Networking method and system of power distribution Internet of Things with multi-energy flow state perception

Abstract

The invention relates to the technical field of power distribution Internet of Things, in particular to a power distribution Internet of Things networking method and system capable of sensing multiple energy flow states. According to the method, an acquisition strategy configuration file is generated by acquiring electric-thermal-hydrogen energy flow characteristic data, SDN channel configuration parameters are generated based on time-sensitive level mapping processing and SDN controller interface calling, and a space-time compensation coefficient matrix is constructed through transmission delay data acquisition and sliding compensation algorithm operation. GIS equipment coordinate data is combined to carry out topological relation analysis and multi-energy flow data alignment operation to form a space-time alignment data flow, an energy flow coupling analysis report and a system optimization suggestion item are generated through coupling feature extraction and coupling degree calculation, and finally closed-loop optimization updating of an acquisition strategy file is realized. The space-time accurate alignment and coupling relation dynamic analysis of the multi-energy flow data are realized, and the sensing precision and the regulation and control efficiency of the power distribution Internet of Things on the electric-thermal-hydrogen multi-energy flow cooperative operation are improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution Internet of Things (IoT) technology, and in particular to a power distribution IoT networking method and system with multi-energy flow state sensing. Background Technology

[0002] In the field of distribution IoT technology, existing solutions typically achieve energy flow status monitoring and management of multi-energy systems through the collaborative work of data acquisition terminals, transmission channels and central processing units. However, these solutions have limitations such as difficulty in dynamically adapting acquisition strategies, insufficient flexibility in transmission channel allocation and inadequate edge computing capabilities.

[0003] Existing methods often rely on fixed acquisition frequencies and preset transmission paths, which can easily lead to data lag and transmission bottlenecks in scenarios with complex changes in multimodal energy flow data, making it difficult to achieve stable improvement in the accuracy of energy flow state perception.

[0004] For the joint processing of multimodal energy flow data, acquisition strategies, hierarchical transmission channels, and edge-side spatiotemporal compensation models, existing technologies generally suffer from slow response speed and poor adaptability in data fusion, strategy synchronization, and spatiotemporal compensation control. This makes it difficult to form a consistent process of acquisition-transmission-compensation-optimization in multi-energy coupled power distribution systems, resulting in insufficient accuracy of energy flow status perception. Summary of the Invention

[0005] This invention provides a multi-energy flow state perception power distribution Internet of Things (IoT) networking method and system to solve the problem of how to improve the accuracy of energy flow state perception in multi-energy coupled power distribution systems based on multi-modal energy flow data, through dynamic adaptation acquisition strategies, hierarchical transmission channel allocation, and edge-side spatiotemporal compensation models.

[0006] To address the aforementioned technical problems, this invention provides a multi-energy flow state-aware power distribution Internet of Things (IoT) networking method, comprising:

[0007] Acquire electrical-thermal-hydrogen energy flow characteristic data, extract dynamic characteristic parameters, and generate a data acquisition strategy configuration file. The energy flow characteristic data includes key indicators such as current, voltage, heat flow, temperature, hydrogen concentration, and pressure. The process of extracting dynamic characteristic parameters includes: using a timestamp synchronization mechanism to handle data time sequence consistency, and using a data cleaning module to remove outliers and noise data. For the dynamic change characteristics of electrical-thermal-hydrogen energy flow, multivariate time series analysis technology is used, combined with wavelet transform and autoregressive model, to extract dynamic characteristic parameters in the time domain and frequency domain, including energy flow fluctuation amplitude, periodicity characteristics, and abrupt change point information.

[0008] Receive the acquisition strategy configuration file, perform time sensitivity level mapping processing and SDN controller interface calls, and generate SDN channel configuration parameters;

[0009] Receive SDN channel configuration parameters, perform transmission delay data acquisition and processing and sliding compensation algorithm calculation, and generate a spatiotemporal compensation coefficient matrix;

[0010] Based on the spatiotemporal compensation coefficient matrix and GIS equipment coordinate data, topological relationship parsing and multi-energy flow data alignment operations are performed to generate a spatiotemporally aligned data stream.

[0011] Receive spatiotemporally aligned data streams, perform coupling feature extraction and coupling degree calculation, calculate energy flow coupling degree, and generate energy flow coupling analysis reports and system optimization suggestions;

[0012] Receive system optimization suggestions, perform strategy parameter comparison and update the acquisition strategy file, generate a new acquisition strategy configuration file, and perform closed-loop optimization of configuration parameters; expressions include:

[0013]

[0014] in, This is the frequency response function of the transmission channel; The optimized spectrum distribution; This is an integration operation over the entire real number field; For dynamic compensation coefficients; It is a complex exponential function; Let be a time infinitesimal element, representing a time... integral; The imaginary unit; Angular frequency; It is a time variable.

[0015] Furthermore, the transmission delay data acquisition and processing includes:

[0016] The real-time latency of each transmission channel is monitored by the edge-side data acquisition module, and the transmission time stamp information of multi-source devices is obtained by using the embedded clock synchronization mechanism.

[0017] The collected raw latency data is preprocessed, including outlier removal and noise filtering. A statistical anomaly detection algorithm is used to identify and remove transmission anomalies.

[0018] The format of multi-source transmission delay data is unified, and timestamp alignment technology is used to map the data from each source to a unified time axis, generating a multi-source time-stamped deviation dataset.

[0019] Furthermore, the process of generating a multi-source timescale bias dataset also includes:

[0020] Time window features were extracted from the time scale deviation dataset, and a sliding time window model was constructed to capture the dynamic characteristics of time delay changes.

[0021] A weighted moving average algorithm is used to smooth the timescale deviation data within each time window;

[0022] Based on the time delay data within the sliding time window, a compensation algorithm is executed, and a time delay compensation function is used to correct the time scale deviation and generate time compensation coefficients.

[0023] Furthermore, the sliding compensation algorithm calculation also includes:

[0024] By combining edge computing resources and utilizing a dynamic threshold adjustment mechanism, the length and sliding step of the time window are dynamically adjusted according to the current network load and transmission quality.

[0025] An error feedback mechanism is introduced to iteratively optimize the compensation coefficient, and corrections are made using the difference between historical delay data and current measurement results.

[0026] Furthermore, the generation of the spatiotemporal compensation coefficient matrix includes:

[0027] By combining GIS equipment coordinate data, spatial coordinate association processing is performed on the time compensation coefficient, mapping the compensation coefficient to the corresponding physical equipment spatial location;

[0028] Using the spatial data processing module, spatial indexing and proximity calculation methods are employed to accurately match each time compensation coefficient with its corresponding sensor node coordinates.

[0029] Consistency verification and anomaly detection are performed on the spatiotemporal compensation coefficient matrix, and spatial statistical analysis methods are used to identify and remove abnormal compensation values.

[0030] Furthermore, the process of performing topological relation resolution includes:

[0031] The geospatial coordinates of all data acquisition terminals and related equipment in the power distribution Internet of Things are obtained through a pre-defined geographic information system interface.

[0032] Perform topological relationship parsing on coordinate data, and establish physical connections and logical associations between devices through spatial adjacency algorithms and graph theory models;

[0033] The topology mapping table is dynamically updated by using spatial indexing and neighborhood search technology, combined with device attributes and connection status.

