Multi-element coupling heat supply monitoring system based on meteorological condition prediction model

Abstract

The invention discloses a multi-element coupling heat supply monitoring system based on a meteorological condition prediction model, belongs to an industrial control system, realizes deep coupling of meteorological data and an equipment operation state, and solves a fusion error problem caused by fixed meteorological element weight in traditional monitoring to a certain extent. By establishing the efficiency evaluation model containing the environment compensation mechanism, the interference of external temperature fluctuation on equipment performance evaluation is eliminated, and the accuracy of the operation efficiency index is improved. A multi-dimensional health assessment system is adopted to integrate equipment maintenance data and real-time operation parameters, potential fault risks can be recognized in advance, and data support is provided for equipment preventive maintenance.

Description

TECHNICAL FIELD

[0001] The present application relates to an industrial control system, in particular to a multi-element coupled heat supply monitoring system based on a meteorological condition prediction model. BACKGROUND

[0002] With the acceleration of urbanization, the scale of heat supply systems is expanding, and traditional heat supply monitoring methods are facing new challenges. Related technologies mainly rely on manual experience or simple control logic for heat supply adjustment, which is difficult to cope with complex and variable meteorological conditions and diversified user demands.

[0003] Current heat supply systems generally have the problem of insufficient utilization of meteorological data. Most systems only use temperature as a single parameter for load prediction, without fully considering the comprehensive influence of humidity, wind speed, sunshine and other multi-dimensional meteorological elements. At the same time, the system lacks the ability to optimize multiple heat supply equipment, and air source heat pumps, electrode boilers and heat storage equipment often operate independently, unable to achieve optimal coupling control of energy efficiency.

[0004] These limitations result in low operating efficiency of the heat supply system and serious energy waste. SUMMARY

[0005] The embodiments of the present application provide a multi-element coupled heat supply monitoring system based on a meteorological condition prediction model, which can improve the operating efficiency of the heat supply system, realize multi-device collaborative optimization and enhance the system's early warning capability. The technical solution is as follows: A multi-element coupled heat supply monitoring system based on a meteorological condition prediction model is provided, the system includes a processor and a memory, the processor is used to execute the following steps: Obtain multi-source meteorological monitoring data and heat supply system operation data of a heat supply area, the multi-source meteorological monitoring data includes real-time meteorological observation data, meteorological forecast data and historical meteorological data, the heat supply system operation data includes device operation parameters, pipe network operation state and user heat consumption data; based on the multi-source meteorological monitoring data and the heat supply system operation data, generate multi-modal meteorological situation data and device operation state characterization parameters, the multi-modal meteorological situation data includes temperature field distribution data, humidity field distribution data, wind speed field distribution data and solar radiation intensity distribution data, the device operation state characterization parameters include device operation efficiency indicators, device energy consumption characteristic parameters and device health state evaluation data; based on the multi-modal meteorological situation data and the device operation state characterization parameters, determine the operation state evaluation result and the fault warning information of the heat supply system, the operation state evaluation result includes energy efficiency evaluation indicators, device health state evaluation and system stability analysis, the fault warning information includes device fault warning, pipe network abnormal warning and system risk warning.

[0006] From the above, the application provides a multi-element coupled heat supply monitoring system and system based on a weather condition prediction model, which generates multi-modal weather situation data by fusing multi-source weather data and equipment operation parameters, and realizes dynamic optimization control of the heat supply system in combination with equipment state evaluation, to a certain extent, solves the problems of insufficient use of weather data and low equipment collaborative efficiency of traditional methods, and has the advantages of improving the operation efficiency of the heat supply system, realizing multi-device collaborative optimization and enhancing the system early warning capability. BRIEF DESCRIPTION OF DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0008] Figure 1 is a structural schematic diagram of a multi-element coupled heat supply monitoring system based on a weather condition prediction model provided by the embodiments of the present application; Figure 2 is a flowchart of the steps executed by the processor provided by the embodiments of the present application; Figure 3 is another flowchart of the steps executed by the processor provided by the embodiments of the present application; Figure 4 is still another flowchart of the steps executed by the processor provided by the embodiments of the present application; Figure 5 is still another flowchart of the steps executed by the processor provided by the embodiments of the present application; Figure 6 is still another flowchart of the steps executed by the processor provided by the embodiments of the present application; DETAILED DESCRIPTION

[0009] In order to make the purpose, technical solutions and advantages of the present application more clear, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0010] In the present application, the terms "first", "second", etc. are used to distinguish the same or similar items with basically the same function, and it should be understood that there is no logical or time sequence relationship between "first", "second", "nth", and the number and execution order are not limited.

[0011] Artificial intelligence (Artificial Intelligence, AI) is to use digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive environment, acquire knowledge and use knowledge to obtain better results.

[0012] Normalization: mapping a series of values with different ranges to the interval (0, 1) for the convenience of data processing. In some cases, the normalized values can be directly implemented as probabilities.

[0013] Embedded coding: Embedded coding represents a corresponding relationship in mathematics, that is, mapping data on X space to Y space through a function F, where the function F is a single function, and the mapping result is structure preserving. The single function means that the mapped data is uniquely corresponding to the pre-mapped data, and the structure preserving means that the size relationship of the pre-mapped data is the same as that of the mapped data, for example, there are data X1 and X2 before mapping, and Y1 corresponding to X1 and Y2 corresponding to X2 are obtained after mapping. If the data X1 > X2 before mapping, then the data Y1 after mapping is greater than Y2 accordingly. For words, it is to map words to another space for subsequent machine learning and processing.

[0014] Attention weight: can represent the importance of certain data in the training or prediction process. Importance represents the size of the influence of input data on output data. The data with high importance has a higher value of the corresponding attention weight, and the data with low importance has a lower value of the corresponding attention weight. In different scenarios, the importance of data is not the same, and the process of training attention weight of the model is also the process of determining the importance of data.

[0015] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.) and signals involved in the present application are authorized by the user or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0016] Figure 1 Fig. 1 is a structural schematic diagram of a system provided by an embodiment of the present application. The system 100 can have great differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPUs) 101 and one or more memories 102. The one or more memories 102 store at least one computer program, which is loaded and executed by the one or more processors 101 to implement the method provided by each method embodiment described above. Of course, the system 100 can also have a wired or wireless network interface, a keyboard, an input and output interface, and other components for realizing the functions of the device, and the details are not described here.

[0017] In the related art, the traditional heating monitoring system mainly relies on manual experience or a single temperature parameter for load prediction, lacking comprehensive consideration of multi-dimensional meteorological elements such as humidity, wind speed, and sunshine. Existing methods usually run various heating equipment independently, making it difficult to achieve collaborative optimization between devices, resulting in low energy utilization efficiency. For example, in the northern winter heating scenario, when strong wind weather occurs, the traditional system cannot timely adjust the pipe network pressure parameters, causing heat loss and the risk of device overload.

[0018] To solve the above problems, the inventors found that the root cause of low heating system efficiency lies in one-sided use of meteorological data and lack of device collaborative control. By analyzing the coupling relationship between meteorological elements and heat load, the inventors proposed to dynamically associate multi-dimensional meteorological data with device state parameters. Further research on the influence mechanism of meteorological field distribution characteristics on pipe network heat conduction, a multi-modal data fusion model is constructed. To solve the problem of device collaborative optimization, a linkage mechanism of energy efficiency evaluation and fault warning is established, forming a closed-loop control system.

[0019] Therefore, the present application proposes a multi-element coupling heating monitoring system based on a meteorological condition prediction model, which comprises a processor and a memory, as shown in Figure 2 The processor can perform the following steps.

[0020] 201. Obtain multi-source meteorological monitoring data and heating system operation data of a heating area, the multi-source meteorological monitoring data including real-time meteorological observation data, meteorological forecast data and historical meteorological data, and the heating system operation data including device operation parameters, pipe network operation state and user heat consumption data; 202. Based on the multi-source meteorological monitoring data and the heating system operation data, generate multi-modal meteorological situation data and device operation state characterization parameters, the multi-modal meteorological situation data including temperature field distribution data, humidity field distribution data, wind speed field distribution data and solar radiation intensity distribution data, and the device operation state characterization parameters including device operation efficiency indicators, device energy consumption characteristic parameters and device health state evaluation data; 203. Based on the multi-modal meteorological situation data and the device operation state characterization parameters, determine the operation state evaluation result and the fault warning information of the heating system, the operation state evaluation result including energy efficiency evaluation indicators, device health state evaluation and system stability analysis, and the fault warning information including device fault warning, pipe network abnormal warning and system risk warning.

[0021] The multi-source meteorological monitoring data refers to a composite data set obtained through meteorological observation stations, satellite remote sensing and numerical prediction models, and can be specifically implemented by using a distributed sensor network and a data assimilation algorithm to construct a three-dimensional meteorological field model. The multi-modal meteorological situation data refers to the spatial distribution characteristics of temperature, humidity, wind speed and radiation intensity, and can be specifically implemented by using a spatio-temporal convolutional neural network to extract features, and is used to quantify the combined influence of environmental thermal load. The device running state representation parameter refers to a multi-dimensional index reflecting the efficiency, energy consumption and health of the device, and can be specifically implemented by using principal component analysis combined with device running logs to model, and is used to evaluate the potential of device collaborative operation. The running state evaluation result refers to a quantitative evaluation of energy efficiency, device state and system stability, and can be specifically implemented by using a multi-objective optimization algorithm combined with real-time monitoring data to dynamically update, and is used to guide the adjustment of heating strategy.

[0022] Specifically, real-time temperature, humidity and wind speed data are collected by a meteorological sensor array deployed in the heating area, combined with forecast data provided by the meteorological department, to form a meteorological situation prediction covering the next 24 hours. At the same time, the running current of the heat pump unit, the pressure fluctuation of the pipe network and the user room temperature data are collected to construct a device running state matrix. The meteorological data is input into a spatio-temporal convolution network to extract temperature field gradient features, and the health status evaluation data is generated by combining the maintenance records in the device running log. By coupling the meteorological field distribution and the pipe network heat conduction model, the heating load demand of different regions is predicted. When the dynamic characteristics of the wind speed field in a certain region are detected to be abnormal, the output power of the heat source equipment in that region is automatically adjusted, and the pipe network flow distribution parameters are optimized. Based on the correlation analysis of device energy consumption characteristic parameters and solar radiation intensity distribution data, solar auxiliary heating equipment is preferentially started during periods of sufficient sunlight to reduce the load of traditional heat sources.

