Remote heat supply management method, system and equipment based on Internet of Things

Abstract

The invention discloses a remote heat supply management method, system and equipment based on the Internet of Things, and relates to the technical field related to heat supply management, and the method comprises the steps: installing terminal equipment on a heat supply pipeline of a user; after the terminal equipment is activated, the unique ID, the position information and the physical topology information of the terminal equipment are uploaded; executing proximity search of the terminal equipment, establishing initial handshake connection, and executing edge communication networking; self-adaptive marshalling reconstruction is executed, and node roles in a group are configured; calling terminal equipment monitoring data, performing monitoring data exception identification based on intra-group node roles by using the reconstructed marshalling, and reporting local exception; and after the local abnormity is uploaded, secondary verification of the local abnormity is executed, and heat supply management early warning is generated. The technical problems of insufficient data acquisition dimension, low network communication efficiency and poor anomaly detection reliability and real-time performance in the prior art are solved, and the technical effects of realizing efficient and accurate monitoring and low-delay communication and improving the reliability and real-time performance of heat supply early warning are achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to heat supply management, and specifically to a remote heat supply management method, system and equipment based on the Internet of Things. Background Art

[0002] Centralized heating systems play a vital role in ensuring winter heating for residents. However, over the long term, pipelines are prone to problems such as blockages, leaks, and reduced insulation. For example, blockages can hinder water flow, impacting heat transfer, while leaks waste heat and water, potentially posing safety risks. Traditional heating systems struggle to precisely adjust heating parameters to meet user needs, resulting in inconsistent heating results. Some users experience excessively high indoor temperatures, leading to energy waste, while others experience insufficient temperatures, impacting comfort. Traditional heating management faces challenges such as thermal imbalances, pipeline leaks, and data monitoring lags, leading to energy waste, increased operational costs, and a decreased user experience. Existing remote monitoring and early warning methods often rely on single sensors or centralized data processing, making it difficult to fully reflect pipeline operating status, effectively identifying complex anomalies (such as leaks, blockages, or equipment failures), and failing to achieve efficient and accurate anomaly detection and dynamic control. This poor reliability of anomaly detection impacts operational decisions.

[0003] Therefore, at the current stage, relevant technologies have technical problems such as insufficient data collection dimensions, low network communication efficiency, poor anomaly detection reliability and real-time performance. Summary of the Invention

[0004] This application solves the technical problems of insufficient data collection dimensions, low network communication efficiency, poor anomaly detection reliability and real-time performance in the existing technology by providing a remote heating management method, system and equipment based on the Internet of Things, and achieves the technical effect of realizing efficient and accurate monitoring, low-latency communication, and improving the reliability and real-time performance of heating early warning.

[0005] The present application provides a remote heating management method based on the Internet of Things, which includes: installing a terminal device on the user's heating pipe, the terminal device integrating a temperature sensor, a vibration sensor, a valve control sensor and a position sensor; after activating the terminal device, uploading the unique ID, location information, and physical topology information of the terminal device; performing a proximity search for the terminal device, establishing an initial handshake connection, and performing edge communication networking; performing adaptive grouping reconstruction based on the uploaded results and the edge communication networking, and configuring the node roles within the group; after calling the monitoring data of the terminal device, using the reconstructed grouping to identify monitoring data anomalies based on the node roles within the group, and reporting local anomalies; after uploading the local anomalies, performing secondary verification of the local anomalies and generating a heating management warning.

[0006] In a possible implementation, the remote heating management method based on the Internet of Things also performs the following processing: using the edge communication networking to perform node communication testing and establish test indicators, the test indicators include signal strength indicator, packet loss rate indicator, round-trip delay indicator, and communication stability time indicator; after parsing the uploaded results, after locating the node position according to the unique ID and location information, establish a node topology structure based on the physical topology information; establish a communication-topology graph structure for the node topology structure and the test indicators; and complete adaptive grouping reconstruction according to the communication-topology graph structure.

[0007] In a possible implementation, the remote heating management method based on the Internet of Things also performs the following processing: reconstructing the edge weights of the communication-topology graph structure according to the topological adjacency and communication status; using the weighted graph partitioning channel to perform initial group division of the reconstructed communication-topology graph structure to establish an initial division result; and performing adaptive local adjustment on the initial division result to complete the adaptive grouping reconstruction.

