Gateway device-based new energy station data acquisition method and system

Abstract

This invention discloses a data acquisition method and system for new energy power plants based on gateway devices, relating to the field of data processing. The method includes: after the gateway device connects to the power generation equipment and power exchange equipment, it parses the communication protocols of each device and acquires equipment operation data; constructs a closed-loop data relationship for the power plant equipment based on electrical connection relationships and environmental correlations; determines the conservation constraint set between different device data in each closed-loop path, performs closed-loop consistency verification, and calculates the data residuals of the closed-loop paths; when a calibration threshold is met, it performs propagation analysis to locate candidate device nodes; it performs data acquisition structure reconstruction, performs additional data acquisition, and performs adaptive data acquisition based on iterative updates of the data residual propagation results and the additional data acquisition results. This application solves the problems of existing data acquisition verification lacking closed-loop functionality, inability to locate fault sources, and non-adaptive acquisition strategies, achieving closed-loop verification of data acquisition, accurate fault location, and adaptive optimization of the acquisition strategy.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method and system for data acquisition in new energy power stations based on gateway devices. Background Technology

[0002] The safe and stable operation and reliable data collection of new energy power plants are directly related to the efficiency of new energy power generation, the quality of power grid dispatch, and the level of equipment operation and maintenance. They are a crucial foundation for the large-scale development of the new energy industry. Currently, the monitoring and data collection of equipment operation status at new energy power plants mainly adopts a distributed terminal collection and centralized back-end aggregation technical model. This involves gateways independently reading data from power generation equipment and power exchange equipment within the plant and uploading it to a monitoring platform for unified display and storage. This independent collection model only performs isolated judgments on data from individual devices, which can easily lead to a lack of consistency verification criteria for the collected data, difficulty in tracing abnormal data, and fixed collection strategies that cannot adapt to changes in equipment operating status, resulting in wasted collection resources and delayed fault identification.

[0003] At present, the data acquisition and anomaly identification of new energy power stations suffer from technical problems such as lack of closed-loop support for data verification, inability to accurately locate the source of faults, and difficulty in adaptively adjusting the acquisition strategy. Summary of the Invention

[0004] This application provides a data acquisition method and system for new energy power stations based on gateway devices. The system connects to various devices in the power station via a gateway device, parses communication protocols, and collects operational data. A closed-loop data relationship is constructed based on the electrical connections of the devices and their environmental correlation. A data conservation constraint set is established based on this closed-loop relationship. The system performs closed-loop consistency verification on the device operational data and calculates path data residuals. When the residuals exceed a calibrated threshold, residual propagation analysis is used to locate candidate device nodes that generate anomalies. The gateway acquisition structure is then reconstructed with these candidate devices as the core, and additional data is collected. The residual propagation results are iteratively verified with the additional collection results. This achieves adaptive and high-precision data acquisition for new energy power stations. It solves the technical problems of existing new energy power station data acquisition and anomaly identification, such as lack of closed-loop support for data verification, inability to accurately locate fault sources, and difficulty in adaptively adjusting acquisition strategies. The system achieves the technical effects of providing closed-loop support for data verification, accurately locating fault sources, and enabling adaptive adjustment of acquisition strategies.

[0005] This application provides a data acquisition method for new energy power plants based on gateway devices, comprising: after the gateway device connects to the power generation equipment and power exchange equipment in the new energy power plant, parsing the communication protocols of each device and acquiring the device operation data; constructing a closed-loop data relationship of the power plant equipment based on the electrical connection relationship and environmental correlation relationship between each device; determining the conservation constraint set between different device data in each closed-loop path based on the closed-loop data relationship of the power plant equipment; performing closed-loop consistency verification of the device operation data according to the conservation constraint set; calculating the data residual of the closed-loop path; when the data residual of the closed-loop path meets the calibration threshold, performing propagation analysis of the data residual of the closed-loop path according to the data change correlation order to locate the candidate device node that generates the residual; using the candidate device node to perform data acquisition structure reconstruction of the gateway device; using the reconstructed gateway device to perform additional data acquisition; and performing adaptive data acquisition of the new energy power plant based on the data residual propagation result and the iterative update of the additional data acquisition result.

[0006] In a possible implementation, the candidate device node is used to reconstruct the data acquisition structure of the gateway device, performing the following processes: Based on the closed-loop path position of the candidate device node in the data closed-loop relationship of the station equipment, a set of neighboring devices with data conservation relationships with the candidate device node is determined; based on the data contribution and residual propagation direction of each device in the neighboring device set in the closed-loop path, an extended acquisition priority sequence of the candidate device node is generated; the data acquisition object set of the gateway device is adjusted according to the extended acquisition priority sequence to establish an additional data acquisition channel; and the operating data in the neighboring device set is obtained using the additional data acquisition channel to establish additional data acquisition results.

[0007] In a possible implementation, the propagation analysis of residual data in the closed-loop path is performed according to the order of data changes to locate candidate device nodes that generate residuals. The following processes are then performed: a residual propagation sequence is established along the closed-loop path of the closed-loop relationship of the station equipment data, and the residual propagation direction is determined according to the energy transfer direction between devices in the closed-loop path; a time alignment window is constructed for each device node in the closed-loop path, and the operating data of each device is synchronously mapped within the time alignment window to form a unified residual sequence; the residual transfer coefficient between adjacent device nodes is calculated based on the unified residual sequence, and the residual transfer coefficient is used to characterize the propagation attenuation characteristics of residuals in the closed-loop path; residual recursive calculation is performed along the closed-loop path according to the residual propagation direction and residual transfer coefficient to obtain the cumulative residual value corresponding to each device node; when the cumulative residual value of any device node and the abrupt change value of the adjacent device node exceed a preset transition threshold, the corresponding device node is determined as a candidate device node that generates residuals.

[0008] In a possible implementation, the propagation analysis of residual data in the closed-loop path is performed according to the order of data changes to locate candidate device nodes that generate residuals. The following processing is also performed: After the residual propagation sequence is established, the cumulative residual values ​​of the same device nodes in multiple closed-loop paths are cross-validated to generate a comprehensive residual index for the device nodes; the comprehensive residual index is fused with the historical residual change trend of the corresponding device node to generate a historical trend-weighted residual value; a preset transition threshold is dynamically adjusted based on the historical trend-weighted residual value to form a dynamic residual judgment threshold; when performing residual recursive calculation along the closed-loop path, the backflow or detour propagation path of the residual is detected simultaneously, and the residual propagation coefficient is adjusted based on the detection results; when the cumulative residual value of any device node and the abrupt change value of adjacent device nodes exceed the corresponding dynamic residual judgment threshold, and the abrupt change remains after adjustment of the residual propagation coefficient, the corresponding device node is determined as a candidate device node that generates residuals.

[0009] In a possible implementation, the following processing is performed: the set of conservation constraints includes node input-output conservation constraints, power balance conservation constraints, voltage and current consistency constraints, and energy accumulation consistency constraints.

