Intelligent Building Commutating Heating Method and System

Abstract

The present invention relates to the field of intelligent heating technology, specifically an intelligent building reverse heating method and system, which includes the following steps: collecting real-time data, including temperature, flow rate, and user demand; adjusting the opening degree of each intelligent valve; alternately performing forward and reverse heating to obtain an adjusted heating direction. In the present invention, through intelligent management and control means, the heat load prediction and network flow reconstruction technology are used to dynamically optimize the heating path. The decision logic "identifying peak demand areas based on heat load prediction results" allows for precise response to fluctuations in heat energy demand, achieving highly accurate energy distribution. The network flow reconstruction technology and the strategy of screening the optimal heating path according to the cost and efficiency principles are adopted to ensure that the heating system automatically optimizes its operating state, optimizes energy distribution, significantly improves the system's adaptability to changing demands, and at the same time optimizes the energy use efficiency, thereby reducing the environmental impact.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent heating, and particularly to an intelligent building reversing heating method and system. Background Art

[0002] In the technical field of intelligent heating, it focuses on using modern information technology, automatic control technology, and network communication technology to intelligently manage and control the building heating system. This method aims to optimize the operation efficiency of the heating system through intelligent means, improve energy utilization efficiency, and ensure user comfort at the same time. Intelligent heating technology not only focuses on the efficiency and energy conservation of heating, but also covers the reliability, flexibility, and user experience of the system.

[0003] Among them, the intelligent building reversing heating method is a technology that realizes the automatic adjustment of the heating quantity in different building areas or different time periods through intelligent control means. Its purpose is to automatically adjust the heating parameters (such as heating temperature, flow rate, etc.) according to the real-time or predicted heat load demand inside the building, so as to achieve the purpose of energy conservation and consumption reduction and improve user comfort. By realizing the intelligent management of the heating system, it aims to improve the economy and environmental friendliness of the entire heating system.

[0004] Traditional heating technology is insufficient in terms of adaptability and regulation accuracy, lacking a rapid response mechanism to real-time or predicted heat load changes, unable to achieve instant optimization of the heating path, resulting in inaccurate energy distribution, unable to effectively avoid energy waste, and reducing the user's comfort experience at the same time. The lack of the ability to dynamically optimize the heating path makes it difficult for the system to maintain high efficiency and energy conservation when facing demand changes, affecting the overall performance and environmental sustainability of the heating system. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose an intelligent building reversing heating method and system.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: The intelligent building reversing heating method includes the following steps:

[0007] S1: Collect real-time data, including temperature, flow rate, and user demand, adjust the opening degree of each intelligent valve, alternately perform forward and reverse heating, and obtain the adjusted heating direction;

[0008] S2: Use the adjusted heating direction to perform information exchange, including valve status and demand changes, construct a network connection diagram between valves, and obtain valve network communication data;

[0009] S3: According to the valve network communication data, adjust the working mode of the intelligent valve and the connection relationship between them, match the current heating demand and system status, and construct a self-organizing regulation strategy;

[0010] S4: Analyze historical heating data and real-time environmental variables, use linear regression analysis to evaluate the future heat load in the building area, combine the impacts of weather conditions, time, and building usage patterns on heat demand, and generate heat load prediction results;

[0011] S5: Utilize the heat load prediction results, perform dynamic optimization of the heating path through network flow reconstruction technology, adjust the connection mode between heat exchange nodes, combine with the adjustment of the heating direction, screen the optimal heating path, and obtain a heating network optimization plan;

[0012] S6: Combine the heating network optimization plan and the self-organizing control strategy, perform final adjustment, optimize for maximizing heating efficiency, and obtain the final heating control plan.

[0013] As a further solution of the present invention, the adjusted heating directions include forward heating and reverse heating, the valve network communication data includes valve identifiers, current opening and closing states, and connection information of its adjacent valves, the self-organizing control strategy includes the target valve opening and closing states and the priority of state adjustment, the heat load prediction results include the predicted heat load values, prediction accuracies, and predicted time ranges for all areas, the heating network optimization plan specifically is the selection of the optimized heating path, adjustment of the heating direction, and expected energy-saving effect, and the final heating control plan includes the finally selected heating path, the final state configuration of the valves, and details of the heating direction.

[0014] As a further solution of the present invention, the steps of collecting real-time data, including temperature, flow rate, and user demand, adjusting the opening degree of each intelligent valve, and alternately performing forward and reverse heating to obtain the adjusted heating direction are as follows.

[0015] S101: Collect real-time data, including temperature, flow rate, and user demand. If the current time is within the set forward heating time period, set valves 1# and 4# to the open state and valves 2# and 3# to the closed state to generate a preliminary setting of the heating direction.