[0034] Furthermore, the multi-energy stream data alignment operation includes:

[0035] Spatial relationships are extracted from the device topology mapping table, and graph traversal algorithms are used to identify the energy flow transmission paths between devices and their spatial proximity.

[0036] For different energy flow types, construct corresponding spatial correlation subgraphs to clarify the upstream and downstream relationships and coupling points of each energy flow node;

[0037] The raw data streams from different energy flow sensors are synchronized in time and spatially corrected according to the spatial relationships shown in the device topology mapping table.

[0038] Furthermore, multi-energy stream data alignment operations also include:

[0039] The spatiotemporal compensation coefficient matrix is ​​invoked, and a weighted sliding window algorithm is used to dynamically adjust the timestamps of multi-source data;

[0040] Interpolation algorithms and anomaly detection mechanisms are introduced to handle missing and anomalies, ensuring the integrity and accuracy of aligned data;

[0041] A preliminary fused data stream is generated, reflecting the spatial coupling relationship and temporal consistency of the multi-energy streams.

[0042] Furthermore, generating a spatiotemporally aligned data stream includes:

[0043] Apply a spatiotemporal compensation coefficient matrix to the initially fused data stream and perform refined spatiotemporal alignment processing;

[0044] By combining GIS equipment coordinate data, the spatial position of each node in the data stream is corrected;

[0045] An iterative optimization algorithm and a sliding time window technique are used to dynamically update the compensation coefficient matrix to adapt to real-time changes in network transmission conditions and equipment status.

[0046] Furthermore, a multi-energy flow state-aware distribution IoT networking system, applied to any of the methods described above, includes:

[0047] The installation and calibration module is used to acquire parameters of the multimode communication components and complete the calibration.

[0048] The sector limitation module is used to set the coverage area of ​​power distribution nodes, energy storage units, and load terminals;

[0049] The threshold and strategy configuration module is used to store and dynamically load overload thresholds and oscillation frequency thresholds.

[0050] The data acquisition module is used to collect data on power distribution nodes and environmental disturbances through a multi-mode communication protocol.

[0051] The time synchronization module is used to eliminate the transmission delay deviation between the first data sequence and the second data sequence;

[0052] The event determination module is used to generate conflict arbitration results based on the dynamic threshold strategy table;

[0053] The arbitration module is used to handle priority conflicts of multi-source events and generate speed limit control commands;

[0054] The speed limit signal output module is used to send power adjustment commands to the edge computing execution unit;

[0055] Brake signal output module, used to trigger audible and visual alarms and emergency braking of feeder switch;

[0056] The alarm management module is used to activate audible and visual alarm devices and record alarm levels.

[0057] The log and parameter update module is used to store effective time window data and update arbitration priority parameters.

[0058] The key innovations of this invention include:

[0059] (1) The strategy parameters in the system optimization suggestions are dynamically compared by the difference detection algorithm to identify the parameter items that need to be adjusted and their change range, so as to ensure the safety and stability of the strategy parameters.

[0060] (2) The incremental update algorithm is used to modify the acquisition strategy configuration file, adjust the acquisition time interval, data precision threshold, sensor type mapping and triggering conditions, and support the real-time update of the acquisition strategy.

[0061] (3) Using the strategy iteration engine combined with the closed-loop optimization strategy, the closed-loop optimization configuration parameters are generated, covering the adjustment of the acquisition strategy, the optimization of the transmission channel and the update of the spatiotemporal compensation model, forming a unified closed-loop optimization configuration scheme.

[0062] The following are its main beneficial effects:

[0063] (1) By analyzing and dynamically comparing the system optimization suggestions, the safety and stability of the strategy parameter adjustment process of the multi-energy flow distribution Internet of Things system are ensured, and system performance fluctuations caused by parameter changes are avoided.

[0064] (2) The application of incremental update algorithm enables the acquisition strategy configuration file to be updated in real time, ensuring the adaptability of the acquisition strategy in sensor configuration and triggering mechanism, and improving the perception accuracy of the system in complex energy flow scenarios.

[0065] (3) The generation and distribution of closed-loop optimization configuration parameters promote the real-time updating and dynamic adjustment of strategy parameters, support the optimization feedback closed loop of multimodal energy flow data dynamic acquisition strategy, and improve the system's adaptability and performance. Attached Figure Description

[0066] Figure 1 A flowchart illustrating a multi-energy flow state-aware distribution IoT networking method provided in this application embodiment;

[0067] Figure 2This is a structural block diagram of a power distribution Internet of Things (IoT) networking system with multi-energy flow state awareness, provided in an embodiment of this application. Detailed Implementation

[0068] Example 1: Refer to Figure 1 This is a flowchart illustrating a multi-energy flow state-aware distribution IoT networking method provided in an embodiment of the present invention. The process may include at least steps S100-S600:

[0069] S100: Acquire electrical-thermal-hydrogen energy flow characteristic data, extract dynamic characteristic parameters, and generate acquisition strategy configuration file;

[0070] S200 receives the acquisition strategy configuration file, performs time sensitivity level mapping processing and SDN controller interface calls, and generates SDN channel configuration parameters.

[0071] S300: Receive SDN channel configuration parameters, perform transmission delay data acquisition and processing and sliding compensation algorithm calculation, and generate a spatiotemporal compensation coefficient matrix;

[0072] S400, based on the spatiotemporal compensation coefficient matrix and GIS equipment coordinate data, performs topological relationship parsing and multi-energy flow data alignment operations to generate a spatiotemporally aligned data stream;

[0073] S500 receives spatiotemporally aligned data streams, performs coupling feature extraction and coupling degree calculation, calculates energy flow coupling degree, and generates energy flow coupling analysis reports and system optimization suggestions.

[0074] S600 receives system optimization suggestions, performs strategy parameter comparison and updates the acquisition strategy file, generates a new version of the acquisition strategy configuration file, and optimizes the configuration parameters in a closed loop.

[0075] Step S100 includes at least steps S110-S130:

[0076] S110. Acquire the electro-thermal-hydrogen energy flow characteristic data, perform dynamic characteristic parameter extraction processing, and obtain the energy flow dynamic characteristic parameters.

[0077] The implementation of the multimodal energy flow data dynamic acquisition strategy generation module begins with acquiring electrical-thermal-hydrogen energy flow characteristic data. Specifically, this step receives multi-source energy flow parameter data from the system's perception layer and historical database as input. Through a preset interface protocol, the energy flow characteristic data includes, but is not limited to, key indicators such as current, voltage, heat flow, temperature, hydrogen concentration, and pressure. First, the system performs format unification and preprocessing on the raw energy flow data, employing a timestamp synchronization mechanism to ensure data temporal consistency. Subsequently, a data cleaning module removes outliers and noise data, and statistical filtering and threshold determination methods ensure data quality. Further, targeting the dynamic changes in electrical-thermal-hydrogen energy flow, multivariate time series analysis technology is used, combined with wavelet transform and autoregressive models, to extract dynamic characteristic parameters in the time and frequency domains, including energy flow fluctuation amplitude, periodicity, and abrupt change information. This process ensures the accuracy and real-time performance of the characteristic parameters by setting dynamic thresholds and anomaly detection rules. Simultaneously, the system normalizes the acquired dynamic characteristic parameters to adapt to the unified input standard of the subsequent multimodal acquisition strategy. The dynamic characteristic parameters, referred to as the output field name "energy flow dynamic characteristic parameters" in this step, are passed to the "input parameters" in the subsequent step S120 to provide basic data support for multimodal acquisition strategy adaptation.