[0010] In a possible implementation, the following processing is performed: when establishing input-output conservation constraints, a tolerance threshold is set for the device node, the tolerance threshold is used to adapt to measurement errors and environmental disturbances, and the tolerance threshold is adaptively adjusted according to the device type and operating status.

[0011] In a possible implementation, the adaptive data acquisition of the new energy power station is performed, and the following processing is also performed: the adaptive data acquisition results are recorded with dual redundancy, and time-series storage verification feedback is generated based on the dual redundancy storage record results; abnormal storage early warning management is performed based on the time-series storage verification feedback.

[0012] In a possible implementation, a closed-loop data relationship for the station equipment is constructed based on the electrical connection relationships and environmental correlations between the various devices, and the following processes are performed: obtaining the electrical connection topology of the power generation equipment and the power exchange equipment, parsing the electrical connection topology to obtain the electrical connection relationships; obtaining the environmental factors of the power generation equipment and the power exchange equipment, and performing correlation analysis between the environmental factors and the corresponding equipment to establish correlation analysis results; identifying the closed-loop paths between the devices based on the electrical connection relationships, and performing mapping correction of the closed-loop paths through the correlation analysis results; and constructing the closed-loop data relationship for the station equipment based on the mapping correction results.

[0013] In a possible implementation, the following process is performed: after the gateway device connects to the power generation equipment and power exchange equipment in the new energy power station, the access status verification is performed, the access verification feedback is configured, the access self-test is completed according to the access verification feedback, and when the access self-test is passed, the device operation data is collected.

[0014] This application also provides a data acquisition system for new energy power plants based on gateway devices, including: a data closed-loop relationship construction module for power plant equipment, used to parse the communication protocols of each device and collect the device operation data after the gateway device is connected to the power generation equipment and power exchange equipment in the new energy power plant, and to construct a data closed-loop relationship for the power plant equipment based on the electrical connection relationship and environmental correlation relationship between each device; a closed-loop consistency verification module, used to determine the set of conservation constraints between different device data in each closed-loop path based on the data closed-loop relationship of the power plant equipment, to perform closed-loop consistency verification of the device operation data according to the set of conservation constraints, and to calculate the data residual of the closed-loop path; a propagation analysis module, used to perform propagation analysis of the data residual of the closed-loop path according to the data change correlation order when the data residual of the closed-loop path meets the calibration threshold, and to locate the candidate device node that generates the residual; and a data adaptive acquisition module, used to perform data acquisition structure reconstruction of the gateway device using the candidate device node, to perform additional data acquisition using the reconstructed gateway device, and to perform adaptive data acquisition of the new energy power plant according to the data residual propagation result and the iterative update of the additional data acquisition result.

[0015] The proposed data acquisition method and system for new energy power plants based on gateway devices, as described in this application, firstly involves the gateway device connecting to the power generation and power exchange equipment within the power plant. This involves parsing the communication protocols of each device and collecting their operational data. Based on the electrical connections and environmental relationships between the devices, a closed-loop data relationship is constructed. Next, based on this closed-loop relationship, a set of conservation constraints is determined for the data from different devices within each closed-loop path. A closed-loop consistency check of the operational data is then performed based on this constraint set, and the data residuals for the closed-loop paths are calculated. When the residuals meet a calibration threshold, a propagation analysis of the residuals is performed according to the data change sequence to identify candidate device nodes that generate the residuals. Finally, the data acquisition structure of the gateway device is reconstructed using these candidate device nodes. Additional data acquisition is then performed using the reconstructed gateway device. Adaptive data acquisition for the new energy power plant is then performed based on the iterative updates of the residual propagation results and the additional data acquisition results. Through this process, the proposed method and system achieve the technical effects of providing closed-loop support for data verification, accurately locating fault sources, and enabling adaptive adjustment of the acquisition strategy. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 This is a flowchart illustrating the data acquisition method for new energy power stations based on gateway devices provided in this application embodiment.

[0018] Figure 2 This is a schematic diagram of the structure of a new energy power station data acquisition system based on a gateway device, provided in an embodiment of this application.

[0019] Figure labeling: Module 10 for building closed-loop relationship of station equipment data, Module 20 for closed-loop consistency verification, Module 30 for propagation analysis, and Module 40 for adaptive data acquisition. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] This application provides a method for data acquisition from new energy power stations based on gateway devices, such as... Figure 1 As shown, the method includes:

[0022] Step S100: After the gateway device connects to the power generation equipment and power exchange equipment in the new energy power station, it parses the communication protocols of each device and collects the device operation data. Based on the electrical connection relationship and environmental correlation relationship between each device, a closed-loop relationship of the power station equipment data is constructed.

[0023] Specifically, the gateway device is a dedicated edge acquisition gateway for new energy power plants. It possesses multi-protocol parsing, data forwarding, local computing, and device access management functions. It is the core hardware connecting the power generation equipment, power exchange equipment, and the back-end monitoring system of the power plant, supporting multiple interface accesses such as Ethernet, RS485, CAN, and Modbus. The power generation equipment in a new energy power plant is the core production equipment, such as photovoltaic inverters, wind turbine generators, energy storage converters, photovoltaic strings, and wind turbine towers. The power exchange equipment is the equipment for power transmission, conversion, and dispatching in the power plant, such as box-type transformers, switchgear, reactive power compensation devices, grid-connected cabinets, and cable branch boxes. Device communication protocols are unified rules for data transmission between devices, including Modbus-RTU, Modbus-TCP, IEC61850, DL / T645, CANopen, and Profinet. After the gateway completes the physical access and protocol docking of the power generation and power exchange equipment in the power station, it first verifies the access validity and then establishes the closed-loop relationship of the power station equipment data. The closed-loop relationship of the power station equipment data is a closed link formed by the electrical connection of the equipment and the influence of the environment, which is a data interconnection and energy transfer between the equipment. The data in the link follows the conservation rules of energy, power and electrical parameters.

[0024] In one possible implementation, step S100 further includes step S110: after the gateway device connects to the power generation equipment and power exchange equipment in the new energy power station, it performs access status verification, configures access verification feedback, completes access self-test based on the access verification feedback, and performs equipment operation data acquisition when the access self-test is passed. Specifically, the gateway first performs hardware level detection on each access port, reads the port voltage and current values, and determines whether they are within the standard electrical threshold range for the corresponding device access. Then, it sends a fixed-format heartbeat handshake frame carrying the device address, communication baud rate, and check bit parameters to each connected device, sending it once every 2 seconds for 3 consecutive times, and waits for the device to return a response frame. The gateway pre-configures three types of verification feedback results: normal response, no response, and communication parameter mismatch. Subsequently, it performs self-test based on the feedback. If all three heartbeats receive a normal response and the port electrical parameters meet the standards, the access self-test is deemed to have passed. If there is no response or parameter mismatch, the device is directly marked as having an access fault, triggering the gateway's local fault indicator light to illuminate, pausing data acquisition for the device, and locking the faulty port.

