layer faults, ensuring the basic reliability of data transmission. The generated preliminary data packet integrity report provides a foundation for subsequent more in-depth data integrity checks. It clarifies which data packets may have problems, allowing subsequent verification work to be targeted, avoiding indiscriminate checks on all data, and improving inspection efficiency.

[0031] For S220, the CRC (Cyclic Redundancy Check) refers to the following: When sending each data frame, the sending end calculates a CRC checksum based on the data frame content and appends it to the end of the data frame. Upon receiving the data frame, the receiving center recalculates the CRC checksum and compares it with the received CRC checksum. If they do not match, it indicates that the data frame may have been corrupted during transmission. The MD5 hash verification refers to the following: The sending end also calculates an MD5 hash value for the data frame content and includes it in the data frame. Upon receiving the data frame, the receiving center similarly recalculates the MD5 hash value and compares it with the received MD5 hash value. If they do not match, it indicates that the data may have been tampered with. Based on the results of the CRC checksum and MD5 hash verification, the data content integrity verification result is output, clearly indicating which data frames may have been corrupted or tampered with.

[0032] Cyclic Redundancy Check (CRC) effectively detects bit errors caused by noise and interference during data transmission. It verifies data integrity through redundant calculations. MD5 hash verification, on the other hand, prevents malicious data tampering because even a single bit change significantly alters the MD5 hash value. Using both methods together provides comprehensive integrity verification from different perspectives. In hydropower station unit operation data applications, data accuracy is paramount. These two verification methods ensure complete consistency between received and transmitted data, preventing erroneous decisions due to data errors. For example, tampering with power data could lead to incorrect power generation scheduling, which effective verification can prevent.

[0033] For S230, specifically, each data frame contains a timestamp to record the time of data acquisition. The target center sets a time window, for example, 10 seconds. The time window sliding algorithm starts from the first data frame and checks each data frame in the time window in turn. Within each time window, it analyzes the timestamp order and interval of the data frames. If a timestamp discontinuity is found, such as the time interval between two adjacent data frames being much larger than the normal sampling interval, or a timestamp jump, then a timestamp discontinuity is determined to exist. If the data sampling interval deviates too much from the preset sampling interval, for example, the preset sampling interval is 1 second, but an actual interval of 3 seconds occurs, then the data sampling interval is determined to be abnormal. The timestamp discontinuities and data sampling interval anomalies within each time window are quantified to form a matrix. The rows of the matrix represent different time windows, and the columns represent different anomaly types (timetamp discontinuities, data sampling interval anomalies, etc.). The elements in the matrix represent the number of occurrences or severity of the corresponding anomaly type within the time window, thus forming a time sequence integrity assessment matrix.

[0034] The time window sliding algorithm can analyze the temporal continuity of data and identify timestamp discontinuities and abnormal data sampling intervals. In the operation of hydropower station units, the temporal information of data is crucial; for example, the changes in certain parameters follow a certain temporal pattern. Timestamp discontinuities may indicate sensor failure or data acquisition interruption, while abnormal sampling intervals may indicate unstable operation of the acquisition equipment. Analysis of temporal continuity can promptly identify these potential problems. The resulting temporal integrity assessment matrix quantitatively evaluates data integrity from a temporal perspective. Combined with the preceding packet counting verification and content verification, it achieves a multi-dimensional data integrity assessment, which helps to gain a more comprehensive understanding of data quality and provides important temporal dimension indicators for subsequent comprehensive scoring. Long-term monitoring and analysis of temporal continuity can reveal potential fault trends, allowing for early maintenance and replacement, reducing the impact of equipment failures on hydropower station operation.

[0035] For S240, the packet integrity rate refers to the ratio of the number of data frames that are neither lost nor duplicated to the total number of data frames sent, calculated based on the preliminary packet integrity report. For example, if 100 data frames are sent, 2 are lost, and 1 is duplicated, the packet integrity rate is (100-2-1) / 100 = 97%. The content verification pass rate refers to the ratio of the number of data frames that pass CRC checksum and MD5 hash value verification to the total number of received data frames, calculated based on the data content integrity verification results. The time sequence continuity index refers to the weighted average of the total number or severity of timestamp breaks and data sampling interval anomalies, obtained by analyzing the time sequence integrity assessment matrix. Furthermore, weights can be assigned to the packet integrity rate, content verification pass rate, and time sequence continuity index, for example, 0.3, 0.4, and 0.3 respectively; the overall integrity score = packet integrity rate × 0.3 + content verification pass rate × 0.4 + time sequence continuity index × 0.3.

[0036] The multi-dimensional integrity evaluation model comprehensively considers data packet integrity rate, content verification pass rate and timing continuity indicators, and can comprehensively and objectively evaluate the integrity of the unit's lower-level machine signal data. Different indicators reflect the quality of data from different aspects, and the comprehensive score obtained by weighted calculation can more accurately reflect the overall integrity level of the data.

[0037] Furthermore, this application also includes: comparing and analyzing the overall integrity score with preset integrity thresholds to generate hierarchical integrity status identifiers and anomaly alarm information, forming a final data integrity evaluation report. Specifically, different levels of integrity thresholds are preset, for example: Excellent: overall integrity score ≥ 90 points; Good: 80 points ≤ overall integrity score < 90 points; Average: 60 points ≤ overall integrity score < 80 points; Poor: overall integrity score < 60 points; Based on the range of the overall integrity score, hierarchical integrity status identifiers are generated, such as "Excellent" and "Good"; If the overall integrity score is lower than a certain threshold (e.g., 70 points), anomaly alarm information is generated, which details the possible problems, such as data packet loss or data content corruption; The final data integrity evaluation report will include the overall integrity score, hierarchical integrity status identifiers, anomaly alarm information, and detailed analysis results of the preceding steps, providing a basis for subsequent decision-making and processing.