[0023] For example, first of all, high-precision meteorological sensor arrays are deployed inside the heating area, which include but are not limited to PT100 temperature sensors (measurement range -40°C to +85°C, accuracy ±0.1°C), capacitive humidity sensors (measurement range 0% to 100% RH, accuracy ±2% RH), ultrasonic wind speed sensors (measurement range 0 to 60 m / s, accuracy ±0.1 m / s) and photovoltaic radiation sensors (measurement range 0 to 2000 W / m², accuracy ±5 W / m²). These sensors are distributed in a grid pattern, covering at least 10 monitoring points per square kilometer in the heating area, with a data collection frequency of every 5 minutes, and transmitting data to the central processor through the LoRa wireless communication protocol. Real-time meteorological observation data is fused with numerical prediction data (such as ECMWF model output) provided by the meteorological department, and a data assimilation algorithm (such as ensemble Kalman filter) is used to form a meteorological situation prediction for the next 24 hours, including the spatio-temporal distribution of temperature field, humidity field, wind speed field and solar radiation intensity.

[0024] Meanwhile, the system collects heating system operation data: device operation parameters are read by PLC (Programmable Logic Controller) to read the operating current of the heat pump unit (using a Hall current sensor, range 0-100A, accuracy ±0.5%), the pipe network operating state is monitored by a pressure transmitter (range 0-2.5MPa, accuracy ±0.1% FS) to monitor pressure fluctuations, and user heat data is collected by an intelligent heat meter (accuracy level 2) to collect room temperature and flow data. These data are stored in JSON format to construct a device operation state matrix, with a time series x device parameter matrix dimension, for example, a system containing 100 devices, the matrix size is 1000x10 (indicating 1000 time points, 10 parameters).

[0025] Meteorological data is input into a spatio-temporal convolutional neural network (STCNN) for feature extraction: this network uses a 3D convolutional layer (convolution kernel size 3x3x3, step 1) to process spatio-temporal data, first calculating the gradient of the temperature field data (using the Sobel operator to extract spatial gradients), then capturing time series features through an LSTM (Long Short-Term Memory) layer, outputting temperature field gradient features, humidity field distribution features, wind speed field dynamic features, and solar radiation intensity variation features. Maintenance records in the device operation log (such as device cumulative operating hours, fault codes) generate health status assessment data through a random forest algorithm, with assessment indicators including device remaining life (calculated based on the Weibull distribution model) and failure probability.

[0026] By coupling the meteorological field distribution with the pipe network heat conduction model (using the finite element method to establish the pipe network heat loss model), the heating load demand in different regions is predicted. For example, when the wind speed field dynamic feature is abnormal (such as wind speed exceeding 10m / s for 30 minutes), the system automatically adjusts the output power of the heat source device in that region (adjusts the frequency of the frequency converter through a PID controller, with a range of 0-50Hz), while optimizing the pipe network flow distribution parameters (uses a linear programming algorithm to calculate the optimal flow, with the objective function being to minimize pumping energy consumption). Based on the correlation analysis (using Pearson correlation coefficient calculation) of device energy consumption characteristic parameters (such as unit time energy consumption curve) and solar radiation intensity distribution data, solar auxiliary heating devices (such as solar collectors) are preferentially activated during periods of sufficient sunlight (solar radiation intensity greater than 500W / m²), and mode switching is achieved through relay control switches to reduce the load of traditional heat sources.

[0027] Compared with the related art, the conventional method only performs load prediction according to a single temperature parameter, while the scheme improves the heat load prediction accuracy to a certain extent by fusing temperature field, humidity field, wind speed field and radiation intensity distribution data. The related art is limited to operating efficiency indicators for equipment state evaluation, while the scheme constructs a multi-dimensional parameter system including energy consumption characteristics and health evaluation to realize accurate evaluation of equipment operation potential. The conventional system uses an independent control strategy, resulting in energy efficiency loss, and the scheme forms a closed-loop optimization control mechanism through dynamic coupling of meteorological situation data and equipment state parameters.

[0028] Through the above technical scheme, the application effectively solves the problem of heat supply load prediction deviation caused by insufficient utilization of meteorological data, accurately quantifies the influence of environmental heat load through multi-dimensional meteorological field modeling. Realize the collaborative optimization control between the heating equipment, reduce the system energy consumption on the premise of ensuring the heating quality. Establish the linkage early warning mechanism of equipment health state and pipe network operation state, significantly reduce the risk of heat supply interruption caused by equipment failure. Through dynamic adjustment of the heating strategy to adapt to the change of meteorological conditions, improve the stability of the system in response to extreme weather.

[0029] The application further proposes the following technical scheme executed by a processor, see Figure 3 , the method comprises: 301, based on multi-modal meteorological situation data and equipment operation state representation parameters, generating energy efficiency optimization objective function, equipment collaborative operation constraint condition and system adjustment parameter; 302, based on the energy efficiency optimization objective function, the equipment collaborative operation constraint condition and the system adjustment parameter, determining the operation state evaluation result and the fault early warning information of the heat supply system.

[0030] Among them, the energy efficiency optimization objective function refers to the conversion of comprehensive energy efficiency, operating cost and carbon emissions into quantifiable optimization objectives through mathematical modeling. Specifically, a multi-objective optimization algorithm can be used to associate and calculate meteorological situation data and equipment operation parameters to balance the weight relationship between different optimization objectives. The equipment collaborative operation constraint condition refers to the limiting condition of the equipment operation range and the safety boundary. Specifically, a dynamic threshold adjustment algorithm can be used in combination with equipment health state evaluation data to generate, which is used to prevent equipment overload operation and ensure system stability. The system adjustment parameter refers to the control parameter that needs to be dynamically adjusted in the operation process of the heat supply system. Specifically, a parameter optimization algorithm can be used in combination with environmental heat load influence factors to generate, which is used to realize accurate control of temperature, pressure and flow of the heat supply pipe network.

[0031] Specifically, by integrating temperature field distribution data, humidity field distribution data, wind speed field distribution data and solar radiation intensity distribution data, combined with equipment operation efficiency indicators, equipment energy consumption characteristic parameters and equipment health status evaluation data, an energy efficiency optimization objective function is constructed, which includes energy consumption efficiency optimization items, operation cost optimization items and environmental benefit optimization items. The device collaborative operation constraint condition is generated by analyzing the device capacity limit, operation safety threshold and start-stop frequency requirement, and the dynamic adjustment safety boundary parameter is generated. The system adjustment parameter is based on the meteorological situation change and the equipment operation efficiency, and the temperature setting parameter, pressure control parameter and flow regulation parameter are generated by optimization algorithm. After the above parameters are input into the heating system digital twin model, the equipment operation parameter optimization scheme is generated by multi-objective optimization algorithm, and the energy efficiency evaluation index and fault warning information are continuously output by combining real-time data assimilation update mechanism.

[0032] For example, real-time integration based on multi-modal meteorological situation data and equipment operation state characterization parameters. Multi-modal meteorological situation data includes temperature field distribution data (grid display with 0.1°C resolution), humidity field distribution data (relative humidity percentage grid), wind speed field distribution data (vector field form) and solar radiation intensity distribution data (W / m² unit), which calculates the environmental thermal load influence factor by weighted fusion algorithm (uses entropy weight method to determine the weight of each meteorological element, for example, temperature weight 0.4, humidity 0.3, wind speed 0.2, radiation 0.1). The formula is: environmental thermal load influence factor = ∑(weight i x normalized meteorological data i). Equipment operation state characterization parameters include equipment operation efficiency indicators (such as COP coefficient, calculated by the ratio of actual output heat power to input electric power), equipment energy consumption characteristic parameters (such as peak energy consumption and average energy consumption, calculated by sliding window method) and equipment health status evaluation data (based on equipment vibration sensor data and maintenance records, using support vector machine classifier to evaluate health score, range 0-100 points).

[0033] The equipment operation efficiency coefficient is determined by multi-dimensional evaluation algorithm (principal component analysis method combined with fuzzy logic after dimension reduction), which integrates efficiency, energy consumption and health status. The calculation formula is: equipment operation efficiency coefficient = α × operation efficiency indicator + β × (1-energy consumption characteristic parameter) + γ × health score, where α, β, γ are weighting coefficients (default values are 0.5, 0.3, 0.2 respectively), which can be adjusted according to actual system. The environmental thermal load influence factor and the equipment operation efficiency coefficient are input into the target function to build the model (using multi-objective optimization framework, such as NSGA-II algorithm), and the energy efficiency optimization objective function is generated, which includes three optimization items: energy consumption efficiency optimization item (minimize total energy consumption, objective function minΣdevice power consumption), operation cost optimization item (minimize fuel or electricity cost, based on real-time electricity price data) and environmental benefit optimization item (minimize carbon emissions, using carbon intensity factor to calculate).

[0034] The device cooperative operation constraint conditions are generated by a constraint condition generation algorithm: based on the device health state evaluation data, set the device operation safety boundary (such as prohibiting high-load operation when the health score is lower than 60 points), the device capacity limit (such as the maximum output power of the heat pump does not exceed the rated value), and the device start-stop frequency limit (such as the start-stop frequency is not more than 3 times per hour to prevent device wear and tear). The system adjustment parameters are generated by a parameter optimization algorithm (such as the gradient descent method), including temperature adjustment parameters (the supply water temperature set value range is 40°C-80°C), pressure control parameters (the pipe network pressure is maintained at 0.4-0.6 MPa), and flow regulation parameters (the flow distribution ratio is optimized by linear programming). These parameters are updated every 15 minutes to ensure that the system dynamically adapts to changes.

[0035] Compared with related technologies, the traditional heating control method only uses a single temperature parameter for load prediction, and does not establish a coupling relationship between multi-modal meteorological data and device operation state. The present scheme dynamically correlates meteorological elements such as temperature, humidity, wind speed, and solar radiation intensity with device operation efficiency by constructing a multi-dimensional optimization objective function, and introduces device health state evaluation data to generate dynamic constraint conditions, to some extent, solving the problems of insufficient use of meteorological data and lack of device cooperative control in traditional methods.

[0036] Through the above technical solutions, the present application realizes dynamic optimization and adjustment of the operation parameters of the heating system, effectively improves the efficiency of device cooperative operation, and reduces energy waste and carbon emissions. By generating device operation safety boundaries and start-stop frequency constraints in real time, the risk of device overload operation is avoided. By dynamically adjusting temperature set parameters and flow distribution parameters in combination with meteorological influence factors, the adaptability of the heating system to complex weather conditions is significantly improved.