[0078] S120. Extract key features from the dynamic characteristic parameters of energy flow, adapt to multi-modal acquisition strategies, and generate acquisition strategy configuration items.

[0079] Based on the "energy flow dynamic characteristic parameters" output in step S110, this step further extracts key features from these parameters. Specifically, the system uses a feature selection algorithm to screen the energy flow dynamic characteristic parameters and employs Principal Component Analysis (PCA) and correlation analysis to identify the feature subset that has the greatest impact on the acquisition strategy. Subsequently, combined with a pre-set multimodal sensor model library, the system performs adaptation matching on the key features to determine the optimal configuration scheme for various sensors in terms of acquisition frequency and accuracy. During the adaptation process, the system considers the sensor's response time, measurement range, and environmental adaptability, and uses a rule engine and machine learning algorithms to dynamically adjust the acquisition parameters to achieve accurate perception of different energy flow types. Furthermore, the system generates acquisition strategy configuration items for multimodal sensors, covering acquisition frequency, data accuracy requirements, acquisition timing, and triggering conditions. All configuration items are encoded using a unified structured format to ensure the parsing and execution efficiency of the strategy. Simultaneously, the system records the decision basis for configuration generation and abnormal adjustment logs for subsequent optimization reference. The acquisition strategy configuration item, as the output field name "Acquisition Strategy Configuration Item" in this step, is passed to the "Configuration Input" in step S130 to provide direct data for the generation of the acquisition strategy configuration file.

[0080] S130. Perform structured coding on the data acquisition strategy configuration items to generate a data acquisition strategy configuration file;

[0081] This step takes the "acquisition strategy configuration item" output from step S120 as input. Specifically, the system performs structured coding on the configuration item, using a standardized configuration description language to convert the acquisition strategy configuration item into a machine-readable configuration file format. During the coding process, the system completes the detailed definition of acquisition frequency, accuracy, sensor type, and acquisition triggering mechanism according to a predefined configuration template, ensuring the integrity and consistency of the configuration file content. Furthermore, the system performs syntax and semantic validation on the configuration file, using automated validation tools to detect the logical rationality and parameter boundaries of the configuration items, preventing acquisition anomalies caused by configuration errors. If an anomaly is detected, the system automatically triggers a configuration rollback mechanism and records an anomaly log for manual review. After coding, the system stores the configuration file in the configuration management module, supporting version control and historical traceability. The acquisition strategy configuration file, as the output field name "Acquisition Strategy Configuration File" of this step, is passed to the "Strategy Configuration Input" of the subsequent step S210, providing a strategic basis for time-sensitive transmission channel allocation and SDN (Software Defined Networking) bandwidth allocation, while also supporting closed-loop feedback and dynamic adjustment of cross-module data flows.

[0082] Step S200 includes at least steps S210-S230:

[0083] S210: Receive the acquisition strategy configuration file, perform time sensitivity level mapping processing, and obtain a three-level channel division scheme;

[0084] This step receives the acquisition strategy configuration file output from step S130 as input for the strategy configuration of time-sensitive transmission channel partitioning and SDN bandwidth allocation. Specifically, the acquisition strategy configuration file contains parameters such as the acquisition frequency, accuracy, and triggering mechanism of multimodal energy flow data. The system first parses the configuration file, using a predefined configuration parsing module to convert the structured configuration content into an internal data structure, ensuring the integrity and consistency of each acquisition parameter. During the parsing process, the system maps indicators such as acquisition frequency and data importance to corresponding time-sensitive levels based on preset time-sensitive level mapping rules, specifically divided into three categories: real-time control level, status monitoring level, and metering and statistics level. The mapping rules are dynamically adjusted based on latency thresholds and data timeliness requirements, combined with historical transmission performance data, to adapt to the heterogeneous characteristics of multimodal energy flow data. Furthermore, the system performs validity verification on the mapping results, confirming the coverage integrity and conflict-free nature of the three transmission levels. If abnormal configuration is found, an exception handling mechanism is triggered, an exception log is recorded, and the default mapping strategy is enabled to ensure system stability. Subsequently, based on the mapping results, the system generates a three-level channel partitioning scheme, clarifying the transmission channel type and priority corresponding to each type of data. The scheme defines in detail the transmission latency requirements, preliminary bandwidth allocation suggestions, and scheduling priorities for each channel, forming a unified three-level channel partitioning scheme. This scheme, as the output field name "Three-Level Channel Partitioning Scheme" of this step, is passed to the "Partitioning Basis" of the subsequent step S220, providing basic data support for bandwidth requirement extraction and SDN controller interface calls, while ensuring the continuity and consistency of data flow across steps.

[0085] S220: Extract bandwidth requirements from the three-level channel partitioning scheme, make SDN controller interface calls, and generate a bandwidth allocation instruction set;

[0086] This step takes the three-level channel partitioning scheme output in step S210 as input. Specifically, the system extracts the bandwidth requirement information of each transmission channel from the scheme as the basis for dynamic SDN bandwidth allocation. First, the system quantifies the bandwidth occupancy of each data stream within the three-level channels through the bandwidth requirement analysis module, using a combination of historical traffic statistics and real-time load monitoring to dynamically assess current and predicted bandwidth requirements. This assessment process combines transmission latency thresholds and data priorities to achieve fine-grained allocation of bandwidth resources. Subsequently, the system calls the SDN controller interface and sends a bandwidth allocation request to the SDN controller based on the open southbound interface protocol. The request includes the required bandwidth, priority level, and target transmission channel identifier. After receiving the request, the SDN controller executes a bandwidth resource scheduling algorithm based on the network topology and current link load to generate a bandwidth allocation instruction set. This instruction set specifies in detail the bandwidth allocation ratio, traffic scheduling strategy, and priority adjustment parameters for each channel, ensuring low-latency transmission of the real-time control channel and bandwidth guarantee for the metering and statistics channel. The system performs integrity and consistency checks on the instruction set to prevent transmission anomalies due to configuration errors. If the check fails, the instruction set rollback mechanism is activated and an anomaly log is recorded. Finally, the bandwidth allocation instruction set, as the output field name "bandwidth allocation instruction set" of this step, is passed to the "instruction input" of the subsequent step S230, providing a direct basis for channel parameter encapsulation and SDN channel configuration parameter generation, and ensuring dynamic adjustment and optimization of the data transmission link.

[0087] S230. Encapsulate the bandwidth allocation instruction set into channel parameters to generate SDN channel configuration parameters;

[0088] This step takes the bandwidth allocation instruction set output in step S220 as input. Specifically, the system encapsulates the instruction set into channel parameters to generate configuration parameters that can be executed by SDN switches and network devices. First, the system parses the bandwidth allocation ratios, priority parameters, and scheduling policies in the bandwidth allocation instruction set. Combining this with the network device's interface standards and configuration templates, it constructs a channel configuration parameter structure that meets the device requirements. This structure includes, but is not limited to, channel identifiers, bandwidth limits, traffic priorities, queue management policies, and latency guarantee parameters. Subsequently, the system formats and encodes the channel configuration parameters using a device-compatible configuration language and protocol format to ensure the executability and compatibility of the configuration. Further, the system performs semantic validation of the configuration parameters, verifying parameter ranges, logical relationships, and dependency constraints to prevent configuration conflicts or unreasonable settings. For detected potential risks, the system automatically adjusts the configuration parameters or triggers a manual intervention process, and fully records the configuration change log. After encapsulation, the system stores the channel configuration parameters in the network configuration management module, supporting version control and remote distribution. The SDN channel configuration parameters, as the output field name "SDN channel configuration parameters" in this step, are passed to the "channel parameters" in the subsequent step S310. This provides a basic transmission environment configuration for the edge-side spatiotemporal compensation model construction module, ensuring the timeliness and bandwidth guarantee of multi-source data transmission, and promoting cross-module collaborative operation and closed-loop optimization.

