Test method and test system for integrated energy management system

Abstract

The present application relates to the technical field of integrated energy energy management, and more particularly to a test method and test system for an integrated energy energy management system. The method comprises the following steps: obtaining energy energy management system operation data, determining integrated energy timing supply and demand data, and evaluating supply and demand deviation too large data; monitoring transmission link dynamic load abnormal conditions according to the supply and demand deviation data, and then determining the link thermal degradation evolution state, and monitoring the energy decomposition device load evolution trend in combination with the above data; determining the device component fatigue damage accumulation condition, identifying the decomposition response performance attenuation condition, and monitoring the multi-energy collaborative decomposition proportion abnormal condition; judging the energy device operation abnormal condition, generating and evaluating energy energy management strategy defect data, and completing the optimization of the integrated energy energy management system. The present application realizes more accurate and efficient management of the integrated energy energy management system through optimization processing of the integrated energy energy management system.

Description

Technical Field

[0001] This invention relates to the field of integrated energy management technology, and in particular to a testing method and testing system for an integrated energy management system. Background Technology

[0002] Integrated energy systems, by integrating multiple energy forms such as electricity, heat, cooling, and gas, and realizing the cross-category flow and coordinated utilization of energy, have become a key support for the construction of new energy systems. However, the operation of integrated energy systems involves diverse energy types, complex supply and demand timing differences, lengthy energy transmission links, and variable equipment operating states. This leads to significant challenges in dynamic load control, stable equipment operation, and policy response adaptation. Traditional energy management systems often employ static supply and demand balance algorithms or scheduling mechanisms based on empirical rules, lacking the ability to identify real-time deviation trends of multiple energy types under complex operating conditions. They struggle to accurately monitor dynamic load fluctuations in energy links and equipment fatigue degradation, resulting in imbalances in the multi-energy collaborative decomposition ratio. This further exacerbates energy supply and demand mismatches and system malfunctions. Furthermore, traditional integrated energy management systems suffer from inaccurate monitoring of the load evolution trends of energy decomposition devices and inaccurate assessment of the defects in energy management strategies. Summary of the Invention

[0003] Therefore, it is necessary to provide a testing method and system for an integrated energy management system to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a testing method for an integrated energy management system includes the following steps: Step S1: Obtain the operating data of the energy management system; determine the comprehensive energy time-series supply and demand data based on the operating data of the energy management system; assess the excessive data of comprehensive energy supply and demand deviation based on the comprehensive energy time-series supply and demand data; Step S2: Monitor the abnormal dynamic load of the transmission link based on the large data of energy supply and demand deviation; determine the thermal loss degradation evolution status of the transmission link based on the abnormal dynamic load of the transmission link; monitor the load evolution trend of the energy decomposition device based on the large data of energy supply and demand deviation and the thermal loss degradation evolution status of the transmission link. Step S3: Determine the cumulative fatigue damage of the device components based on the load evolution trend of the energy decomposition device; determine the degradation of the device's decomposition response performance based on the cumulative fatigue damage of the device components and the load evolution trend of the energy decomposition device; monitor abnormalities in the multi-energy synergistic decomposition ratio based on the degradation of the device's decomposition response performance. Step S4: Determine the abnormal operating conditions of energy devices based on the abnormal situation of multi-energy collaborative decomposition ratio; generate energy management strategies based on the abnormal operating conditions of energy devices in the integrated energy management system to obtain energy management strategy data; conduct energy management strategy defect assessment and testing based on the energy management strategy data to obtain energy management strategy defect data; optimize the integrated energy management system based on the energy management strategy defect data to obtain integrated energy management system optimization data.

[0005] This invention constructs a complete monitoring chain, from sensing the time-series supply and demand deviation of integrated energy, identifying dynamic anomalies in transmission links, tracking the load evolution of decomposition devices, to optimizing energy management strategies, thereby achieving dynamic tracking and systematic evaluation of the operating status of integrated energy systems. Starting with energy supply and demand deviations, it identifies dynamic load anomalies and heat loss evolution trends in transmission links, accurately locating structural imbalances and power transmission bottlenecks in energy flow, providing a foundation for subsequent device load characteristic analysis. Fatigue damage and response performance degradation parameters enable coupled judgment of the physical degradation and response behavior of decomposition devices, and based on the abnormal status of the collaborative decomposition ratio, it achieves an overall judgment of the multi-source collaborative stability of the system. A data closed-loop mechanism is formed, from operational anomaly identification, strategy generation, strategy defect testing to system optimization, improving the accuracy and adaptability of energy management strategies, and achieving rapid response to operational anomalies and continuous improvement of system performance. The above steps collectively construct a causal chain from supply and demand disturbances to energy efficiency degradation and then to strategy adjustment, ensuring the logical coordination and integrity of state feedback between various links of the integrated energy system. Therefore, this invention is an optimization of the traditional integrated energy management system, which solves the problems of inaccurate monitoring of the load evolution trend of the energy decomposition device and inaccurate assessment of the defects in the energy management strategy. It improves the accuracy of monitoring the load evolution trend of the energy decomposition device and the accuracy of assessing the defects in the energy management strategy.

[0006] The present invention also provides a test system for an integrated energy management system, used to perform the test method for the integrated energy management system as described above, wherein the test of the integrated energy management system includes: The supply-demand deviation assessment module is used to acquire energy management system operation data; determine comprehensive energy time-series supply and demand data based on the energy management system operation data; and assess excessive comprehensive energy supply-demand deviation data based on the comprehensive energy time-series supply and demand data. The device load evolution monitoring module is used to monitor the abnormal dynamic load of the transmission link based on the large data of energy supply and demand deviation; determine the thermal loss degradation evolution status of the transmission link based on the abnormal dynamic load of the transmission link; and monitor the load evolution trend of the energy decomposition device based on the large data of energy supply and demand deviation and the thermal loss degradation evolution status of the transmission link. The energy co-decomposition anomaly monitoring module is used to determine the cumulative fatigue damage of the device components based on the load evolution trend of the energy decomposition device; determine the degradation of the device's decomposition response performance based on the cumulative fatigue damage of the device components and the load evolution trend of the energy decomposition device; and monitor the anomaly of the multi-energy co-decomposition ratio based on the degradation of the device's decomposition response performance. The integrated energy management system optimization module is used to determine abnormal operating conditions of energy devices based on abnormal multi-energy synergistic decomposition ratios; generate energy management strategies based on the abnormal operating conditions of energy devices using the integrated energy management system to obtain energy management strategy data; conduct defect assessment tests on energy management strategies based on the energy management strategy data to obtain energy management strategy defect data; and optimize the integrated energy management system based on the energy management strategy defect data to obtain integrated energy management system optimization data.

[0007] The present invention provides a test system for an integrated energy management system. This system is capable of implementing the test method for any integrated energy management system of the present invention. It is used to combine the operation and signal transmission media between various modules to complete the test method for the integrated energy management system. The internal modules of the system cooperate with each other. By constructing a full-process test method covering supply and demand deviation identification, link thermal loss monitoring, device degradation diagnosis and strategy closed-loop optimization, the system can achieve accurate evaluation and dynamic control of the operating status of the integrated energy management system. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating the steps of a testing method for an integrated energy management system. Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3. Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S4. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0009] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0010] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0011] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0012] To achieve the above objectives, please refer to Figures 1 to 3 A test method for an integrated energy management system includes the following steps: Step S1: Obtain the operating data of the energy management system; determine the comprehensive energy time-series supply and demand data based on the operating data of the energy management system; assess the excessive data of comprehensive energy supply and demand deviation based on the comprehensive energy time-series supply and demand data; In this embodiment of the invention, an energy data access gateway is deployed in the actual testing platform to collect operational data of distributed energy supply devices. The collected data includes electrical output power (unit: kilowatts), thermal energy supply (unit: megajoules), gas energy volumetric flow rate (unit: standard cubic meters / hour), and load demand data related to each type of energy. This operational data is transmitted in real-time to the central data management system via an Ethernet interface, with a collection cycle of once per minute. After data collection, the system archives and groups the data based on timestamps and energy types, forming a comprehensive energy time-series supply and demand data sequence measured in minutes. Data fields include parameters such as energy type, supply-side energy output value, demand-side forecast value, and data timestamp. The supply and demand data are then used to calculate the difference, employing the algorithm formula ΔE = |Esupply - EDemand| to calculate the supply and demand deviation at each time point, where ΔE is the supply and demand deviation at a specific point in time, with the unit uniformly set to kilowatt-minutes. Periods with consecutive supply and demand deviations greater than 50 kilowatt-minutes are marked as supply and demand deviation clusters, and their start and end times are recorded, generating an excessive supply and demand deviation data sequence.