[0037] The present application further proposes the following technical solutions executed by a processor, referring to Figure 4 , the method comprises: 401. Based on the temperature field distribution data, humidity field distribution data, wind speed field distribution data, and solar radiation intensity distribution data in the multi-modal meteorological situation data, determine the environmental thermal load influence factor by a weighted fusion algorithm; 402. Based on the device operation efficiency index, device energy consumption characteristic parameter, and device health state evaluation data in the device operation state representation parameter, determine the device operation efficiency coefficient by a multi-dimensional evaluation algorithm; 403. Input the environmental thermal load influence factor and the device operation efficiency coefficient into a target function construction model to generate an energy efficiency optimization objective function including an energy consumption efficiency optimization item, an operation cost optimization item, and an environmental benefit optimization item; 404. Based on the device health state evaluation data in the device running state characterization parameter, the device collaborative running constraint condition including the device running safety boundary, the device capacity limit and the device start-stop frequency limit is generated by the constraint condition generation algorithm; 405. Based on the environmental thermal load influence factor and the device running efficiency coefficient, the system regulation parameter including the temperature regulation parameter, the pressure control parameter and the flow regulation parameter is generated by the parameter optimization algorithm.

[0038] Wherein, the weighted fusion algorithm refers to the method of superimposing and calculating the spatio-temporal distribution data of different meteorological elements according to dynamic weights, and the weight coefficients of each meteorological element can be determined by entropy weight method or analytic hierarchy process, which is used to quantify the comprehensive influence of environmental thermal load on the heating system. The multi-dimensional evaluation algorithm refers to the algorithm for joint analysis of device running efficiency, energy consumption characteristics and health state, and the weight coefficients of each meteorological element can be determined by entropy weight method or analytic hierarchy process, which is used to quantify the comprehensive influence of environmental thermal load on the heating system. The target function construction model refers to the mathematical model of multi-objective optimization function constructed by taking environmental thermal load influence factor and device running efficiency coefficient as input variables, and the linear weighting method or Pareto frontier method can be used to balance the optimization objectives of energy consumption efficiency, running cost and environmental benefit. The constraint condition generation algorithm refers to the algorithm for dynamically setting the running safety boundary and capacity limit according to the device health state evaluation data, and the interval constraint method or probability constraint method can be used to avoid overloading of the device. The parameter optimization algorithm refers to the algorithm for dynamically adjusting temperature, pressure and flow parameters based on environmental thermal load and device efficiency, and the genetic algorithm or particle swarm algorithm can be used to realize adaptive matching of system regulation parameters.

[0039] Specifically, the temperature field distribution data, humidity field distribution data, wind speed field distribution data and solar radiation intensity distribution data are processed by weighted fusion to generate the influence factor reflecting the dynamic change of environmental thermal load. The device running efficiency index, energy consumption characteristic parameter and health state evaluation data are evaluated by multi-dimensional evaluation to form the running efficiency coefficient representing the comprehensive performance of the device. The environmental thermal load influence factor and the device running efficiency coefficient are input into the target function construction model to generate a multi-objective optimization function considering energy consumption efficiency, running cost and environmental benefit. The device health state evaluation data is converted into the device running safety boundary, capacity limit and start-stop frequency threshold by the constraint condition generation algorithm to form the constraint system of device collaborative running. The environmental thermal load influence factor and the device running efficiency coefficient are further dynamically adjusted by the parameter optimization algorithm to realize real-time optimization configuration of the heating system running parameters.

[0040] Compared with the related art, the conventional method only uses a single temperature parameter for load prediction, without integrating multi-dimensional meteorological elements such as humidity, wind speed and solar radiation, resulting in inaccurate construction of the energy efficiency optimization objective function. The related art is limited to a single dimension for the evaluation of the device state, and cannot reflect the comprehensive influence of the device efficiency, energy consumption and health state, resulting in incomplete device collaborative constraint conditions. The existing parameter adjustment method lacks dynamic correlation between the environmental thermal load and the device performance, resulting in insufficient adaptability of the system adjustment parameters. The present scheme constructs a precise energy efficiency optimization objective function through multi-dimensional meteorological data fusion and multi-dimensional evaluation of the device state; improves the boundary restrictions of the device collaborative operation through the generation of constraint conditions driven by the device health state; and improves the dynamic matching capability of the system adjustment parameters through the joint optimization of the environmental thermal load and the device performance.

[0041] Through the above technical scheme, the present application solves the problem that the existing heating system is not fully integrated with multi-dimensional meteorological elements and device state data, resulting in inaccurate construction of the energy efficiency optimization objective function. The environmental thermal load influence factor is quantified through a weighted fusion algorithm to ensure that the optimization objective function can reflect the comprehensive effect of multi-dimensional meteorological elements. The problem of incomplete device collaborative constraint conditions is solved to some extent by generating a device operation performance coefficient through a multi-dimensional evaluation algorithm, and dynamically setting the device operation safety boundary and capacity limit in combination with the health state data. The problem of poor adaptability of system adjustment parameters is solved to some extent by realizing the dynamic matching of temperature, pressure and flow parameters with environmental thermal load and device performance through a parameter optimization algorithm, thereby improving the adjustment precision and response speed of the heating system operation parameters.

[0042] The present application further proposes the following technical scheme executed by a processor, referring to Figure 5 , the method comprises: 501. Based on the energy efficiency optimization objective function, the device collaborative operation constraint condition and the system adjustment parameter, determine the meteorological influence factor, the load demand prediction result and the prediction uncertainty index through the meteorological condition prediction model, the meteorological influence factor includes the temperature influence coefficient, the humidity influence coefficient and the wind speed influence coefficient, the load demand prediction result includes the short-term load prediction value, the medium-term load trend and the long-term load change rule, the prediction uncertainty index includes the prediction error range and the confidence level; 502. Determine the multi-element device collaborative control strategy, the operation parameter optimization scheme and the heating mode switching instruction based on the meteorological influence factor, the load demand prediction result and the prediction uncertainty index, the multi-element device collaborative control strategy includes the heat source device start-stop strategy, the load distribution strategy and the operation mode combination strategy, the operation parameter optimization scheme includes the temperature set value optimization, the pressure control optimization and the flow distribution optimization, the heating mode switching instruction includes the mode identification instruction, the switching time instruction and the transition control instruction; 503. The multi-element device collaborative control strategy, the operation parameter optimization scheme and the heat supply mode switching instruction are executed, and the operation state evaluation result and the fault early warning information are output through real-time data assimilation update and abnormality monitoring, the operation state evaluation result includes energy efficiency evaluation index, device health state evaluation and system stability analysis, and the fault early warning information includes device fault early warning, pipe network abnormality early warning and system risk early warning.

[0043] The meteorological condition prediction model is a prediction model for extracting the space-time features of meteorological data through a space-time convolution network and dynamically adjusting the weights of various meteorological elements in combination with an attention mechanism, and can be implemented by using a multi-layer neural network structure, and is used to solve the load prediction deviation problem caused by traditional single temperature prediction. The meteorological influence factor is a quantitative parameter reflecting the influence of temperature, humidity and wind speed on heat load, and the contribution of different meteorological elements to heat load can be calculated by a weighted fusion algorithm, and is used to correct the environmental heat load prediction value. The load demand prediction result is a heat demand prediction sequence generated based on historical data and real-time meteorological conditions, and can be implemented by using time series analysis combined with a regression prediction algorithm, and is used to guide the adjustment of the operation parameters of the heat supply equipment. The prediction uncertainty index is a quantitative parameter representing the credibility of the prediction result, and can be generated by residual analysis and confidence interval calculation, and is used to evaluate the robustness of the control strategy. The multi-element device collaborative control strategy is an optimization scheme for coordinating the start-stop timing, load distribution ratio and operation mode combination of heat source equipment, and can be generated by using a multi-objective optimization algorithm combined with digital twin simulation, and is used to eliminate the energy efficiency loss caused by independent equipment operation. The real-time data assimilation update is an iterative optimization process for dynamically integrating system operation feedback data into the prediction model, and can be implemented by using a Kalman filter or a particle filter algorithm, and is used to improve the dynamic adaptability of the prediction model.

[0044] Specifically, the weather condition prediction model first extracts the spatio-temporal features of the temperature field, humidity field, wind speed field, and solar radiation intensity data to generate a multi-dimensional weather spatio-temporal feature matrix. The device operation state representation parameters are normalized to form a device state feature vector, which is input into the prediction model together with the weather features. The spatio-temporal convolution network extracts the spatial distribution and temporal evolution law of the weather data, and the attention mechanism dynamically allocates the weight coefficients of each weather element to generate temperature, humidity, and wind speed influence factors. The regression prediction algorithm combines the weather influence factors and device state features to output a load demand sequence containing short-term, medium-term, and long-term prediction values. The prediction uncertainty index determines the confidence interval by analyzing the historical prediction error distribution, providing a reliability evaluation basis for the control strategy. Based on the above prediction results, the multi-element coupled optimization algorithm generates device start-stop strategies, load distribution ratios, and operation mode combinations, while optimizing temperature, pressure, and flow parameter set points. The heating mode switching instruction triggers mode conversion according to the change trend of the weather influence factor, and ensures smooth transition through transition control parameters. Real-time collected operation data updates the prediction model parameters through data assimilation algorithms, and the abnormal monitoring module synchronously detects the device state deviation from the threshold, dynamically adjusts the control strategy, and triggers the warning signal.

[0045] For example, the implementation of the weather condition prediction model first extracts the spatio-temporal features of the multi-modal weather situation data. The temperature field distribution data, humidity field distribution data, wind speed field distribution data, and solar radiation intensity distribution data are input in grid form (grid size 1 km x 1 km, time resolution 1 hour), and the spatio-temporal convolution network (STCNN) is used to extract features: the network structure includes 3 convolution layers (convolution kernel size 5 x 5 x 5, activation function ReLU) and 2 pooling layers (maximum pooling, pooling size 2 x 2), which output a weather spatio-temporal feature matrix with dimensions time x space x feature (for example, 24-hour data generate a 24 x 100 x 4 matrix, representing 100 spatial points and 4 weather elements). At the same time, the device operation state representation parameters (device operation efficiency indicators, device energy consumption characteristic parameters, and device health state evaluation data) are normalized (scaled to the [0, 1] interval using Min-Max), generating a device state feature vector, the length of which depends on the number of parameters (e.g., 10 parameters generate a 10-dimensional vector).