[0089] Step S300 includes at least steps S310-S330:

[0090] S310: Receive SDN channel configuration parameters, perform transmission delay data acquisition and processing, and obtain a multi-source time-stamped deviation dataset;

[0091] This step receives the "SDN channel configuration parameters" output from step S230. Specifically, these parameters serve as input, and the system first performs latency data acquisition processing on the transmission channels defined in the configuration parameters through the edge-side data acquisition module. This processing includes monitoring the real-time latency of each transmission channel and using an embedded clock synchronization mechanism to obtain the transmission time stamp information of multi-source devices, ensuring consistent time base for data acquisition. Specifically, the system uses a multimodal sensor network deployed at edge nodes and a distributed clock synchronization protocol to achieve accurate measurement of the data transmission latency of electrical, thermal, and hydrogen energy flows. During the latency data acquisition process, the system preprocesses the acquired raw latency data, including outlier removal and noise filtering, and uses a statistical anomaly detection algorithm to identify and remove transmission anomalies, ensuring the accuracy and integrity of the data. Furthermore, the system unifies the format of the multi-source transmission latency data, using timestamp alignment technology to map the data from each source to a unified time axis, forming a multi-source time stamp deviation dataset. This dataset contains latency difference information for different transmission channels and different energy flow types, serving as the basic input for subsequent spatiotemporal compensation algorithms. The multi-source timescale deviation dataset, named "Timescale Deviation Dataset" as the output field name of this step, is passed to the "Input Data" of step S320 to provide data support for the sliding compensation algorithm operation and ensure the continuity and consistency of data flow across steps.

[0092] S320. Extract time window features from the multi-source timescale deviation dataset, perform sliding compensation algorithm calculations, and generate time compensation coefficients.

[0093] This step takes the "timescale deviation dataset" output in step S310 as input. Specifically, the system first performs time window feature extraction processing on the dataset to construct a sliding time window model to capture the dynamic characteristics of latency changes. The sliding time window moves gradually across the dataset according to a predetermined time step, setting a fixed-length time interval, and updating the statistical characteristics of latency deviation in real time. The system uses a weighted moving average algorithm to smooth the timescale deviation data within each time window, reducing the impact of instantaneous abnormal fluctuations. Furthermore, the system combines edge computing resources and utilizes a dynamic threshold adjustment mechanism to dynamically adjust the length and sliding step of the time window according to the current network load and transmission quality to adapt to the latency characteristics under different transmission environments. Subsequently, based on the latency data within the sliding time window, the system executes a compensation algorithm calculation. Specifically, a latency compensation function is used to correct the timescale deviation and calculate the corresponding time compensation coefficient. This compensation coefficient reflects the dynamic changes in transmission latency within different time periods and has temporal continuity and adaptive adjustment capabilities. To ensure the stability and accuracy of the algorithm, the system introduces an error feedback mechanism during the calculation process to iteratively optimize the compensation coefficients and correct them using the differences between historical time delay data and current measurement results. The time compensation coefficient, as the output field name "Time Compensation Coefficient" in this step, is passed to the "Compensation Parameter" in step S330, providing key parameters for the generation of the spatiotemporal compensation coefficient matrix and ensuring the dynamic adaptability and accuracy of data processing.

[0094] S330. Perform spatial coordinate correlation processing on the time compensation coefficients to generate a spatiotemporal compensation coefficient matrix;

[0095] This step takes the "time compensation coefficient" output from step S320 as input. Specifically, the system first combines GIS (Geographic Information System) device coordinate data to perform spatial coordinate association processing on the time compensation coefficient. This processing couples the time compensation parameters with the device's geographic information by mapping the compensation coefficient to the corresponding physical device spatial location. The system utilizes a spatial data processing module, employing spatial indexing and proximity calculation methods to accurately match each time compensation coefficient with its corresponding sensor node coordinates, constructing a spatiotemporal compensation coefficient matrix. This matrix, with spatial coordinates as rows and time compensation coefficients as columns, forms a multi-dimensional set of compensation parameters that reflects the dynamic characteristics of transmission delay within different geographical locations and time periods. Furthermore, the system performs consistency checks and anomaly detection on the spatiotemporal compensation coefficient matrix, using spatial statistical analysis methods to identify and remove abnormal compensation values, ensuring the reliability and validity of the matrix data. The system supports dynamic updates and version management of this matrix, facilitating real-time responses to changes in network status. The spatiotemporal compensation coefficient matrix, as the output field name "spatiotemporal compensation coefficient matrix" in this step, is passed to the "compensation matrix" in step S410 to provide spatiotemporal alignment basic data for multi-energy flow GIS topology association and data fusion, and realize effective connection and closed-loop control of cross-step data.

[0096] Step S400 includes at least steps S410-S430:

[0097] S410. Based on the spatiotemporal compensation coefficient matrix and GIS equipment coordinate data, perform topological relationship parsing to obtain the equipment topology mapping table;

[0098] This step receives the "spatiotemporal compensation coefficient matrix" output from step S330 as input. Combined with the GIS device coordinate data provided by the system configuration module, specifically, the system obtains the geospatial coordinate information of all acquisition terminals and related devices in the power distribution IoT through a preset geographic information system interface, including latitude, longitude, elevation, and relative positional relationships. The GIS device coordinate data is standardized into a uniform spatial coordinate format by a format conversion module to ensure data consistency and parsability. Subsequently, the system performs topological relationship parsing processing on the coordinate data. Specifically, through spatial adjacency algorithms and graph theory models, it establishes physical connections and logical associations between devices, identifying upstream and downstream connection nodes and loop structures. During the parsing process, the system uses spatial indexing and neighborhood search technology, combined with device attributes and connection status, to dynamically update the topology mapping table, supporting real-time change perception of the topology structure. To ensure the accuracy of the parsing results, the system sets a topology error threshold and automatically detects and marks abnormal connections. Abnormal information is recorded in the topology anomaly log for subsequent maintenance and optimization. Finally, after multiple rounds of data verification and consistency checks, the system generates a complete device topology mapping table, which reflects the correspondence between GIS device coordinate data and energy flow transmission paths. This device topology mapping table, as the output field name of this step, is passed to the "Topology Input" in step S420, providing a spatial correlation basis for multi-energy flow data alignment operations, and also supporting cross-step spatiotemporal compensation and coupling analysis module calls.

[0099] S420. Extract spatial relationships from the device topology mapping table, perform multi-energy flow data alignment operations, and generate a preliminary fused data flow.