[0025] In one possible implementation, a closed-loop data relationship of the station equipment is constructed based on the electrical connection relationship and environmental correlation relationship between the various devices. Step S100 further includes step S120, which involves obtaining the electrical connection topology of the power generation equipment and the power exchange equipment, and parsing the electrical connection topology to obtain the electrical connection relationship. Specifically, the gateway's built-in site topology configuration module is activated, and the topology generation method is selected according to the site conditions. When CAD drawings are available, the site electrical CAD drawings are directly imported into the module, which automatically identifies the equipment legends, wiring lines, and equipment numbers in the drawings, and sorts out the equipment wiring relationships according to the drawings. When there are no CAD drawings, the gateway's device address binding function is used to first bind the inverters, photovoltaic strings, switch cabinets, and combiner boxes under the same box-type transformer into an associated group according to the power supply affiliation, distinguishing the equipment clusters of different transformers. Then, the IEC61850 protocol messages of the connected equipment are parsed to extract the master-slave connection, series connection, parallel connection, and upper and lower level transformer relationships marked in the protocol's logical nodes. After completing the above operations, the module automatically generates a visual topology structure, and then extracts the hierarchical connection relationship from the generation end to the grid connection end from the topology, clarifying the one-to-one and one-to-many association logic between equipment.

[0026] Step S130: Obtain environmental factors for power generation equipment and power exchange equipment, and perform correlation analysis between environmental factors and corresponding equipment to establish correlation analysis results. Specifically, the gateway connects external temperature and humidity, light intensity, wind speed, cabinet temperature, and rain / snow sensors through an analog signal acquisition module. The analog signals collected by the sensors are converted into digital signals. First, the environmental impact area is divided according to the layout of the site equipment. The light intensity and component temperature data of the photovoltaic array area are bound to the inverters in the corresponding area. The wind speed, vibration, and temperature data of the wind turbine nacelle are bound to the corresponding wind turbines. Then, a linear fitting algorithm is used to calculate the correlation coefficient between environmental parameters and equipment output power and operating efficiency. The coefficient range is set to 0-1. The higher the coefficient, the greater the environmental impact. Finally, a standardized correlation table containing environmental parameters, associated equipment, impact coefficients, and impact thresholds is formed and stored in the gateway's local database.

[0027] Step S140: Identify the closed-loop path between devices based on the electrical connection relationship, and perform mapping correction of the closed-loop path based on the correlation analysis results. Specifically, a depth-first traversal algorithm is used, starting with the grid-connected cabinet and main transformer as the core nodes, and traversing all power generation devices in reverse along the electrical connection relationship to sort out the complete closed loop of energy transfer and form an initial closed-loop path. Then, the environmental correlation table generated in step S130 is retrieved, and device nodes with environmental impact coefficients exceeding the preset coefficient threshold are screened out. The data weight of these nodes in the closed-loop path is increased, and environmental interference items are included in the path correction rules. If a single device is greatly affected by the environment, it and its directly related devices are separately divided into sub-closed-loop paths to avoid interfering with the main closed-loop data verification. After the correction is completed, the final closed-loop path is locked.

[0028] Step S150: Construct a closed-loop data relationship for the site equipment based on the mapping correction results. Specifically, a time-series closed-loop relationship database is built locally on the gateway. A unique ID is assigned to each closed-loop path. The list of devices included in the closed loop, communication addresses, collection points, energy transfer directions, environmental correction coefficients, and conservation verification rules are entered into the database one by one. The database supports real-time access and dynamic updates. When new devices are added, the gateway automatically traverses the closed-loop relationship and assigns the new devices to the corresponding closed-loop paths, ensuring that all devices are included in the data closed-loop management.

[0029] Step S200: Based on the closed-loop relationship of the station equipment data, determine the set of conservation constraints between different equipment data in each closed-loop path. Perform closed-loop consistency verification of equipment operation data according to the set of conservation constraints, and calculate the data residuals of the closed-loop path. The set of conservation constraints includes node input / output conservation constraints, power balance conservation constraints, voltage and current consistency constraints, and energy accumulation consistency constraints. When establishing input / output conservation constraints, a tolerance threshold is set for the equipment nodes. This tolerance threshold is used to adapt to measurement errors and environmental disturbances, and it is adaptively adjusted according to the equipment type and operating status.

[0030] Specifically, the gateway retrieves all closed-loop paths from the local closed-loop relation database, decomposes the device data association logic for each path, and constructs four types of conservation constraint sets for each item. The first type is node input-output conservation constraint, which limits the deviation range between the total amount of input data and the total amount of output data for intermediate equipment nodes such as combiner boxes, switch cabinets, and transformers. The second type is power balance conservation constraint, which limits the total output power of the power generation equipment to be balanced with the sum of the power loss power of the power exchange equipment and the grid-connected output power for the entire closed-loop path. The third type is voltage and current consistency constraint, which limits the voltage and current values ​​at the interfaces of adjacent equipment to match and the phase to match for series-connected and directly connected equipment. The fourth type is energy accumulation consistency constraint, which limits the energy accumulation output value within the closed-loop path to match the cumulative transmission and grid connection value according to a fixed time period. When constructing input-output conservation constraints, the gateway first presets basic tolerance thresholds for various devices, then initiates adaptive adjustment logic, and reads the current operating load rate and environmental impact coefficient of the devices in real time. When the devices are running at full load, the tolerance thresholds float downwards; when the devices are running at light load or the environment fluctuates significantly, the tolerance thresholds float upwards. After completing the configuration of the conservation constraint set and tolerance thresholds, the gateway retrieves the real-time operating data of each device, performs consistency verification one by one along the closed-loop path, substitutes the actual collected data into the conservation constraint formula to calculate the theoretical standard value, and then subtracts the theoretical standard value from the actual data to obtain the data deviation of a single device node. The gateway summarizes the deviation values ​​of all nodes along the entire closed-loop path, removes normal fluctuations within the tolerance range, and the remaining cumulative deviation value is the data residual of the closed-loop path. The residual value is stored locally in the gateway in real time for subsequent anomaly detection.

[0031] Step S300: When the closed-loop path data residual meets the calibration threshold, the propagation analysis of the closed-loop path data residual is performed according to the data change association sequence to locate the candidate device node that generates the residual.

[0032] Specifically, for abnormal residuals calculated from the closed-loop path that exceed the normal tolerance range, the generation, transmission, and attenuation patterns of the residuals are tracked comprehensively along the energy transfer logic of the closed-loop path. By eliminating non-equipment fault factors such as environmental interference, short-term data fluctuations, and normal transmission losses through multi-dimensional verification, the source candidate device node causing the residual abnormality is accurately located. This solves the problem of abnormal residuals in traditional acquisition modes but inability to locate the source and misjudgment of equipment faults. At the same time, by combining historical operating data and multi-closed-loop cross-validation, the accuracy of anomaly location is improved, and misjudgment caused by a single data deviation is avoided.