[0046] The meteorological spatio-temporal feature matrix and the equipment state feature vector are input into a meteorological condition prediction model. The model is based on a deep learning architecture: after the spatio-temporal convolution network extracts the meteorological spatio-temporal features, the weight coefficients of each meteorological element are calculated through an attention mechanism (multi-head self-attention layer, the number of heads is set to 8). The calculation of attention weight adopts the dot product attention formula: Attention(Q, K, V) = softmax(QK^T / √d)V, where Q, K, and V are query, key, and value matrices, respectively, which are derived from the meteorological feature matrix. The weight coefficients are used to generate meteorological influence factors, including temperature influence coefficients (calculated based on the correlation between temperature field gradient and historical load), humidity influence coefficients (considering the influence of humidity on heat conduction), and wind speed influence coefficients (reflecting the effect of wind speed on heat loss), which are quantified through a linear regression model.

[0047] Based on the meteorological influence factors and the equipment state feature vector, the load demand prediction results are generated through a regression prediction algorithm (such as an ARIMA model or an LSTM network): short-term load prediction values (future 1-6 hours, resolution 15 minutes) are calculated through time series analysis; medium-term load trends (future 7-24 hours) are calculated using a trend decomposition algorithm (STL decomposition); long-term load change rules (future 1 week) are predicted based on a seasonal model. Prediction uncertainty indicators are generated through prediction residual analysis (calculating the root mean square error of historical prediction errors) and confidence interval calculation (using t-distribution to generate 95% confidence intervals), such as a prediction error range of ±5% load value, and a confidence level evaluated through probability distribution.

[0048] Compared with related technologies, the traditional heating control method only relies on a single temperature parameter for load prediction, without considering the coupling effects of humidity, wind speed, and other meteorological elements, resulting in insufficient prediction accuracy. The existing equipment control strategy uses fixed start-stop logic and cannot dynamically adjust the operation mode combination according to meteorological changes. The present scheme realizes the accurate quantification of meteorological influence factors through the deep coupling of multi-dimensional meteorological data fusion and equipment state parameters. Based on the risk assessment mechanism of prediction uncertainty, the control strategy has robustness to respond to meteorological fluctuations. The combination of digital twin simulation and real-time data assimilation ensures that the control instructions can adapt to the dynamic changes of the actual operating state, overcoming the hysteresis problem of traditional static control strategies.

[0049] Through the above technical solutions, the present application solves the problem of low prediction accuracy of traditional heating systems due to the use of single meteorological data, improves the load prediction accuracy through coupling analysis of multi-dimensional meteorological elements, optimizes the equipment collaborative operation efficiency, generates optimal start-stop strategies and load distribution schemes based on dynamic prediction results, realizes the dynamic adaptation of the heating system to meteorological condition changes through mode switching instructions to ensure smooth transition under different working conditions, and enhances the real-time performance of fault warning by using abnormal monitoring and data assimilation mechanisms to identify abnormal equipment states in advance.

[0050] The application further proposes a technical scheme for determining meteorological influence factors, load demand prediction results and prediction uncertainty indicators based on multi-modal meteorological situation data and equipment operation state characterization parameters through a meteorological condition prediction model. Specifically, it includes: extracting the spatio-temporal features of temperature field distribution data, humidity field distribution data, wind speed field distribution data and solar radiation intensity distribution data to generate a meteorological spatio-temporal feature matrix; normalizing equipment operation efficiency indicators, equipment energy consumption characteristic parameters and equipment health state evaluation data to generate an equipment state feature vector; after inputting the two types of data into the meteorological condition prediction model, extracting features through a spatio-temporal convolution network and calculating meteorological element weight coefficients combined with an attention mechanism; generating temperature, humidity and wind speed influence factors based on the weight coefficients; generating load demand prediction results containing multiple time scales combined with a regression prediction algorithm; and finally obtaining prediction uncertainty indicators through residual analysis and confidence interval calculation.

[0051] Among them, spatio-temporal feature extraction refers to modeling the dynamic change law of meteorological data in spatial distribution and time series, which can be realized by sliding calculation of three-dimensional convolution kernel in spatial dimension and time dimension, and is used to capture the time correlation of temperature field gradient change and solar radiation intensity. Normalization refers to unifying the numerical range of equipment state parameters to a standardized interval, which can specifically use the min-max scaling method to eliminate dimensional differences, so that the equipment efficiency indicators and energy consumption characteristics have comparability. Spatio-temporal convolution network refers to a deep learning model with time dimension convolution kernel and space dimension convolution kernel, for example, using a separable convolution layer to process time series and spatial grid data respectively, for extracting spatio-temporal correlation features of meteorological elements. The attention mechanism refers to a calculation module that dynamically allocates the importance of different meteorological elements, which can specifically use a multi-head self-attention structure to calculate the interaction influence weight of temperature and humidity data, for identifying key meteorological factors that dominate heating load changes. The regression prediction algorithm refers to a mathematical model that establishes the mapping relationship between meteorological parameters and load demand, for example, using support vector regression or long short-term memory network, for fusing equipment state data and meteorological influence factors to generate multi-stage prediction results. Prediction residual analysis refers to modeling the statistical distribution of prediction errors, which can specifically use a sliding window method to calculate the standard deviation of historical prediction bias, for quantifying the fluctuation range of prediction results.

[0052] Specifically, the temperature field distribution data is formed into a spatial thermal map through gridding interpolation, the humidity field distribution data is constructed into a two-dimensional humidity field using Kriging interpolation, and the wind speed vector field is used to form a multi-dimensional meteorological feature. The device state feature vector aligns the running efficiency index with the energy consumption curve, eliminating the influence of device type differences on the prediction. When the spatio-temporal convolution network performs convolution operation on the meteorological spatio-temporal feature matrix, the time dimension convolution kernel can capture the trend characteristics of temperature change in the next three hours, and the space dimension convolution kernel can identify the spatial correlation of the humidity field in the adjacent area. The attention mechanism dynamically adjusts the contribution weight of different meteorological elements in the load prediction model by calculating the cross-correlation degree of temperature data and wind speed data. The regression prediction algorithm takes the device health status as an input feature, and combines the meteorological influence factors to construct the dynamic coupling relationship between load demand and device operation state. The prediction uncertainty index calculates the confidence interval of the historical prediction error to provide a risk warning threshold setting basis for the system.

[0053] Compared with related technologies, the traditional heating load prediction only uses a single temperature parameter to establish a linear regression model, without considering the influence of humidity on heat conduction efficiency and the effect of wind speed on pipe network heat loss. Related technologies lack quantitative evaluation of the device health status, and cannot reflect the dynamic influence of device aging on heating efficiency. The present scheme fuses four-dimensional meteorological field data and multi-dimensional state parameters of the device, uses a spatio-temporal convolution network to capture the spatio-temporal evolution law of meteorological elements, and combines an attention mechanism to identify key influencing factors, so that the load prediction model can adaptively adjust the weight distribution under different meteorological conditions and device operating conditions. In addition, by introducing a prediction uncertainty index, a risk quantitative evaluation basis is provided for system scheduling, overcoming the defect that traditional point prediction lacks reliability judgment.

[0054] Through the above technical solutions, the present application effectively solves the problem of multi-dimensional meteorological element comprehensive modeling to a certain extent, and constructs a dynamic correlation model of meteorological parameters and device state through spatio-temporal feature extraction. The attention mechanism is used to realize dynamic distribution of the influence weight of different meteorological elements, which to a certain extent solves the prediction deviation caused by fixed weight in traditional methods. The joint input of the device state feature vector and the meteorological influence factor makes the load prediction result reflect the change of the actual running efficiency of the device. The generation of the prediction uncertainty index provides a risk evaluation basis for the heating system, so that the scheduling strategy can dynamically adjust the operating parameters according to the prediction reliability.

[0055] The present application further proposes a technical scheme based on meteorological influence factors, load demand prediction results and prediction uncertainty indexes, generating an energy efficiency optimization objective function, device collaborative operation constraint conditions and system adjustment parameters through a multivariate coupling optimization algorithm, and determining a multivariate device collaborative control strategy, an operating parameter optimization scheme and a heating mode switching instruction based on these elements.

[0056] The multi-element coupling optimization algorithm refers to a mathematical optimization method for comprehensively processing meteorological elements, load demand and prediction reliability, and can be specifically implemented by using a multi-objective genetic algorithm or a particle swarm optimization algorithm, and is used to solve the nonlinear coupling relationship between multi-dimensional data. The energy efficiency optimization objective function refers to a comprehensive optimization model including energy consumption efficiency, operation cost and environmental benefit, and can be specifically constructed by using weighted summation or a Pareto frontier method, and is used to balance the priority of different optimization objectives. The device collaborative operation constraint condition refers to a boundary condition for limiting the operation range of the device, and can be specifically modeled by using a linear inequality or a dynamic threshold, and is used to ensure that the optimization result meets the actual operation capacity of the device. The system regulation parameter refers to a set of physical parameters for controlling the operation of the heating system, and specifically includes a temperature set value, a pressure control threshold and a flow distribution ratio, and is used to convert the optimization result into an executable control instruction.

[0057] Specifically, the temperature, humidity and wind speed coefficients in the meteorological influence factor are corrected by weighted calculation of the environmental thermal load, the load demand prediction result is extracted by trend analysis, and the prediction uncertainty index is quantified by a risk assessment model. After the three types of data are input into the multi-element coupling optimization algorithm, an optimization function considering energy efficiency, cost and environmental protection is generated under a multi-objective framework, and the operation constraint boundary is dynamically adjusted in combination with the device health state data. The optimized system regulation parameter is converted into a device start-stop strategy and a load distribution scheme through a digital twin model, and the heating mode switching instruction is automatically triggered to trigger the transition control logic according to the change of the meteorological factor. During the whole process, the environmental thermal load correction factor and the device operation efficiency coefficient interact in real time to ensure that the control strategy responds to the meteorological change and adapts to the device state.

[0058] Compared with related technologies, the traditional heating control method usually adopts single target optimization and fixed constraint condition, and cannot dynamically adapt to meteorological parameter fluctuation and device state change. The present scheme realizes the collaborative optimization of meteorological elements, device state and load demand by establishing a multi-objective optimization framework and a dynamic constraint mechanism. In related technologies, the heating mode switching depends on artificial experience or simple threshold judgment, while in the present scheme, the prediction uncertainty index and reliability coefficient are used to quantify the control risk, so that the mode switching time and transition parameter are accurately matched.