[0038] By using tiered integrity status indicators such as "Excellent," "Good," "Average," and "Poor," managers can quickly and intuitively understand the level of data integrity. This tiering method aligns with human cognitive habits, facilitating rapid assessment and comparison of data quality. Anomaly alarms promptly notify relevant personnel when serious data integrity issues arise. Maintenance personnel can then take swift action based on these alarms, such as checking transmission lines or replacing faulty equipment, preventing erroneous decisions and equipment failures caused by incomplete data and ensuring the safe and stable operation of the hydropower station. The final data integrity evaluation report includes a comprehensive integrity score, tiered status indicators, anomaly alarm information, and detailed analysis results. It is a complete record of the entire data integrity inspection process. These reports can be archived as historical data for subsequent auditing, data analysis, and system optimization, providing strong support for the long-term operation and management of the hydropower station.

[0039] Reference Figure 4 The method for S300 to "obtain the availability of unit lower-level machine signal data received by the target center" specifically includes: S310: Acquire the end-to-end delay time from data acquisition to data reception at the target center for the unit's lower-level machine signal data, identify expired data exceeding the preset timeliness threshold, and generate a data timeliness analysis report; S320, based on data timeliness analysis reports, intelligently judges the validity of signal data, uses machine learning algorithms to detect the reasonable range of data values, abnormal trends and sensor failure modes, and outputs a data validity verification matrix; S330, based on the data validity verification matrix, constructs a multi-source data fusion model, and identifies redundant and complementary information by cross-validating similar parameter data from different sensors, thus forming a data credibility assessment result; S340, based on the data credibility assessment results, adopts the fuzzy comprehensive evaluation method to establish an availability quantification model, comprehensively considers four dimensions of data timeliness, effectiveness, credibility and business applicability, and calculates the availability weight coefficient of various types of unit signal data; S350 matches availability weight coefficients with actual business needs, generates data availability level classifications and quality labels, and forms data availability evaluation reports for different operation and maintenance decision-making scenarios.

[0040] Specifically for S310, when the unit's lower-level machine collects data, it records the timestamp T of the data collection. collect When the target center receives the data, it records the receiving timestamp T. receive End-to-end delay time T delay =T receive -T collectThis recording process can be achieved by adding a timestamp field to the data frame. The lower-level machine inserts the acquisition timestamp into the data frame after acquiring data, and the target center reads and records this timestamp when receiving data, and then performs calculations. A preset timeliness threshold T is used. threshold This threshold is determined based on specific business needs and the real-time requirements of the data; iterate through all received data and set T... delay With T threshold Compare, if T delay >T threshold If the data is expired, mark it as expired data; generate a data timeliness analysis report. The report should include information such as the total amount of data received, the amount of expired data, the percentage of expired data, and the average delay time for different time periods. This data can be presented in the form of tables and charts, such as using a bar chart to show the average delay time for different time periods and a pie chart to show the percentage of expired data.

[0041] For S320, specifically, historical data is collected to determine the reasonable value range for each signal data point; for example, temperature sensor data typically falls within a specific range. For newly received data, it is determined whether it falls within the reasonable value range; if it exceeds the range, it is marked as potentially invalid data. Machine learning algorithms, such as time series analysis algorithms (e.g., ARIMA, LSTM), are used to model the historical data, learning the normal trend of data change. For newly received data, it is compared with the trend predicted by the model; if the deviation exceeds a certain threshold, the data trend is considered abnormal. Data characteristics of sensor failures are collected to construct a fault mode library. Specifically, classification algorithms (e.g., decision trees, support vector machines) are used to classify newly received data and determine whether it matches the characteristics in the fault mode library; if it does, it is marked as invalid data possibly caused by sensor failure. Finally, a data validity verification matrix is ​​output. The rows of the matrix represent different signal data, and the columns represent different validity judgment indicators (e.g., data value reasonableness, trend, sensor failure, etc.). The elements in the matrix represent the verification result (valid or invalid) of the data under the corresponding indicator.

[0042] Specifically, for S330, similar parameter data from different sensors are collected, such as temperature sensor data from different locations. For each sensor's data, it is compared with similar data from other sensors to calculate indicators such as correlation and consistency. If the data from a certain sensor differs significantly from the data from most other sensors, the data is considered potentially problematic. For the identification of redundant and complementary information, redundant information refers to similar and repetitive data from multiple sensors, which can be identified through correlation analysis. Complementary information refers to data from different sensors that complement and enhance each other. Based on the identification results, the data is filtered and integrated, removing redundant information and retaining complementary information. Based on the results of cross-validation and information identification, a credibility score is assigned to each sensor's data; the higher the score, the more reliable the data. The score can be determined based on factors such as data consistency and stability.

[0043] For S340, specifically, the evaluation factor set U = {data timeliness, data validity, data reliability, business applicability} is determined; a weight vector W = (w1, w2, w3, w4) is determined for each evaluation factor. The weights can be determined based on expert experience, analytic hierarchy process (AHP), etc. For each evaluation factor, its membership function is determined, mapping the actual data value to the membership degree. For example, for data timeliness, the membership degree can be determined based on the relationship between end-to-end delay time and timeliness threshold. Then, according to the principle of fuzzy comprehensive evaluation, the membership degree matrix R of each unit's signal data under each evaluation factor is calculated. The comprehensive evaluation result is calculated using B = W × R, where B is the comprehensive membership degree vector. Next, based on the comprehensive membership degree vector B, the availability weight coefficient is calculated for each unit's signal data. The comprehensive membership degree vector can be normalized, and the normalized value is the availability weight coefficient.