[0008] In a possible implementation, the remote heating management method based on the Internet of Things also performs the following processing: performing isolated node identification based on the initial division result to generate an isolated node identification result; performing adsorption identification of adjacent groups based on the isolated node identification result, and establishing a first adjustment result based on the adsorption identification result; performing intra-group key edge identification on the initial division result, performing communication stability analysis based on the intra-group key edge identification result, and generating a second adjustment result based on the communication stability analysis result; and completing adaptive local adjustment based on the first adjustment result and the second adjustment result.

[0009] In a possible implementation, the remote heating management method based on the Internet of Things also performs the following processing: establishing a node role set, the node role set including a group leader node, a sentinel node, an auxiliary node, and a redundant node; calculating the node capability characteristic indicators within each reconstructed grouping respectively, the capability characteristic indicators including a communication quality indicator, a topology centrality indicator, and an energy stability indicator; performing adaptation analysis of the node role set based on the node capability characteristic indicators, and completing the node role configuration within the group based on the adaptation analysis results.

[0010] In a possible implementation, the remote heating management method based on the Internet of Things also performs the following processing: establishing a verification cycle, performing health status verification of the leader node in each reconstructed grouping within the verification cycle, and generating a health status verification result; if the health status verification result is a verification failure result, generating a switching instruction, and replacing and updating the redundant node with the leader node according to the switching instruction.

[0011] In a possible implementation, the remote heating management method based on the Internet of Things also performs the following processing: using the terminal device to perform periodic collection of temperature data, valve control data, vibration data, node communication data, and power consumption data to establish the monitoring data; performing independent node abnormality verification based on the monitoring data for each node within the reconstructed formation to generate an independent abnormality verification result; performing collaborative analysis of the independent abnormality verification results according to the role of the node within the group within the reconstructed formation to establish local abnormalities.

[0012] In a possible implementation, the remote heating management method based on the Internet of Things also performs the following processing: locating the user according to the local anomaly and reading the user's historical behavior data; performing local anomaly authentication based on the historical behavior data and establishing a historical authentication result; performing data backtracking of the reconstructed grouping corresponding to the local anomaly and establishing a neighborhood data backtracking result; performing local anomaly authentication with the neighborhood data backtracking result and establishing a neighborhood backtracking authentication result; and completing secondary verification of the local anomaly based on the historical authentication result and the neighborhood backtracking authentication result.

[0013] The present application also provides a remote heating management system based on the Internet of Things, which includes: a terminal device installation module for installing a terminal device on a user's heating pipe, wherein the terminal device is integrated with a temperature sensor, a vibration sensor, a valve control sensor, and a position sensor; a device information upload module for uploading the unique ID, location information, and physical topology information of the terminal device after activating the terminal device; an edge communication networking module for performing a proximity search for the terminal device, establishing an initial handshake connection, and performing edge communication networking; an adaptive grouping reconstruction module for performing adaptive grouping reconstruction based on the uploaded results and the edge communication networking, and configuring the node roles within the group; a monitoring data anomaly identification module for, after calling the monitoring data of the terminal device, using the reconstructed grouping to perform monitoring data anomaly identification based on the node roles within the group, and reporting local anomalies; a local anomaly secondary verification module for performing secondary verification of the local anomaly after uploading the local anomaly, and generating a heating management warning.

[0014] The present application also provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing a remote heating management method based on the Internet of Things when executing the executable instructions stored in the memory.

[0015] The remote heating management method, system and equipment based on the Internet of Things proposed in this application are intended to install terminal devices on the user's heating pipes; after activating the terminal device, upload the terminal device's unique ID, location information, and physical topology information; perform a proximity search for the terminal device, establish an initial handshake connection, and perform edge communication networking; perform adaptive grouping reconstruction and configure the node roles within the group; call the terminal device monitoring data, use the reconstructed grouping to identify anomalies in the monitoring data based on the node roles within the group, and report local anomalies; after uploading the local anomaly, perform secondary verification of the local anomaly and generate a heating management warning. This solves the technical problems of insufficient data collection dimensions, low network communication efficiency, poor anomaly detection reliability and real-time performance in the existing technology, and achieves the technical effect of realizing efficient and accurate monitoring, low-latency communication, and improving the reliability and real-time performance of heating warnings. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0017] Figure 1 A flow chart of a remote heating management method based on the Internet of Things provided in an embodiment of the present application.

[0018] Figure 2 A schematic structural diagram of a remote heating management system based on the Internet of Things provided in an embodiment of the present application.