[0059] Through the above technical scheme, the present application solves the problem of insufficient device collaborative control under multi-dimensional meteorological elements to a certain extent, and ensures that the optimization strategy meets the actual working condition of the device through dynamic constraint condition modeling, thereby improving the accuracy of operation parameter optimization. The heating mode switching instruction and the environmental influence factor are dynamically associated, the response ability of the system to meteorological mutation is enhanced, and a complete control instruction system is formed. The introduction of the prediction uncertainty index effectively reduces the decision risk of the control strategy, and improves the reliability and stability of the operation of the heating system.

[0060] The application further proposes that based on the temperature influence coefficient, humidity influence coefficient and wind speed influence coefficient in the meteorological influence factor, an environmental heat load correction factor is generated through weighted calculation; based on the short-term load prediction value, medium-term load trend and long-term load change law in the load demand prediction result, a load demand change feature is generated through trend analysis algorithm; based on the prediction error range and confidence level in the prediction uncertainty index, a prediction reliability coefficient is generated through a risk assessment model; the environmental heat load correction factor, load demand change feature and prediction reliability coefficient are input into a multi-element coupling optimization algorithm, and an energy efficiency optimization objective function including energy consumption efficiency optimization item, operation cost optimization item and environmental benefit optimization item is generated through multi-objective optimization calculation; based on the equipment health state evaluation data in the equipment operation state characterization parameter, a device collaborative operation constraint condition including device operation safety boundary, device capacity limit and device start-stop frequency limit is generated through constraint condition modeling; based on the environmental heat load correction factor and load demand change feature, system adjustment parameters including temperature setting parameters, pressure control parameters and flow regulation parameters are generated through parameter optimization calculation.

[0061] Among them, the environmental heat load correction factor refers to the dynamic adjustment parameter of the influence of temperature, humidity and wind speed on heat load, which can be specifically weighted and distributed to multiple meteorological elements by using entropy weight method, and then realized by linear weighted calculation. This factor can quantify the comprehensive effect of different meteorological elements on heating demand, and solve the problem of insufficient prediction accuracy of single temperature parameter. The load demand change feature refers to the periodic and trend components extracted from the prediction result by time series decomposition algorithm, for example, using STL decomposition algorithm to separate short-term fluctuations and long-term trends. This feature can reflect the load change law at different time scales and provide dynamic input for the optimization algorithm. The prediction reliability coefficient is a risk assessment index calculated based on the prediction error distribution and confidence interval, for example, using Monte Carlo simulation to generate error probability distribution. This coefficient can quantify the credibility of the prediction result and guide the optimization algorithm to avoid risks. The device collaborative operation constraint condition refers to the operation boundary condition dynamically adjusted according to the equipment health state, for example, setting the maximum running time limit based on the equipment remaining life model. This constraint ensures that the optimization scheme meets the actual operation capacity of the equipment and avoids overloading operation. The system adjustment parameter refers to the temperature, pressure and flow setting value generated through multi-objective optimization, for example, using NSGA-II algorithm to solve the Pareto optimal solution set. These parameters can realize the fine adjustment of the running state of the heating system.

[0062] Specifically, during the operation of the heating system, the environmental heat load correction factor is first generated by weighted calculation of temperature, humidity, and wind speed influence coefficients. For example, when the humidity increases, the correction factor automatically adjusts the heat load baseline value. Subsequently, the load prediction results are trend decomposed to extract the load fluctuation characteristics and seasonal change trend within the next 24 hours. The prediction reliability coefficient is dynamically updated based on the statistical analysis results of historical prediction errors, for example, reducing the confidence level threshold during the cold wave warning period. After inputting the above parameters into the multi-coupling optimization algorithm, the algorithm considers energy efficiency, operating cost, and carbon emissions as three optimization objectives, and generates multiple feasible solutions through non-dominated sorting. The device health status evaluation data real-time constraints the optimization process, for example, when the health score of a certain heat pump is lower than the threshold, the algorithm automatically excludes schemes that include high load operation of the device. The final generated temperature setting parameters are dynamically adjusted according to the correction factor, for example, in low temperature and strong wind weather, the water supply temperature setting value is increased by 3-5℃, and the flow regulation parameters are controlled according to the load change characteristics in different time periods.

[0063] For example, during the operation of the heating system, the implementation of the multi-coupling optimization algorithm first generates an environmental heat load correction factor based on the temperature influence coefficient, humidity influence coefficient, and wind speed influence coefficient in the meteorological influence factor through weighted calculation. The weighted calculation uses dynamic weight distribution: the temperature influence coefficient weight is 0.5 (based on its dominant effect on heat load), the humidity influence coefficient weight is 0.3 (considering the humidity evaporation cooling effect), and the wind speed influence coefficient weight is 0.2 (reflecting the influence of wind speed on convective heat loss), the calculation formula is: environmental heat load correction factor = 0.5 x temperature influence coefficient + 0.3 x humidity influence coefficient + 0.2 x wind speed influence coefficient. This factor is used to correct the basic heat load value, for example, when the correction factor is greater than 1, the heat load demand is increased by 10%.

[0064] Based on the short-term load prediction value (future 1-6 hours), medium-term load trend (future 7-24 hours), and long-term load change law (future 1 week) in the load demand prediction results, the load demand change characteristics are generated by trend analysis algorithm (such as Holt-Winters exponential smoothing method), including the extraction of seasonal component, trend component, and residual component. The prediction error range (such as ±5% load value) and confidence level (such as 95%) in the prediction uncertainty index are generated by the risk assessment model (Monte Carlo simulation) to generate the prediction reliability coefficient, which is expressed as a value between 0 and 1, used to quantify the prediction confidence.

[0065] The environmental thermal load correction factor, load demand change characteristics and prediction reliability coefficient are input into a multi-element coupling optimization algorithm (a multi-objective particle swarm optimization algorithm PSO) to generate an energy efficiency optimization objective function through multi-objective optimization calculation. The function includes an energy consumption efficiency optimization item (aiming to minimize total energy consumption, formula min∑(P_i×t_i), wherein P_i is device power consumption and t_i is running time), an operation cost optimization item (combined with a real-time electricity price model) and an environmental benefit optimization item (calculated using a carbon emission factor). Device collaborative operation constraint conditions are based on device health state evaluation data in device operation state representation parameters to generate: a device operation safety boundary set to limit the load to 80% of the rated value when the health score is lower than 70 points; device capacity limitation is set through hardware nameplate parameters; and device start-stop frequency limitation is realized through a timer, such as a minimum start-stop interval of 10 minutes. System adjustment parameters are based on the environmental thermal load correction factor and load demand change characteristics to generate temperature setting parameters (such as water supply temperature adjustment according to outdoor temperature compensation), pressure control parameters (PID control parameters Kp=0.8, Ki=0.1, Kd=0.05) and flow regulation parameters (flow valve opening degree optimization) through parameter optimization calculation (sequential quadratic programming method).

[0066] Compared with related technologies, the traditional method only uses a single temperature parameter to calculate the thermal load, while the present scheme can accurately reflect the actual influence of humidity and wind speed on building heat loss through weighted correction of multiple meteorological elements. In related technologies, device constraint conditions are mostly fixed thresholds, while the present scheme is based on dynamic modeling of device health state, which can avoid forced operation of devices in sub-health state. Existing optimization algorithms mostly use a single energy efficiency indicator, while the present scheme generates adjustment parameters that take into account economy and environmental protection through multi-objective optimization, to a certain extent solving the problem that energy efficiency improvement and cost control are difficult to balance in traditional schemes.

[0067] Through the above technical scheme, the present application realizes dynamic optimization control of the heating system under multiple meteorological conditions. Through the environmental thermal load correction factor, the comprehensive influence of meteorological elements is accurately quantified, to a certain extent solving the problem of thermal load calculation deviation caused by neglecting humidity and wind speed in traditional prediction models. Dynamic constraint conditions based on device health state avoid the risk of device failure caused by overload operation. System adjustment parameters generated through multi-objective optimization realize dual optimization of energy consumption and operation cost on the premise of ensuring heating quality. The introduction of the prediction reliability coefficient enables the system to automatically adjust the degree of conservatism of the control strategy according to prediction uncertainty, improving the operation stability under extreme weather conditions.

[0068] The application further proposes inputting the energy efficiency optimization objective function, the device collaborative operation constraint condition and the system adjustment parameter into the heat supply system digital twin model, generating a device operation parameter optimization scheme through a multi-objective optimization algorithm, the device operation parameter optimization scheme including a temperature set value optimization parameter, a pressure control optimization parameter and a flow distribution optimization parameter; generating a multi-element device collaborative control strategy through digital twin simulation calculation based on the device operation parameter optimization scheme and the device collaborative operation constraint condition, the multi-element device collaborative control strategy including a heat source device start-stop timing strategy, a load distribution proportion strategy and an operation mode combination strategy; generating a heat supply mode switching instruction through a pattern recognition algorithm based on the meteorological influence factor and the load demand prediction result, the heat supply mode switching instruction including a pattern recognition condition, a switching opportunity judgment criterion and a transition control parameter; wherein the multi-element device collaborative control strategy and the operation parameter optimization scheme are associated through a device operation state feedback mechanism, and the heat supply mode switching instruction and the meteorological influence factor are associated through an environmental adaptability algorithm to form a complete control instruction system.