[0013] Step S2: Monitor the abnormal dynamic load of the transmission link based on the large data of energy supply and demand deviation; determine the thermal loss degradation evolution status of the transmission link based on the abnormal dynamic load of the transmission link; monitor the load evolution trend of the energy decomposition device based on the large data of energy supply and demand deviation and the thermal loss degradation evolution status of the transmission link. In this embodiment of the invention, the excessive supply-demand deviation data obtained in step S1 is used as input and mapped to the corresponding transmission link control cycle. Based on the structural attributes of each energy transmission path, corresponding cables (electrical energy), heating networks (thermal energy), and gas transmission pipelines (gas energy) are matched as transmission links. Using the control command execution frequency and output amplitude data recorded by the embedded control node, the link control rhythm disturbance parameters are calculated, and a disturbance amplitude growth curve is constructed in conjunction with the deviation data. Based on the disturbance curve, the fluctuation of electrical parameters in the transmission link is detected, and the monitoring data includes voltage transient rate, load mutation amplitude, and current density changes at corresponding time points. The above-mentioned electrical parameter changes are used to determine abnormal electrical transmission behavior of the link, and combined with mechanical strain and thermosensitive fiber optic temperature data recorded by the sensor array, a multi-physics field coupled response dataset is constructed. Through data interpolation and time series analysis, the coordinates of local stress concentration areas are extracted, corresponding to specific segments in the transmission link structure diagram. Further analysis is conducted on the correlation between the main branch current surge data and the insufficient pressure-bearing capacity of the secondary branches, and the current imbalance index is used to calculate the uneven distribution of current between the main and secondary branches. The current imbalance data is matched with the thermal capacity coefficient of the link structure to obtain the heat growth trend. Combined with the temperature rise distribution map measured by a high-precision infrared thermal imager, the cumulative thermal effect of the cable structure is analyzed. Based on the duration of the thermal effect and the temperature rise gradient, the damage risk value of the cable joint is derived, along with the damage value of each cable unit, to output the thermal loss degradation evolution status data of the transmission link. This data, along with the aforementioned supply-demand deviation data, is used to evaluate the load response behavior of the energy decomposition device. The above thermal loss status is compared with the output power data of the decomposition device to extract the continuous high-load operation period of the decomposition device, analyze its redundant energy output, and calculate the internal pressure value of the system (read by the cavity pressure sensor). If the redundant energy is not effectively dissipated, resulting in the cavity pressure continuously exceeding 0.3 MPa, it is determined to be a high-pressure backflush state. Further analysis of the reaction process instability behavior using the reaction cavity disturbance coefficient outputs the device load evolution trend data as input for step S3.

[0014] Step S3: Determine the cumulative fatigue damage of the device components based on the load evolution trend of the energy decomposition device; determine the degradation of the device's decomposition response performance based on the cumulative fatigue damage of the device components and the load evolution trend of the energy decomposition device; monitor abnormalities in the multi-energy synergistic decomposition ratio based on the degradation of the device's decomposition response performance. In this embodiment of the invention, the load evolution trend data of the energy decomposition device output in step S2 is used as input. Based on the historical analysis method, the thermal and electrical coupling loads of the pressure-bearing components of the device are evaluated for cross-action, specifically including parameters such as the rate of change of temperature gradient (unit: ℃ / min) and the rate of increase in current density (unit: A / mm² / min). The composite stress value of the local area of ​​the connection structure is calculated using these parameters and compared with the material yield limit. When the stress value is greater than 70% of the material safety limit, the structure is extracted as a local yield node. Based on the distribution of local yield nodes and the stress change trend, a crack propagation statistical model is applied to evaluate the metal fatigue crack growth rate. If a crack length is detected to continuously increase and exceed 4.8 mm within a certain period, the corresponding location is recorded as a high-risk node. If the elastic modulus of the sealing surface is simultaneously detected to decrease to below 50% of the initial state, the seal failure state is confirmed, and the cumulative fatigue damage status data of the components is generated. The fatigue damage data is correlated with the device load response trend to extract the device's instantaneous response lag time and the degree of distortion of the power output curve. The magnitude of the decrease in response performance is determined by calculating the decomposition response hysteresis coefficient. If the hysteresis coefficient is greater than 30% of the average value under normal operating conditions, the response performance is considered to have significantly degraded. In this case, the result of the degraded response performance is input into the energy coordination control module to analyze the degree of deviation in the decomposition ratio of the three types of energy. If the output ratio of electricity, heat, and gas deviates from the original design coordination value by more than 20% and the duration exceeds 10 minutes, it is recorded as an abnormal situation of multi-energy coordination decomposition ratio.

[0015] Step S4: Determine the abnormal operating conditions of energy devices based on the abnormal situation of multi-energy collaborative decomposition ratio; generate energy management strategies based on the abnormal operating conditions of energy devices in the integrated energy management system to obtain energy management strategy data; conduct energy management strategy defect assessment and testing based on the energy management strategy data to obtain energy management strategy defect data; optimize the integrated energy management system based on the energy management strategy defect data to obtain integrated energy management system optimization data.

[0016] In this embodiment of the invention, detected abnormal data on the multi-energy synergistic decomposition ratio is input to the energy operation status identification module, which analyzes the resulting device operation risk indicators. If a deviation in the energy decomposition ratio causes carbon emissions to exceed environmental protection thresholds (e.g., CO2 emission rate exceeding 220 g / kWh), it is determined to be a carbon emission overload state in the decomposition process. Combining the persistence and impact range of the energy synergistic ratio deviation, the overall operational deviation of the device is deduced, forming abnormal energy device operation status data. Next, the abnormal status data is loaded into the central energy control module, initiating the energy management strategy generation process based on a rule set. The rule set includes strategy templates such as decomposition mode switching, load limit adjustment, and synergistic factor callback. Energy management strategy data is generated by matching the strategy templates with the abnormal indicators. Subsequently, the generated strategy is dynamically simulated and tested in the strategy testing module, with test indicators including response time, stability coefficient, and over-adjustment. After the test is completed, an energy management strategy defect assessment result is generated, and the degree of defect is graded. Finally, based on the defect assessment results, the parameter settings, scheduling logic, and strategy combination weights of the integrated energy management system are automatically adjusted to generate an optimized set of system operating parameters, which is output as optimized data for the integrated energy management system. This optimized data will be fed back to the energy management platform as the basis for reconstructing system operating parameters, forming a complete closed-loop energy strategy assessment and optimization process.

[0017] Preferably, step S1 includes the following steps: Step S11: Obtain the operating data of the energy management system; In this embodiment of the invention, during the operation of the integrated energy management system, real-time operational data of various energy types within the system are acquired through data acquisition devices deployed at key nodes of different energy types. Energy types include electricity, heat, and gas. Corresponding key acquisition nodes include substation outgoing lines, the main circulation pump inlet of heat exchange stations, and downstream of the flow meter in the main natural gas supply pipeline. Each type of energy node is equipped with an industrial-grade acquisition instrument adapted to its physical characteristics. Electricity nodes use three-phase power acquisition instruments, heat nodes are equipped with a combination unit of heat flow meter and temperature sensor, and gas nodes are equipped with a volumetric flow meter with automatic temperature and pressure compensation. The data acquisition frequency is set to once every 10 seconds. Data packaging is completed through an edge control device, and the packaging fields include: timestamp, device number, energy type, measured value, unit, measurement accuracy, and measurement status flag. All acquired data is transmitted to a regional-level data aggregator via an industrial control bus and uploaded to the system's main server. On the server side, data is stored in separate databases using energy type as the index label, and each data entry is given a unique internal identifier for subsequent tracking and cross-source comparison operations. This step forms the raw dataset for running a fully structured energy management system.

[0018] Step S12: Perform energy management system operation data preprocessing on the energy management system operation data to obtain energy management system operation preprocessing data; In this embodiment of the invention, the operational data obtained in step S11 is used as input and processed according to energy type. For electrical energy data, the preprocessing process includes data denoising, missing data imputation, and unit standardization. During data denoising, a moving median filtering method is used to eliminate high-frequency interference caused by instantaneous fluctuations in the sensor; missing data imputation is based on historical trend interpolation, combined with linear trend fitting to complete the data using data from similar devices over the past hour; unit standardization converts all electrical energy data into kilowatts (kW) through power standardization. The preprocessing methods for thermal and gas energy data are similar. Thermal energy data processing requires calculating the inlet and outlet temperature difference and flow rate to obtain the thermal power output per unit time; gas energy data is converted into equivalent thermal power output based on the gas calorific value, and is also stored in kW after processing. All processed data includes the source device number, energy type label, and processing time identifier, and is stored in the "Energy Preprocessing Data Table". Furthermore, to eliminate the order-of-magnitude differences between different types of data, the preprocessing process also includes normalization processing. The normalization method employs a maximum-minimum scaling approach, scaling the maximum and minimum values ​​of each energy type within the current monitoring period to ensure a consistent numerical response range in subsequent time-series analyses. The energy management system preprocessing data output from this step features a unified structure, good data continuity, and standardized format.

[0019] Step S13: Determine the integrated energy time-series supply and demand data based on the preprocessing data of the energy management system; In this embodiment of the invention, based on the energy operation preprocessing data output in step S12, corresponding comprehensive energy time-series supply and demand data is constructed. In the system structure, the supply side consists of various energy supply devices, such as grid substation output, total gas pressure regulation output, and hot water pump output from heating stations; the demand side consists of end-user energy devices, including industrial electrical equipment, residential heating load, electric vehicle charging piles, and domestic hot water systems. To achieve supply and demand data matching, the total energy output of all supply-side devices and the total energy request of all demand-side energy devices are statistically analyzed every minute, and summarized by energy type. Specifically, electrical load demand is obtained through analysis of data returned by smart meters, heat demand is estimated based on the secondary side outlet water temperature and flow rate of the heat exchanger, and gas demand is formed by the gas consumption rate fed back by the smart gas controller. After statistical analysis, a "Comprehensive Energy Time-Series Supply and Demand Data Table" is formed, with time as the primary index and energy type as the secondary index. Data fields include: time point, energy type, total supply power, total demand power, supply-demand difference, load ratio coefficient, etc. In addition, to support subsequent anomaly detection operations, a fluctuation flag field is added to the data table to record the absolute value of the supply and demand change rate at different times. If the rate change at a certain point in time exceeds 30%, it is automatically marked as "severe fluctuation," serving as an important basis for supply and demand stability analysis. The supply and demand data output in this step is synchronized in time according to energy type, providing a dynamic baseline for deviation detection and link fault diagnosis.

[0020] Step S14: Assess the excessive size of the comprehensive energy supply and demand deviation based on the comprehensive energy time series supply and demand data.