[0016] S102: Based on the preliminary setting of the heating direction, if the current temperature is lower than the set heating threshold, it is determined that the demand has increased, and a delayed opening strategy is executed for the closed valves to prepare for reverse heating, obtaining a reverse heating preparation state.

[0017] S103: Based on the reverse heating preparation state, if the set reverse heating time period is reached, alternately open and close the valves, close the originally open valves 1# and 4#, and open valves 2# and 3# to obtain the adjusted heating direction.

[0018] As a further solution of the present invention, according to the valve network communication data, adjusting the working modes and the connection relationships among the intelligent valves to match the current heating demand and the system state, the steps of constructing the self-organizing regulation strategy are as follows:

[0019] S301: Based on the valve network communication data, analyze the current working states of each intelligent valve and the connection information among them, adjust the valves to match the current heating demand, and generate a list of valves to be adjusted;

[0020] S302: For each valve in the list of valves to be adjusted, determine whether its current state is consistent with the demand. If not, adjust its opening or closing state according to the heating demand and the system state to obtain the adjusted valve state;

[0021] S303: Based on the adjusted valve states, reconstruct the connection relationship diagram among the valves to ensure that the working state of each valve matches the system heating demand and the overall state, and construct the self-organizing regulation strategy.

[0022] As a further solution of the present invention, analyzing the historical heating data and the real-time environmental variables, and using linear regression analysis to evaluate the future building area heat load, and combining the influence of weather conditions, time, and building usage patterns on the heat demand, the steps of generating the heat load prediction result are as follows:

[0023] S401: Collect historical heating data and real-time environmental variable data, including past temperature and flow rate records and current meteorological conditions, to obtain the basic data collection record;

[0024] S402: Analyze the information in the basic data collection record, combine the influence of weather conditions, time, and building usage patterns on the heat demand, assign weights to each factor, and generate an influence factor weight table;

[0025] S403: According to the influence factor weight table, combine the historical heating data and the real-time environmental variables, and evaluate the future building area heat load by means of linear regression analysis to generate the heat load prediction result.

[0026] As a further solution of the present invention, the linear regression analysis method is as follows according to the formula:

[0027] y′ = β0 + β1x 温度 + β2x 湿度 + β3x 历史热负荷 + β4x 日照时长 + β5x 风速 + ò

[0028] Calculate the heat load prediction result of the building area, where y′ is the improved predicted heat load value, and x 温度 is the real-time temperature, x湿度 is the real-time humidity, x 历史热负荷 is the recent historical heat load data, x 日照时长 is the sunshine duration, x 风速 is the wind speed, β0 is the intercept term, β1, β2, β3, β4, β5 are the weights of influencing factors, and ò is the prediction error.

[0029] As a further solution of the present invention, using the heat load prediction result, through the network flow reconstruction technology, the dynamic optimization of the heating path is carried out, the connection mode between heat exchange nodes is adjusted, combined with the adjustment of the heating direction, the optimal heating path is screened, and the steps to obtain the heating network optimization plan are as follows:

[0030] S501: Based on the heat load prediction result, identify the building areas with future demand peaks, mark the areas as the nodes with the highest heating priority, and generate high-demand node marks;

[0031] S502: Using the high-demand node marks, evaluate the heating paths from heat exchange nodes to high-demand nodes in the existing heating network, and according to the efficiency and heating capacity of the paths, use the network flow reconstruction technology for optimization and adjustment to obtain a list of candidate optimized paths;

[0032] S503: For each path in the list of candidate optimized paths, combined with the adjustment requirements of the heating direction, select the optimal heating path according to the principle of the lowest cost and the highest efficiency to obtain the heating network optimization plan.

[0033] As a further solution of the present invention, the network flow reconstruction technology is calculated according to the formula:

[0034]

[0035] Calculate the total optimized cost of the heating network, where C t ′ otal represents the improved total cost, E represents the set of edges in the network, c ij represents the unit flow cost of the edge from node i to node j, f ij represents the flow of this edge, k ij represents the reliability coefficient of the path, d ij represents the unit distance cost, ΔT ij represents the temperature difference from node i to node j.