[0019] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0020] Explanation of the accompanying drawings: terminal device installation module 10, device information upload module 20, edge communication networking module 30, adaptive grouping reconstruction module 40, monitoring data anomaly identification module 50, local anomaly secondary verification module 60, input device 401, processor 402, memory 403, output device 404. DETAILED DESCRIPTION

[0021] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0022] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0023] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0024] The present application embodiment provides a remote heating management method based on the Internet of Things, such as Figure 1 As shown, the method includes:

[0025] Step S100: Install a terminal device on the user's heating pipe, wherein the terminal device is integrated with a temperature sensor, a vibration sensor, a valve control sensor, and a position sensor.

[0026] Preferably, terminal devices such as temperature sensors, vibration sensors, valve control sensors and position sensors are integrated and installed on the user's heating pipeline to sense and manage the operating status of the heating pipeline in real time. Specifically, the temperature sensor is used to monitor the water temperature or steam temperature in the heating pipeline in real time and convert the temperature value into an electrical signal; blockage, water hammer, loose connection, etc. inside the pipeline may cause abnormal vibration. The vibration sensor is used to detect the vibration of the heating pipeline (including vibration frequency and intensity) and convert the vibration data into an electrical signal and transmit it to the terminal device; the valve control sensor is used to monitor and control the opening and closing status of the valve in the heating pipeline, including real-time monitoring of the opening and closing angle of the valve, and can remotely control the opening and closing of the valve through the terminal device to adjust the heating flow. If the valve is abnormal (such as stuck or leaking), the valve control sensor can detect it in time and alarm; the position sensor is used to determine the specific installation location information (such as longitude and latitude coordinates) of the terminal device in the heating pipeline network and transmit it to the control center. The location information can clearly understand the location of each terminal device in the network, which facilitates the construction of a topological map of the heating system. When a terminal device detects an abnormality, the position sensor can quickly locate the fault point. By integrating multiple sensors into terminal devices, all-round monitoring of heating pipelines can be achieved. Based on the monitoring data, valves can be controlled through remote commands to achieve intelligent heating regulation. At the same time, potential faults can be discovered in a timely manner, improving the operating efficiency and safety of the heating system.

[0027] Step S200: After activating the terminal device, upload the unique ID, location information, and physical topology information of the terminal device.

[0028] Preferably, after activating the terminal device in the heating management system, the unique ID, location information and physical topology information of the terminal device are uploaded to the control center for unified management and monitoring. Specifically, each terminal device has a unique identifier (i.e., unique ID) to distinguish other devices, which is equivalent to the device's ID number. The unique ID can accurately identify each terminal device and realize individualized management of the device. When the device fails or is abnormal, the unique ID can help quickly locate the problem device and improve the efficiency of fault handling. The location sensor installed on the terminal device obtains the device's geographical location information, which usually includes longitude and latitude coordinates, etc., to provide accurate device location data for the construction of the topology map of the heating system. At the same time, according to the location information of the device, heating resources can be reasonably allocated, the heating plan can be optimized, and the heating efficiency can be improved. The physical topology information describes the physical connection relationship and position relationship of the terminal device in the heating pipe network, including the connection method and distance between the device and adjacent pipes and other devices, so as to accurately understand the layout and structure of the heating pipe. When a device fails, the impact range on the surrounding equipment and the heating area is evaluated based on the physical topology information, which facilitates the formulation of emergency treatment plans, thereby realizing precise control of the equipment and optimized scheduling of the heating system.

[0029] Step S300: perform a proximity search of the terminal device, establish an initial handshake connection, and perform edge communication networking.

[0030] Preferably, a proximity search of terminal devices is performed, that is, in the heating system, the activated terminal device detects other adjacent terminal devices within a certain range around it, determines whether it is within the communication distance and the communication signal strength, etc., determines which terminal devices exist in the surrounding area that can communicate and interact, and constructs a local network topology structure in the heating system based on the location, distance and other information of the searched adjacent devices; then an initial handshake connection is established, that is, when the terminal device determines the adjacent devices through proximity search, it sends a connection request to these devices, and the adjacent devices respond and confirm after receiving the connection request. The two parties exchange basic device information and communication parameters, etc. to establish a reliable communication link between the terminal devices. During the handshake connection process, the devices can verify each other's identity to ensure that the communicating parties are legitimate terminal devices, prevent illegal devices from accessing the heating network, and ensure the security and stability of the heating system. At the same time, the two parties can negotiate and determine key parameters such as communication frequency, rate, encryption method, etc. based on their own hardware conditions and network environment to ensure the efficiency and reliability of subsequent data transmission.