[0069] The energy efficiency optimization objective function refers to a mathematical expression for optimizing the energy efficiency of the heating system, which can be implemented using a multi-objective function containing energy consumption efficiency optimization items, operation cost optimization items, and environmental benefit optimization items to balance different optimization objectives in device collaborative control. The device collaborative operation constraint condition refers to the boundary condition that limits the operation of the device, which can be implemented using device capacity constraints, operation safety constraints, and start-stop frequency constraints to ensure that the device operates within a safe range. The system regulation parameter refers to a parameter set for adjusting the operating state of the heating system, which can be implemented using temperature setting parameters, pressure control parameters, and flow regulation parameters to dynamically adjust the output of the heating system. The heating system digital twin model refers to a virtual mapping model of the physical heating system, which can be implemented using a simulation model based on real-time data synchronization to verify the effectiveness of the control strategy in a virtual environment. The multi-objective optimization algorithm refers to an algorithm that optimizes multiple objectives simultaneously, which can be implemented using a genetic algorithm or a particle swarm optimization algorithm to generate parameter schemes that meet different optimization objectives. The device operation parameter optimization scheme refers to an optimized set of device operation parameters, which can be implemented using an optimized combination of temperature set values, pressure control values, and flow distribution values to guide actual device operation. The digital twin simulation calculation refers to the process of verifying the strategy in the digital twin model, which can be implemented using real-time data-driven dynamic simulation technology to predict the execution effect of the control strategy. The multi-element device collaborative control strategy refers to a coordinated control scheme for multiple heating devices, which can be implemented using an optimized strategy of heat source device start-stop timing, load distribution ratio, and operation mode combination to improve device collaboration efficiency. The pattern recognition algorithm refers to an algorithm for recognizing the operation mode of the heating system, which can be implemented using a classification algorithm based on meteorological influence factor threshold determination to trigger heating mode switching. The heating mode switching instruction refers to the trigger condition and control parameter for switching the heating mode, which can be implemented using a combination of mode recognition conditions, switching timing judgment criteria, and transition control parameters to achieve smooth switching of the heating mode. The device operation state feedback mechanism refers to a mechanism that feeds back the actual operation state of the device to the control system, which can be implemented using real-time sensor data acquisition and transmission technology to dynamically adjust the control strategy. The environmental adaptability algorithm refers to an algorithm for adjusting control parameters according to meteorological conditions, which can be implemented using an adaptive algorithm based on meteorological influence factor weights to enhance the response capability of the system to meteorological changes.

[0070] Specifically, the energy efficiency optimization objective function, device collaborative operation constraints, and system adjustment parameters are input into the digital twin model of the heating system, and a device operation parameter optimization scheme is generated through a multi-objective optimization algorithm. For example, the temperature set value optimization parameter can be dynamically adjusted based on the meteorological influence factor, the pressure control optimization parameter can be optimized according to the load demand prediction result, and the flow distribution optimization parameter is determined through the device collaborative operation constraint condition. Subsequently, the device operation parameter optimization scheme and the device collaborative operation constraint condition are used for digital twin simulation calculation to generate a multi-device collaborative control strategy. For example, the heat source device start-stop timing strategy verifies the influence of device start-stop on system stability through simulation, the load distribution ratio strategy determines the load sharing ratio of different devices through an optimization algorithm, and the operation mode combination strategy selects the best operation mode combination according to the energy efficiency optimization objective. At the same time, the meteorological influence factor and the load demand prediction result generate a heating mode switching instruction through a pattern recognition algorithm, for example, when the temperature influence coefficient exceeds a preset threshold, the mode switching is triggered, the switching timing criterion is determined based on the prediction uncertainty index, and the transition control parameter is dynamically adjusted through an environmental adaptability algorithm. The device operation state feedback mechanism compares the actual operation parameters with the optimization scheme in real time, for example, when the actual energy consumption of the device deviates from the optimization objective, the feedback mechanism triggers dynamic correction of the control strategy. The environmental adaptability algorithm associates the meteorological influence factor with the heating mode switching instruction, for example, when the wind speed influence coefficient changes, the transition control parameter is adjusted to ensure smooth mode switching process. Thus, through the combination of digital twin model and multi-objective optimization, dynamic optimization of the control strategy is achieved; through the cooperation of simulation calculation and pattern recognition, the coupling of meteorological conditions and device operation is enhanced; through the linkage of feedback mechanism and environmental adaptability, a complete closed-loop control system is formed.

[0071] For example, the energy efficiency optimization objective function, device collaborative operation constraints, and system adjustment parameters are input into the digital twin model of the heating system for implementation. The digital twin model uses a physics-based modeling method and is constructed using the Modelica language, including heat source devices (such as boilers, heat pumps), pipe network modules, and user end models. Model parameters are calibrated through actual systems, for example, pipe network heat loss coefficients are determined through heat balance tests. A multi-objective optimization algorithm (such as the ε-constraint method) generates a device operation parameter optimization scheme: temperature set value optimization parameters are optimized in the range of 40°C-90°C through a search algorithm (such as a genetic algorithm); pressure control optimization parameters are set based on safety thresholds; and flow distribution optimization parameters are calculated through a linear distribution algorithm to ensure balanced flow in each branch.

[0072] Based on the device operation parameter optimization scheme and the device collaborative operation constraint condition, a multi-device collaborative control strategy is generated through digital twin simulation calculation: the simulation step is set to 1 minute, and the running period is 24 hours. The heat source device start-stop timing strategy is realized through event-driven logic, for example, when the load is less than 50%, the standby device is started up with delay; the load distribution ratio strategy distributes the load based on the device efficiency ranking; the operation mode combination strategy is realized through a state machine, for example, when switching to the "economic mode", the high-efficiency device is used preferentially. The heating mode switching instruction is generated through a pattern recognition algorithm (decision tree classifier) based on the meteorological influence factor and the load demand prediction result: the pattern recognition condition is set to trigger the "windproof mode" when the outdoor temperature is lower than 5°C and the wind speed is greater than 5m / s; the switching opportunity judgment criterion is based on the prediction uncertainty index, for example, when the confidence level is lower than 90%, the switching is delayed; the transition control parameters include the gradual temperature change rate (not more than 2°C / min) and the pressure stabilization time.

[0073] The multi-device collaborative control strategy and the operation parameter optimization scheme are associated through a device operation state feedback mechanism: the feedback data is collected in real time through the OPC protocol, and the parameters are adjusted after comparison with the optimization scheme. The heating mode switching instruction and the meteorological influence factor are associated through an environmental adaptability algorithm (adaptive filtering) to ensure that the instruction responds to meteorological changes. This implementation provides specific modeling languages, simulation parameters and feedback mechanisms, and technicians can use a digital twin platform (such as ANSYS TwinBuilder) to deploy the model.

[0074] Compared with related technologies, the traditional heating system control strategy usually uses a static mathematical model, lacks dynamic feedback of the actual device state, and causes deviation between the optimization target and the actual operation effect. The present scheme realizes real-time mapping of virtual simulation and actual system through a digital twin model, to a certain extent, solving the problem of disconnection between mathematical model and physical system. In related technologies, the device collaborative control strategy is mostly based on fixed rules, which cannot adapt to the dynamic changes of meteorological conditions, while the present scheme embeds the meteorological influence factor directly into the control instruction generation process through a pattern recognition algorithm and an environmental adaptability algorithm, enhancing the response ability of the system to complex meteorological conditions. In addition, in traditional methods, the heating mode switching usually relies on manual experience, and the switching opportunity and control parameters lack scientific basis, while the present scheme realizes intelligent decision-making of heating mode switching through dynamic calculation of the prediction uncertainty index and the transition control parameters.

[0075] By the technical solution, dynamic optimization of the multi-element equipment collaborative control strategy of the heating system is achieved, and the problem of insufficient coupling of meteorological conditions and equipment operation states in traditional methods is solved to some extent. Through the combination of digital twin simulation and multi-objective optimization, the control strategy is verified and adjusted in a virtual environment, avoiding the trial and error risk of the actual system. The linkage of the equipment operation state feedback mechanism and the environmental adaptability algorithm enables the control instruction to respond to meteorological changes and equipment state fluctuations in real time, improving the operation stability and energy efficiency level of the heating system. The intelligent generation of the heating mode switching instruction ensures the rapid switching and smooth transition of the heating mode under different meteorological conditions.

[0076] The application further proposes the following technical solution executed by a processor, referring to Figure 6 , the method comprises: 601, real-time meteorological observation data, meteorological forecast data and historical meteorological data in multi-source meteorological monitoring data are subjected to spatio-temporal feature extraction, a meteorological spatio-temporal feature matrix is generated, the meteorological spatio-temporal feature matrix comprises temperature field gradient features, humidity field distribution features, wind speed field dynamic features and solar radiation intensity variation features; 602, equipment operation parameters, pipe network operation states and user heat consumption data in heating system operation data are subjected to multi-scale analysis, an equipment operation state feature vector is generated, the equipment operation state feature vector comprises equipment efficiency feature parameters, energy consumption distribution features and equipment health state indicators; 603, the meteorological spatio-temporal feature matrix and the equipment operation state feature vector are input into a multi-modal data fusion model, and multi-modal meteorological situation data is generated through an adaptive weighted fusion algorithm; 604, equipment operation state representation parameters are generated through a state evaluation algorithm based on the equipment operation state feature vector.

[0077] Among them, the spatio-temporal feature extraction refers to extracting dynamic change features in time and space dimensions from meteorological data, which can be realized by a spatio-temporal convolution network, and is used to capture the gradient change of the temperature field, the spatial heterogeneity of the humidity distribution and the dynamic evolution law of the wind speed field. Multi-scale analysis refers to multi-level data processing from equipment level parameters, pipe network level states to user level heat consumption data, which can be realized by a wavelet transform algorithm, and is used to construct a feature vector containing equipment efficiency features, energy consumption distribution features and health state indicators. The adaptive weighted fusion algorithm refers to dynamically adjusting the data fusion weight according to the importance of meteorological elements and the influence of equipment state, which can be realized by combining the attention mechanism and correlation analysis, and is used to balance the objective importance of meteorological data and the actual operation demand of equipment. The state evaluation algorithm refers to generating representation parameters through an equipment efficiency and environmental temperature correlation model and a health state multi-factor evaluation, which can be realized by a random forest regression model, and is used to establish a correlation system of equipment operation efficiency indicators and energy consumption feature parameters.

[0078] Specifically, the temperature, humidity, wind speed and solar radiation data are subjected to three-dimensional convolution operation by a spatio-temporal convolution network to extract composite features containing time series fluctuations and spatial distribution differences, forming a weather spatio-temporal feature matrix. At the same time, wavelet transform is used to perform time-frequency decomposition on the equipment operation parameters to extract energy consumption fluctuation features of different time scales, and a multi-dimensional equipment state feature vector is constructed in combination with the pipe network pressure fluctuation data and user heat curve. After the weather feature matrix and the equipment feature vector are input into the fusion model, the correlation coefficients of each weather element and equipment efficiency are calculated, the fusion weights of the temperature field and humidity field data are dynamically allocated, and multi-modal weather situation data reflecting the interaction between weather and equipment are generated. Further, a random forest model is used to analyze the correlation between the equipment load rate and the ambient temperature, a health state evaluation tree is constructed in combination with maintenance records and fault history data, and finally complete characterization parameters including efficiency indicators, energy consumption features and health assessment are output.