[0033] In one possible implementation, the propagation analysis of the residual data in the closed-loop path is performed according to the order of data changes to locate candidate device nodes that generate residuals. Step S300 further includes step S310, establishing a residual propagation sequence along the closed-loop path of the closed-loop relationship of the station equipment data, and determining the residual propagation direction according to the energy transfer direction between devices in the closed-loop path. Specifically, according to the hierarchical order of devices in the closed-loop path from the generation end to the grid connection end, device nodes are arranged sequentially to form a one-dimensional linear residual propagation sequence. Each sequence node corresponds to one device. Energy in the closed-loop path flows from photovoltaic strings, wind turbines, and other power generation equipment to power exchange equipment such as inverters, transformer substations, and grid-connected cabinets. The residual propagation direction is directly set to be consistent with the energy transfer direction. A linked list structure is used to store the sequence, and each node is bound to the initial residual value of the corresponding device, which is updated synchronously in real time.

[0034] Step S320: A time alignment window is constructed for each device node in the closed-loop path. The operating data of each device is synchronized and mapped within the time alignment window to form a unified residual sequence. Specifically, the gateway uniformly sets a time alignment window of fixed duration to adapt to the conventional acquisition interval of the site. For device data with different acquisition frequencies, a linear interpolation algorithm is used for time synchronization. Devices with high acquisition frequencies retain the original data points, while devices with low acquisition frequencies have missing data points interpolated within the window. All device data are uniformly aligned to the time points within the window. The residuals of each node are calculated based on the synchronized data to form a unified residual sequence with the same time reference. The duration of the time alignment window is adaptively adjusted according to the dynamic operating conditions of the site. For example, when large fluctuations in the grid frequency or large changes in device power are detected, the window duration is automatically shortened to half of the original duration to improve time sensitivity; when the system is in steady-state operation, the window duration is restored to the preset value.

[0035] Step S330: Calculate the residual propagation coefficient between adjacent device nodes based on the unified residual sequence. The residual propagation coefficient characterizes the propagation attenuation characteristics of the residual in the closed-loop path. Specifically, the upstream and downstream relationships of adjacent devices are defined. According to the energy flow direction in the closed-loop path, the device that sends out energy / data first is the upstream device, and the device that receives it later is the downstream device. For example, a photovoltaic string is upstream, and the corresponding inverter is downstream. The residual propagation coefficient is calculated using the ratio method. The formula is: Residual propagation coefficient = Single-node residual of downstream device node ÷ Single-node residual of upstream device node. Simultaneously, the gateway pre-sets a fixed reference coefficient according to the device type. For example, the reference coefficient for power generation equipment is set to 0.9, and the reference coefficient for power exchange and transfer equipment is set to 0.85. This reference coefficient is used to determine whether the residual has decayed normally. The closer the residual propagation coefficient is to 1, the less the residual decays from upstream to downstream, which is likely due to a device malfunction causing persistent residuals. If the residual propagation coefficient is much smaller than the reference coefficient, the residual decays significantly from upstream to downstream, which is more likely due to normal system dynamic response or measurement noise, rather than device malfunction. To avoid distortion of the transmission coefficient caused by single-point data jitter, a sliding window mean filter is used to preprocess the residual input values ​​of upstream and downstream nodes. The window width is set to 3 sampling periods. Isolated noise points with sudden changes are removed before being substituted into the ratio formula for calculation.

[0036] Step S340: Based on the residual propagation direction and residual propagation coefficient, perform residual recursive calculation along the closed-loop path to obtain the cumulative residual value corresponding to each device node. Specifically, a recursive accumulation algorithm is adopted, starting from the power generation device node at the beginning of the closed-loop path. The cumulative residual value of the starting node is equal to its own initial residual. The cumulative residual value of each subsequent device node is equal to its own node residual plus the product of the cumulative residual value of the upstream node and the residual propagation coefficient. The entire calculation process is completed by the gateway's local edge computing unit. After each node is calculated, the cumulative residual value is stored in the corresponding sequence node in real time.

[0037] Step S350: When the cumulative residual value of any device node and the abrupt change value of its adjacent device node exceed a preset transition threshold, the corresponding device node is identified as a candidate device node generating residuals. Specifically, the gateway presets differentiated fixed residual transition thresholds according to device type. These thresholds are critical values ​​statistically derived from a large amount of normal operation data from the site, used to distinguish between normal fluctuations and abnormal deviations. During calculation, the cumulative residual value of the current device node is taken, and the cumulative residual value of the adjacent upstream device node is subtracted. The absolute value of the result is taken to obtain the difference between adjacent residuals, which is then compared with the threshold: if the difference between adjacent residuals is ≤ the transition threshold of the corresponding device, it indicates that the residual is normal transmission attenuation and a small measurement error, and is not abnormal, so it is skipped directly; if the difference between adjacent residuals is > the transition threshold, it indicates that the residual has suddenly changed and is not normal attenuation, so the device node is marked as a candidate device node.

[0038] In one possible implementation, the propagation analysis of data residuals in closed-loop paths is performed according to the order of data changes to locate candidate device nodes that generate residuals. Step S300 further includes step S360, where, after the residual propagation sequence is established, the cumulative residual values ​​of the same device nodes in multiple closed-loop paths are cross-validated to generate a comprehensive residual index for the device nodes. Specifically, in a new energy power station, the same key equipment, such as a box-type transformer, grid-connected cabinet, or main inverter, may belong to multiple data closed-loop paths simultaneously. The gateway traverses all local closed-loop paths, filters out common device nodes shared across paths, and extracts the cumulative residual values ​​of these nodes in different closed-loop paths. A weighted fusion algorithm is used to calculate the comprehensive residual index, assigning path weights to each associated path. The weight is positively correlated with the electrical coupling strength of the path to the device; the tighter the coupling, the higher the weight. The formula for calculating the comprehensive residual index is: Comprehensive Residual Index = Σ (Cumulative Residual Value of a Single Path × Corresponding Path Weight), thereby eliminating abnormal misjudgments caused by data deviations in a single closed-loop path and achieving multi-closed-loop cross-validation. The path weights are allocated based on the electrical distance and energy exchange ratio of the equipment in each path. The closer the electrical distance and the higher the energy exchange ratio, the greater the weight. The electrical distance is quantified according to the per-unit impedance of the connecting lines, and the energy exchange ratio is determined by the proportion of the power transmitted through that path to the total power transmitted through the equipment.