[0044] For S350, specifically, the actual business needs of different operation and maintenance decision-making scenarios should be clearly defined. For example, real-time control scenarios have high requirements for data timeliness and validity, while historical data analysis scenarios have high requirements for data reliability. The availability weight coefficients should be matched with the actual business needs to determine whether the signal data of each unit meets the requirements of the specific business scenario. Based on the matching results, the data should be divided into different availability levels, such as high availability, medium availability, and low availability. Each level should be assigned a corresponding quality label, such as "available," "partially available," and "unavailable." The report should include the data availability level classification, quality labels, availability weight coefficients for the signal data of each unit, and a detailed process of matching analysis. The report can be presented in document form for easy viewing and use by operation and maintenance personnel.

[0045] Reference Figure 5The method for ensuring the security of data transmission from the lower-level generator signal data of the S400 hydropower station to the target center includes: S410 performs security situation awareness analysis on the data transmission channel. By deploying network traffic monitoring probes, it detects abnormal access behavior, malicious attack characteristics and network intrusion patterns on the transmission path in real time, identifies potential security threat sources and generates a transmission channel security threat assessment report. S420, based on the transmission channel security threat assessment report, performs multi-layer security verification on the data encryption transmission process, uses national cryptographic algorithms to encrypt data packets end-to-end, and verifies the legality of the identities of the communicating parties through digital certificates and PKI public key infrastructure, and outputs the encryption transmission security verification result; S430, based on the encrypted transmission security verification results, constructs a transmission path trustworthiness assessment model. By analyzing the security level of network nodes, transmission hop count, routing stability and bandwidth utilization, it identifies vulnerable links and security risk points in the transmission path and forms a path security trustworthiness matrix. S440, based on the path security trust matrix, uses a dynamic risk assessment algorithm to establish a comprehensive evaluation system for transmission security. It integrates threat detection accuracy, encryption strength level, identity authentication success rate and path trust index to calculate a multi-dimensional security score for the data transmission process.

[0046] For S410, this specifically includes: deploying network traffic monitoring probes at key nodes (such as network border routers and switches) along the monitored data transmission path. These probes can be hardware devices or software programs, capable of deep analysis of network packets; configuring the probes to capture all network traffic passing through the node in real time, including source IP address, destination IP address, port number, protocol type, packet size, and other information; and using rule-based or machine learning-based detection methods to detect abnormal access behavior. Rule-based detection involves pre-setting a series of rules for normal access behavior, such as allowed IP address ranges and rules for using specific ports. When detected traffic violates these rules, it is judged as abnormal access behavior. Machine learning-based detection involves collecting a large amount of normal network traffic data for training, building a normal behavior model, and using clustering algorithms (such as K-Means) to classify normal traffic. When new traffic data deviates from these classifications by more than a certain threshold, it is identified as abnormal.

[0047] Then, a malicious attack signature database is established, containing characteristic information of common attacks (such as DDoS attacks, SQL injection attacks, etc.). Through pattern matching algorithms, the monitored traffic is compared with the features in the signature database to identify potential malicious attacks. An intrusion detection system (IDS) or intrusion prevention system (IPS) is used, combined with known network intrusion patterns and behavioral patterns, to monitor and identify network intrusion behaviors in real time. Correlation analysis is performed on detected abnormal behaviors and attack events to determine possible sources of security threats. By tracing the source IP address of the traffic and combining it with geographical information and network logs, the location and nature of the threat source are determined.

[0048] Finally, a security threat assessment report for the transmission channel is generated. The report should include the number of abnormal access behaviors detected, the types and frequencies of malicious attacks, and detailed information on potential security threat sources (such as IP addresses and geographical locations). Each threat source is assessed for risk level, which can be divided into three levels: high, medium, and low, based on the severity and scope of the threat. It is preferable to present this information intuitively in the form of charts and tables.

[0049] For S420, the specific steps include: selecting a suitable national cryptographic algorithm, such as SM2, SM3, or SM4. For data encryption, the SM4 symmetric encryption algorithm can be used. An encryption key is generated at the data sending end to encrypt the data packets. A Key Management System (KMS) is used to securely manage the encryption keys, ensuring the security of key generation, storage, distribution, and updating. During data transmission, the SM2 asymmetric encryption algorithm is used to encrypt the symmetric encryption key, ensuring secure key exchange. Both communicating parties apply for digital certificates, issued by a trusted Certificate Authority (CA), which contain the public key and identity information of the communicating parties. During the communication establishment phase, both parties exchange digital certificates and verify the legitimacy of each other's certificates through a Public Key Infrastructure (PKI). The verification process includes checking the certificate's validity period, signature validity, and whether the certificate has been revoked. The authentication information is encrypted using the other party's public key, and the other party decrypts it using their private key, completing the authentication process. Finally, the encrypted transmission security verification result is output, recording various parameters during the encryption process, such as the encryption algorithm, key length, and encryption time. The verification result includes information such as whether the authentication was successful and whether the encryption process was normal. Output the verification results in text format, clearly indicating whether the encrypted transmission is secure.