[0021] In this embodiment of the invention, based on the comprehensive energy time-series supply and demand data constructed in step S13, a deviation assessment process is executed, defining supply and demand deviation thresholds for each type of energy: 20kW for electricity, 15kW for heat, and 10kW for gas. Based on this, the supply and demand difference at each time point is calculated, i.e., the absolute value of the supplied power minus the demanded power, and compared with the aforementioned thresholds. If the supply and demand deviation exceeds the corresponding threshold for five consecutive time points, it is recorded as an excessive supply and demand deviation interval. Further, within this interval, the average, maximum, and duration of the supply and demand deviation are statistically analyzed in relation to the energy type, and the historical average of the supply-demand ratio is used to determine whether there is a systemic anomaly. For example, if the electricity supply-demand ratio consistently shows an oversupply for more than 10 minutes, it is judged as a structural deviation being excessive; if gas experiences a sudden, short-term supply shortage, it is recorded as an instantaneous deviation anomaly. The assessment process employs a rolling analysis approach with a 5-minute cycle, updating a new time point each time to ensure the continuity and real-time nature of data detection. This results in a large data record table of supply-demand deviations, with fields including: deviation start time, end time, energy type, peak deviation, average deviation, involved equipment number, and anomaly type (structural / sudden). This table serves as the core input dataset for subsequent transmission link analysis and assessment of device load evolution trends.

[0022] Preferably, step S14 includes the following steps: Step S141: Divide the comprehensive energy time series supply and demand data into energy types to obtain multi-type energy time series supply and demand data, which includes electricity, heat, and gas energy. In this embodiment of the invention, based on the input comprehensive energy time-series supply and demand data, the data is divided according to energy type to obtain multi-type energy time-series supply and demand data. The energy types include, but are not limited to, electricity, heat, and gas energy. This step uses pre-defined energy type tags to classify and archive each record in the time-series data, ensuring that supply and demand data for different energy types are stored and processed separately. The division process employs data filtering and classification algorithms, splitting the data into independent energy time series based on the inherent energy type field in the energy data. After division, electricity time-series data, heat time-series data, and gas energy time-series data are obtained, each containing a timestamp and corresponding supply and demand information. This operation is completed using data structuring tools to ensure that the time-series supply and demand data format for each energy type is consistent, facilitating subsequent analysis. The output of this step is a set of multi-type energy time-series supply and demand data, which serves as the input for difference calculation in subsequent steps.

[0023] Step S142: Calculate the supply and demand difference of multiple types of energy based on the time series supply and demand data of multiple types of energy to obtain the supply and demand difference data of multiple types of energy; In this embodiment of the invention, based on the multi-type energy time-series supply and demand data obtained in step S141, multi-type energy supply and demand difference calculation is performed to obtain multi-type energy supply and demand difference data. The difference calculation is performed for the time-series supply and demand data of each energy type, using a method for calculating the supply and demand difference between consecutive time points. Specifically, for each energy type, the supply and demand difference between adjacent time points is calculated in chronological order, i.e., the supply and demand value at the current time point minus the supply and demand value at the previous time point. By traversing the entire time-series dataset, a complete supply and demand difference sequence is formed. The difference calculation process is implemented using numerical calculation tools to ensure the accuracy and completeness of data processing. After the difference calculation is completed, the resulting multi-type energy supply and demand difference data includes supply and demand change sequences corresponding to multiple energy types, providing basic data for subsequent sliding window segmented processing. The output of this step is the multi-type energy supply and demand difference data, which serves as the input for sliding window processing.

[0024] Step S143: Set a time series sliding window of fixed length, and use the time series sliding window to segment the energy supply and demand difference data of multiple types to obtain the energy supply and demand difference sliding window matrix; In this embodiment of the invention, a fixed-length time-series sliding window is set to segment the multi-type energy supply-demand difference data obtained in step S142, resulting in an energy supply-demand difference sliding window matrix. The sliding window length and sliding step size are preset by system parameters to ensure complete coverage of the time-series data. Specifically, the processing method involves extracting data segments according to a fixed-length window, starting from the beginning of the multi-type energy supply-demand difference sequence, and sliding the window forward with a certain step size to gradually extract data segments covering the entire time range. Each data segment contains supply-demand difference information for all energy types within that time window. All data segments are arranged sequentially to form a two-dimensional matrix structure, where rows represent different time windows and columns represent supply-demand differences for different energy types. This operation employs time-series data segmentation technology and is implemented through data processing algorithms. The generated sliding window matrix provides the distribution of supply-demand differences across multiple time periods and energy types, providing fundamental data for subsequent fluctuation analysis. The output of this step is the energy supply-demand difference sliding window matrix, which serves as input for supply-demand fluctuation analysis.

[0025] Step S144: Analyze the fluctuation of energy supply and demand deviation based on the sliding window matrix of energy supply and demand difference; In this embodiment of the invention, the energy supply and demand difference sliding window matrix generated in step S143 is used to analyze the fluctuation of energy supply and demand deviation. The analysis method is achieved by calculating the statistical characteristics of the supply and demand differences of multiple types of energy within each sliding window. The statistical characteristics include mean, variance, extreme values, etc., which are used to characterize the central tendency and dispersion of the supply and demand differences. Combined with time series analysis methods, the periodicity, persistence, and abrupt change characteristics of the fluctuations are identified. Signal processing technology is used to extract the fluctuation trend and determine the change amplitude and fluctuation frequency of the supply and demand difference within each time window. This process is executed by data statistics tools and time series analysis tools, which can accurately reveal the fluctuation characteristics of the supply and demand deviation of multiple types of energy in different time periods. After the analysis is completed, supply and demand deviation fluctuation description data is generated for subsequent trend feature extraction. The output of this step is the energy supply and demand deviation fluctuation description data, which serves as the input for the extraction of trend features of multiple types of energy deviation.

[0026] Step S145: Extract trend features of multiple types of energy deviations based on the fluctuation of energy supply and demand deviations; In this embodiment of the invention, based on the energy supply and demand deviation fluctuation description data obtained in step S144, trend features of deviations for multiple energy types are extracted. This process utilizes a trend recognition algorithm, comprehensively considering multiple statistical indicators to evaluate the fluctuation data and form a continuous sequence of deviation trend features. Trend features include deviation increase / decrease trends, trend duration, and trend intensity, and the trend slope and trend change points for each time period are calculated using mathematical methods. For each energy type, the changing trend of its supply and demand deviation is identified separately, and further combined with the synergistic effects of multiple energy sources to generate a multi-dimensional trend feature vector. Trend feature extraction is performed using data mining tools to ensure the accuracy and representativeness of the trend features. The extracted trend features serve as the foundational data for subsequent cluster segment identification and high-fluctuation time point location. The output of this step is multi-type energy deviation trend feature data, which is used for subsequent deviation cluster segment identification.

[0027] Step S146: When the trend characteristics of the multi-type energy deviation exceed ±50kW·min, identify the energy supply and demand deviation clustering segment; In this embodiment of the invention, when the multi-type energy deviation trend features extracted in step S145 exceed a preset threshold, an energy supply and demand deviation cluster segment is identified. This threshold is a system parameter, defined as the quantification boundary of the trend features, used to determine the degree of abnormality in the supply and demand deviation. Specifically, the trend feature sequence is continuously detected to identify time periods where the trend intensity exceeds the threshold. Trend deviations within continuous time periods manifest as concentrated anomalies in supply and demand differences, forming cluster segments. Time interval division technology is used to locate the boundaries of abnormal time periods, clarifying the start and end times and duration of the cluster segments. The cluster segment identification process combines trend features with the positional relationship of the time series and is executed through an algorithm. After identification, cluster segment information data is output, including the segment's time range and corresponding trend features, for subsequent identification of high-fluctuation point data and deviation assessment. The output of this step is the energy supply and demand deviation cluster segment data.

[0028] Step S147: Identify high-fluctuation energy supply and demand data based on the trend characteristics of various energy deviations; In this embodiment of the invention, based on the multi-type energy deviation trend characteristics of step S145, data on high fluctuations in energy supply and demand are identified. This identification is based on extreme points and abrupt change points in the trend characteristics, combined with the rate of change of the supply-demand difference for location. Specifically, a peak detection algorithm is executed on the trend sequence to filter out the time points with the fastest or largest deviation changes. High fluctuation points are marked as key time nodes of drastic changes in the energy supply and demand status of the system, and the corresponding timestamps and supply-demand difference data are recorded. This process is implemented using time-series data analysis technology and extreme value detection algorithms. After completing the identification of high fluctuation points, a high fluctuation point dataset is formed, containing high fluctuation status information of various energy types at different times. This data provides an important basis for comprehensive large-scale assessment of energy supply and demand deviations. The output of this step is the high fluctuation point data for energy supply and demand.

[0029] Step S148: Evaluate the comprehensive energy supply and demand deviation data based on the time data of high fluctuations in energy supply and demand and the energy supply and demand deviation clusters.

[0030] In this embodiment of the invention, based on the energy supply and demand deviation cluster data identified in step S146 and the energy supply and demand high-fluctuation point data obtained in step S147, the overall energy supply and demand deviation excessive data is assessed. The assessment process comprehensively considers the time span of the clustered segments, the trend intensity, and the distribution density and amplitude of high-fluctuation points, calculating the overall supply and demand deviation status through multi-factor analysis. Specifically, this includes integrating the spatial-temporal distribution characteristics of the supply and demand deviation to form an overall deviation assessment index. This process employs data fusion technology to merge deviation data from different times and different energy types, obtaining a unified excessive deviation assessment result. The assessment result serves as key reference data for subsequent monitoring and adjustment of the integrated energy management system. This step outputs the overall energy supply and demand deviation excessive data.

[0031] Preferably, step S2, which involves monitoring the dynamic load anomalies of the transmission link based on excessive energy supply-demand deviation data, includes: Based on large data on energy supply and demand deviations, determine the transmission link control rhythm disturbance data; In this embodiment of the invention, transmission link control rhythm disturbance data is determined based on large data of energy supply and demand deviation. Specifically, based on the comprehensive large data of energy supply and demand deviation obtained in previous steps, time-series signal features related to transmission link control are extracted. Time-frequency analysis is performed on the large data of supply and demand deviation, and Fourier transform or short-time Fourier transform methods are used to separate the fluctuation components of different frequency bands, thereby identifying periodic disturbance signals related to the transmission link control rhythm. This disturbance signal manifests as periodic fluctuations in the supply and demand data, and its frequency and amplitude information constitute the transmission link control rhythm disturbance data. This step employs frequency domain analysis techniques combined with time-series signal processing tools to ensure accurate capture of rhythm disturbance information in the transmission link. The output transmission link control rhythm disturbance data includes timestamps, disturbance frequencies, and corresponding amplitude values, providing input for subsequent disturbance amplitude analysis.