[0039] Step S370 involves fusing the comprehensive residual index with the historical residual change trend of the corresponding device node to generate a historical trend-weighted residual value. Specifically, the gateway retrieves historical residual data from the local time-series database for the device node over the past N collection cycles, uses a moving average algorithm to fit the historical residual change trend, and identifies the normal fluctuation range, periodic fluctuation pattern, and sudden anomaly characteristics of the residual. The value of N is set differently according to the device type; for example, N is 20 cycles for power generation equipment to cover diurnal fluctuations, and N is 10 cycles for power exchange equipment to quickly respond to changes in operating conditions. The comprehensive residual index obtained in step S360 is fused with the historical trend curve over time. When the current comprehensive residual index is consistent with the historical trend, the weight of the current residual is reduced; when the current comprehensive residual index deviates significantly from the historical normal range, the weight of the current residual is increased to strengthen the significance of the anomaly, ultimately generating a historical trend-weighted residual value. This value simultaneously integrates real-time multipath data and long-term operating patterns, effectively distinguishing between inherent equipment fluctuations and actual fault deviations.

[0040] Step S380: The preset transition threshold is dynamically adjusted based on the historical trend-weighted residual value to form a dynamic residual judgment threshold. Specifically, the gateway uses the preset transition threshold as a benchmark and, in conjunction with the historical trend-weighted residual value obtained in step S370, adaptively corrects the threshold: when the overall fluctuation of the historical trend-weighted residual value is small, the transition threshold is narrowed to improve the sensitivity of anomaly detection; when the overall fluctuation of the historical trend-weighted residual value is large, the transition threshold is widened to avoid normal operating condition fluctuations being judged as anomalies. Through the above correction, the fixed preset transition threshold is adjusted to a dynamic residual judgment threshold that adapts to the current operating state of the device.

[0041] Step S390: During the residual recursive calculation along the closed-loop path, the backflow or detour propagation path of the residual is detected simultaneously, and the residual propagation coefficient is adjusted according to the detection results. Specifically, during the residual recursive calculation process, the gateway synchronously traverses the closed-loop path in both the reverse and forward directions to detect whether there is a residual backflow from the downstream device to the upstream device or a detour propagation path between multiple devices. If a residual backflow or detour path is detected, the original residual propagation coefficient is corrected according to the backflow intensity and detour delay to reduce the interference of abnormal circulation on the residual calculation; if no backflow or detour path is detected, the original residual propagation coefficient remains unchanged to ensure the accuracy of the residual recursive calculation. Here, the backflow intensity is defined as the ratio of the amplitude of the reverse propagation residual to the amplitude of the forward residual, the detour delay is defined as the number of sampling periods from the occurrence of the residual to the completion of the detour propagation, and the corrected propagation coefficient = original propagation coefficient × (1 - backflow intensity × delay coefficient), with the delay coefficient positively correlated with the detour delay.

[0042] Step S3100: When the cumulative residual value of any device node and the abrupt change value of its adjacent device node exceed the corresponding dynamic residual judgment threshold, and the abrupt change remains after adjustment by the residual propagation coefficient, the corresponding device node is identified as a candidate device node generating residuals. Specifically, the gateway calculates the absolute value of the abrupt change between the cumulative residual value of the current device node and the cumulative residual value of its adjacent device nodes, and compares this abrupt change value with the dynamic residual judgment threshold obtained in step S380. If the abrupt change value exceeds the dynamic residual judgment threshold, and the abrupt change still exists after adjustment by the residual propagation coefficient in step S390, normal data fluctuations, environmental interference, and measurement errors are excluded, and the device node is identified as a candidate device node generating residuals.

[0043] Step S400: Utilize the candidate device node to perform data acquisition structure reconstruction of the gateway device, utilize the reconstructed gateway device to perform additional data acquisition, and perform adaptive data acquisition of the new energy power station based on the data residual propagation results and the iterative update of the additional data acquisition results.

[0044] Specifically, after locating candidate device nodes that generate data residuals, the gateway device dynamically reconstructs the original globally unified data acquisition structure based on the closed-loop relationship of data from the site equipment and the residual propagation path, strengthening the data acquisition efforts for candidate device nodes and related devices. The additional high-resolution data is then used for bidirectional iterative verification with the previous residual propagation results: the first round uses the additional data to verify the accuracy of the residual propagation path and correct the candidate device node determination; the second round, based on the verified residual conclusions, readjusts the acquisition frequency / location until the additional data and residual propagation patterns perfectly match, ultimately accurately identifying the true abnormal device node. Simultaneously, the verified acquisition strategy is solidified into adaptive rules. When this device / similar devices subsequently exhibit residuals, the gateway automatically triggers enhanced acquisition, achieving dynamic optimization of acquisition resources and ensuring the accuracy and economy of site data acquisition.

[0045] In one possible implementation, the gateway device's data acquisition structure is reconstructed using the candidate device node. Step S400 further includes step S410, which determines a set of neighboring devices that have a data conservation relationship with the candidate device node based on the closed-loop path position of the candidate device node in the data closed-loop relationship of the station equipment. Specifically, the gateway retrieves the closed-loop path where the candidate device node is located from the local time-series closed-loop relationship database. Based on the electrical connection relationship and data conservation constraints of the devices within the closed-loop path, devices that have a direct or indirect data relationship with the candidate device node and satisfy at least one of the constraints of node input-output conservation, power balance conservation, voltage and current consistency constraints, or energy accumulation consistency constraints are uniformly classified into a set of neighboring devices, forming an associated device domain with the candidate device node as the core. The division of the neighboring device set can consider topological distance constraints, only including devices that are no more than 3 nodes away from the candidate device node in terms of electrical topology, to avoid excessive expansion of the acquisition range and waste of resources.

[0046] Step S420: Based on the data contribution and residual propagation direction of each device in the neighboring device set within the closed-loop path, an extended acquisition priority sequence for candidate device nodes is generated. Specifically, the gateway calculates the data contribution of each device in the neighboring device set based on its energy transfer level, data coupling strength, and impact on overall closed-loop consistency verification within the closed-loop path. A higher data contribution indicates a greater impact of the device on the integrity of the closed-loop data. The formula for calculating the data contribution is: Contribution = α × Energy Proportion + β × Verification Weight + γ × Historical Anomaly Frequency, where α, β, and γ are weighting coefficients, the energy proportion is the percentage of energy transmitted by the device in the path relative to the total energy, the verification weight is the number of conservation constraint terms the device participates in, and the historical anomaly frequency is the number of times the device triggered anomalies in the past 24 hours. Combining the residual propagation direction, devices located upstream of the residual propagation path and having a more direct impact on the candidate device node data are assigned higher priority. These devices are then sorted from highest to lowest priority to form the extended acquisition priority sequence for candidate device nodes.