[0050] For S430, this specifically includes: assessing the security level of each network node (such as routers, switches, servers, etc.) on the transmission path, with assessment indicators including the node's operating system security, whether a firewall is installed, and whether security updates are performed regularly; assigning a security level to each node based on the assessment indicators, which can be divided into high, medium, and low levels; recording the number of hops during data transmission, as a higher number of hops indicates a more complex transmission path and a relatively higher security risk; monitoring the stability of the route by analyzing indicators such as the frequency of changes in the routing table and the packet loss rate; and monitoring the bandwidth utilization on the transmission path in real time, as excessively high bandwidth utilization may lead to network congestion, affecting the security and stability of data transmission.

[0051] Based on factors such as network node security level, hop count, routing stability, and bandwidth utilization, a comprehensive analysis of transmission paths is conducted to identify potential vulnerabilities and security risks; for example, nodes with low security levels or segments with high hop counts. Finally, a path security reliability matrix is ​​formed. The rows of the matrix represent different transmission paths, and the columns represent different evaluation metrics (such as network node security level, hop count, routing stability, and bandwidth utilization). Each element in the matrix represents the path's score or level under the corresponding metric. By combining these metrics, a security reliability score is calculated for each path.

[0052] For S440, the dynamic risk assessment algorithm includes: using a dynamic risk assessment algorithm to dynamically adjust risk assessment results based on real-time monitored indicators such as threat detection accuracy, encryption strength level, authentication success rate, and path trustworthiness; and using Bayesian network probabilistic models to treat each indicator as a variable and conduct risk assessment based on their causal relationships and conditional probabilities. For the multi-dimensional security score calculation, each assessment indicator is assigned a corresponding weight, which can be determined based on expert experience, historical data statistics, etc. The actual value of each indicator is multiplied by its weight, and then summed to obtain the multi-dimensional security score. For example, if the weight of threat detection accuracy is 0.3 and the actual accuracy is 80%, then the score for this item is 0.3 × 0.8 = 0.24, and so on. Finally, the scores of each item are added together to obtain the total score.

[0053] Furthermore, the application also includes: benchmarking and analyzing multi-dimensional security scores against industry security standards and graded protection requirements, generating a transmission security level assessment and risk warning mechanism, and forming a comprehensive transmission security evaluation report that includes security protection recommendations and emergency response plans.

[0054] The benchmarking analysis includes: collecting industry security standards and graded protection requirements, comparing the calculated multi-dimensional security scores with these standards, analyzing the gap between the scores and the standards, and determining whether the data transmission process meets the relevant requirements.

[0055] The assessment of transmission security levels includes: based on benchmarking analysis results, classifying data transmission security levels into different levels, such as Level 1 (secure), Level 2 (basically secure), Level 3 (some risk exists), and Level 4 (high risk); establishing clear level classification standards, for example, a security score of 90 or above is Level 1, and 70-90 is Level 2, etc. The establishment of a risk warning mechanism includes: setting different risk warning thresholds for different levels; when the security score falls below a certain threshold, a corresponding warning is triggered. Warning methods may include SMS notifications, email alerts, system pop-ups, etc., ensuring that operations and maintenance personnel are promptly informed of security risks.

[0056] For the generation of a comprehensive evaluation report on transmission security, the report should include benchmarking analysis results, transmission security level assessment, risk warning information, etc.; provide security protection recommendations, such as strengthening the security configuration of network nodes and optimizing encryption algorithms; formulate emergency response plans, clarify emergency handling procedures and responsible personnel for different levels of security risks, and present the report in document form for easy reference and archiving.

[0057] Reference Figure 6 The method for "determining the evaluation information of the collected lower-level machine signal data of hydropower station units based on integrity, availability, and transmission security" in S500, that is, the method for obtaining the evaluation information of the collected lower-level machine signal data of hydropower station units, specifically includes: S510 performs data standardization processing on integrity score, availability and transmission security, and determines the importance coefficient of each dimension in the comprehensive evaluation through weight allocation algorithm, generating a standardized evaluation index matrix. S520, based on a standardized evaluation index matrix, constructs a hierarchical analysis model to decompose each evaluation dimension hierarchically and outputs a multi-dimensional weight allocation vector. S530, based on the multi-dimensional weight allocation vector, adopts the improved TOPSIS comprehensive evaluation method to establish the ideal solution and negative ideal solution reference model, forming a dynamic comprehensive evaluation score sequence; S540, based on the dynamic comprehensive evaluation score sequence, introduces the time decay factor and trend prediction algorithm to construct an adaptive evaluation index model, and calculates a comprehensive evaluation index with time-series characteristics. S550 maps the comprehensive evaluation index to the preset evaluation level standards and generates a comprehensive evaluation index report of the lower-level machine signal data of hydropower station units, which includes the evaluation level, confidence interval and trend of change.

[0058] Specifically, S510 includes: standardizing the data for integrity score, availability weight coefficient and transmission security score; using the Z-score standardization method to eliminate the differences in dimensionality between different evaluation dimensions; and using a weight allocation algorithm to determine the importance coefficient of each dimension in the comprehensive evaluation, thereby generating a standardized evaluation index matrix.

[0059] Furthermore, let the integrity score data be x. i1 (i=1,2,⋯,n, where n is the number of evaluation objects), the availability weight coefficient data is x i2 The transmission security score is x. i3 For each dimension of the data, calculate its mean μ. j and standard deviation σ j Standardized data z ij = (x ij −μ j ) / σ j This eliminates the dimensional differences between different evaluation dimensions.