[0032] Based on the data of transmission link control rhythm disturbance and the large data of energy supply and demand deviation, the amplitude of transmission link control interference was enhanced, and the trend of the amplitude of link control interference was obtained. In this embodiment of the invention, an enhancement analysis of the transmission link control interference amplitude is performed based on transmission link control rhythm disturbance data and large data on overall energy supply and demand deviation, yielding an enhancement trend in the transmission link control interference amplitude. This step identifies the changing trend of the interference amplitude by comparing and analyzing the correlation between the transmission link control rhythm disturbance data and the overall large data on energy supply and demand deviation. Specifically, a time-series correlation analysis method is used to calculate the correlation coefficient and time lag relationship between the two types of data, revealing the strength of the role of transmission link control rhythm disturbance in energy supply and demand anomalies. Furthermore, through amplitude statistical analysis, the maximum value, average value, and fluctuation range of the interference signal are monitored, and the changing trend of the interference amplitude over time is extracted. This analysis process uses statistical tools and time-series data processing algorithms to ensure the accuracy and reliability of the results. The output data on the enhancement trend of the transmission link control interference amplitude provides a basis for subsequent anomaly monitoring.

[0033] Monitor abnormal electrical transmission conditions of the link based on the increasing trend of link control interference amplitude and the disturbance data of transmission link control rhythm; In this embodiment of the invention, abnormal electrical transmission conditions of the link are monitored based on the increasing trend of link control interference amplitude and the transmission link control rhythm disturbance data. This step identifies abnormal transmission events by comparing the control interference amplitude and rhythm disturbance data in the electrical transmission state of the transmission link. Specific operations include continuous sampling and recording of voltage and current waveforms, combined with the aforementioned interference trend analysis, and employing anomaly detection algorithms, such as threshold determination and change point detection, to determine whether electrical parameters exceed the normal operating range. This monitoring process relies on a high-precision electrical parameter acquisition device and a signal processing module to acquire electrical operating data in real time and perform dynamic comparisons. The results form abnormal electrical transmission state data for the link, including the time point of the anomaly, the anomaly type, and its severity, providing input for subsequent mechanical load analysis.

[0034] The link mechanical load fluctuation status is determined based on the increasing trend of link control interference amplitude; In this embodiment of the invention, the mechanical load fluctuation status of the transmission link is determined based on the increasing trend of the link modulation interference amplitude. The determination of mechanical load fluctuation is based on the mechanical response analysis of the transmission link structure, combined with the amplitude variation trend of the link modulation interference, to infer the dynamic changes in the mechanical load. Specific operations include collecting force sensor data and strain gauge signals on the link, and calculating the amplitude and frequency characteristics of the mechanical load variation using time-series signal analysis techniques. Using the link modulation interference amplitude as an input variable, a data fitting method is used to correlate the mechanical load fluctuation, forming a mechanical load fluctuation curve. This step utilizes physical quantity measurement devices and signal analysis instruments to ensure accurate measurement of the mechanical load fluctuation. The output link mechanical load fluctuation status data is used for multiphysics response analysis.

[0035] Based on the link mechanical load fluctuation and link electrical transmission anomaly, multi-physics response curve fitting is performed to obtain link multi-physics coupling response data; In this embodiment of the invention, multi-physics response curve fitting is performed based on the link's mechanical load fluctuations and electrical transmission anomalies to obtain multi-physics coupled response data. This step comprehensively considers the combined effects of mechanical loads and electrical anomalies, establishing response models for both mechanical and electrical fields through multi-physics coupling analysis. Specifically, based on the collected mechanical load fluctuation and electrical anomaly data, curve fitting techniques, such as polynomial fitting or interpolation, are used to jointly model the response characteristics of the two physical fields, generating a coupled response curve. This response curve reflects the state changes of the link under the interaction of multiple physical fields, demonstrating the combined effects of stress, deformation, and electrical disturbances. High-precision data processing equipment and numerical analysis tools are used to complete the fitting process, outputting multi-physics coupled response data for the link, providing data support for the identification of local stress concentrations.

[0036] Identify local stress concentration points in the link based on multi-physics coupling response data; In this embodiment of the invention, local stress concentration points in a link are identified based on multi-physics coupling response data. This step utilizes the coupling response data to locate key areas of stress concentration in the link structure. The operation includes mapping the multi-physics response curve onto the link geometry and identifying stress peak regions through stress field distribution calculations. Specifically, a stress distribution analysis method is used, combined with data visualization tools, to label the spatial coordinates and stress intensity of stress concentration points. This process relies on physical quantities collected by high-precision sensors and their corresponding spatiotemporal data to ensure the accuracy and timeliness of stress concentration point identification, generating local stress concentration point data for the link, providing a foundation for voltage fluctuation estimation and dynamic load anomaly monitoring.

[0037] Estimate the growth of voltage fluctuations in the transmission link based on abnormal electrical transmission conditions. In this embodiment of the invention, the growth of voltage fluctuations in the transmission link is estimated based on abnormal electrical transmission conditions. This step analyzes the amplitude and growth trend of voltage fluctuations based on the specific manifestations of the abnormal electrical transmission conditions. By collecting time-series data of the link voltage and combining it with the time points of the abnormal state, statistical analysis methods are used to calculate the rate of change and peak fluctuation range of voltage fluctuations. This analysis process combines power system monitoring equipment and data acquisition terminals to ensure the accuracy and real-time nature of the data. The output data on the growth of voltage fluctuations reflects the stability changes of the link's electrical state, providing an important indicator for dynamic load anomaly monitoring.

[0038] Monitor the dynamic load anomalies of the transmission link based on the growth of transmission link voltage fluctuations and data on local stress concentration points in the link.

[0039] In this embodiment of the invention, the dynamic load anomaly status of the transmission link is monitored based on the growth of transmission link voltage fluctuations and data on local stress concentration points. This step integrates the aforementioned voltage fluctuation and stress concentration information, employing a multi-parameter fusion analysis method to comprehensively monitor the dynamic load anomalies of the transmission link. Specifically, this involves combining voltage fluctuation data with the spatial distribution of stress concentration points to analyze the impact of load changes on the structure and potential risks. Through time series comparison and spatial distribution correlation, the occurrence time and location of dynamic load anomalies are identified. This process utilizes multi-sensor data fusion technology and a dynamic condition monitoring system to ensure the accuracy and continuity of the monitoring results, outputting dynamic load anomaly data for the transmission link, providing a basis for the subsequent maintenance and optimization of the integrated energy management system.

[0040] Preferably, step S2, determining the thermal loss degradation evolution state of the transmission link based on the abnormal dynamic load condition of the transmission link, includes: Determine the sudden increase in current in the main branch of the link based on the abnormal dynamic load of the transmission link. In this embodiment of the invention, the surge in current in the main branch of the transmission link is determined based on the abnormal dynamic load conditions of the transmission link. Specifically, based on the current time-series data obtained from the dynamic load anomaly monitoring of the transmission link, the main branch current signal is continuously sampled and recorded. A high-precision current sensor is used to collect the current intensity data of the main branch, and signal processing methods are used to remove noise and interference to obtain an accurate current change curve. Change rate analysis technology is used to identify abrupt changes in the current data to determine whether there is a rapid increase in the main branch current within a short period. This step uses time-series data analysis tools to extract the start and end times, amplitude changes, and duration of the current surge, forming data on the surge in current in the main branch of the link, providing a foundation for subsequent load pressure analysis of non-main branch lines.

[0041] Detect the insufficient load capacity of non-main branch circuits based on abnormal dynamic load conditions and sudden increases in local current in the transmission link. In this embodiment of the invention, the underload condition of non-main branch loads is detected based on the abnormal dynamic load conditions and local current surges in the transmission link. This step combines the current load data of non-main branches with the current surge information of main branches, using a multi-point current monitoring device to collect the current values ​​of non-main branches in real time. By comparing the load current of non-main branches with a preset load threshold, it is determined whether the non-main branches are underloaded. The load distribution balance is analyzed by combining the spatial location and temporal information of the local current surge area. During the operation, a current acquisition module and a data synchronization processing device are used to ensure the temporal consistency between the load data of non-main branches and the current surge data of main branches. The underload condition data of non-main branches is output to support subsequent current load imbalance determination.

[0042] The link current load imbalance is determined based on the insufficient load on the non-main branch and the sudden increase in the link main branch current. In this embodiment of the invention, the link current load imbalance is determined based on the insufficient load capacity of non-main branch circuits and the sudden increase in current in the main branch circuit. By fusing the current load states of the main and non-main branches, the deviation of the current distribution is calculated. Specifically, the current load ratio analysis method is used to normalize the current amplitude of each branch circuit and assess the balance of the load distribution. This method quantitatively describes the link current load imbalance by calculating statistical indicators of load current differences, such as standard deviation and maximum difference. The operation employs a current data processing unit and statistical analysis software to ensure the accurate calculation of the load imbalance data. This generates link current load imbalance data, which serves as the basis for locating bottleneck node segments.

[0043] Identify local bottleneck nodes in the transmission link based on the link current load imbalance. In this embodiment of the invention, local bottleneck nodes in the transmission link are identified based on the link current load imbalance. Combining link current load imbalance data with link topology information, a node current load distribution map is constructed, and a regional clustering analysis method is used to locate local nodes with abnormally concentrated loads. Specifically, the spatial clustering degree of current load anomalies is used to determine the boundaries and range of the bottleneck node segments. This process employs a multi-point current acquisition device, a node topology database, and spatial analysis tools to achieve accurate identification of bottleneck segments. Data on local bottleneck nodes in the transmission link is output, providing spatial location data for heat growth assessment.