[0047] Step S430: Adjust the data acquisition object set of the gateway device according to the extended acquisition priority sequence and establish an additional data acquisition channel. Specifically, the gateway dynamically adjusts the data acquisition object set based on the original regular acquisition objects according to the extended acquisition priority sequence, increasing the acquisition weight of high-priority neighboring devices and releasing some acquisition resources of low-priority non-critical devices. Simultaneously, an additional data acquisition channel is independently opened within the gateway. This channel is isolated from the regular acquisition channel, uses independent thread scheduling and caching space, and is dedicated to enhanced acquisition of high-priority neighboring devices, ensuring the real-time performance and stability of data acquisition.

[0048] Step S440: The gateway uses the additional data acquisition channel to acquire operational data from the neighboring device set and establishes additional data acquisition results. Specifically, the gateway uses the additional data acquisition channel to perform high-density, multi-point acquisition of device operational data from the neighboring device set. The acquired data includes key operational parameters such as voltage, current, power, temperature, switch status, and communication quality. This data is then structured according to a unified time base, device number, and closed-loop path ID to form additional data acquisition results containing timestamps, device identifiers, point values, and closed-loop association information, providing data support for residual iterative verification.

[0049] In one possible implementation, the adaptive data acquisition of the new energy power station is performed. Step S400 further includes step S450, which involves performing dual-redundant storage recording on the adaptive data acquisition results and generating time-series storage verification feedback based on the dual-redundant storage recording results. Specifically, the gateway writes the adaptively acquired operating data to both the local independent storage area and the edge computing cache area, forming dual-redundant storage records. The two storage data maintain consistency in timing and content. After each round of storage writing, the gateway performs timing comparison, verification, and integrity verification on the two storage data. Based on the verification results, it generates time-series storage verification feedback, which includes statuses such as normal storage, timing misalignment, data missing, and verification mismatch. The dual-redundant storage areas use different storage media: the local independent storage area uses an industrial-grade SD card, and the edge computing cache area uses DDR4 memory. The write operations for the two storage areas are executed by different CPU cores to avoid simultaneous failure of both storage areas due to a single-core failure.

[0050] Step S460: Perform abnormal storage early warning management based on the time-series storage verification feedback. Specifically, after receiving the time-series storage verification feedback, the gateway classifies the storage anomaly type: if it is a minor anomaly, i.e., a single verification mismatch lasting less than 3 seconds, automatic data repair and rewriting are performed; if it is a moderate or severe anomaly, while marking the abnormal storage location and recording the anomaly log, a local storage fault prompt is triggered, and the anomaly information is uploaded to the background monitoring system. Among them, three consecutive verification mismatches or a data missing rate exceeding 5% are considered moderate anomalies; simultaneous write failures of dual redundancy or verification misalignments that cannot be repaired are considered severe anomalies. After a severe anomaly is triggered, the gateway automatically switches to the backup storage channel and issues a hardware alarm. Through abnormal storage early warning management, the security, integrity, and traceability of data storage in the operation of new energy power stations are ensured, providing a reliable data foundation for data closed-loop consistency verification and equipment anomaly location.

[0051] This application embodiment connects to various devices in a new energy power station through a gateway device, parses communication protocols, and collects operational data. Based on the electrical connections of the devices and their environmental relationships, a closed-loop data relationship for the power station equipment is constructed. A data conservation constraint set is established based on this closed-loop relationship. Closed-loop consistency verification of the equipment operational data is performed, and path data residuals are calculated. When the residual exceeds a calibrated threshold, residual propagation analysis is used to locate candidate device nodes that generate anomalies. The gateway acquisition structure is then reconstructed with these candidate devices as the core, and additional data is collected. The residual propagation results and the additional collection results are iteratively verified. This achieves adaptive and high-precision data acquisition for new energy power stations, solving the technical problems of lack of closed-loop support for data verification, inability to accurately locate fault sources, and difficulty in adaptively adjusting acquisition strategies in existing new energy power station data acquisition and anomaly identification. It achieves the technical effects of providing closed-loop support for data verification, accurately locating fault sources, and enabling adaptive adjustment of acquisition strategies.

[0052] In the above text, refer to Figure 1 This paper describes in detail a data acquisition method for new energy power stations based on gateway devices according to embodiments of the present invention. Next, we will refer to... Figure 2 A new energy power station data acquisition system based on a gateway device according to an embodiment of the present invention is described.

[0053] The new energy power station data acquisition system based on gateway devices according to embodiments of the present invention addresses the technical problems of existing new energy power station data acquisition and anomaly identification, such as lack of closed-loop support for data verification, inability to accurately locate fault sources, and difficulty in adaptively adjusting acquisition strategies. It achieves the technical effects of providing closed-loop support for data verification, accurately locating fault sources, and enabling adaptive adjustment of acquisition strategies. The new energy power station data acquisition system based on gateway devices includes: a power station equipment data closed-loop relationship construction module 10, a closed-loop consistency verification module 20, a propagation analysis module 30, and a data adaptive acquisition module 40.

[0054] The station equipment data closed-loop relationship construction module 10 is used to parse the communication protocols of each device and collect the device operation data after the gateway device is connected to the power generation equipment and power exchange equipment in the new energy station. It constructs the station equipment data closed-loop relationship based on the electrical connection relationship and environmental correlation relationship between each device. The closed-loop consistency verification module 20 is used to determine the conservation constraint set between different device data in each closed-loop path based on the station equipment data closed-loop relationship. It performs closed-loop consistency verification of device operation data according to the conservation constraint set and calculates the closed-loop path data residual. The propagation analysis module 30 is used to perform propagation analysis of the closed-loop path data residual according to the data change correlation order when the closed-loop path data residual meets the calibration threshold, and locate the candidate device node that generates the residual. The data adaptive acquisition module 40 is used to perform data acquisition structure reconstruction of the gateway device using the candidate device node, perform additional data acquisition using the reconstructed gateway device, and perform adaptive data acquisition of the new energy station according to the data residual propagation result and the iterative update of the additional data acquisition result.

[0055] The detailed description of the specific configuration of the adaptive data acquisition module 40 is explained as follows: As described above, the data acquisition structure reconstruction of the gateway device is performed using the candidate device node. The adaptive data acquisition module 40 may further include: a neighboring device set determination unit, used to determine the neighboring device set that has a data conservation association with the candidate device node based on the closed-loop path position of the candidate device node in the data closed-loop relationship of the station equipment; an extended acquisition priority sequence generation unit, used to generate an extended acquisition priority sequence of the candidate device node based on the data contribution and residual propagation direction of each device in the neighboring device set in the closed-loop path; an additional data acquisition channel establishment unit, used to adjust the data acquisition object set of the gateway device according to the extended acquisition priority sequence and establish an additional data acquisition channel; and an additional data acquisition result establishment unit, used to obtain the running data in the neighboring device set using the additional data acquisition channel and establish an additional data acquisition result.