[0060] To determine the importance coefficients using a weighting algorithm, an entropy weighting method with equal weights can be employed. Specifically, the weight of the j-th indicator... ; , ; Standardized data and corresponding weights Combined into matrix A=( ) n×3 ,in = This matrix is ​​the standardized evaluation index matrix.

[0061] Specifically, S520 includes: constructing a hierarchical analysis model based on a standardized evaluation index matrix to decompose each evaluation dimension hierarchically; determining the relative weight values ​​of integrity, availability, and transmission security through expert evaluation and historical data statistical analysis; using consistency checks to ensure the rationality of weight allocation; and outputting a multi-dimensional weight allocation vector.

[0062] Furthermore, the comprehensive evaluation objective is taken as the highest level, integrity, availability, and transmission security as the intermediate layers, and the specific indicators under each dimension as the bottom layer. A judgment matrix is ​​constructed, and the relative importance of each dimension in the intermediate layer is compared pairwise through expert evaluation and historical data statistical analysis. For example, for integrity (C1), availability (C2), and transmission security (C3), experts judge that integrity is slightly more important than availability, which can be reflected in the judgment matrix B=(b ij ) 3×3 Command b 12 =3, b 21=1 / 3, and so on, to complete the construction of the entire judgment matrix.

[0063] The determination of relative weight values ​​specifically includes: calculating the largest eigenvalue λ of the judgment matrix B. max The eigenvectors W and their corresponding eigenvectors are normalized to obtain the relative weights of each dimension. The maximum eigenvalue and eigenvector can be solved using numerical calculation methods such as the power method.

[0064] Then calculate the consistency index CI=(λ) max The weights are calculated as follows: (-n) / (n-1), where n=3 is the order of the judgment matrix; the average random consistency index RI is found (RI=0.58 for n=3); the consistency ratio CR=CI / RI is calculated, and when CR<0.1, the consistency of the judgment matrix is ​​considered acceptable and the weight allocation is reasonable. Finally, the relative weight values ​​after consistency testing are combined into a vector W=(w1′,w2′,w3′), which is the multi-dimensional weight allocation vector.

[0065] Specifically, S530 includes: establishing ideal and negative ideal solution reference models based on multi-dimensional weight allocation vectors and using an improved TOPSIS comprehensive evaluation method; quantifying the data quality differences between different time periods and different units by calculating the relative closeness of each evaluation object to the ideal solution; and forming a dynamic comprehensive evaluation score sequence.

[0066] The construction of the ideal solution and negative ideal solution reference models includes: for the standardized evaluation index matrix A=( ) n×3 Determine the ideal solution A + and negative ideal solution A − The ideal solution A + =(a1 + a2 + a3 + ), where a j + =max 1≤i≤n a ij Negative ideal solution A − =(a1 - a2 - a3 - ), where a j - =min 1≤i≤n a ij .

[0067] The relative closeness calculation includes: calculating the closeness of each evaluation object. i Distance to the ideal solution Distance to the negative ideal solution Calculate the relative closeness. , The closer the value is to 1, the closer the evaluated object is to the ideal solution, and the better the data quality. Finally, the relative similarity of the evaluated objects at different time periods and for different units is calculated. Arranged in order, they form a dynamic comprehensive evaluation score sequence.

[0068] Specifically, S540 includes: constructing an adaptive evaluation index model based on a dynamic comprehensive evaluation score sequence, introducing a time decay factor and a trend prediction algorithm, analyzing the changing trend and fluctuation characteristics of the evaluation index through a sliding time window, identifying the periodic patterns and abnormal change patterns of data quality, and calculating a comprehensive evaluation index with time-series characteristics.

[0069] The time decay factor α(t) can be expressed as the exponential decay function α(t) = e −βt Where β is the decay coefficient and t is the time interval. As time progresses, the influence of earlier data on the current evaluation gradually decreases. Trend prediction algorithms can use methods such as moving averages and exponential smoothing to predict the trend of dynamic comprehensive evaluation score sequences.

[0070] Adaptive Evaluation Index Model Construction: An adaptive evaluation index model is constructed by comprehensively considering the time decay factor and trend prediction results. ,in Let be the comprehensive evaluation index at time t.

[0071] A sliding time window is used to analyze the comprehensive evaluation index, with a window size of m. Within each window, the changing trend and fluctuation characteristics of the evaluation index are analyzed to identify periodic patterns and abnormal change patterns in data quality. For example, statistics such as the mean and standard deviation within the window are calculated to determine whether abnormal fluctuations exist.

[0072] Specifically, S550 includes: mapping the comprehensive evaluation index with time-series characteristics to the preset evaluation level standards, establishing a five-level evaluation system (excellent, good, average, poor, and extremely poor) using fuzzy membership functions, and generating a comprehensive evaluation index report of the hydropower station unit's lower-level machine signal data that includes the evaluation level, confidence interval, and trend of change.

[0073] Furthermore, the energy storage control system information transmission and index evaluation method disclosed in this application also includes: determining the unit status and operation and maintenance strategy based on the evaluation information; Based on the evaluation information, determine the unit status and operation and maintenance strategies, including: A100 intelligently analyzes and maps the comprehensive evaluation index report to generate a real-time status diagnosis matrix for the unit.

[0074] Specifically, the comprehensive evaluation index report is intelligently analyzed and mapped to its status. By establishing a knowledge graph of the unit's operating status, different evaluation levels and confidence intervals are mapped to the corresponding unit health status categories, including five status levels: normal operation, early warning status, abnormal operation, fault risk, and emergency shutdown, generating a real-time status diagnosis matrix for the unit.