[0036] Intelligent building reverse heating system, the system includes:

[0037] The data collection module collects real-time data based on temperature, flow rate and user needs, conducts preliminary analysis and classification on the data, and generates real-time data analysis results;

[0038] The status marking module marks the current status of each intelligent valve based on the real-time data analysis results, including open or closed and the current heating direction, and generates a valve status identifier;

[0039] The demand analysis module analyzes the changes in the heating demand of the valves based on the valve status identifier, including temperature changes, flow rate requirements, and updates of user-specific requirements, and generates a demand change analysis record;

[0040] The load forecasting module evaluates the future heat load of the building area based on the demand change analysis record, combined with historical heating data and real-time environmental variables, using linear regression analysis, and generates a heat load forecasting result;

[0041] The path optimization module identifies the building areas with future demand peaks based on the heat load forecasting result, and uses network flow reconstruction technology to adjust the connection mode between heat exchange nodes, and generates a heating network optimization plan;

[0042] The efficiency optimization module analyzes the energy efficiency distribution in the current heating network based on the heating network optimization plan, marks the areas with low energy efficiency, designs specific adjustment plans to improve energy efficiency, and generates an energy efficiency optimization plan;

[0043] The control strategy module makes the final adjustment to the heating system based on the energy efficiency optimization plan, combined with the self-organizing control strategy, optimizes the heating efficiency, and generates a final heating control plan.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0045] In the present invention, through intelligent management and control means, the heating path is dynamically optimized by using heat load forecasting and network flow reconstruction technology. The determination logic "identifying the demand peak area based on the heat load forecasting result" allows for precise response to fluctuations in heat energy demand, realizes highly accurate energy distribution, adopts network flow reconstruction technology and the strategy of screening the optimal heating path according to the cost and efficiency principles, ensures the automatic optimization of the operating state of the heating system, optimizes energy distribution, significantly improves the adaptability of the system to changing demands, and at the same time optimizes the energy use efficiency, thereby reducing the environmental impact. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a schematic diagram of the working process of the present invention;

[0047] Figure 2 is a detailed flowchart of S1 of the present invention;

[0048] Figure 3 is a detailed flowchart of S2 of the present invention;

[0049] Figure 4 is a detailed flowchart of S3 of the present invention;

[0050] Figure 5 It is the detailed flowchart of S4 of the present invention;

[0051] Figure 6 It is the detailed flowchart of S5 of the present invention;

[0052] Figure 7 It is the detailed flowchart of S6 of the present invention;

[0053] Figure 8 It is the system flowchart of the present invention;

[0054] Figure 9 It is the schematic diagram of the initial operating condition of the system of the present invention;

[0055] Figure 10 It is the schematic diagram of the commutation operating condition of the system of the present invention. Specific embodiments

[0056] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0057] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0058] Embodiment 1

[0059] Please refer to Figure 1-10 , the present invention provides a technical solution: an intelligent building commutation heating method, including the following steps:

[0060] S1: Collect real-time data, including temperature, flow rate and user requirements, adjust the opening degree of each intelligent valve, and alternately perform forward and reverse heating to obtain the adjusted heating direction;

[0061] S2: Use the adjusted heating direction to perform information exchange, including valve status and demand changes, construct a network connection diagram between valves, and obtain valve network communication data;

[0062] S3: Adjust the working modes of the intelligent valves and their connection relationships according to the valve network communication data, match the current heating demand and system status, and construct a self-organizing regulation strategy;

[0063] S4: Analyze the historical heating data and real-time environmental variables, use linear regression analysis to evaluate the future heat load of the building area, and generate a heat load prediction result by combining the impacts of weather conditions, time, and building usage patterns on the heat demand;

[0064] S5: Utilize the heat load prediction result to dynamically optimize the heating path through network flow reconstruction technology, adjust the connection modes between heat exchange nodes, combine with the adjustment of the heating direction, screen the optimal heating path, and obtain a heating network optimization plan;

[0065] S6: Combine the heating network optimization plan and the self-organizing regulation strategy to perform a final adjustment, optimize for maximizing the heating efficiency, and obtain a final heating regulation plan.

[0066] The adjusted heating directions include forward heating and reverse heating. The valve network communication data includes valve identifiers, current opening / closing states, and connection information of their adjacent valves. The self-organizing regulation strategy includes the target valve opening / closing states and the priorities of state adjustments. The heat load prediction result includes the predicted heat load values, prediction accuracies, and prediction time ranges for all areas. The heating network optimization plan specifically refers to the optimized heating path selection, heating direction adjustment, and expected energy-saving effects. The final heating regulation plan includes the finally selected heating path, the final state configuration of the valves, and the details of the heating direction.

[0067] Please refer to Figure 2 and collect real-time data, including temperature, flow rate, and user demand. Adjust the opening degrees of each intelligent valve, and alternately perform forward and reverse heating. The specific steps for obtaining the adjusted heating direction are as follows:

[0068] S101: Collect real-time data, including temperature, flow rate, and user demand. If the current time is within the set forward heating time period, set valves 1# and 4# to the open state and valves 2# and 3# to the closed state to generate a preliminary setting of the heating direction;

[0069] In step S101, based on the data collected by the temperature sensor, flow rate sensor, and user input device, a threshold comparison algorithm is used to set a preset heating threshold for the temperature data. If the measured temperature is lower than this threshold, it is determined that the heating demand has increased. At this time, the algorithm will specify that valves 1# and 4# are in the open state, and valves 2# and 3# are in the closed state. This process is implemented on the microcontroller through programming. The threshold comparison algorithm is written in Python, and the running environment is Raspberry Pi. The sensor data is read through the GPIO interface, and specific parameters are set for each sensor: the temperature sensor parameter is set to read at an interval of 500 ms, the flow rate sensor is set to read once per second, and the user's demand is input through a simple interface implemented using the tkinter library, generating a preliminary setting of the heating direction.