[0044] The heat growth of the transmission link is determined based on the local bottleneck node section of the transmission link and the unbalanced link current load. In this embodiment of the invention, the heat growth status of the transmission link is determined based on the local bottleneck node segment and the unbalanced current load of the link. This step combines the current load data of the bottleneck node and applies thermodynamic calculation methods to estimate the heat generation of the corresponding segment. Specifically, the current load is converted into heat generation power, and the principles of energy conservation and heat conduction are used to calculate the heat energy accumulation in the bottleneck segment. Actual temperature data is collected using thermal sensors and verified in conjunction with the calculation results. This step relies on thermocouples or infrared temperature measurement equipment and a thermal energy calculation module to generate data on the heat growth status of the transmission link, reflecting the local heat energy accumulation.

[0045] Estimate the cumulative thermal effect of the transmission link cable based on the heat growth of the transmission link; In this embodiment of the invention, the cumulative thermal effect of the transmission link cable is estimated based on the heat growth status of the transmission link. By continuously monitoring the heat growth of the link and combining it with the thermophysical property parameters of the cable material, the temperature change and number of thermal cycles of the cable under thermal load are calculated. Specifically, a thermal stress analysis method is used to evaluate the cumulative impact of thermal effects on the cable material structure. This operation is based on temperature sensor data and a database of thermophysical property parameters, and uses numerical integration to calculate the cumulative heat load. The cumulative thermal effect data of the transmission link cable is output, reflecting the cumulative process of thermal damage to the cable.

[0046] The damage status of the transmission link cable joints is estimated based on the cumulative thermal effects of the transmission link cable. In this embodiment of the invention, the damage status of the transmission link cable joint is estimated based on the cumulative thermal effects of the transmission link cable. This step utilizes the cumulative thermal effect data, combined with the thermal expansion and mechanical stress characteristics of the joint structure, to assess the degree of fatigue damage at the joint. The damage factor is calculated by analyzing the temperature gradient and stress concentration in the joint area. Temperature and strain data at the joint are collected, and the joint damage status is quantified using damage mechanics theory. Dedicated sensors and strain gauges are used to complete data acquisition, outputting the damage status data of the transmission link cable joint, providing an important basis for determining the thermal degradation evolution state.

[0047] The thermal loss degradation evolution state of the transmission link is determined based on the damage status of the transmission link cable joints and the cumulative thermal effect of the transmission link cable.

[0048] In this embodiment of the invention, the thermal loss degradation evolution state of the transmission link is determined based on the damage status of the cable joints and the cumulative thermal effects of the transmission link cable. By integrating the data on cumulative cable thermal effects and joint damage, a multi-index fusion analysis technique is employed to form an overall assessment result of the thermal loss degradation. Specifically, a thermal loss degradation state index system is established, and different data are weighted to synthesize a comprehensive evaluation score. A data fusion device and a state assessment system are applied to achieve dynamic monitoring and judgment of the thermal loss degradation evolution of the transmission link. The thermal loss degradation evolution state data of the transmission link is output to support the maintenance and optimization decisions of the integrated energy management system.

[0049] Preferably, step S2, which monitors the load evolution trend of the energy decomposition device based on excessive data of energy supply and demand deviation and the thermal loss degradation evolution status of the transmission link, includes: The continuous operating status of the energy decomposition device is estimated based on large data on the energy supply and demand deviation. In this embodiment of the invention, the continuous operating status of the energy decomposition device is estimated based on large data of energy supply and demand deviation. Specifically, real-time operating data of the energy decomposition device in the integrated energy system is acquired, focusing on the device's start-up time, operating duration, and energy output parameters. Using the large data of energy supply and demand deviation, the operating time period of the decomposition device is divided using time series analysis to determine whether the device is in a continuous operating state. This analysis, based on time series data collected by the system and combined with the device's operating logs, employs a continuity judgment algorithm to accurately define the continuous operating range of the decomposition device, thereby obtaining continuous operating status data of the device as the basis for subsequent energy output statistics.

[0050] Based on the continuous operation status of the energy decomposition device and the excessive data of energy supply and demand deviation, the excessive energy output of the decomposition device is statistically analyzed to obtain the excessive energy output data of the decomposition device. In this embodiment of the invention, excessive energy output of the energy decomposition device is statistically analyzed based on the continuous operating status of the device and the large data on the energy supply-demand deviation, resulting in large data on the excessive energy output of the decomposition device. In this step, the total energy output and real-time power data of the decomposition device within the continuous operating period are extracted and compared with the actual demand of the integrated energy system. By establishing a statistical model of the difference between output and demand, the excess energy of the device in each time period is calculated. During data processing, a high-precision metering device records the energy flow, and statistical analysis methods are used to accumulate and calculate the excess energy, forming large data on the excessive energy output of the decomposition device. This data clearly reflects the degree of deviation between the device's energy output and the system's demand.

[0051] Determine abnormal energy output conditions of the decomposition device based on the thermal loss degradation evolution state of the transmission link; In this embodiment of the invention, abnormal energy output of the decomposition device is determined based on the thermal loss degradation evolution state of the transmission link. During implementation, the changes in transmission link efficiency are analyzed in conjunction with the monitoring results of the thermal loss degradation evolution state, with particular attention paid to the increased energy transmission loss caused by thermal loss. Abnormal energy output fluctuations are identified through real-time monitoring of energy flow at key nodes of the transmission link. Specifically, thermal imaging technology and power quality analysis equipment are used to simultaneously collect data on link temperature changes and voltage fluctuations. This data is then used to compare the output energy of the decomposition device with the actual transmitted energy of the link to determine the time period and magnitude of the output anomaly, thus obtaining data on the abnormal energy output of the decomposition device.

[0052] By using abnormal energy output of the decomposition unit, the excess energy produced by the decomposition unit can be estimated to determine the redundant energy accumulation status of the decomposition unit. In this embodiment of the invention, the excess energy output of the decomposition device is estimated using data on abnormal energy output to assess the redundant energy accumulation status of the device. By matching the abnormal energy output time period with the excess energy statistics, it is determined whether the abnormal output has led to the accumulation of redundant energy within the device or surrounding systems. This step employs an energy balance analysis method to dynamically compare the energy input, output, and system consumption of the decomposition device, identifying unutilized redundant energy. Through energy flow monitoring and energy storage device status detection, the accumulated redundant energy is calculated, forming redundant energy accumulation status data.

[0053] Determine the excessive pressure within the device cavity based on the redundant energy accumulation status of the decomposition device. In this embodiment of the invention, the excessive internal pressure of the device cavity is determined based on the redundant energy accumulation status of the decomposition device. In practice, the pressure changes within the device cavity caused by redundant energy are monitored, and pressure sensors are used to collect cavity pressure data in real time. By comparing the normal operating pressure range with the current pressure value, it is determined whether an overpressure phenomenon exists. A continuous pressure monitoring system and pressure change rate analysis are used to obtain the real-time status and trend of the internal pressure. Data on excessive internal pressure in the device cavity is output, providing a basis for subsequent energy reaction backflow analysis.

[0054] Estimate the high-pressure backflow of energy based on the redundant energy accumulation status of the decomposition device and the excessive internal pressure of the device cavity. In this embodiment of the invention, the high-pressure reaction backflow status is estimated based on the redundant energy accumulation status of the decomposition device and the excessive internal pressure within the device cavity. The redundant energy data is combined with the cavity pressure data, and the generation and propagation of the high-pressure reaction force are analyzed using fluid mechanics and energy transfer principles. Stress and pressure sensors installed at key structural locations are used to collect real-time data on changes in mechanical stress caused by the reaction backflow. Noise is removed using signal processing methods, and the characteristic signals of the reaction backflow are extracted. The amplitude and duration of the reaction backflow are calculated based on the sensor data to obtain the high-pressure reaction backflow status data.

[0055] Predicting the disorder of the original reaction process in the decomposition unit based on the high-pressure reaction backflow condition; In this embodiment of the invention, the disturbance of the original reaction process in the decomposition device is predicted based on the high-pressure reaction backflushing status. The backflushing data is compared with the parameters of the device under normal reaction conditions to analyze its impact on the stability of the reaction process. Abnormal fluctuations are monitored by setting thresholds for key parameters of the reaction process. High-frequency sampling equipment is used to collect data on the physical and chemical states of the reaction, and combined with changes in the backflushing signal, the timing and extent of the reaction process disturbance are inferred. This forms the original reaction process disturbance data, clarifying the cause and manifestation of the reaction anomaly.

[0056] Monitor the load evolution trend of the energy decomposition unit based on the disorder of the original reaction process and the backflow of high-pressure energy.

[0057] In this embodiment of the invention, the load evolution trend of the energy decomposition device is monitored based on the disorder of the original reaction process and the high-pressure backflow of energy. Combining the aforementioned data, time series analysis and trend extraction methods are used to depict the evolution trend of the device load over time. Load evolution trend data is generated by statistically analyzing the frequency, amplitude, and duration of load fluctuations. This data reflects the load variation pattern of the decomposition device under different operating conditions, providing an important basis for the operational optimization of the integrated energy management system.

[0058] Preferably, step S3 includes the following steps: Step S31: Determine the cumulative fatigue damage of the device components based on the load evolution trend of the energy decomposition device; In this embodiment of the invention, the cumulative fatigue damage of the energy splitting device components is determined based on the load evolution trend of the device. Specifically, load evolution trend data of the energy splitting device is acquired, including the magnitude, frequency, and duration of load changes over different time periods. Cumulative damage analysis is performed on the load change data using the material performance parameters and fatigue life curves of the device components. Stress-strain states of key components are collected using strain gauges and vibration sensors, and combined with historical operating data, a cumulative fatigue damage calculation method based on Miner's rule is used to quantify the degree of fatigue damage in each key component. This analysis generates data on the cumulative fatigue damage of the device components, reflecting the damage accumulation during the fatigue process under load, and providing fundamental data for subsequent performance evaluation.