[0079] For example, the implementation of the multi-modal data fusion model first performs three-dimensional convolution operation on the temperature, humidity, wind speed and solar radiation data by a spatio-temporal convolution network. The network structure includes an input layer (receiving gridded weather data, grid size 1km x 1km), a convolution layer (using 3D convolution kernel, size 3 x 3 x 3, channel number 32), a batch normalization layer and a ReLU activation function, and outputs a weather spatio-temporal feature matrix, which captures spatial gradient features (such as temperature field gradient calculated by Sobel operator) and time dynamic features (such as wind speed field extracted by LSTM network for sequence dependence). The equipment operation state feature vector is generated by multi-scale analysis of equipment operation parameters, pipe network operation state and user heat data: wavelet transform (Daubechies wavelet basis) is used to decompose energy consumption data into different frequency components, to extract equipment efficiency feature parameters (such as instantaneous COP), energy consumption distribution features (peak energy consumption detected by sliding window) and equipment health state indicators (based on vibration frequency analysis and maintenance records, using a neural network classifier).

[0080] The meteorological spatiotemporal feature matrix and the equipment operation state feature vector are input into a multi-modal data fusion model, and multi-modal meteorological situation data is generated through an adaptive weighted fusion algorithm. The algorithm calculates the weight coefficients of each meteorological element based on the attention mechanism: a multi-head attention layer (number of heads = 4) is used to calculate the attention scores of temperature, humidity, wind speed and radiation, and the weight coefficients are normalized by the softmax function. At the same time, the influence factors of equipment state on meteorological data are determined through correlation analysis (Pearson correlation coefficient), for example, when the correlation between equipment efficiency and temperature is greater than 0.7, the fusion weight is adjusted. The adaptive weighted fusion algorithm dynamically adjusts the weight, and the formula is: fusion weight = a x attention weight + b x influence factor, where a and b are adaptive coefficients (initial value 0.5, optimized by gradient descent). The output includes temperature field distribution data (grid precision 0.1°C), humidity field distribution data, wind speed field distribution data and solar radiation intensity distribution data.

[0081] Based on the equipment operation state feature vector, the equipment operation state representation parameters are generated through a state evaluation algorithm: the efficiency evaluation model is associated with the equipment load rate (measured by the current sensor), the running time (recorded by the timer) and the environmental temperature influence factor, and the running efficiency index is calculated using multiple linear regression; the energy consumption analysis algorithm includes peak energy consumption analysis (finding the daily maximum energy consumption), average energy consumption calculation (moving average method) and energy consumption trend prediction (ARIMA model); the health state evaluation model integrates the equipment running time, maintenance record (such as the last maintenance date) and fault history (fault code database), and outputs a health score (0-100 points). This embodiment enables technicians to use a deep learning framework (such as Keras) to realize data fusion and integrate it into a database system through specific network parameters, algorithm formulas and evaluation indicators.

[0082] Compared with related technologies, the traditional method only uses a single temperature parameter for load prediction, does not analyze the influence of humidity field distribution characteristics on heat transfer efficiency, and does not consider the effect of wind speed dynamic changes on pipe network heat loss. The present scheme synchronously captures the spatiotemporal evolution law of multi-dimensional meteorological elements through spatiotemporal feature extraction, and constructs a feature vector containing pipe network pressure fluctuation and user heat mode through multi-scale analysis of equipment operation, breaking through the limitations of single data source. In related technologies, equipment state evaluation is mostly dependent on artificial experience, while the present scheme establishes a quantitative correlation model between equipment efficiency and environmental temperature, and realizes automatic evaluation of equipment health status by combining maintenance records and energy consumption trend prediction, which to some extent improves the objectivity and comprehensiveness of the evaluation system.

[0083] By the technical solution, the application effectively solves the problem of insufficient use of meteorological data to a certain extent, and realizes the collaborative analysis of multi-dimensional meteorological elements by fusing the temperature field gradient characteristics, humidity distribution characteristics and wind speed dynamic characteristics. At the same time, based on the multi-dimensional evaluation system of equipment efficiency characteristic parameters and health state indexes, the defects of one-sidedness of traditional equipment state evaluation are overcome. Through the adaptive weighted fusion algorithm, the correlation weight of meteorological data and equipment state is dynamically adjusted, which to a certain extent improves the accuracy of multi-source data fusion, and provides accurate and reliable data support for the optimization control of the heating system.

[0084] The application further proposes a technical scheme of inputting the meteorological space-time feature matrix and the equipment operation state feature vector into the multi-modal data fusion model, generating multi-modal meteorological situation data through the adaptive weighted fusion algorithm, and generating equipment operation state characteristic parameters through the state evaluation algorithm based on the equipment operation state feature vector.

[0085] Among them, the adaptive weighted fusion algorithm refers to a fusion method of dynamically adjusting data weight according to the importance of meteorological elements and the influence of equipment state, which can be realized by combining a dynamic weight adjustment module with correlation analysis results, and is used to solve the fusion deviation problem caused by fixed meteorological element weight. The attention mechanism refers to a technology of assigning weights by calculating the contribution of each meteorological element to the heating system, which can be realized by using a multi-head attention network, and is used to quantify the influence difference of different meteorological elements such as temperature and humidity. The efficiency evaluation model refers to an algorithm of analyzing the operation efficiency by relating the device load rate and environmental temperature factors, which can be realized by training a multiple regression model combined with historical operation data, and is used to eliminate the interference of environmental factors on equipment efficiency evaluation. The health state evaluation model refers to an algorithm of integrating equipment maintenance records and fault history to predict the health degree of equipment, which can be realized by training a random forest classifier combined with maintenance data, and is used to break through the limitations of single operation parameter evaluation.

[0086] Specifically, the temperature field gradient feature, humidity field distribution feature, wind speed field dynamic feature and solar radiation intensity change feature in the meteorological spatio-temporal feature matrix are input into the attention mechanism module, and the weight coefficient is obtained by calculating the correlation of each meteorological element and the heating load. At the same time, the device efficiency feature parameter, energy consumption distribution feature and device health state index in the device running state feature vector are analyzed by Pearson correlation coefficient to determine the influence factor of the device state on the meteorological data. The weight coefficient and the influence factor are input into the dynamic weight adjustment module, and the fusion weight of the temperature field, humidity field and other data is optimized in real time according to the real-time importance of the meteorological elements and the change of the sensitivity of the device running state to the meteorological data. At the device state representation level, the device efficiency feature parameter is input into the regression model containing the ambient temperature compensation term to generate the running efficiency index eliminating the environmental interference; the energy consumption distribution feature is extracted by the sliding window analysis method to generate the energy consumption trend parameter combined with the load prediction result; and the device health state index is integrated with the maintenance cycle data and fault code by the classifier to generate the multi-dimensional health evaluation data.

[0087] In some embodiments, the above process can be represented in the form of formula (1) and formula (2) as follows.

[0088]

[0089] Wherein, M represents multi-modal meteorological situation data, m represents meteorological spatio-temporal feature matrix, n represents device running state feature vector, autoweight() represents the function corresponding to the adaptive weighted fusion algorithm, k represents device running state feature vector, and stateU() represents the function corresponding to the state evaluation algorithm.

[0090] Compared with related technologies, the traditional method fuses meteorological data with fixed weight, without considering the dynamic change of the sensitivity of the device state to the meteorology, resulting in deviation between the fused data and the actual heat load demand. The existing device evaluation is mainly based on a single running parameter, without establishing a correlation model between environmental factors and device state, and cannot accurately distinguish the difference between device performance decline and environmental influence. The present scheme realizes cross verification of meteorological data and device state through a dynamic weight adjustment mechanism, synchronously optimizes the meteorological element weight and device influence factor in the data fusion stage, and improves the spatio-temporal consistency of multi-modal data. An environmental compensation mechanism is constructed in the evaluation dimension to associate and model the device efficiency and the environmental temperature, effectively distinguishing the difference between the inherent performance of the device and the environmental influence.

[0091] All the optional technical solutions described above can be combined to form optional embodiments of the present application, which will not be described here.

[0092] By the technical solution, the meteorological data and the equipment operation state are deeply coupled, and the fusion error problem caused by the fixed weight of meteorological elements in the traditional monitoring is solved to a certain extent. By establishing an efficiency evaluation model including an environment compensation mechanism, the interference of external temperature fluctuation on the equipment performance evaluation is eliminated, and the accuracy of the operation efficiency index is improved. The multi-dimensional health evaluation system integrates the equipment maintenance data and the real-time operation parameters, and can identify potential fault risks in advance, thereby providing data support for preventive maintenance of the equipment.

[0093] In an example embodiment, a computer readable storage medium, such as a memory including a computer program, is also provided, and the computer program can be executed by a processor to complete the multi-element coupling heat supply monitoring method based on the weather condition prediction model in the above-mentioned embodiment. For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a mobile hard disk, a solid state disk, an optical data storage device, etc. In addition, since the technical solution provided by the embodiment of the present application requires a large amount of data, pure local storage may not be able to meet the requirements, and on this basis, the computer readable storage medium can also be in the form of cloud storage or AI database, and the cloud storage and AI database can also use cloud computing or AI retrieval to realize data retrieval.

[0094] In an example embodiment, a computer program product or computer program is also provided, and the computer program product or computer program includes program code stored in a computer readable storage medium, and a processor of a computer device reads the program code from the computer readable storage medium, and the processor executes the program code to make the computer device execute the multi-element coupling heat supply monitoring system based on the weather condition prediction model.

[0095] In some embodiments, the computer program related to the embodiments of the present application can be deployed on one computer device for execution, or on multiple computer devices located in one place for execution, or on multiple computer devices distributed in multiple places and interconnected through a communication network for execution. The multiple computer devices distributed in multiple places and interconnected through a communication network can constitute a blockchain system.

[0096] Those of ordinary skill in the art can understand that all or part of the steps of the above-mentioned embodiments can be completed by hardware, or by a program instructing related hardware, and the program can be stored in a computer readable storage medium, and the storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0097] The above merely is the optional embodiment of the present application, and does not use to limit the present application, any modification, equivalent replacement, improvement and so on, which are made in the spirit and principle of the present application, should be included in the protection scope of the present application.