[0070] S102: Based on the preliminary setting of the heating direction, if the current temperature is lower than the set heating threshold, it is determined that the demand has increased, and a delayed opening strategy is executed for the closed valves to prepare for reverse heating, obtaining the reverse heating preparation state.

[0071] In step S102, using the preliminary setting of the heating direction from the previous step, a timer control algorithm is adopted, and the delay parameter is set to the current time plus the preset reverse heating preparation time. If the current system time reaches the time point set by the delay parameter, the reverse heating preparation action is executed, that is, a delayed opening strategy is executed for the originally closed valves 2# and 3#. This strategy is written in C language and runs on an embedded system. The timer interrupt service program is used to achieve time control, and the delay parameter is obtained through the system clock to ensure the accuracy of time control, generating the reverse heating preparation state.

[0072] S103: Based on the reverse heating preparation state, if the set reverse heating time period is reached, the valves are alternately opened and closed, the originally open valves 1# and 4# are closed, and valves 2# and 3# are opened, obtaining the adjusted heating direction.

[0073] In step S103, based on the reverse heating preparation state, a state switching algorithm is adopted to check whether the current system time is within the set reverse heating time period. This algorithm is implemented in Java and runs on the server side. The current time is obtained by accessing the built-in calendar service in the system and compared with the preset reverse heating time period. If the current time falls within the reverse heating time period, a control signal is sent through a network instruction to close the originally open valves 1# and 4#, and at the same time, valves 2# and 3# are opened to complete the adjustment of the heating direction. The network instruction is sent using the TCP / IP protocol to ensure the reliability of the instruction transmission, generating the adjusted heating direction.

[0074] Please refer to Figure 3, using the adjusted heat supply direction to conduct information exchange, including valve status and demand changes, and constructing a network connection diagram between valves. The specific steps to obtain valve network communication data are as follows:

[0075] S201: Based on the adjusted heat supply direction, perform status marking on each intelligent valve, mark it as open or closed, and record its current heat supply direction, which is forward heat supply or reverse heat supply, to generate valve status marks;

[0076] In step S201, based on the adjusted heat supply direction, use the label assignment method to perform status marking on each intelligent valve. The specific operations include accessing the valve control unit, reading its current status (open or closed) and heat supply direction (forward or reverse), assigning a unique identifier to each valve in the central control system through programming, and recording its status and direction at the same time. This process is written in C# and runs on the Windows operating system platform. The valve control unit is connected to the central control system through the Modbus communication protocol to generate valve status marks.

[0077] S202: Based on the valve status marks, collect and record the changes in heat supply demand of each valve, including temperature changes, flow rate requirements, and user-specific requirements, and update the working status of the valve to obtain updated valve demand data;

[0078] In step S202, based on the valve status marks, use the data collection and update algorithm. The specific operations are to monitor the changes in heat supply demand of each intelligent valve, including real-time collection of temperature changes, flow rate requirements, and user-specific requirements, collect data from various sensors and user interfaces using Python scripts through the RESTful API, and perform preliminary filtering and formatting on the collected data to meet the requirements of subsequent processing. This script executes the data collection and update task every 5 minutes to ensure that the working status of the valve responds to actual demand changes in a timely manner and generates updated valve demand data.

[0079] S203: Based on the updated valve demand data, construct a network connection diagram between valves. The nodes represent valves, and the edges represent the communication paths between valves, including the direction and intensity of data transmission, to obtain valve network communication data;

[0080] In step S203, based on the updated valve demand data, a graph construction algorithm is adopted. The specific operation is to construct a network connection graph between valves. A program written in Java analyzes the updated valve demand data to determine the logical connection relationships between valves, including the direction and intensity of data transmission. Each valve is a node in the graph, and the communication paths between valves are edges. The weight of an edge is determined by the data transmission intensity. This program uses the JGraphT library to implement graph construction and analysis, and the graph structure is automatically updated every 10 minutes to reflect the latest network status, generating valve network communication data.