[0075] The intelligent analysis and status mapping process includes: extracting and cleaning the evaluation levels and confidence intervals from the comprehensive evaluation index report, removing invalid or erroneous data; and establishing a knowledge graph of the unit's operating status, which contains mapping relationships between different evaluation levels, confidence intervals, and unit health status categories (normal operation, warning status, abnormal operation, fault risk, and emergency shutdown). These mapping relationships can be determined through expert experience and statistical analysis of historical data. For example, when the comprehensive evaluation index is at an excellent level and the confidence interval is high, it is mapped to a normal operating status; when the evaluation level is poor and the confidence interval is wide, it is mapped to a fault risk status.

[0076] The generation of the real-time status diagnosis matrix for the generating units includes: constructing a matrix with generating units as rows and different status levels as columns; based on the mapping results, marking the status level corresponding to each generating unit as 1 in the matrix, and the rest as 0. For example, if generating unit 1 is in an early warning state, then the early warning state column corresponding to generating unit 1 in the matrix is ​​marked as 1, and the other columns are marked as 0; finally, the real-time status diagnosis matrix for the generating units is formed.

[0077] A200, based on the real-time status diagnosis matrix of the unit, outputs a unit status evolution prediction report that includes the time dimension.

[0078] Specifically, based on the real-time status diagnosis matrix of the unit, a status evolution prediction model is constructed using multiple regression analysis and machine learning algorithms. By analyzing historical status change trajectories, equipment aging curves, and external environmental factors, the development trend and potential risk points of the unit status are predicted, and a unit status evolution prediction report containing the time dimension is output.

[0079] The application of multiple regression analysis and machine learning algorithms includes: collecting historical status data of the unit, including historical status change trajectories, equipment aging curves, and external environmental factors (such as temperature and humidity); using multiple regression analysis to establish a linear regression model between the unit status (i.e., the unit's real-time status diagnostic matrix) and these factors; and combining this with machine learning algorithms, such as decision trees and neural networks. Taking neural networks as an example, a network structure of input layer, hidden layer, and output layer is constructed, with historical status data and influencing factors as inputs and the predicted unit status as output.

[0080] The prediction of the status development trend and potential risk points includes: using a trained model to predict the future status of the unit; analyzing the prediction results to determine the development trend of the unit status, such as whether the status is gradually improving or deteriorating; and identifying potential risk points, such as when the model predicts that the unit status will change from normal operation to abnormal operation, this transition point is a potential risk point.

[0081] The unit status evolution prediction report output includes: compiling the prediction results into a report, which includes a time dimension, i.e. the predicted status of the unit at different time points. The report content may also include charts of status development trends, detailed descriptions of potential risk points, etc.

[0082] A300 establishes a hierarchical operation and maintenance decision tree model based on the unit state evolution prediction report, forming a multi-dimensional set of operation and maintenance strategy candidates.

[0083] Specifically, based on the unit status evolution prediction report, a hierarchical operation and maintenance decision tree model is established, and differentiated operation and maintenance response strategies are formulated for different status levels, including preventive maintenance plans, predictive maintenance arrangements, emergency response plans and spare parts inventory optimization schemes, forming a multi-dimensional operation and maintenance strategy candidate set.

[0084] The hierarchical operation and maintenance decision tree model is established by taking the unit status hierarchy (normal operation, early warning status, abnormal operation, fault risk, and emergency shutdown) as the root node, and gradually expanding the decision branches according to the characteristics and needs of different statuses. For example, for the normal operation status, the decision branches may include regular inspections and equipment maintenance; for the early warning status, the decision branches may include strengthening monitoring and preparing spare parts in advance. Each decision branch corresponds to a corresponding operation and maintenance response strategy. By continuously refining and improving the decision tree, a hierarchical operation and maintenance decision tree model is formed.

[0085] The differentiated operation and maintenance response strategy includes: developing regular equipment inspection, maintenance, and calibration plans for units in normal operating conditions to prevent potential failures; arranging targeted maintenance work based on condition evolution prediction reports for units in early warning or abnormal operating conditions to address potential problems in advance; developing detailed emergency response plans, including fault diagnosis procedures, maintenance steps, and personnel allocation, when units are at risk of failure or in emergency shutdown conditions; and optimizing spare parts inventory management based on the spare parts that may be needed at different condition levels to ensure timely supply of spare parts while reducing inventory costs.

[0086] The formation of the multi-dimensional operation and maintenance strategy candidate set includes: summarizing various operation and maintenance response strategies formulated for different state levels to form a multi-dimensional operation and maintenance strategy candidate set.

[0087] A400, based on a multi-dimensional set of operation and maintenance strategy candidates, constructs an operation and maintenance strategy optimization model using cost-benefit analysis and risk assessment algorithms. It then calculates the comprehensive benefit index of each strategy scheme through a multi-objective optimization algorithm and outputs the optimal operation and maintenance strategy recommendation scheme.

[0088] Specifically, based on a multi-dimensional set of operation and maintenance strategy candidates, an operation and maintenance strategy optimization model is constructed using cost-benefit analysis and risk assessment algorithms. Taking into account maintenance costs, downtime losses, security risks, and resource constraints, the comprehensive benefit index of each strategy scheme is calculated through a multi-objective optimization algorithm, and the optimal operation and maintenance strategy recommendation scheme is output.