[0100] This step takes the "device topology mapping table" output in step S410 as input and combines it with the "spatiotemporal compensation coefficient matrix" from step S330. First, the system extracts spatial relationships from the device topology mapping table. Specifically, it uses a graph traversal algorithm to identify energy flow transmission paths and their spatial proximity between devices, forming a spatial dependency model for multi-energy flow data. For different energy flow types (electrical, thermal, and hydrogen), the system constructs corresponding spatial relationship subgraphs to clarify the upstream and downstream relationships and coupling points of each energy flow node. Subsequently, the system performs multi-energy flow data alignment operations. Specifically, it synchronizes and spatially corrects the raw data streams from different energy flow sensors according to the spatial relationships shown in the device topology mapping table. During alignment, the system calls the spatiotemporal compensation coefficient matrix output in step S330 and uses a weighted sliding window algorithm to dynamically adjust the timestamps of multi-source data, compensating for transmission delays and acquisition timescale deviations, thus achieving spatiotemporal synchronization of multimodal data. To handle data loss and anomalies, the system introduces interpolation algorithms and anomaly detection mechanisms to ensure the integrity and accuracy of the aligned data. The alignment results are processed by the fusion engine, which integrates the spatial data features and time series information of the multi-energy streams to generate a preliminary fused data stream. This preliminary fused data stream fully reflects the spatial coupling relationship and temporal consistency of the multi-energy streams, and possesses high timeliness and accuracy. This preliminary fused data stream, as the output field name of this step, is passed to the "input data" in step S430 for further spatiotemporal compensation applications and state-aware analysis, supporting subsequent calls to the coupling analysis engine.

[0101] S430. Apply a spatiotemporal compensation coefficient matrix to the initially fused data stream to generate a spatiotemporally aligned data stream;

[0102] This step receives the "preliminary fused data stream" output from step S420 and the "spatiotemporal compensation coefficient matrix" output from step S330 as input. Specifically, the system first applies the spatiotemporal compensation coefficient matrix to the preliminary fused data stream to perform refined spatiotemporal alignment processing. This processing includes dynamically adjusting the timestamps of each energy stream data point in the fused data stream based on the compensation coefficients to eliminate data timing misalignment caused by transmission delays and acquisition time stamp errors. Spatially, the system combines GIS device coordinate data to correct the spatial position of each node in the data stream, ensuring accurate correspondence of multi-energy stream data in physical space. The spatiotemporal compensation calculation uses an iterative optimization algorithm combined with sliding time window technology to dynamically update the compensation coefficient matrix to adapt to real-time changes in network transmission conditions and device status. To ensure the compensation effect, the system sets error thresholds and convergence conditions, automatically adjusting compensation parameters until the accuracy requirements are met. All calculation steps in the process are recorded in the spatiotemporal compensation log for subsequent performance evaluation and fault diagnosis. Finally, the system generates a spatiotemporally aligned data stream, which possesses highly consistent time series and spatial distribution characteristics, accurately reflecting the dynamic state and spatial coupling relationship of multiple energy flows. This spatiotemporally aligned data stream, as the output field name of this step, is passed to the "input data" in step S510, providing the energy flow coupling analysis engine with accurate basic data on the state of multiple energy flows, while also supporting the closed-loop operation of the cross-module dynamic strategy optimization feedback mechanism.

[0103] Step 500 includes at least steps S510-S530:

[0104] S510: Receive the spatiotemporally aligned data stream, perform coupling feature extraction processing, and obtain the energy flow coupling feature vector;

[0105] This step receives the "spatiotemporally aligned data stream" from step S430 as input. Specifically, the spatiotemporally aligned data stream contains multi-energy flow fusion data corrected by a spatiotemporal compensation coefficient matrix, covering multimodal sensing information of the electric-thermal-hydrogen energy flow and its corresponding spatial coordinates and timestamps. First, the system preprocesses the spatiotemporally aligned data stream, employing a data integrity verification mechanism to ensure that the data is complete and the time series is continuous. Then, based on preset coupling feature extraction rules, the system filters key variables in the multi-energy flow data, focusing on interaction factors between energy flows, such as the correlation between current fluctuations and heat flow changes, and the coupling trend between hydrogen pressure and temperature. Specifically, a multidimensional feature mapping algorithm is used to map the multimodal data to a unified feature space, forming an energy flow coupling feature vector. This mapping process includes feature normalization, principal component extraction, and nonlinear transformation to ensure that the scale and distribution differences of different energy flow data are effectively eliminated. Furthermore, the system uses a dynamic threshold determination mechanism to eliminate abnormal coupling feature points, avoiding interference from abnormal data in subsequent analysis. During the coupling feature extraction process, all operations are completed on edge computing nodes to reduce transmission latency and computational load. Finally, the system uses the processed energy-flow coupling feature vector as the output field name "Energy-flow Coupling Feature Vector" for this step and passes it to the "Feature Input" in step S520 to provide basic data support for energy-flow coupling degree calculation, while also supporting cross-module data closed-loop feedback and dynamic adjustment.

[0106] S520. Extract key parameters from the energy flow coupling feature vector, calculate the coupling degree, and generate an energy flow coupling analysis report;

[0107] This step takes the "energy flow coupling feature vector" output from step S510 as input. Specifically, the system first performs key parameter analysis on the feature vector, employing a rule-based parameter extraction algorithm to identify core influencing factors in energy flow coupling, such as coupling strength, coupling frequency, and coupling delay. During the analysis, the system dynamically adjusts the parameter extraction threshold by combining historical coupling models and real-time data to adapt to changes in coupling characteristics under different operating conditions. Further, the system performs coupling degree calculation, using a multi-energy flow coupling metric function combined with statistical and machine learning methods to achieve a quantitative assessment of the energy flow coupling relationship. This calculation covers time and frequency domain analysis, specifically including cross-correlation analysis, mutual information calculation, and time-frequency joint decomposition, ensuring the accuracy and robustness of the coupling degree results. To ensure the real-time nature of the calculation results, the system adopts a parallel computing architecture and distributed processing of the input feature vector, significantly improving computational efficiency. After the calculation is completed, the system generates an energy flow coupling analysis report according to a preset format, including coupling degree values, coupling pattern recognition results, and abnormal coupling warning information. The report format supports structured storage and rapid retrieval, facilitating subsequent state awareness and strategy optimization. The energy flow coupling analysis report, named "Energy Flow Coupling Analysis Report" as the output field name of this step, is passed to the "Report Input" of step S530 for state awareness accuracy assessment and system optimization suggestion generation, while realizing cross-step data sharing and closed-loop feedback.

[0108] S530. Evaluate the state-aware accuracy of the energy flow coupling analysis report and generate system optimization suggestions.

[0109] This step receives the "Energy Flow Coupling Analysis Report" output from step S520 as input. Specifically, the system first performs a state-aware accuracy assessment on the coupling degree data and coupling modes in the analysis report, employing a multi-dimensional evaluation index system, including error rate, confidence interval, and response time, to comprehensively measure the reliability and real-time performance of the coupling analysis results. During the evaluation process, the system combines historical operating data and field feedback, utilizing statistical testing and trend analysis methods to identify potential biases and uncertainties in the analysis results. Further, based on the evaluation results, the system automatically generates system optimization suggestions, covering aspects such as acquisition strategy adjustment, transmission channel optimization, and spatiotemporal compensation parameter updates. Specifically, the optimization suggestions are formed through a combination of a rule engine and a machine learning model, considering both the current coupling situation and predicting future energy flow trends to ensure the suggestions' foresight and practicality. The system prioritizes and assesses the risks of the generated suggestions, ensuring that key optimization measures are implemented first. All suggestions are output in a structured format, supporting automated processing and manual review in subsequent steps. The system optimization suggestion item, as the output field name "System Optimization Suggestion Item" in this step, is passed to the "Suggestion Input" in step S610, providing a decision basis for dynamic strategy optimization feedback, forming a cross-module closed-loop link, and promoting continuous improvement of system performance.