[0056] The detailed description of the specific configuration of the propagation analysis module 30 is explained below: As mentioned above, the propagation analysis of the residual data in the closed-loop path is performed according to the data change association sequence to locate the candidate device nodes that generate the residuals. The propagation analysis module 30 may further include: a residual propagation direction determination unit for establishing a residual propagation sequence along the closed-loop path of the closed-loop relationship of the station equipment data, and determining the residual propagation direction according to the energy transfer direction between devices in the closed-loop path; and a unified residual sequence forming unit for constructing a time alignment window for each device node in the closed-loop path, and performing the operation data of each device within the time alignment window. The system performs synchronous mapping to form a unified residual sequence. The residual propagation coefficient calculation unit calculates the residual propagation coefficient between adjacent device nodes based on the unified residual sequence. The residual propagation coefficient characterizes the propagation attenuation characteristics of the residual in the closed-loop path. The residual recursive calculation unit performs residual recursive calculation along the closed-loop path based on the residual propagation direction and the residual propagation coefficient to obtain the cumulative residual value corresponding to each device node. The first candidate device node determination unit determines the corresponding device node as a candidate device node that generates residuals when the cumulative residual value of any device node and the abrupt change value of the adjacent device node exceed a preset transition threshold.

[0057] The propagation analysis module 30 further includes: a comprehensive residual index generation unit, which performs cross-validation of the cumulative residual values ​​of the same device nodes in multiple closed-loop paths after the residual propagation sequence is established, to generate a comprehensive residual index for the device nodes; a historical trend weighted residual value generation unit, which integrates the comprehensive residual index with the historical residual change trend of the corresponding device nodes to generate a historical trend weighted residual value; and a dynamic residual judgment threshold formation unit. The unit is used to dynamically adjust the preset transition threshold based on the historical trend weighted residual value to form a dynamic residual judgment threshold; the residual propagation coefficient adjustment unit is used to simultaneously detect the backflow or detour propagation path of the residual when performing residual recursive calculation along the closed-loop path, and adjust the residual propagation coefficient according to the detection result; the second candidate device node determination unit is used to determine the corresponding device node as a candidate device node that generates residuals when the cumulative residual value of any device node and the abrupt change value of the adjacent device node exceed the corresponding dynamic residual judgment threshold, and the abrupt change state is still maintained after the residual propagation coefficient adjustment.

[0058] The detailed description of the specific configuration of the closed-loop consistency verification module 20 is explained as follows: As mentioned above, the closed-loop consistency verification module 20 may further include: a set of conservation constraints including node input-output conservation constraints, power balance conservation constraints, voltage and current consistency constraints, and energy accumulation consistency constraints.

[0059] The closed-loop consistency verification module 20 may further include: setting a tolerance threshold for the device node when establishing input-output conservation constraints, wherein the tolerance threshold is used to adapt to measurement errors and environmental disturbances, and the tolerance threshold is adaptively adjusted according to the device type and operating status.

[0060] The adaptive data acquisition module 40 for new energy power stations may further include: a dual-redundant storage recording unit for performing dual-redundant storage recording on the adaptive data acquisition results and generating time-series storage verification feedback based on the dual-redundant storage recording results; and an abnormal storage early warning management unit for performing abnormal storage early warning management based on the time-series storage verification feedback.

[0061] The detailed description of the specific configuration of the station equipment data closed-loop relationship construction module 10 is as follows: As mentioned above, the station equipment data closed-loop relationship is constructed based on the electrical connection relationship and environmental correlation relationship between each device. The station equipment data closed-loop relationship construction module 10 may further include: an electrical connection relationship acquisition unit for acquiring the electrical connection topology of the power generation equipment and the power exchange equipment, and parsing the electrical connection topology to acquire the electrical connection relationship; a correlation analysis result establishment unit for acquiring the environmental factors of the power generation equipment and the power exchange equipment, and performing correlation analysis between the environmental factors and the corresponding equipment to establish the correlation analysis result; a closed-loop path identification unit for identifying the closed-loop path between the devices based on the electrical connection relationship, and performing mapping correction of the closed-loop path through the correlation analysis result; and a closed-loop relationship construction unit for constructing the station equipment data closed-loop relationship based on the mapping correction result.

[0062] The data closed-loop relationship construction module 10 for the power station equipment may further include: an access status verification unit, which performs access status verification, configures access verification feedback, completes access self-test according to the access verification feedback, and performs equipment operation data collection when the access self-test is passed.

[0063] The new energy power station data acquisition system based on gateway devices provided in this embodiment of the invention can execute the new energy power station data acquisition method based on gateway devices provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0064] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A data acquisition method for new energy power stations based on gateway devices, characterized in that, The method includes: After the gateway device connects to the power generation equipment and power exchange equipment in the new energy power station, it parses the communication protocols of each device and collects the equipment operation data. Based on the electrical connection relationship and environmental correlation relationship between each device, it constructs a closed-loop relationship of the power station equipment data. Based on the closed-loop relationship of the station equipment data, determine the set of conservation constraints between different equipment data in each closed-loop path, perform closed-loop consistency verification of equipment operation data according to the set of conservation constraints, and calculate the data residual of the closed-loop path. When the closed-loop path data residual meets the calibration threshold, the propagation analysis of the closed-loop path data residual is performed according to the data change association order to locate the candidate device node that generates the residual. The candidate device nodes are used to reconstruct the data acquisition structure of the gateway device, and the reconstructed gateway device is used to perform additional data acquisition. Based on the data residual propagation results and the iterative update of the additional data acquisition results, adaptive data acquisition of the new energy power station is performed. The data acquisition structure reconstruction of the gateway device is performed using the candidate device nodes, including: Based on the closed-loop path position of the candidate device node in the closed-loop relationship of the station equipment data, determine the set of neighboring devices that have a data conservation association with the candidate device node; Based on the data contribution and residual propagation direction of each device in the neighboring device set in the closed-loop path, an extended acquisition priority sequence of candidate device nodes is generated. This includes: the gateway calculating the data contribution of each device in the neighboring device set based on the energy transmission level, data coupling strength, and impact on the overall closed-loop consistency verification of each device in the closed-loop path. The higher the data contribution, the greater the impact of the device on the integrity of the closed-loop data. The calculation formula for the data contribution is: contribution = α × energy proportion + β × verification weight + γ × historical anomaly frequency, where α, β, and γ are weighting coefficients, energy proportion is the proportion of energy transmitted by the device in the path to the total energy, verification weight is the number of conservation constraint terms that the device participates in, and historical anomaly frequency is the number of times the device has triggered anomalies in the past 24 hours. Combined with the residual propagation direction, higher priority is assigned to devices located upstream of the residual propagation that have a more direct impact on the candidate device node data. The devices are sorted from high to low priority to form an extended acquisition priority sequence of candidate device nodes. Adjust the set of data acquisition objects of the gateway device according to the extended acquisition priority sequence, and establish additional data acquisition channels; The additional data acquisition channel is used to acquire the operating data of the neighboring device set, and the additional data acquisition results are established.