[0081] Please refer to Figure 4 , according to the valve network communication data, adjust the working modes of intelligent valves and their connection relationships with each other to match the current heating demand and system status. The specific steps for constructing the self-organizing regulation strategy are as follows:

[0082] S301: Based on the valve network communication data, analyze the current working status of each intelligent valve and the connection information between them, adjust the valves to match the current heating demand, and generate a list of valves to be adjusted;

[0083] In step S301, based on the valve network communication data, a network analysis method is adopted to parse the data. The operations include traversing the current working status of each intelligent valve and the connection information between them. A Python script uses the NetworkX library to analyze the graph structure, identify valves whose heating demand does not match the existing status. This analysis is based on the opening status of the valve, the heating direction, and the connection intensity with other valves. Assign a unique identifier to each unmatched valve and record its current status and connection information, generating a list of valves to be adjusted.

[0084] S302: For each valve in the list of valves to be adjusted, judge whether its current status is consistent with the demand. If not, adjust its opening or closing status according to the heating demand and system status to obtain the adjusted valve status;

[0085] In step S302, for each valve in the list of valves to be adjusted, a status adjustment strategy is adopted. The specific operation is to evaluate the consistency between the current status of each valve and the actual heating demand. If inconsistency is found, the opening or closing status of the valve is adjusted by sending a network command. This process is implemented in Java and interacts with the database through JDBC to obtain the heating demand information of each valve. Use if-else logic to judge whether the valve status needs to be adjusted. For valves that need to be adjusted, specify the parameters of the network command in detail, including the valve ID, target status (open / close), and timestamp of the execution action, to obtain the adjusted valve status.

[0086] S303: Based on the adjusted valve states, reconstruct the connection relationship diagram between valves to ensure that the working state of each valve matches the system's heating demand and overall state, and construct a self-organizing control strategy;

[0087] In step S303, based on the adjusted valve states, use a graph reconstruction algorithm. The operations include reconstructing the network connection relationship diagram between valves to ensure that the state of each node (i.e., valve) in the graph matches the overall heating demand and state of the system. This process is written in MATLAB and utilizes its powerful graph theory and network analysis toolboxes. Analyze each valve node, update the node attributes according to the adjusted state, and recalculate the edges between nodes, i.e., the direction and intensity of data transmission, to reflect the latest heating network structure and generate a self-organizing control strategy.

[0088] Please refer to Figure 5 , analyze historical heating data and real-time environmental variables, and use linear regression analysis to evaluate the future heat load of the building area. Considering the impact of weather conditions, time, and building usage patterns on heat demand, the specific steps to generate the heat load prediction result are as follows.

[0089] S401: Collect historical heating data and real-time environmental variable data, including past temperature and flow rate records, as well as current meteorological conditions, to obtain the basic data collection record;

[0090] In step S401, based on the data provided by temperature sensors, flow meters, and weather stations, use a data aggregation method. The operations include using SQL queries to extract past temperature and flow rate records from the database, and at the same time obtaining current meteorological conditions, including air temperature, wind speed, and sunshine duration, through an API. These data are automatically collected using a Python script that runs once an hour to ensure the real-time and accuracy of the data. The collected data is stored in a central data warehouse for further analysis to generate the basic data collection record.

[0091] S402: Analyze the information in the basic data collection record, considering the impact of weather conditions, time, and building usage patterns on heat demand, assign weights to each factor, and generate an influence factor weight table;

[0092] In step S402, based on the basic data collection record, use a weight assignment method. The operation is to analyze the specific impact of weather conditions, time, and building usage patterns on heat demand, and use a Python script for data processing to assign weights to each influencing factor. The determination of the weights is based on the correlation analysis of historical data. Use the statistical software R for linear regression analysis to determine the correlation coefficients of each factor, and assign weights according to these coefficients to generate the influence factor weight table.

[0093] S403: According to the influence factor weight table, combined with historical heating data and real-time environmental variables, evaluate the future heat load of the building area through linear regression analysis method, and generate the heat load prediction result;

[0094] The linear regression analysis method is carried out according to the formula:

[0095] y′ = β0 + β1x 温度 + β2x 湿度 + β3x 历史热负荷 + β4x 日照时长 + β5x 风速 + ò

[0096] Calculate the heat load prediction result of the building area, where y′ is the improved predicted heat load value, and x 温度 is the real-time temperature, x 湿度 is the real-time humidity, x 历史热负荷 is the recent historical heat load data, x 日照时长 is the sunshine duration, x 风速 is the wind speed, β0 is the intercept term, β1, β2, β3, β4, β5 are the weights of the influencing factors, and ò is the prediction error.