[0110] Step S600 includes at least steps S610-S630:

[0111] S610: Receive system optimization suggestions, perform strategy parameter comparison processing, and generate a strategy adjustment instruction set;

[0112] This step receives system optimization suggestions from step S530 as input. Specifically, the system first parses and structures the strategy parameters contained in the system optimization suggestions, extracting key content such as acquisition frequency adjustment, transmission bandwidth optimization, and spatiotemporal compensation parameter updates. Then, the system compares these key contents with the currently running strategy parameters, using a difference detection algorithm to identify the parameters that need adjustment and their range of change. Specifically, the difference detection algorithm, based on the strategy parameter version management module and combined with historical adjustment records, dynamically evaluates the potential impact of each parameter adjustment on the overall system performance, ensuring the safety and stability of the adjustment process. Further, the system automatically generates a set of strategy adjustment instructions for the acquisition strategy, transmission channel, and compensation model according to preset adjustment rules and priority strategies. The instructions include parameter names, adjustment values, adjustment methods, and effective times. During generation, the system performs multiple checks, including parameter validity verification, dependency checks, and conflict detection, to ensure the integrity and consistency of the instruction set. If an anomaly or conflict is detected, the system automatically triggers a rollback mechanism and records the anomaly details to the log management module for subsequent manual intervention. The policy adjustment instruction set, as the output field name "policy adjustment instruction set" in this step, is passed to the "instruction input" in the subsequent step S620, providing a direct basis for the update of the policy file and supporting the construction of a dynamic policy optimization feedback loop.

[0113] S620: Extract key parameters from the strategy adjustment instruction set, update the data acquisition strategy file, and generate a new version of the data acquisition strategy configuration file;

[0114] This step takes the strategy adjustment instruction set output in step S610 as input. Specifically, the system first parses the key parameters contained in the strategy adjustment instruction set to identify relevant adjustment items such as acquisition frequency, acquisition accuracy, sensor configuration, and triggering mechanism. Then, the system calls the acquisition strategy configuration management module to load the currently valid acquisition strategy configuration file as the update baseline. Based on the key parameters, the system modifies the acquisition strategy configuration file using an incremental update algorithm, specifically including adjusting the acquisition time interval, modifying the data accuracy threshold, updating the sensor type mapping, and reconstructing the acquisition triggering conditions. During the update process, the system performs syntax and semantic consistency checks, using the configuration rule engine to detect whether the updated configuration file meets the preset format specifications and business logic. For any conflicts or anomalies that may occur in the configuration file, the system automatically performs rollback processing and records the anomaly information in the anomaly log module. After the update is complete, the system increments the version number of the new acquisition strategy configuration file and persists it in the configuration version repository, supporting the query and backtracking of historical versions. Furthermore, the system pushes the new acquisition strategy configuration file to relevant acquisition terminals and data processing nodes through a configuration release mechanism to ensure that the strategy update takes effect in real time. The new version of the data acquisition strategy configuration file, named "New Version Data Acquisition Strategy Configuration File" as the output field name of this step, is passed to the "Configuration Input" of the subsequent step S630 to achieve iterative upgrades and closed-loop optimization of the system version.

[0115] S630: Iterate the system version of the new acquisition strategy configuration file to generate closed-loop optimized configuration parameters;

[0116] This step takes the new acquisition strategy configuration file output in step S620 as input. Specifically, the system first performs version management processing on the new acquisition strategy configuration file, generates a new system version identifier according to predefined version iteration rules, and updates the version index in the configuration management database. Subsequently, the system combines the closed-loop optimization strategy to perform version compatibility testing, confirming the compatibility of the new configuration file with the existing system environment and related modules, preventing operational anomalies caused by version incompatibility. Further, the system automatically generates closed-loop optimization configuration parameters based on parameter adjustments in the new configuration file through the strategy iteration engine. This parameter set covers acquisition strategy adjustments, transmission channel optimization, and spatiotemporal compensation model updates, forming a unified closed-loop optimization configuration scheme. During the generation process, the system uses a rule engine and model inference technology, comprehensively considering operational indicators, historical optimization effects, and strategy adjustment suggestions to ensure the scientific validity and effectiveness of the closed-loop optimization configuration parameters. After the configuration generation is completed, the system distributes the closed-loop optimization configuration parameters to the multimodal energy flow data dynamic acquisition strategy generation module (step S110) to achieve real-time updates and dynamic adjustments of the strategy parameters. The closed-loop optimization configuration parameters are passed to the "characteristic data update" step S110 as the output field name "closed-loop optimization configuration parameters" to build a complete dynamic strategy optimization feedback closed loop, promoting the continuous adaptation and performance improvement of the system.

[0117] In another embodiment:

[0118] S610: Receive system optimization suggestions, perform strategy parameter comparison processing, and generate a strategy adjustment instruction set;

[0119] The system receives a "system optimization suggestion" from step S530 as input, which includes the amount of sampling frequency adjustment. Incremental transmission bandwidth Time and space compensation parameter update coefficients Key content includes: The system retrieves historical adjustment records from the policy parameter version management module to extract the currently running policy parameter set. and with the suggested parameter set Perform difference detection.

[0120] Formula ① (Calculation of parameter adjustment):

[0121]

[0122] in:

[0123] : No. System optimization suggestion parameters, with a value range of [0,1].

[0124] The currently running number Class strategy parameters;

[0125] : Parameter adjustment amount, output variable;

[0126] The weight matrix reflects the priority of each parameter.

[0127] Regularization coefficient, used to smooth the adjustment process;

[0128] The gradient of parameter adjustment is used to prevent overly drastic adjustments.

[0129] : indicates with To optimize the problem of minimizing variables;

[0130] : The square of the L2 norm.

[0131] Note: This step calculates a reasonable parameter adjustment amount by comparing the suggested parameters with the current parameters, combined with weights and regularization. .

[0132] Formula ② (Generation of strategy adjustment instructions):

[0133]

[0134] in:

[0135] : No. Class-based policy adjustment command values;

[0136] : No. The maximum allowable adjustment amount for class parameters;

[0137] Adjust the trigger threshold;

[0138] : The floor function discretizes continuous values ​​into integers;

[0139] Instruction category identifier;

[0140] :Instruction instance index;

[0141] Output: Policy Adjustment Instruction Set It is passed to the "Instruction Input" field of S620.

[0142] S610 → S620: S610 calculates the parameter adjustment amount through difference detection. And generate a specific set of policy adjustment instructions. This instruction set serves as input to the S620, used to analyze specific acquisition frequencies and bandwidth adjustments, and then to optimize resource allocation.

[0143] S620: Extract key parameters from the strategy adjustment instruction set, update the data acquisition strategy file, and generate a new version of the data acquisition strategy configuration file;

[0144] Taking the strategy adjustment instruction set output by S610 as input, analyze the acquisition frequency adjustment item and bandwidth increment:

[0145]

[0146] in: New sampling frequency; Basic acquisition frequency; : Allocate bandwidth; Basic bandwidth; : No. Adjustment value for class instructions (bandwidth increment);

[0147] Formula ③ (Resource Allocation Optimization):

[0148]

[0149] in:

[0150] : No. Resource allocation ratio;

[0151] Weighting coefficients reflect the importance of resources;

[0152] The constraint coefficient matrix is ​​derived from the delay-bandwidth relationship.