2. The data acquisition method for new energy power stations based on gateway devices as described in claim 1, characterized in that, Based on the data changes and their associated order, perform propagation analysis of the closed-loop path data residuals to locate candidate device nodes that generate residuals, including: A residual propagation sequence is established along the closed-loop path of the data closed-loop relationship of the station equipment, and the residual propagation direction is determined according to the energy transfer direction between the equipment in the closed-loop path. For each device node in the closed-loop path, a time alignment window is constructed, and the operating data of each device is synchronously mapped within the time alignment window to form a unified residual sequence. The residual propagation coefficient between adjacent device nodes is calculated based on the unified residual sequence. The residual propagation coefficient is used to characterize the propagation attenuation characteristics of the residual in the closed-loop path. Based on the residual propagation direction and residual propagation coefficient, perform residual recursive calculation along the closed-loop path to obtain the cumulative residual value corresponding to each device node; When the cumulative residual value of any device node and the mutation value of adjacent device nodes exceed a preset transition threshold, the corresponding device node is identified as a candidate device node that generates residuals.

3. The data acquisition method for new energy power stations based on gateway devices as described in claim 2, characterized in that, Based on the data changes and their associated order, perform propagation analysis of the closed-loop path data residuals to locate candidate device nodes that generate residuals, including: After the residual propagation sequence is established, the cumulative residual values ​​of the same device nodes in multiple closed-loop paths are cross-validated to generate a comprehensive residual index for the device nodes. The comprehensive residual index is integrated with the historical residual change trend of the corresponding equipment node to generate a historical trend weighted residual value. The preset transition threshold is dynamically adjusted based on the historical trend weighted residual value to form a dynamic residual judgment threshold. When performing residual recursive calculations along the closed-loop path, the backflow or detour propagation path of the residual is detected simultaneously, and the residual propagation coefficient is adjusted according to the detection results. When the cumulative residual value of any device node and the abrupt change value of the adjacent device node exceed the corresponding dynamic residual judgment threshold, and the abrupt change remains after adjustment by the residual propagation coefficient, the corresponding device node is determined as a candidate device node that generates residuals.

4. The data acquisition method for new energy power stations based on gateway devices as described in claim 1, characterized in that, The set of conservation constraints includes node input-output conservation constraints, power balance conservation constraints, voltage and current consistency constraints, and energy accumulation consistency constraints.

5. The data acquisition method for new energy power stations based on gateway devices as described in claim 4, characterized in that, When establishing input-output conservation constraints, a tolerance threshold is set for the device nodes. The tolerance threshold is used to adapt to measurement errors and environmental disturbances, and the tolerance threshold is adaptively adjusted according to the device type and operating status.

6. The data acquisition method for new energy power stations based on gateway devices as described in claim 1, characterized in that, The implementation of adaptive data acquisition for new energy power plants also includes: The data adaptive acquisition results are recorded with dual redundancy, and time-series storage verification feedback is generated based on the results of the dual redundancy storage records. Based on the time-series storage verification feedback, perform abnormal storage early warning management.

7. The data acquisition method for new energy power stations based on gateway devices as described in claim 1, characterized in that, Based on the electrical connections and environmental relationships between various devices, a closed-loop data relationship for the site equipment is constructed, including: Obtain the electrical connection topology of the power generation equipment and the power exchange equipment, and parse the electrical connection topology to obtain the electrical connection relationships; Obtain environmental factors of power generation equipment and power exchange equipment, perform correlation analysis between environmental factors and corresponding equipment, and establish correlation analysis results; Identify closed-loop paths between devices based on the electrical connection relationships, and perform mapping corrections of the closed-loop paths based on the correlation analysis results; Based on the mapping correction results, a closed-loop relationship of station equipment data is constructed.

8. The data acquisition method for new energy power stations based on gateway devices as described in claim 1, characterized in that, After the gateway device connects to the power generation equipment and power exchange equipment in the new energy power station, it performs access status verification, configures access verification feedback, completes access self-test based on the access verification feedback, and performs equipment operation data collection when the access self-test is passed.

9. A data acquisition system for new energy power stations based on gateway devices, characterized in that, The system is used to implement the data acquisition method for new energy power stations based on gateway devices as described in any one of claims 1-8, and the system includes: The data closed-loop relationship construction module for power station equipment is used to parse the communication protocols of each device and collect the equipment operation data after the gateway device is connected to the power generation equipment and power exchange equipment in the new energy power station, and to construct the data closed-loop relationship of the power station equipment based on the electrical connection relationship and environmental correlation relationship between each device. The closed-loop consistency verification module is used to determine the set of conservation constraints between different equipment data in each closed-loop path based on the closed-loop relationship of the station equipment data, perform closed-loop consistency verification of equipment operation data according to the set of conservation constraints, and calculate the data residual of the closed-loop path. The propagation analysis module is used to perform propagation analysis of the closed-loop path data residuals according to the data change association order when the closed-loop path data residuals meet the calibration threshold, and to locate the candidate device nodes that generate the residuals. The adaptive data acquisition module is used to reconstruct the data acquisition structure of the gateway device using the candidate device node, perform additional data acquisition using the reconstructed gateway device, and perform adaptive data acquisition of the new energy power station based on the data residual propagation results and the iterative update of the additional data acquisition results. The data acquisition structure reconstruction of the gateway device is performed using the candidate device nodes, and the adaptive data acquisition module further includes: The neighboring device set determination unit is used to determine the neighboring device set that has a data conservation association with the candidate device node based on the closed-loop path position of the candidate device node in the closed-loop relationship of the station equipment data; The extended acquisition priority sequence generation unit is used to generate an extended acquisition priority sequence for candidate device nodes based on the data contribution and residual propagation direction of each device in the neighboring device set in the closed-loop path. This includes: the gateway calculating the data contribution of each device in the neighboring device set based on the energy transmission level, data coupling strength, and impact on the overall closed-loop consistency verification of each device in the closed-loop path. The higher the data contribution, the greater the impact of the device on the integrity of the closed-loop data. The calculation formula for the data contribution is: contribution = α × energy proportion + β × verification weight + γ × historical anomaly frequency, where α, β, and γ are weighting coefficients, the energy proportion is the proportion of energy transmitted by the device in the path to the total energy, the verification weight is the number of conservation constraint terms that the device participates in, and the historical anomaly frequency is the number of times the device has triggered anomalies in the past 24 hours. Combined with the residual propagation direction, higher priority is assigned to devices located upstream of the residual propagation that have a more direct impact on the candidate device node data. The devices are sorted from high to low priority to form an extended acquisition priority sequence for candidate device nodes. The additional data acquisition channel establishment unit is used to adjust the set of data acquisition objects of the gateway device according to the extended acquisition priority sequence, and to establish an additional data acquisition channel. The additional data acquisition result establishment unit is used to acquire the operating data in the set of neighboring devices using the additional data acquisition channel, and establish additional data acquisition results.

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