[0097] The execution process is as follows:

[0098] First, collect and sort out the real-time environmental variables and historical heating data, including real-time temperature, real-time humidity, recent historical heat load data, sunshine duration and wind speed;

[0099] Then, use statistical methods to determine the weights of each influencing factor, namely β1, β2, β3, β4, β5, and the weights are obtained by analyzing the relationship between historical data and heat load;

[0100] Next, substitute the collected real-time data and historical data into the improved formula;

[0101] Finally, calculate the heat load prediction result of the building area, that is, y′,

[0102] This process includes comprehensively analyzing the influence of real-time environmental variables and historical data, and considering the contribution of new parameters such as sunshine duration and wind speed to heat load prediction, which greatly improves the prediction accuracy.

[0103] Please refer to Figure 6 , using the heat load prediction result, through the network flow reconstruction technology to dynamically optimize the heating path, adjust the connection mode between heat exchange nodes, combined with the adjustment of the heating direction, screen the optimal heating path, and the specific steps to obtain the heating network optimization scheme are as follows,

[0104] S501: Based on the heat load prediction results, identify the building areas with future demand peaks, mark the areas as the nodes with the highest heating priority, and generate high-demand node marks.

[0105] In step S501, based on the heat load prediction results, using the Geographic Information System (GIS) analysis method, the operations performed include using ArcGIS software to conduct spatial analysis on the heat load data of the building areas, identifying the building areas with future demand peaks through the set heat load threshold, assigning a unique spatial identifier to each identified high-demand area. This operation depends on the building geographical location and the predicted heat load data, and the spatial identifier is generated through the longitude and latitude information of the building to ensure the accurate identification of each high-demand area and generate high-demand node marks.

[0106] S502: Utilize the high-demand node marks to evaluate the heating paths from the heat exchange nodes to the high-demand nodes in the existing heating network, and perform optimization and adjustment using the network flow reconstruction technology according to the efficiency and heating capacity of the paths to obtain a list of candidate optimized paths.

[0107] For the network flow reconstruction technology, according to the formula:

[0108]

[0109] Calculate the optimized total cost of the heating network, where C′ total represents the improved total cost, E represents the set of edges in the network, c ij represents the unit flow cost of the edge from node i to node j, f ij represents the flow of this edge, k ij represents the reliability coefficient of the path, d ij represents the unit distance cost, and ΔT ij represents the temperature difference between node i and node j.

[0110] The execution process is as follows:

[0111] First, calculate the unit flow cost c ij of each path, and the flow f ij of this path;

[0112] Then, introduce the reliability coefficient k ij , considering that some paths in the heating network may have high reliability due to maintenance or other factors, and the coefficient can be obtained through historical data analysis;

[0113] Next, calculate the unit distance cost d ij and the temperature difference ΔT ij between nodes. The temperature difference can be estimated based on the heating demand and the actual heating capacity, and the unit distance cost takes into account the energy loss during transmission;

[0114] Finally, combine all these factors, calculate the optimized total cost of the heating network according to the improved formula, and obtain a list of candidate optimized paths.

[0115] S503: For each path in the list of candidate optimized paths, in combination with the adjustment requirements of the heating direction, select the optimal heating path by applying the principle of the lowest cost and the highest efficiency, and obtain the heating network optimization plan;

[0116] In step S503, for each path in the list of candidate optimized paths, use the cost-benefit analysis method. The operations performed are to evaluate the heating cost and expected benefits of each path in combination with the adjustment requirements of the heating direction, and calculate through Excel using a custom cost-benefit model. The model takes into account factors such as the length of the heating path, expected heat loss, heating efficiency, and maintenance cost, calculates a cost-benefit ratio for each path, and selects the path with the lowest cost and the highest benefit as the optimal heating path to generate the heating network optimization plan.

[0117] Please refer to Figure 7 , in combination with the heating network optimization plan and the self-organizing control strategy, perform the final adjustment to maximize the heating efficiency. The specific steps to obtain the final heating control plan are as follows.

[0118] S601: Based on the heating network optimization plan, analyze the energy efficiency distribution in the current heating network, mark the areas with low energy efficiency, and generate an energy efficiency distribution map;

[0119] In step S601, based on the heating network optimization plan, use the thermodynamic analysis method. The operations performed include using the dedicated software Simulink to simulate the energy efficiency of the heating network. In the simulation, the thermodynamic parameters of each heating node, such as temperature, pressure, and flow rate, are set in detail. The software simulates the flow and distribution of thermal energy in the network according to these parameters. Through the analysis of the simulation results, identify the areas with low thermal efficiency, generate unique energy efficiency identifiers for these areas to ensure the accurate visualization of the energy efficiency distribution, and generate the energy efficiency distribution map.