[0089] The cost-benefit analysis and risk assessment algorithms applied include: for each strategy option in the multi-dimensional operation and maintenance strategy candidate set, a cost-benefit analysis is performed. Costs include maintenance costs and downtime losses, while benefits include improved equipment reliability and reduced failure probability. The cost and benefits of each strategy option can be quantified by establishing a cost-benefit function. Simultaneously, risk assessment algorithms are used to evaluate the potential security risks and other potential risks faced by each strategy option; for example, a risk matrix method can be used to assess risks based on their probability of occurrence and impact.

[0090] The multi-objective optimization algorithm for calculating the comprehensive benefit index involves: comprehensively considering maintenance costs, downtime losses, safety risks, and resource constraints to establish a multi-objective optimization model. The objective is to maximize the comprehensive benefit index while satisfying various constraints; multi-objective optimization algorithms, such as genetic algorithms and particle swarm optimization, are used to solve the multi-objective optimization model to obtain the comprehensive benefit index for each strategy.

[0091] The optimal operation and maintenance strategy recommendation output includes: selecting the strategy with the highest comprehensive benefit index as the optimal operation and maintenance strategy recommendation based on the comprehensive benefit index.

[0092] The A500 matches and verifies the optimal operation and maintenance strategy recommendation with existing operation and maintenance resources and scheduling plans. Through resource availability analysis and time window optimization, it generates executable operation and maintenance operation instructions and resource configuration lists, forming a unit intelligent operation and maintenance strategy that covers execution sequence, division of responsibilities and quality standards.

[0093] The matching verification process includes: matching the recommended optimal operation and maintenance strategy with existing operation and maintenance resources (such as manpower, material resources, and financial resources) and scheduling plans; checking whether existing resources can meet the implementation requirements of the strategy and whether the scheduling plan allows the strategy to be executed at an appropriate time; and conducting resource availability analysis to determine the availability of various resources at different times. For example, analyzing the work arrangements of maintenance personnel and the inventory of spare parts.

[0094] The time window optimization includes: optimizing the execution time window of the operation and maintenance strategy based on the resource availability analysis results; and selecting the time when resources are sufficient and the impact on the normal operation of the unit is minimal for operation and maintenance work.

[0095] The generation of executable operation and maintenance instructions and resource configuration lists includes: generating executable operation and maintenance instructions based on the results of matching verification and time window optimization, specifying the specific content, steps and requirements of the operation and maintenance tasks; and generating a resource configuration list, listing the various resources required to execute the operation and maintenance tasks and their quantities.

[0096] The implementation plan for intelligent operation and maintenance strategy of the unit includes: integrating operation and maintenance work instructions, resource configuration list, execution sequence, division of responsibilities and quality standards, etc., to form an implementation plan for intelligent operation and maintenance strategy of the unit covering execution sequence, division of responsibilities and quality standards. The plan should be operable and able to guide actual operation and maintenance work.

[0097] Secondly, this application discloses an information transmission and performance evaluation system for an energy storage control system, used to execute the information transmission and performance evaluation method for an energy storage control system disclosed in the first aspect of this application. The system includes: The data acquisition module is used to acquire signal data from the lower-level machines of the hydropower station units; The transmission module is used to transmit the collected signal data from the lower-level machines of the hydropower station units to the target center of the remote centralized control system. The analysis module is used to obtain the integrity of the unit lower-level machine signal data received by the target center, the availability of the unit lower-level machine signal data received by the target center, the transmission security of the collected hydropower station unit lower-level machine signal data transmitted to the target center, and, based on integrity, availability, and transmission security, to determine the evaluation information of the collected hydropower station unit lower-level machine signal data.

[0098] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0099] In this disclosure, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, devices, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as "comprising," "including," "having," etc., are open-ended terms meaning "including but not limited to," and are used interchangeably with them. The terms "or" and "and" as used herein refer to the terms "and / or," and are used interchangeably with them unless the context clearly indicates otherwise. The term "such as" as used herein refers to the phrase "such as but not limited to," and is used interchangeably with it.

[0100] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0101] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for energy storage control system information transmission and index evaluation, characterized in that, The method comprises the following steps: Collecting the signal data of the hydropower unit lower computer and transmitting to the target center of remote centralized control; Obtaining the integrity of the signal data of the hydropower unit lower computer received by the target center; Obtaining the availability of the signal data of the hydropower unit lower computer received by the target center; Obtaining the transmission security of the collected signal data of the hydropower unit lower computer transmitted to the target center; Based on the integrity, availability and transmission security, the evaluation information of the collected signal data of the hydropower unit lower computer is determined.

2. The energy storage control system information transmission and index evaluation method of claim 1, wherein, The method of collecting the signal data of the hydropower unit lower computer and transmitting to the target center of remote centralized control comprises the following steps: Based on the real-time collected operation parameters of the hydropower unit of the distributed sensor network, the original signal data is obtained; The original signal data is preprocessed to generate structured data packets conforming to the transmission protocol; Based on the structured data packets, a time stamp synchronization mechanism and a data integrity checking algorithm are used to construct a secure data frame with a unique identifier; The secure data frame is sent to the target center of remote centralized control through an encrypted transmission channel.

3. The energy storage control system information transmission and index evaluation method of claim 1, wherein, The method of obtaining the integrity of the signal data of the hydropower unit lower computer received by the target center comprises the following steps: Data packet counting verification is performed on the data frame received by the target center, and a data packet integrity preliminary report is generated by comparing the sending end sequence number with the receiving end sequence number; Based on the data packet integrity preliminary report, CRC cyclic redundancy check and MD5 hash value verification are performed on the check code in each data frame to output the data content integrity verification result; According to the data content integrity verification result, a time window sliding algorithm is used to analyze the time sequence continuity of the data to form a time sequence integrity evaluation matrix; A multi-dimensional integrity evaluation model is constructed based on the time sequence integrity evaluation matrix, and a comprehensive integrity score of the signal data of the hydropower unit lower computer is obtained based on the multi-dimensional integrity evaluation model.