[0059] Step S32: Determine the degradation of the device's decomposition response performance based on the cumulative fatigue damage of device components and the load evolution trend of the energy decomposition device; In this embodiment of the invention, the degradation of the device's decomposition response performance is determined based on the cumulative fatigue damage of device components and the load evolution trend of the energy decomposition device. In practice, the cumulative fatigue damage data of device components is correlated with the load evolution trend, with a focus on examining the impact of fatigue damage on the device's response speed, response stability, and response amplitude. Dynamic performance testing equipment is used to collect the device's response parameters under different load conditions, including output power fluctuations, response time delays, and output stability indicators. By comparing the response data over different time periods and combining it with fatigue damage indicators, the degree of degradation of the device's decomposition response performance is calculated. Data on the degradation of the device's decomposition response performance is generated, clarifying the specific manifestations and trends of performance decline.

[0060] Step S33: Monitor the energy efficiency conversion degradation status of the decomposition device based on the decomposition response performance degradation of the device; In this embodiment of the invention, the energy efficiency conversion degradation state of the decomposition device is monitored based on the degradation of the device's decomposition response performance. Specific operations include collecting actual energy input and output data of the device and calculating the device's energy efficiency conversion ratio. By comparing the energy efficiency data under normal operating conditions with the current operating data, the magnitude and duration of energy efficiency reduction are identified. Combined with the decomposition response performance degradation data, the inherent correlation of energy efficiency reduction is analyzed, and energy loss points are located using energy balance analysis. High-precision electrical energy metering instruments and heat flow measurement equipment are used to acquire real-time data on input and output energy, forming energy efficiency conversion degradation state data of the decomposition device, reflecting the dynamic changes in the device's energy conversion capability.

[0061] Step S34: Monitor the abnormality of the multi-energy synergistic decomposition ratio based on the energy efficiency conversion degradation status of the decomposition device.

[0062] In this embodiment of the invention, abnormalities in the multi-energy synergistic decomposition ratio are monitored based on the energy efficiency conversion degradation state of the decomposition device. During implementation, real-time flow and ratio data of various energy inputs and outputs, including electrical energy, thermal energy, and gaseous energy, are collected based on the device's energy efficiency conversion degradation state. A benchmark for the multi-energy synergistic decomposition ratio is established, compared with the current actual decomposition ratio, and anomalies are identified using a ratio deviation analysis method. Multi-channel flow meters and energy metering equipment are used to accurately measure the flow changes of each energy source, generating multi-energy synergistic decomposition ratio data. Based on this data, it is determined whether there are abnormal fluctuations or deviation trends in the decomposition ratio, generating multi-energy synergistic decomposition ratio anomaly data, providing data support for the adjustment and optimization of the integrated energy management system.

[0063] Preferably, step S31 includes the following steps: Step S311: Estimate the cross-effect of structural thermal and electrical loads based on the load evolution trend of the energy decomposition device; In this embodiment of the invention, the cross-interaction of thermal and electrical loads on a structure is estimated based on the load evolution trend of an energy splitting device. Specifically, load evolution trend data of the energy splitting device is acquired, including changes in the electrical load of the device over different time periods and changes in the thermal load on the device's surface and interior. Multi-channel current sensors are used to measure the dynamic changes in the electrical load, and thermocouples and infrared thermal imaging equipment are used to monitor temperature changes on the structural surface and key components. Combining the load data and temperature data, a cross-correlation analysis method is used to assess the cross-interaction of thermal and electrical loads on the structure. This process generates data on the cross-interaction of thermal and electrical loads on the structure, providing a foundation for subsequent structural condition assessment.

[0064] Step S312: Detect the local yielding condition of the connecting structure based on the cross-action of thermal and electrical loads; In this embodiment of the invention, the local yielding condition of the connecting structure is detected based on the cross-effect of thermal and electrical loads. Specifically, strain gauges, acoustic emission sensors, and digital image correlation (DIR) technology are used to monitor key nodes and weak points of the connecting structure in real time. Strain gauges collect local strain data, acoustic emission sensors capture acoustic signals generated by material yielding, and DIR technology is used to analyze the initiation and propagation of microcracks. Combining the thermal-electrical load cross-effect data, yielding criteria are used to identify the local yielding state of the connecting structure, determine the existence of yielding zones, and generate local yielding condition data for the connecting structure.

[0065] Step S313: Determine the metal crack growth status of the device structure based on the local yielding condition of the connection structure and the cross-action of thermal and electrical loads on the structure. In this embodiment of the invention, the crack growth status of the device structure's metal is determined based on the local yielding condition of the connecting structure and the interaction of thermal and electrical loads. In practice, an ultrasonic testing instrument and magnetic particle testing equipment are used to detect cracks in the connecting structure and its adjacent areas. The ultrasonic testing instrument identifies the location and length of internal cracks by sending and receiving high-frequency sound waves, while the magnetic particle testing instrument detects surface and near-surface cracks by magnetizing the structural surface. Combining the aforementioned local yielding data and thermal-electrical load data, crack growth rate and trend are calculated using crack propagation theory, generating data on the crack growth status of the device structure's metal, thus clarifying the degree of crack development and propagation rate.

[0066] Step S314: When the growth of the metal crack in the device structure exceeds 4.8 mm, check the aging and failure status of the device seals; In this embodiment of the invention, when the crack growth in the device structure metal exceeds 4.8 mm, the aging and failure status of the device's seals is detected. In this step, the threshold is determined based on the crack length data. If the threshold is exceeded, the hardness, elastic modulus, and sealing effect of the seal material are tested using a seal performance testing device. The mechanical property degradation of the seal is determined using a tensile testing machine and a compression testing machine, and changes in sealing performance are detected using an airtightness tester. The surface of the seal is observed using a scanning electron microscope to identify microscopic aging characteristics. Combined with the test results, data on the aging and failure status of the device's seals is generated.

[0067] Step S315: Determine the cumulative fatigue damage of the device components based on the aging and failure status of the device seals and the growth status of metal cracks in the device structure.

[0068] In this embodiment of the invention, the cumulative fatigue damage of device components is determined based on the aging and failure status of device seals and the crack growth status of device structural metals. Specifically, aging data of seals and crack data of structures are comprehensively analyzed, and the fatigue condition of the overall device components is evaluated using fatigue damage accumulation theory. By quantitatively analyzing the impact of seal performance degradation on the overall sealing performance and operational stability of the device, and combining crack propagation rate and distribution, the degree of fatigue damage accumulation is determined to form fatigue damage accumulation data for device components, providing data support for device maintenance and energy management strategies.

[0069] Of particular importance, step S2 includes the following steps: Step S321: Determine the stiffness decay of the device components based on the cumulative fatigue damage of the device components and the load evolution trend of the energy decomposition device; In this embodiment of the invention, during long-term monitoring of the energy splitting device's operating status, a fatigue damage accumulation layer is constructed by periodically collecting stress-strain response data, dynamic deformation data, and microcrack evolution maps of the device components. This layer covers all load-bearing parts, including the frame structure, connecting flanges, support arms, and thermoelectric conversion interfaces. Combining the load evolution trend data of the energy splitting device, especially the load peak change sequence under high-frequency, high-amplitude loading conditions, and comparing historical operating conditions with structural response data, the elastic recovery rate and hysteresis loss ratio within each monitoring cycle are extracted. A component stiffness time series curve is constructed using a displacement stiffness function, and the linear decrease ratio of load corresponding to unit displacement is calibrated, thus forming a component stiffness attenuation curve to obtain the device component stiffness attenuation data, represented as the "structural stiffness attenuation distribution layer," which serves as input data for subsequent identification of the deterioration diffusion state.

[0070] Step S322: Estimate the degradation and diffusion of the energy decomposition device based on the stiffness decay of the device components; In this embodiment of the invention, based on the "structural stiffness attenuation distribution layer" obtained in step S321, areas where the stiffness reduction rate increases significantly are extracted, such as thermal stress concentration areas, weld perimeters, bolt fixing nodes, and weak areas of the cavity. Using a region topology mapping method, these high-risk stiffness attenuation points are marked with spatial grid encoding and multidimensionally overlaid with historical thermal cycle frequency distributions and vibration acceleration sequences. By performing time-extended analysis on the stiffness change trends in the grid cells, degradation propagation paths are identified, and a "component degradation spatial diffusion map" is constructed based on the diffusion boundary change law. The output is "energy decomposition device degradation diffusion status data," characterizing the range and expansion trend of mechanical performance degradation within the entire device.

[0071] Step S323: Predict the sensor abnormality of the energy decomposition device based on the deterioration and diffusion status of the energy decomposition device; In this embodiment of the invention, the structural risk level of the sensor installation area is screened using the "deterioration and diffusion status data of the energy decomposition device" generated in step S322, including the components to which components such as temperature probes, pressure sensors, and current detection terminals are attached. The sensor node locations covered in the degradation map are compared to extract their corresponding stiffness degradation level and local thermal loss evolution state. Then, based on the degradation matrix of the sensor thermal-load coupling region, the stability fluctuation amplitude in the signal transmission path is detected. Combined with signal drift data and delay response data, time alignment is performed to extract the sensor response offset time series, obtaining sensor anomaly early warning data for the energy decomposition device, which is used for performance degradation analysis in the next step.

[0072] Step S324: Determine the degradation of the device's decomposition response performance based on the abnormal sensing conditions and the deterioration and diffusion status of the energy decomposition device.

[0073] In this embodiment of the invention, the sensor anomaly early warning data and the energy decomposition device degradation and diffusion status data are fused at the parameter level. The sensor anomaly amplitude is mapped to the topological location of the degraded node according to spatial correspondence, and high-degradation-high-anomaly response coupling segments are selected. Subsequently, the energy input response hysteresis value and output amplitude reduction ratio are extracted from these segments to establish a three-segment link feature table of input-response-sensor. A comprehensive evaluation is performed based on indicators such as response amplitude reduction, response time delay, and frequency adjustment mismatch in the energy efficiency transfer chain to form a device decomposition response performance degradation status layer, which is the device decomposition response performance degradation data result required in step S324.