Claims

1. A multi-element coupled heating monitoring system based on a meteorological condition prediction model, characterized in that, The system includes a processor and a memory, the processor being configured to perform the following steps: Acquire multi-source meteorological monitoring data and heating system operation data of the heating area. The multi-source meteorological monitoring data includes real-time meteorological observation data, meteorological forecast data and historical meteorological data. The heating system operation data includes equipment operating parameters, pipeline operation status and user heat consumption data. Based on the multi-source meteorological monitoring data and heating system operation data, multimodal meteorological situation data and equipment operation status characterization parameters are generated. The multimodal meteorological situation data includes temperature field distribution data, humidity field distribution data, wind speed field distribution data, and solar radiation intensity distribution data. The equipment operation status characterization parameters include equipment operation efficiency indicators, equipment energy consumption characteristic parameters, and equipment health status assessment data. Based on the multimodal meteorological situation data and the equipment operation status characterization parameters, the operation status assessment results and fault early warning information of the heating system are determined.

2. The system according to claim 1, characterized in that, The determination of the heating system's operational status assessment results and fault early warning information based on the multimodal meteorological situation data and the equipment operating status characterization parameters includes: Based on the multimodal meteorological situation data and the equipment operating status characterization parameters, an energy efficiency optimization objective function, equipment collaborative operation constraints, and system adjustment parameters are generated. Based on the energy efficiency optimization objective function, the equipment collaborative operation constraints, and the system adjustment parameters, the operating status assessment results of the heating system and the fault early warning information are determined.

3. The system according to claim 2, characterized in that, The process of generating an energy efficiency optimization objective function, equipment collaborative operation constraints, and system adjustment parameters based on the multimodal meteorological situation data and the equipment operating status characterization parameters includes: Based on the temperature field distribution data, humidity field distribution data, wind speed field distribution data, and solar radiation intensity distribution data in the multimodal meteorological situation data, the environmental heat load influencing factors are determined by a weighted fusion algorithm; Based on the equipment operating efficiency index, equipment energy consumption characteristic parameters and equipment health status assessment data in the equipment operating status characterization parameters, the equipment operating efficiency coefficient is determined through a multi-dimensional evaluation algorithm. The environmental heat load influencing factor and the equipment operating efficiency coefficient are input into the objective function to construct a model, generating an energy efficiency optimization objective function that includes energy consumption efficiency optimization terms, operating cost optimization terms, and environmental benefit optimization terms; Based on the equipment health status assessment data in the equipment operation status characterization parameters, a constraint generation algorithm is used to generate equipment cooperative operation constraints that include equipment operation safety boundaries, equipment capacity limits, and equipment start-stop frequency limits. Based on the environmental heat load influence factor and the equipment operating efficiency coefficient, system regulation parameters including temperature regulation parameters, pressure control parameters and flow regulation parameters are generated through parameter optimization algorithm.

4. The system according to claim 2, characterized in that, The process of determining the operating status assessment results and fault early warning information of the heating system based on the energy efficiency optimization objective function, the equipment collaborative operation constraints, and the system adjustment parameters includes: Based on the energy efficiency optimization objective function, the equipment collaborative operation constraints, and the system adjustment parameters, the meteorological influencing factors, load demand forecast results, and forecast uncertainty indicators are determined through a meteorological condition prediction model. Based on the meteorological influencing factors, the load demand forecast results, and the forecast uncertainty index, a multi-equipment collaborative control strategy, an operating parameter optimization scheme, and a heating mode switching command are determined. The system executes the multi-device collaborative control strategy, the operating parameter optimization scheme, and the heating mode switching command, and outputs the operating status evaluation results and the fault warning information through real-time data assimilation updates and anomaly monitoring.

5. The system according to claim 4, characterized in that, Based on the multimodal meteorological situation data and the equipment operating status characterization parameters, the meteorological condition prediction model is used to determine meteorological influencing factors, load demand prediction results, and prediction uncertainty indicators, including: Spatiotemporal features are extracted from the temperature field distribution data, humidity field distribution data, wind speed field distribution data, and solar radiation intensity distribution data in the multimodal meteorological situation data to generate a meteorological spatiotemporal feature matrix; The equipment operating efficiency index, equipment energy consumption characteristic parameter and equipment health status assessment data in the equipment operating status characterization parameters are normalized to generate an equipment status feature vector. The meteorological spatiotemporal feature matrix and the equipment status feature vector are input into the meteorological condition prediction model. The meteorological spatiotemporal features are extracted through a spatiotemporal convolutional network, and the weight coefficients of each meteorological element are calculated through an attention mechanism. Based on the weighting coefficients and the meteorological spatiotemporal characteristics, meteorological influence factors such as temperature influence coefficient, humidity influence coefficient, and wind speed influence coefficient are generated. Based on the meteorological influencing factors and the equipment status feature vector, a load demand forecast result containing short-term load forecast values, medium-term load trends and long-term load change patterns is generated through a regression prediction algorithm. Based on prediction residual analysis and confidence interval calculation, a prediction uncertainty index is generated that includes the prediction error range and confidence level.

6. The system according to claim 4, characterized in that, The process of determining a multi-device collaborative control strategy, operating parameter optimization scheme, and heating mode switching instructions based on the meteorological influencing factors, the load demand forecast results, and the forecast uncertainty index includes: Based on the meteorological influencing factors, the load demand forecast results, and the forecast uncertainty index, an energy efficiency optimization objective function, equipment collaborative operation constraints, and system adjustment parameters are generated through a multivariate coupled optimization algorithm. Based on the energy efficiency optimization objective function, the equipment collaborative operation constraints, and the system adjustment parameters, a multi-equipment collaborative control strategy, an operating parameter optimization scheme, and a heating mode switching command are determined.

7. The system according to claim 6, characterized in that, Based on the meteorological influencing factors, the load demand forecast results, and the forecast uncertainty index, a multivariate coupled optimization algorithm is used to generate an energy efficiency optimization objective function, equipment collaborative operation constraints, and system adjustment parameters, including: Based on the temperature influence coefficient, humidity influence coefficient and wind speed influence coefficient among the meteorological influence factors, an environmental heat load correction factor is generated by weighted calculation. Based on the short-term load forecast, medium-term load trend, and long-term load change pattern in the load demand forecast results, load demand change characteristics are generated through a trend analysis algorithm. Based on the prediction error range and confidence level in the prediction uncertainty index, a prediction reliability coefficient is generated through a risk assessment model. The environmental heat load correction factor, load demand variation characteristics, and prediction reliability coefficient are input into a multivariate coupled optimization algorithm, and an energy efficiency optimization objective function containing energy consumption efficiency optimization terms, operating cost optimization terms, and environmental benefit optimization terms is generated through multi-objective optimization calculation. Based on the equipment health status assessment data in the equipment operation status characterization parameters, the equipment cooperative operation constraints, including equipment operation safety boundaries, equipment capacity limits, and equipment start-stop frequency limits, are generated by constraint modeling. Based on the environmental heat load correction factor and the characteristics of load demand changes, system regulation parameters, including temperature setting parameters, pressure control parameters, and flow regulation parameters, are generated through parameter optimization calculations.

8. The system according to claim 6, characterized in that, The determination of a multi-device collaborative control strategy based on the energy efficiency optimization objective function, the equipment collaborative operation constraints, and the system adjustment parameters includes: The energy efficiency optimization objective function, the equipment collaborative operation constraints, and the system adjustment parameters are input into the digital twin model of the heating system. A multi-objective optimization algorithm is used to generate an equipment operating parameter optimization scheme, which includes temperature setpoint optimization parameters, pressure control optimization parameters, and flow distribution optimization parameters. Based on the optimized operating parameters of the equipment and the constraints of the equipment's coordinated operation, a multi-element equipment coordinated control strategy is generated through digital twin simulation calculation. The multi-element equipment coordinated control strategy includes a heat source equipment start-stop timing strategy, a load distribution ratio strategy, and an operating mode combination strategy. Based on the meteorological influencing factors and the load demand forecast results, a heating mode switching instruction is generated through a pattern recognition algorithm. The heating mode switching instruction includes pattern recognition conditions, switching timing judgment criteria, and transition control parameters. The multi-device collaborative control strategy and the operating parameter optimization scheme are linked through a device operating status feedback mechanism, and the heating mode switching command and the meteorological influencing factors are linked through an environmental adaptability algorithm.

9. The system according to claim 1, characterized in that, The generation of multimodal meteorological situation data and equipment operating status characterization parameters based on the multi-source meteorological monitoring data and heating system operation data includes: Spatiotemporal features are extracted from the real-time meteorological observation data, meteorological forecast data, and historical meteorological data in the multi-source meteorological monitoring data to generate a meteorological spatiotemporal feature matrix. The meteorological spatiotemporal feature matrix includes temperature field gradient features, humidity field distribution features, wind speed field dynamic features, and solar radiation intensity variation features. Multi-scale analysis is performed on the equipment operating parameters, pipeline operating status and user heat consumption data in the heating system operation data to generate equipment operating status feature vectors. The equipment operating status feature vectors include equipment efficiency feature parameters, energy consumption distribution characteristics and equipment health status indicators. The meteorological spatiotemporal feature matrix and the equipment operating status feature vector are input into the multimodal data fusion model, and the multimodal meteorological situation data is generated through an adaptive weighted fusion algorithm. Based on the device's operating state feature vector, the device's operating state characterization parameters are generated through a state evaluation algorithm.

10. The system according to claim 9, characterized in that, The step of inputting the meteorological spatiotemporal feature matrix and the equipment operating status feature vector into a multimodal data fusion model, and generating multimodal meteorological situation data through an adaptive weighted fusion algorithm, includes: Based on the temperature field gradient characteristics, humidity field distribution characteristics, wind speed field dynamic characteristics, and solar radiation intensity variation characteristics in the meteorological spatiotemporal feature matrix, the weight coefficients of each meteorological element are calculated through an attention mechanism. Based on the equipment efficiency characteristic parameters, energy consumption distribution characteristics and equipment health status indicators in the equipment operation status characteristic vector, the influencing factors of equipment status on meteorological data are determined by correlation analysis. The weighting coefficients and influencing factors are input into an adaptive weighted fusion model, and temperature field distribution data, humidity field distribution data, wind speed field distribution data, and solar radiation intensity distribution data are generated through dynamic weight adjustment. The adaptive weighted fusion algorithm dynamically adjusts the fusion weights based on the importance of meteorological elements and the impact of equipment status.

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