4. The energy storage control system information transmission and index evaluation method of claim 1, wherein, The method of obtaining the availability of the signal data of the hydropower unit lower computer received by the target center comprises the following steps: The end-to-end delay time of the signal data of the hydropower unit lower computer from collection to reception by the target center is obtained, expired data exceeding the preset time threshold is identified, and a data time effectiveness analysis report is generated; Based on the data time effectiveness analysis report, the validity of the signal data is intelligently discriminated, and a data validity verification matrix is outputted; According to the data validity verification matrix, a multi-source data fusion model is constructed, redundant information and complementary information are identified by cross-verification of similar parameter data of different sensors, and a data credibility evaluation result is formed; Based on the data credibility evaluation result, the availability weight coefficient of each type of signal data of the hydropower unit is determined; The availability weight coefficient is matched and analyzed with the actual business demand to obtain the availability information of the signal data of the hydropower unit lower computer received by the target center.

5. The energy storage control system information transmission and index evaluation method of claim 1, wherein, The method of obtaining the transmission security of the collected signal data of the hydropower unit lower computer transmitted to the target center comprises the following steps: Security situation awareness analysis is performed on the data transmission channel to identify potential security threat sources and generate a transmission channel security threat evaluation report; Based on the transmission channel security threat evaluation report, multi-level security verification is performed on the data encryption transmission process, and an encryption transmission security verification result is outputted; According to the encrypted transmission security verification result, a path security credibility matrix is formed; Based on the path security credibility matrix, a dynamic risk assessment algorithm is used to establish a transmission security comprehensive evaluation system to calculate the multi-dimensional security score of the data transmission process.

6. The energy storage control system information transmission and index evaluation method of claim 5, wherein, According to the encrypted transmission security verification result, a path security credibility matrix is formed, including: according to the encrypted transmission security verification result, a transmission path credibility evaluation model is constructed, the security level of the network node, the transmission hop number, the route stability and the bandwidth occupancy rate are analyzed, the weak link and the security risk point of the transmission path are identified, and the path security credibility matrix is formed.

7. The energy storage control system information transmission and index evaluation method of claim 6, wherein, The evaluation information of the collected hydropower unit lower machine signal data is determined based on the integrity, the availability and the transmission security, including: The integrity score, the availability and the transmission security are subjected to data standardization processing to generate a standardized evaluation index matrix; Based on the standardized evaluation index matrix, an analytic hierarchy process model is constructed to hierarchically decompose each evaluation dimension and output a multi-dimensional weight distribution vector; According to the multi-dimensional weight distribution vector, a dynamic comprehensive evaluation score sequence is formed; Based on the dynamic comprehensive evaluation score sequence, a time decay factor and a trend prediction algorithm are introduced to construct an adaptive evaluation index model to calculate a comprehensive evaluation index with time sequence characteristics; The comprehensive evaluation index is mapped and analyzed with a preset evaluation level standard to generate a hydropower unit lower machine signal data comprehensive evaluation index report containing evaluation level, confidence interval and change trend.

8. The energy storage control system information transmission and index evaluation method of claim 7, wherein, The integrity score, the availability and the transmission security are subjected to data standardization processing to generate a standardized evaluation index matrix, including: The integrity score, the availability and the transmission security are subjected to data standardization processing by Z-score standardization method, and the importance coefficient of each dimension in comprehensive evaluation is determined by weight distribution algorithm to generate a standardized evaluation index matrix. 9.The energy storage control system information transmission and index evaluation method of claim 7, wherein, Further comprising: Based on the evaluation information, the unit state and the operation and maintenance strategy are determined; Based on the evaluation information, the unit state and the operation and maintenance strategy are determined, including: The comprehensive evaluation index report is intelligently analyzed and state-mapped to generate a unit real-time state diagnosis matrix; Based on the unit real-time state diagnosis matrix, a unit state evolution prediction report containing time dimension is outputted; According to the unit state evolution prediction report, a hierarchical operation and maintenance decision tree model is established to form a multi-dimensional operation and maintenance strategy candidate set; Based on the multi-dimensional operation and maintenance strategy candidate set, a cost-benefit analysis and risk assessment algorithm is used to construct an operation and maintenance strategy optimization model, and a comprehensive benefit index of each strategy scheme is calculated by a multi-objective optimization algorithm to output an optimal operation and maintenance strategy recommendation scheme; The optimal operation and maintenance strategy recommendation scheme is matched and verified with existing operation and maintenance resources and scheduling plan, and through resource availability analysis and time window optimization, an executable operation and maintenance job instruction and resource configuration list are generated to form a unit intelligent operation and maintenance strategy covering execution time sequence, responsibility division and quality standard.

10. An energy storage control system information transmission and index evaluation system, characterized in that, It comprises: A collection module for collecting hydropower unit lower machine signal data; The transmission module is configured to transmit the collected hydropower unit lower computer signal data to a target center of remote centralized control. The analysis module is configured to acquire completeness of the unit lower computer signal data received by the target center, acquire availability of the unit lower computer signal data received by the target center, acquire transmission safety of the collected hydropower unit lower computer signal data to the target center, and determine evaluation information of the collected hydropower unit lower computer signal data based on the completeness, the availability and the transmission safety.

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