[0074] Of particular importance, step S33 includes the following steps: Step S331: Determine the lag in the response speed of the decomposition device based on the degradation of the device's decomposition response performance; In this embodiment of the invention, based on the "device decomposition response performance degradation" data obtained in the previous stage, a sequence of time response indicators for the reaction components is extracted. This sequence mainly includes the input energy excitation time, the decomposition chamber reaction start-up delay, the initial slope of the thermal response heating curve, and the output energy flow stabilization time node. In specific operation, a high-frequency data acquisition module is used to timestamp the main energy conversion nodes within the device at the millisecond level, recording the corresponding heat current density, chamber pressure growth rate, and output voltage formation time at each time point. By calculating the time difference between the input energy and the output energy and establishing a "response delay matrix," the degree of device response speed lag is quantified. The output data is named "response speed lag data," which serves as the basis for subsequent energy fluctuation identification.

[0075] Step S332: Monitor the instantaneous energy fluctuation at the output end based on the lag in the response speed of the decomposition device; In this embodiment of the invention, based on the "response speed lag data" generated in step S331, real-time recorded data of electrical energy, current, and heat flow at the device output are retrieved, and a 10ms resolution time window is used to scan the power output curve for fluctuations. In actual operation, an instantaneous current sensor, an infrared thermal radiation recorder, and a pressure-capacitance detection head deployed at the device end are used to synchronously acquire the fluctuation amplitude and frequency change curves of the output energy from 5 seconds before startup to 10 seconds after stable operation. The fluctuation data is aligned and analyzed with the response speed lag time to screen out abnormal fluctuation peak segments caused by response mismatch. The three output characteristics of heat, electricity, and pressure are combined to form "instantaneous fluctuation data of output energy," which is used as input for energy loss calculation and efficiency degradation judgment.

[0076] Step S333: Estimate the energy loss growth based on the instantaneous energy fluctuation at the output end; In this embodiment of the invention, the output power curve is integrally compared and analyzed based on "instantaneous energy fluctuation data at the output end." During execution, the energy output deviation per unit time is constructed by comparing the theoretical output power trajectory of the device (determined by the device nameplate output and operating temperature range) with the actual output power value curve. This deviation value is then segmented and statistically analyzed to extract the ineffective energy transfer portion caused by the early output fluctuations due to response lag. This is combined with the cavity energy dissipation output from the heat loss rate measurement module to form energy loss growth data. This data directly reflects the energy waste caused by control response misalignment during device operation.

[0077] Step S333: Determine the reduction in energy conversion efficiency of the device based on the lag in the response speed of the decomposition device; In this embodiment of the invention, the "response speed lag data" in step S331 is used to compare the ratio of the device's input energy to its effective output energy within the same operating cycle. The input energy flow data (including thermal input and mechanical drive input) is integrated with the output electrical and thermal energy effective load curves. An "instantaneous conversion efficiency curve" construction method is employed to extract the energy conversion ratio change trend with second-level precision and identify characteristic segments where conversion efficiency drops sharply during the response lag period. This method determines the efficiency decline region in the overall efficiency curve caused by response lag, calculates the efficiency loss index per unit time, and outputs energy conversion efficiency reduction data, providing a basic input for subsequent comprehensive device performance degradation assessment.

[0078] Step S335: Monitor the energy conversion degradation status of the decomposition unit based on the decrease in energy conversion efficiency and the increase in energy loss.

[0079] In this embodiment of the invention, the energy loss growth data generated in step S333 and the energy conversion efficiency reduction data obtained in step S334 are cross-integrated. Synchronization is performed along a time axis, and time periods with overlapping fluctuations in the two types of data are focused on, extracting segments where high energy consumption growth and drastic efficiency reduction occur simultaneously as feature index areas. A multi-dimensional mapping table is constructed by combining the device operating temperature, cavity pressure, and electrical load within these feature segments, and these are identified as energy efficiency conversion degradation segment layers. The resulting decomposed device energy efficiency conversion degradation state data serves as the core basis for judging the degree of degradation in the device's overall energy efficiency level.

[0080] Preferably, step S4 includes the following steps: Step S41: Estimate the overload status of carbon emissions from energy decomposition based on the abnormal situation of multi-energy synergistic decomposition ratio; In this embodiment of the invention, the carbon emission overload status of energy decomposition is estimated based on abnormal conditions of multi-energy synergistic decomposition ratios. Specifically, data on abnormal multi-energy synergistic decomposition ratios are acquired, reflecting deviations in the proportions of different energy types within the decomposition device from the normal range. An online gas analyzer is used to continuously monitor the gas composition emitted from the decomposition device, measuring the carbon emission concentration and its changing trend. Using a carbon emission calculation model, the abnormal multi-energy synergistic decomposition ratio data is combined with the carbon emission concentration data to estimate the carbon emission overload status of the decomposition device. This estimation process establishes a correspondence between carbon emissions and energy decomposition ratios, generating energy decomposition carbon emission overload status data, providing a basis for subsequent anomaly assessment.

[0081] Step S42: Determine the abnormal operating status of the energy device based on the overload state of carbon emissions from energy decomposition and the abnormal situation of the multi-energy synergistic decomposition ratio; In this embodiment of the invention, abnormal operating conditions of energy devices are determined based on the overload status of carbon emissions from energy decomposition and the abnormality of the multi-energy synergistic decomposition ratio. Specifically, data on the overload status of carbon emissions from energy decomposition and the abnormality of the multi-energy synergistic decomposition ratio are input into the energy device operating anomaly identification module. This module determines whether an operating anomaly exists by defining thresholds and rules for operating anomalies and combining multi-dimensional characteristics of carbon emission exceedances and energy decomposition ratio deviations. This process involves multi-parameter fusion analysis, combining time-series data and statistical characteristics to obtain data on the abnormal operating conditions of energy devices, providing an accurate basis for adjusting energy management strategies.

[0082] Step S43: Based on the integrated energy management system, perform energy management strategy generation and processing on the abnormal operation status of energy devices to obtain energy management strategy data; In this embodiment of the invention, an energy management strategy is generated based on an integrated energy management system to address abnormal operating conditions of energy devices, resulting in energy management strategy data. During implementation, the abnormal operating condition data of the energy devices is input into the strategy generation module of the integrated energy management system. This module uses a rule engine and decision tree algorithm to generate targeted strategies for the abnormal conditions. The strategies include adjusting the energy decomposition ratio, optimizing load allocation, and implementing carbon emission limits. The generated energy management strategy data specifically describes the adjusted parameters and operational steps, forming an executable management scheme and providing a data foundation for subsequent strategy testing and execution.

[0083] Step S44: Conduct an energy management strategy defect assessment test based on the energy management strategy data to obtain energy management strategy defect data; In this embodiment of the invention, energy management strategy defect assessment tests are conducted based on energy management strategy data to obtain energy management strategy defect data. Specifically, a simulation platform is used to simulate and test the energy management strategy, with the simulation environment based on actual energy operation data and equipment characteristics. By comprehensively analyzing indicators such as the time-series changes in system response after strategy execution, the stability of electricity and heat supply, and changes in carbon emissions, the defective performance of the strategy in different operating scenarios is identified. Test results include response delay, insufficient regulation, and abnormal triggering frequency, forming energy management strategy defect data, providing a basis for strategy optimization.

[0084] Step S45: Optimize the integrated energy management system based on the energy management strategy defect data to obtain optimized integrated energy management system data.

[0085] In this embodiment of the invention, the integrated energy management system is optimized based on energy management strategy defect data to obtain optimized integrated energy management system data. Specifically, an iterative optimization method is used to adjust the defective parts of the energy management strategy. Based on the defect data, the rule parameters and decision thresholds of the strategy generation module are modified to improve the strategy's adaptability and execution efficiency. The optimization process employs a system feedback mechanism, verifying the optimization results through a monitoring system to ensure that the optimized strategy meets the expected operational requirements, thus forming the optimized integrated energy management system data. This data includes improved strategy parameters and their corresponding performance indicators, supporting the continuous and stable operation of the system.

[0086] The present invention also provides a test system for an integrated energy management system, used to perform the test method for the integrated energy management system as described above, wherein the test of the integrated energy management system includes: The supply-demand deviation assessment module is used to acquire energy management system operation data; determine comprehensive energy time-series supply and demand data based on the energy management system operation data; and assess excessive comprehensive energy supply-demand deviation data based on the comprehensive energy time-series supply and demand data. The device load evolution monitoring module is used to monitor the abnormal dynamic load of the transmission link based on the large data of energy supply and demand deviation; determine the thermal loss degradation evolution status of the transmission link based on the abnormal dynamic load of the transmission link; and monitor the load evolution trend of the energy decomposition device based on the large data of energy supply and demand deviation and the thermal loss degradation evolution status of the transmission link. The energy co-decomposition anomaly monitoring module is used to determine the cumulative fatigue damage of the device components based on the load evolution trend of the energy decomposition device; determine the degradation of the device's decomposition response performance based on the cumulative fatigue damage of the device components and the load evolution trend of the energy decomposition device; and monitor the anomaly of the multi-energy co-decomposition ratio based on the degradation of the device's decomposition response performance. The integrated energy management system optimization module is used to determine abnormal operating conditions of energy devices based on abnormal multi-energy synergistic decomposition ratios; generate energy management strategies based on the abnormal operating conditions of energy devices using the integrated energy management system to obtain energy management strategy data; conduct defect assessment tests on energy management strategies based on the energy management strategy data to obtain energy management strategy defect data; and optimize the integrated energy management system based on the energy management strategy defect data to obtain integrated energy management system optimization data.