[0120] S602: Combine the energy efficiency distribution map and the self-organizing control strategy, improve the energy efficiency by adjusting the heating path or changing the heating direction, design a specific adjustment plan for each area, and obtain an adjustment schedule;

[0121] In step S602, combining the energy efficiency distribution map and the self-organizing control strategy, using the path adjustment algorithm, the operations include analyzing the inefficient areas marked on the energy efficiency map, calculating the heating paths from these areas to the efficient areas through a written MATLAB script. The heating direction adjustment logic is introduced in the script, which dynamically adjusts the heating paths according to the heating demand and system status. The script uses the Floyd-Warshall shortest path algorithm in graph theory to calculate the heating paths with the lowest cost and highest efficiency for each area, and generates an adjustment schedule.

[0122] S603: Execute each plan in the adjustment schedule to dynamically adjust the heating network, optimize the heating efficiency, ensure that each area obtains the optimal heating effect according to the actual demand, and construct the final heating control scheme;

[0123] In step S603, execute each plan in the adjustment schedule, adopt the dynamic network configuration technology. The operations include using control system software, such as SCADA, to send configuration instructions to each node in the heating network. The instructions contain the adjustment information of the heating path and the change instructions of the heating direction. The software dynamically configures the network according to the information in the adjustment schedule, and adjusts the heating parameters in real time to adapt to the actual demand, ensuring the flexible response and efficient operation of the heating system, and generating the final heating control scheme.

[0124] Please refer to Figure 8 , the intelligent building reverse heating system includes:

[0125] The data collection module collects real-time data based on temperature, flow rate and user demand, conducts preliminary analysis and classification on the data, and generates real-time data analysis results;

[0126] The status marking module marks the current status of each intelligent valve based on the real-time data analysis results, including open or closed and the current heating direction, and generates valve status identifiers;

[0127] The demand analysis module analyzes the changes in the heating demand of the valves based on the valve status identifiers, including temperature changes, flow rate requirements and updates of user-specific requirements, and generates a demand change analysis record;

[0128] The load forecasting module evaluates the future heat load of the building area based on the demand change analysis record, combines historical heating data and real-time environmental variables, and uses linear regression analysis to generate a heat load forecasting result;

[0129] The path optimization module identifies the building areas with future demand peaks based on the heat load forecasting result, and uses the network flow reconstruction technology to adjust the connection mode between the heat exchange nodes, and generates a heating network optimization scheme;

[0130] Based on the heating network optimization plan, the efficiency optimization module analyzes the energy efficiency distribution in the current heating network, marks the areas with low energy efficiency, designs specific adjustment plans to improve energy efficiency, and generates an energy efficiency optimization plan.

[0131] Based on the energy efficiency optimization plan and combined with the self-organizing control strategy, the control strategy module makes final adjustments to the heating system, optimizes the heating efficiency, and generates a final heating control plan.