[0153] Total resource limit, real-time monitoring data;

[0154] :by To optimize the problem of maximizing variables;

[0155] "Subject to" is an abbreviation for constraint conditions, indicating the constraint part of the optimization problem.

[0156] for all This indicates that the constraint must be true for all resource types.

[0157] Note: Based on the adjustment instructions, resource allocation is optimized to obtain the optimal solution. .

[0158] Formula ④ (Version Compatibility Check):

[0159]

[0160] in, Version compatibility metrics;

[0161] Indicator function, 1 for compatible functions, 0 otherwise;

[0162] New configuration parameters item;

[0163] Existing environmental parameters item;

[0164] Total number of compatibility test items.

[0165] Output: New data collection strategy configuration file (including...) , ), which is passed to the "Configuration Input" field of S630.

[0166] S620 → S630: S620 optimizes resource allocation according to the adjustment instructions and generates a new version of the data acquisition strategy configuration file (including resource allocation ratio). and compatibility metrics This configuration file serves as input to the S630, guiding the dynamic updating of spatiotemporal compensation parameters and closed-loop frequency domain optimization.

[0167] S630: Iterate the system version of the new acquisition strategy configuration file to generate closed-loop optimized configuration parameters;

[0168] Based on the S620 configuration file, calculate the dynamic update amount of the spatiotemporal compensation parameters:

[0169] Formula ⑤ (Calculation of dynamic compensation coefficient):

[0170]

[0171] in:

[0172] : Spatiotemporal compensation parameter update coefficients, derived from S610 input suggestions;

[0173] The latency correlation coefficient reflects the impact of historical latency.

[0174] Historical latency deviation, statistical analysis using a sliding window.

[0175] : Dynamic compensation coefficient;

[0176] Take historical time delay deviation The maximum value in;

[0177] Formula 6 (Cross-spectral density frequency domain optimization):

[0178]

[0179] in:

[0180] Frequency response function of the transmission channel, describing the signal transmission characteristics.

[0181] The optimized spectrum distribution is used for closed-loop parameter updates.

[0182] Integration over the entire real number field;

[0183] Complex exponential function, representing a frequency of oscillation component ( 2 is the imaginary unit).

[0184] Time infinitesimal element, representing the time... integral;

[0185] Imaginary unit;

[0186] Angular frequency, representing the spectral components of a signal;

[0187] : Time variable.

[0188] Output: Closed-loop optimized configuration parameters The data is then passed to the "Feature Data Update" field in step S110.

[0189] S630 → S110: S630 calculates the dynamic compensation coefficient. And generate closed-loop optimization parameters based on spectrum analysis. This feedback is used to update system characteristic data, achieving closed-loop control.

[0190] Technical Results: Through S610's difference detection and parameter adjustment, the system can accurately identify and generate targeted strategy adjustment commands; S620 optimizes resource allocation and verifies version compatibility based on these commands, ensuring safe and reliable iterative updates; S630 utilizes historical latency data and dynamic compensation coefficients, combined with spectrum optimization technology, to achieve adaptive adjustment of closed-loop parameters. Overall, an adaptive closed-loop control system for multi-energy flow data acquisition and transmission is constructed, ensuring stable operation and continuous performance improvement throughout the system's lifecycle.

[0191] Example 2: Figure 2 A structural block diagram of a multi-energy flow state-aware distribution Internet of Things (IoT) networking system according to an embodiment of the present invention is shown. Figure 2 As shown, the structure may include:

[0192] The installation and calibration module 01 is used to acquire parameters of the multimode communication component and complete the calibration. Specifically, it receives an input object from the multimode communication component, acquires its communication parameters, and analyzes and calibrates the parameters through an interface protocol under the configured calibration constraints to form a calibrated communication parameter object. The communication parameter object is recorded as calibration data and maintains a consistent association with the current state. The calibration data is transmitted to the sector limitation module as input data, while the calibration log is retained for subsequent traceability.

[0193] The sector limitation module 02 is used to set the coverage range of power distribution nodes, energy storage units, and load terminals. Specifically, it receives calibration data and environmental information output from the installation and calibration module, performs area division, node configuration, and coverage calculation on the calibration data, and forms a coverage object according to the configuration strategy. The coverage object is then passed as coverage data to the threshold and strategy configuration module for invocation, and the corresponding time information is registered in storage for subsequent modules to read.

[0194] The threshold and policy configuration module 03 is used to store and dynamically load overload thresholds and oscillation frequency thresholds. Specifically, it receives coverage data from the sector limitation module, performs threshold loading and policy matching in conjunction with the policy library, and generates a threshold configuration object when the policy table requirements are met. The threshold configuration object is then passed to the data acquisition module as configuration data and its correspondence with the policy is recorded in the storage object.

[0195] The data acquisition module 04 is used to acquire power distribution node and environmental disturbance data through a multi-mode communication protocol. Specifically, it completes data acquisition and protocol parsing based on configuration data from the threshold and strategy configuration module, generating a data acquisition object; the data acquisition object is output to the time synchronization module, and the acquisition status is sent back to the event determination module for registration.

[0196] The time synchronization module 05 is used to eliminate the transmission delay deviation between the first data sequence and the second data sequence. Specifically, it receives the acquired data object and timestamp information from the data acquisition module, performs sequence alignment and delay compensation to obtain a synchronized data sequence; and provides the synchronized data sequence to the event determination module as input data to maintain an index relationship consistent with time.

[0197] Event determination module 06 is used to generate conflict arbitration results based on the dynamic threshold strategy table. Specifically, it receives the synchronization data sequence from the time synchronization module, performs matching and conflict analysis on the strategy table, generates an arbitration result object, passes the arbitration result object to the arbitration module for invocation, and registers the corresponding status information in the buffer for the arbitration module to read.

[0198] Arbitration module 07 is used to handle priority conflicts of multi-source events and generate speed limit control commands. Specifically, based on the arbitration result object from the event determination module, priority sorting and conflict handling are completed, and speed limit control commands are generated; the speed limit control commands are output to the speed limit signal output module, and the arbitration status is fed back to the log and parameter update module for registration.

[0199] The speed limit signal output module 08 is used to send power adjustment commands to the edge computing execution unit. Specifically, it receives speed limit control commands from the arbitration module, generates a power adjustment signal through the interface protocol, and transmits it to the edge computing execution unit; at the same time, it sends the execution status information back to the brake signal output module for registration.

[0200] The brake signal output module 09 is used to trigger the audible and visual alarm and the feeder switch for emergency braking. Specifically, it receives the execution status information from the speed limit signal output module, generates a brake signal based on the emergency braking conditions, triggers the audible and visual alarm and the feeder switch, and transmits the braking status information to the alarm management module for use.

[0201] The alarm management module 10 is used to activate the audible and visual alarm device and record the alarm level. Specifically, it receives braking status information from the brake signal output module, activates the audible and visual alarm device according to the alarm level, generates an alarm record, provides the alarm record to the log and parameter update module, and maintains an archive relationship consistent with time.

[0202] The log and parameter update module 11 is used to store effective time window data and update arbitration priority parameters. Specifically, it receives alarm records from the alarm management module and arbitration status information from the arbitration module, performs data storage and parameter updates to obtain an update log object; the update log object is provided to the installation and calibration module as backfeed data, and the records are updated.