[0087] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A test method for an integrated energy management system, characterized in that, Includes the following steps: Step S1: Obtain the operating data of the energy management system; determine the integrated energy time-series supply and demand data based on the operating data of the energy management system; Based on comprehensive energy time-series supply and demand data, assess excessively large deviations in comprehensive energy supply and demand; Step S2: Monitor the abnormal dynamic load of the transmission link based on the large data of energy supply and demand deviation; determine the thermal loss degradation evolution status of the transmission link based on the abnormal dynamic load of the transmission link; monitor the load evolution trend of the energy decomposition device based on the large data of energy supply and demand deviation and the thermal loss degradation evolution status of the transmission link. Step S3: Determine the cumulative fatigue damage of the device components based on the load evolution trend of the energy decomposition device; The degradation of the device's decomposition response performance is determined based on the cumulative fatigue damage of device components and the load evolution trend of the energy decomposition device. Monitor abnormalities in the multi-energy synergistic decomposition ratio based on the degradation of the device's decomposition response performance. Step S4: Determine the abnormal operating status of energy devices based on the abnormal situation of multi-energy synergistic decomposition ratio; Based on the integrated energy management system, energy management strategies are generated and processed to address abnormal operating conditions of energy devices, resulting in energy management strategy data. Based on the energy management strategy data, defect assessment tests are conducted to obtain energy management strategy defect data. Based on the data on the defects in the energy management strategy, the integrated energy management system is optimized to obtain optimized data for the integrated energy management system.

2. The test method for the integrated energy management system according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain the operating data of the energy management system; Step S12: Perform energy management system operation data preprocessing on the energy management system operation data to obtain energy management system operation preprocessing data; Step S13: Determine the integrated energy time-series supply and demand data based on the preprocessing data of the energy management system; Step S14: Assess the excessive size of the comprehensive energy supply and demand deviation based on the comprehensive energy time series supply and demand data.

3. The test method for the integrated energy management system according to claim 2, characterized in that, Step S14 includes the following steps: Step S141: Divide the comprehensive energy time series supply and demand data into energy types to obtain multi-type energy time series supply and demand data, which includes electricity, heat, and gas energy. Step S142: Calculate the supply and demand difference of multiple types of energy based on the time series supply and demand data of multiple types of energy to obtain the supply and demand difference data of multiple types of energy; Step S143: Set a time series sliding window of fixed length, and use the time series sliding window to segment the energy supply and demand difference data of multiple types to obtain the energy supply and demand difference sliding window matrix; Step S144: Analyze the fluctuation of energy supply and demand deviation based on the sliding window matrix of energy supply and demand difference; Step S145: Extract trend features of multiple types of energy deviations based on the fluctuation of energy supply and demand deviations; Step S146: When the trend characteristics of the multi-type energy deviation exceed ±50kW·min, identify the energy supply and demand deviation clustering segment; Step S147: Identify high-fluctuation energy supply and demand data based on the trend characteristics of various energy deviations; Step S148: Evaluate the comprehensive energy supply and demand deviation data based on the time data of high fluctuations in energy supply and demand and the energy supply and demand deviation clusters.

4. The test method for the integrated energy management system according to claim 1, characterized in that, Step S2, which involves monitoring abnormal dynamic load conditions of transmission links based on excessive energy supply and demand deviation data, includes: Based on large data on energy supply and demand deviations, determine the transmission link control rhythm disturbance data; Based on the data of transmission link control rhythm disturbance and the large data of energy supply and demand deviation, the amplitude of transmission link control interference was enhanced, and the trend of the amplitude of link control interference was obtained. Monitor abnormal electrical transmission conditions of the link based on the increasing trend of link control interference amplitude and the disturbance data of transmission link control rhythm; The link mechanical load fluctuation status is determined based on the increasing trend of link control interference amplitude; Based on the link mechanical load fluctuation and link electrical transmission anomaly, multi-physics response curve fitting is performed to obtain link multi-physics coupling response data; Identify local stress concentration points in the link based on multi-physics coupling response data; Estimate the growth of voltage fluctuations in the transmission link based on abnormal electrical transmission conditions. Monitor the dynamic load anomalies of the transmission link based on the growth of transmission link voltage fluctuations and data on local stress concentration points in the link.

5. The test method for the integrated energy management system according to claim 1, characterized in that, Step S2, determining the thermal loss degradation evolution state of the transmission link based on the abnormal dynamic load conditions of the transmission link, includes: Determine the sudden increase in current in the main branch of the link based on the abnormal dynamic load of the transmission link. Detect the insufficient load capacity of non-main branch circuits based on abnormal dynamic load conditions and sudden increases in local current in the transmission link. The link current load imbalance is determined based on the insufficient load on the non-main branch and the sudden increase in the link main branch current. Identify local bottleneck nodes in the transmission link based on the link current load imbalance. The heat growth of the transmission link is determined based on the local bottleneck node section of the transmission link and the unbalanced link current load. Estimate the cumulative thermal effect of the transmission link cable based on the heat growth of the transmission link; The damage status of the transmission link cable joints is estimated based on the cumulative thermal effects of the transmission link cable. The thermal loss degradation evolution state of the transmission link is determined based on the damage status of the transmission link cable joints and the cumulative thermal effect of the transmission link cable.

6. The test method for the integrated energy management system according to claim 1, characterized in that, Step S2 involves monitoring the load evolution trend of the energy decomposition device based on excessive data of energy supply and demand deviation and the thermal loss degradation evolution status of the transmission link, including: The continuous operating status of the energy decomposition device is estimated based on large data on the energy supply and demand deviation. Based on the continuous operation status of the energy decomposition device and the excessive data of energy supply and demand deviation, the excessive energy output of the decomposition device is statistically analyzed to obtain the excessive energy output data of the decomposition device. Determine abnormal energy output conditions of the decomposition device based on the thermal loss degradation evolution state of the transmission link; By using abnormal energy output of the decomposition unit, the excess energy produced by the decomposition unit can be estimated to determine the redundant energy accumulation status of the decomposition unit. Determine the excessive pressure within the device cavity based on the redundant energy accumulation status of the decomposition device. Estimate the high-pressure backflow of energy based on the redundant energy accumulation status of the decomposition device and the excessive internal pressure of the device cavity. Predicting the disorder of the original reaction process in the decomposition unit based on the high-pressure reaction backflow condition; Monitor the load evolution trend of the energy decomposition unit based on the disorder of the original reaction process and the backflow of high-pressure energy.

7. The test method for the integrated energy management system according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Determine the cumulative fatigue damage of the device components based on the load evolution trend of the energy decomposition device; Step S32: Determine the degradation of the device's decomposition response performance based on the cumulative fatigue damage of device components and the load evolution trend of the energy decomposition device; Step S33: Monitor the energy efficiency conversion degradation status of the decomposition device based on the decomposition response performance degradation of the device; Step S34: Monitor the abnormality of the multi-energy synergistic decomposition ratio based on the energy efficiency conversion degradation status of the decomposition device.

8. The test method for the integrated energy management system according to claim 7, characterized in that, Step S31 includes the following steps: Step S311: Estimate the cross-effect of structural thermal and electrical loads based on the load evolution trend of the energy decomposition device; Step S312: Detect the local yielding condition of the connecting structure based on the cross-action of thermal and electrical loads; Step S313: Determine the metal crack growth status of the device structure based on the local yielding condition of the connection structure and the cross-action of thermal and electrical loads on the structure. Step S314: When the growth of the metal crack in the device structure exceeds 4.8 mm, check the aging and failure status of the device seals; Step S315: Determine the cumulative fatigue damage of the device components based on the aging and failure status of the device seals and the growth status of metal cracks in the device structure.

9. The test method for the integrated energy management system according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Estimate the overload status of carbon emissions from energy decomposition based on the abnormal situation of multi-energy synergistic decomposition ratio; Step S42: Determine the abnormal operating status of the energy device based on the overload state of carbon emissions from energy decomposition and the abnormal situation of the multi-energy synergistic decomposition ratio; Step S43: Based on the integrated energy management system, perform energy management strategy generation and processing on the abnormal operation status of energy devices to obtain energy management strategy data; Step S44: Conduct an energy management strategy defect assessment test based on the energy management strategy data to obtain energy management strategy defect data; Step S45: Optimize the integrated energy management system based on the energy management strategy defect data to obtain optimized integrated energy management system data.

10. A testing system for an integrated energy management system, characterized in that, The test method for performing the integrated energy management system as described in claim 1, wherein the test of the integrated energy management system includes: The supply-demand deviation assessment module is used to acquire energy management system operation data; determine comprehensive energy time-series supply and demand data based on the energy management system operation data; and assess excessive comprehensive energy supply-demand deviation data based on the comprehensive energy time-series supply and demand data. The device load evolution monitoring module is used to monitor the abnormal dynamic load of the transmission link based on the large data of energy supply and demand deviation; determine the thermal loss degradation evolution status of the transmission link based on the abnormal dynamic load of the transmission link; and monitor the load evolution trend of the energy decomposition device based on the large data of energy supply and demand deviation and the thermal loss degradation evolution status of the transmission link. The energy co-decomposition anomaly monitoring module is used to determine the cumulative fatigue damage of the device components based on the load evolution trend of the energy decomposition device; determine the degradation of the device's decomposition response performance based on the cumulative fatigue damage of the device components and the load evolution trend of the energy decomposition device; and monitor the anomaly of the multi-energy co-decomposition ratio based on the degradation of the device's decomposition response performance. The integrated energy management system optimization module is used to determine abnormal operating conditions of energy devices based on abnormal multi-energy synergistic decomposition ratios; generate energy management strategies based on the abnormal operating conditions of energy devices using the integrated energy management system to obtain energy management strategy data; conduct defect assessment tests on energy management strategies based on the energy management strategy data to obtain energy management strategy defect data; and optimize the integrated energy management system based on the energy management strategy defect data to obtain integrated energy management system optimization data.

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