[0132] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. Intelligent building commutation heating system, characterized in that, The system includes: The data collection module collects real-time data based on temperature, flow rate, and user requirements, performs preliminary analysis and classification on the data, and generates real-time data analysis results; The status marking module marks the current status of each intelligent valve based on the real-time data analysis results, including open or closed and the current heating direction, and generates valve status identifiers; The demand analysis module analyzes the changes in the heating demand of the valves based on the valve status identifiers, including temperature changes, flow rate requirements, and updates of user-specific requirements, and generates demand change analysis records; The load prediction module evaluates the future heat load of the building area based on the demand change analysis records, combines historical heating data and real-time environmental variables, and uses linear regression analysis to generate heat load prediction results; The path optimization module identifies the building areas with future demand peaks based on the heat load prediction results, and uses network flow reconstruction technology to adjust the connection mode between heat exchange nodes to generate a heating network optimization plan; The efficiency optimization module analyzes the energy efficiency distribution in the current heating network based on the heating network optimization plan, marks the areas with low energy efficiency, designs specific adjustment plans to improve energy efficiency, and generates an energy efficiency optimization plan; The control strategy module makes final adjustments to the heating system based on the energy efficiency optimization plan, combines self-organizing control strategies, optimizes the heating efficiency, and generates a final heating control plan; Among them, the opening degree of each intelligent valve is adjusted according to the collected real-time data, and forward and reverse heating are alternately performed to obtain the adjusted heating direction. The adjusted heating direction includes forward heating and reverse heating. The valve network communication data includes valve identifiers, the current opening and closing status, and the connection information of its adjacent valves. The self-organizing control strategy includes the target valve opening and closing status and the priority of status adjustment. The heat load prediction results include the predicted heat load values, prediction accuracy, and prediction time range of all areas. The heating network optimization plan is specifically the optimized heating path selection, heating direction adjustment, and expected energy-saving effect. The final heating control plan includes the finally selected heating path, the final status configuration of the valves, and the details of the heating direction; The steps of collecting real-time data, including temperature, flow rate, and user requirements, and adjusting the opening degree of each intelligent valve to alternately perform forward and reverse heating to obtain the adjusted heating direction are as follows: Collect real-time data, including temperature, flow rate, and user requirements. If the current time is within the set forward heating time period, set valves 1# and 4# to the open state and valves 2# and 3# to the closed state to generate a preliminary setting of the heating direction; Based on the preliminary setting of the heating direction, if the current temperature is lower than the set heating threshold, it is determined that the demand has increased, and a delayed opening strategy is executed for the closed valves to prepare for reverse heating to obtain a reverse heating preparation state; Based on the reverse heating preparation state, if the set reverse heating time period is reached, the valves are alternately opened and closed, valves 1# and 4# that were originally open are closed, and valves 2# and 3# are opened to obtain the adjusted heating direction; Based on the status and demand changes of the valves, obtain the valve network communication data. According to the valve network communication data, adjust the working modes and connection relationships among the intelligent valves to match the current heating demand and system status. The steps for constructing the self-organizing control strategy are as follows: Based on the valve network communication data, analyze the current working status of each intelligent valve and the connection information among them, adjust the valves to match the current heating demand, and generate a list of valves to be adjusted; For each valve in the list of valves to be adjusted, judge whether its current status is consistent with the demand. If not, adjust its opening or closing status according to the heating demand and system status to obtain the adjusted valve status; Based on the adjusted valve status, reconstruct the connection relationship diagram among the valves to ensure that the working status of each valve matches the heating demand and overall status of the system, and construct the self-organizing control strategy; Analyze the historical heating data and real-time environmental variables, and use linear regression analysis to evaluate the future heat load of the building area. Combine the influence of weather conditions, time, and building usage patterns on the heat demand to generate the heat load prediction result. The steps are as follows: Collect historical heating data and real-time environmental variable data, including past temperature and flow rate records and current meteorological conditions, to obtain the basic data collection record; Analyze the information in the basic data collection record, combine the influence of weather conditions, time, and building usage patterns on the heat demand, assign weights to each factor, and generate the influence factor weight table; According to the influence factor weight table, combine the historical heating data and real-time environmental variables, and use the linear regression analysis method to evaluate the future heat load of the building area to generate the heat load prediction result; The linear regression analysis method calculates the predicted result of the heat load in the building area according to the formula: y' = β0 + β1x 温度 + β2x 湿度 + β3x 历史热负荷 + β4x 日照时长 + β5x 风速 + ò, where y′ is the predicted heat load value after improvement, x temperature is the real-time temperature, x 湿度 is the real-time humidity, x 历史热负荷 is the recent historical heat load data, x 日照时长 is the sunshine duration, x 风速 is the wind speed, β0 is the intercept term, β1, β2, β3, β4, β5 are the weights of the influencing factors, and ò is the prediction error; Use the heat load prediction result to dynamically optimize the heating path through the network flow reconstruction technology, adjust the connection mode between the heat exchange nodes, combine the adjustment of the heating direction, screen the optimal heating path, and obtain the steps for the heating network optimization plan as follows: Based on the heat load prediction result, identify the building areas with future demand peaks, mark the areas as the nodes with the highest heating priority, and generate the high-demand node marks; Use the high-demand node marks to evaluate the heating paths from the heat exchange nodes to the high-demand nodes in the existing heating network, and use the network flow reconstruction technology to optimize and adjust according to the efficiency and heating capacity of the paths to obtain a list of candidate optimized paths; The network flow reconstruction technology calculates the total optimized cost of the heating network according to the formula: where C′ total represents the total cost after improvement, E represents the set of edges in the network, c ij represents the unit flow cost of the edge from node i to node j, f ij represents the flow of this edge, k ij represents the reliability coefficient of the path, d ij represents the unit distance cost, and ΔT ij represents the temperature difference from node i to node j; The execution process of the heating network optimization plan is as follows: First, calculate the unit flow cost c of each path ij , and the flow f of that path ij ; Then, introduce the reliability coefficient k ij ; Next, calculate the unit distance cost d ij and the temperature difference ΔT between nodes ij ; Finally, combine all these factors, calculate the optimized total cost of the heating network according to the improved formula, and obtain the list of candidate optimized paths; For each path in the list of candidate optimized paths, combine the adjustment requirements of the heating direction, and select the optimal heating path according to the principle of the lowest cost and the highest efficiency to obtain the heating network optimization plan; For each path in the list of candidate optimized paths, use the cost-benefit analysis method. The operation is to combine the adjustment requirements of the heating direction, evaluate the heating cost and expected benefits of each path, calculate a cost-benefit ratio for each path using a custom cost-benefit model through Excel; For each path in the list of candidate optimized paths, in combination with the adjustment requirements of the heating direction, the optimal heating path is selected by applying the principles of the lowest cost and the highest efficiency, and an optimized heating network scheme is obtained.

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