Claims

1. A method for networking a power distribution Internet of Things (IoT) with multi-energy flow state sensing, characterized in that, include: Acquire electrical-thermal-hydrogen energy flow characteristic data, extract dynamic characteristic parameters, and generate acquisition strategy configuration files; The energy flow characteristic data includes key indicators such as current, voltage, heat flow, temperature, hydrogen concentration, and pressure. The process of extracting dynamic characteristic parameters includes: using a timestamp synchronization mechanism to handle data time sequence consistency, and using a data cleaning module to remove outliers and noise data. For the dynamic change characteristics of the electric-thermal-hydrogen energy flow, multivariate time series analysis technology is used, combined with wavelet transform and autoregressive model, to extract dynamic characteristic parameters in the time domain and frequency domain, including energy flow fluctuation amplitude, periodicity characteristics, and information on abrupt change points. Receive the acquisition strategy configuration file, perform time sensitivity level mapping processing and SDN controller interface calls, and generate SDN channel configuration parameters; Receive SDN channel configuration parameters, perform transmission delay data acquisition and processing and sliding compensation algorithm calculation, and generate a spatiotemporal compensation coefficient matrix; Based on the spatiotemporal compensation coefficient matrix and GIS equipment coordinate data, topological relationship parsing and multi-energy flow data alignment operations are performed to generate a spatiotemporally aligned data stream. Receive spatiotemporally aligned data streams, perform coupling feature extraction and coupling degree calculation, calculate energy flow coupling degree, and generate energy flow coupling analysis reports and system optimization suggestions; Receive system optimization suggestions, perform strategy parameter comparison and update the acquisition strategy file, generate a new acquisition strategy configuration file, and perform closed-loop optimization of configuration parameters; expressions include: in, This is the frequency response function of the transmission channel; The optimized spectrum distribution; This is an integration operation over the entire real number field; For dynamic compensation coefficients; It is a complex exponential function; Let be a time infinitesimal element, representing a time... integral; The imaginary unit; Angular frequency; It is a time variable.

2. The multi-energy flow state sensing distribution IoT networking method according to claim 1, characterized in that, The process of acquiring and processing transmission delay data includes: The real-time latency of each transmission channel is monitored by the edge-side data acquisition module, and the transmission time stamp information of multi-source devices is obtained by using the embedded clock synchronization mechanism. The collected raw latency data is preprocessed, including outlier removal and noise filtering. A statistical anomaly detection algorithm is used to identify and remove transmission anomalies. The format of multi-source transmission delay data is unified, and timestamp alignment technology is used to map the data from each source to a unified time axis, generating a multi-source time-stamped deviation dataset.

3. The multi-energy flow state sensing power distribution Internet of Things networking method according to claim 2, characterized in that, The process of generating a multi-source timescale bias dataset also includes: Time window features were extracted from the time scale deviation dataset, and a sliding time window model was constructed to capture the dynamic characteristics of time delay changes. A weighted moving average algorithm is used to smooth the timescale deviation data within each time window; Based on the time delay data within the sliding time window, a compensation algorithm is executed, and a time delay compensation function is used to correct the time scale deviation and generate time compensation coefficients.

4. The multi-energy flow state sensing distribution IoT networking method according to claim 1, characterized in that, The sliding compensation algorithm also includes the following calculations: By combining edge computing resources and utilizing a dynamic threshold adjustment mechanism, the length and sliding step of the time window are dynamically adjusted according to the current network load and transmission quality. An error feedback mechanism is introduced to iteratively optimize the compensation coefficient, and corrections are made using the difference between historical delay data and current measurement results.

5. The multi-energy flow state sensing distribution IoT networking method according to claim 1, characterized in that, The generated spatiotemporal compensation coefficient matrix includes: By combining GIS equipment coordinate data, spatial coordinate association processing is performed on the time compensation coefficient, mapping the compensation coefficient to the corresponding physical equipment spatial location; Using the spatial data processing module, spatial indexing and proximity calculation methods are employed to accurately match each time compensation coefficient with its corresponding sensor node coordinates. Consistency verification and anomaly detection are performed on the spatiotemporal compensation coefficient matrix, and spatial statistical analysis methods are used to identify and remove abnormal compensation values.

6. The multi-energy flow state sensing distribution IoT networking method according to claim 1, characterized in that, The process of performing topological relation resolution includes: The geospatial coordinates of all data acquisition terminals and related equipment in the power distribution Internet of Things are obtained through a pre-defined geographic information system interface. Perform topological relationship parsing on coordinate data, and establish physical connections and logical associations between devices through spatial adjacency algorithms and graph theory models; The topology mapping table is dynamically updated by using spatial indexing and neighborhood search technology, combined with device attributes and connection status.

7. The multi-energy flow state sensing distribution IoT networking method according to claim 1, characterized in that, Multi-energy stream data alignment operations include: Spatial relationships are extracted from the device topology mapping table, and graph traversal algorithms are used to identify the energy flow transmission paths between devices and their spatial proximity. For different energy flow types, construct corresponding spatial correlation subgraphs to clarify the upstream and downstream relationships and coupling points of each energy flow node; The raw data streams from different energy flow sensors are synchronized in time and spatially corrected according to the spatial relationships shown in the device topology mapping table.

8. The multi-energy flow state sensing distribution IoT networking method according to claim 1, characterized in that, Multi-stream data alignment operations also include: The spatiotemporal compensation coefficient matrix is ​​invoked, and a weighted sliding window algorithm is used to dynamically adjust the timestamps of multi-source data; Interpolation algorithms and anomaly detection mechanisms are introduced to handle missing and anomalies, ensuring the integrity and accuracy of aligned data; A preliminary fused data stream is generated, reflecting the spatial coupling relationship and temporal consistency of the multi-energy streams.

9. The multi-energy flow state sensing distribution IoT networking method according to claim 1, characterized in that, Generating a spatiotemporally aligned data stream includes: Apply a spatiotemporal compensation coefficient matrix to the initially fused data stream and perform refined spatiotemporal alignment processing; By combining GIS equipment coordinate data, the spatial position of each node in the data stream is corrected; An iterative optimization algorithm and a sliding time window technique are used to dynamically update the compensation coefficient matrix to adapt to real-time changes in network transmission conditions and equipment status.

10. A multi-energy flow state-aware distribution Internet of Things (IoT) networking system, applied to the method of any one of claims 1 to 9, characterized in that, include: The installation and calibration module is used to acquire parameters of the multimode communication components and complete the calibration. The sector limitation module is used to set the coverage area of ​​power distribution nodes, energy storage units, and load terminals; The threshold and strategy configuration module is used to store and dynamically load overload thresholds and oscillation frequency thresholds. The data acquisition module is used to collect data on power distribution nodes and environmental disturbances through a multi-mode communication protocol. The time synchronization module is used to eliminate the transmission delay deviation between the first data sequence and the second data sequence; The event determination module is used to generate conflict arbitration results based on the dynamic threshold strategy table; The arbitration module is used to handle priority conflicts of multi-source events and generate speed limit control commands; The speed limit signal output module is used to send power adjustment commands to the edge computing execution unit; Brake signal output module, used to trigger audible and visual alarms and emergency braking of feeder switch; The alarm management module is used to activate audible and visual alarm devices and record alarm levels. The log and parameter update module is used to store effective time window data and update arbitration priority parameters.

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