[0031] Preferably, edge communication networking is finally performed. Specifically, edge computing technology is used to connect terminal devices at the edge of the heating network (usually close to the user end or the on-site equipment end) to form a communication network with collaborative working capabilities. After the communication networking, the terminal devices can first process and analyze the collected data locally, and then transmit the processed key information to the superior control center or central server. At the same time, collaborative decision-making and control operations can also be performed within the local network. Among them, since the data can be preliminarily processed and transmitted locally on the edge device, there is no need to upload all of it to the central server and then return the control instructions, which greatly shortens the time. The delay time of communication and control is reduced, thereby realizing real-time monitoring and rapid response of the heating system, so as to timely discover and deal with abnormal conditions of the heating pipeline, and ensure the stability and safety of the heating; even if the central server fails or the network connection is interrupted, the terminal devices in the edge communication network can perform simple collaborative work and data interaction to maintain the basic operation of the heating system, ensure the continuity of user heating and the reliability of the heating system; through edge communication networking, the control logic is dispersed to each edge device node, and according to the heating demand and actual situation of different areas, a more flexible and accurate distributed control strategy is implemented to improve the heating quality and energy utilization efficiency.

[0032] Step S400 : performing adaptive grouping reconstruction according to the uploaded result and the edge communication networking, and configuring the node roles within the group.

[0033] Step S400 further includes step S410, using the edge communication networking to perform node communication testing and establish test indicators, the test indicators including signal strength indicator, packet loss rate indicator, round-trip delay indicator, and communication stability time indicator; step S420, after parsing the uploaded results, locating the node position according to the unique ID and location information, and establishing a node topology structure based on the physical topology information; step S430, establishing a communication-topology graph structure for the node topology structure and the test indicators; step S440, completing adaptive grouping reconstruction according to the communication-topology graph structure.

[0034] Preferably, edge communication networking is used to conduct node communication testing, and test indicators such as signal strength index, packet loss rate index, round-trip delay index, and communication stability time index are established. Specifically, the signal strength index measures the strength of the wireless signal between node devices. The higher the signal strength, the better the communication quality and the more stable and reliable the data transmission. For example, in Wi-Fi networks, signal strength is often expressed in dBm. The larger the value, the stronger the signal. The packet loss rate index refers to the ratio of data packets that fail to successfully reach the receiving end to the total number of data packets sent during network communication. A high packet loss rate will lead to incomplete data transmission, affecting the smoothness and reliability of communication, which may be caused by network congestion, signal interference, etc. The round-trip delay index refers to the time it takes for data to start from the sending node, pass through the network to reach the receiving node, and then return to the sending node. It reflects the real-time and response speed of the network and is crucial for application scenarios such as remote operation of heating valves that require real-time control or interaction. The communication stability time index is a statistical node device that can maintain continuous and stable communication during a communication process, which intuitively reflects the stability of the communication link. The example communication test indicator data is shown in Table 1:

[0035] Table 1 Communication test index data table

[0036]

[0037] Preferably, the unique ID, location information, and physical topology information uploaded by the terminal device are decoded and analyzed to extract key information. The specific terminal device is identified by the unique ID of the device, and then combined with its location information, the location of the device is accurately determined on the heating system layout map. Then, based on the physical topology information, that is, the actual connection relationship, distance, pipeline layout, etc. between the devices, a topological structure diagram of the interconnection of each terminal device in the entire heating system is constructed, that is, a node topology structure is established. For example, if a terminal device is connected to a branch of the main pipeline, the distance to the adjacent terminal device is obtained at the same time, and the terminal device is placed and connected in the correct position in the topology map based on this information. The node topology structure is combined with the various indicators obtained from the node communication test to form a communication-topology map structure. Not only can the physical location and connection relationship of each terminal device in the heating system be intuitively seen, but also its communication quality, such as signal strength, packet loss rate, round-trip delay, etc., can be understood. For example, different colored lines or marks are used to represent connection links with different communication quality levels, and the communication status of the entire heating network is clearly displayed on the topology map.

[0038] Preferably, adaptive grouping reconstruction is completed according to the communication-topology graph structure, that is, the grouping and role of the terminal devices are dynamically adjusted and optimized according to the actual situation of the current heating network reflected by the communication-topology graph structure. For example, devices with good communication quality and adjacent positions are divided into the same group, and certain devices are designated as coordinators or data aggregation nodes in the group, etc., to improve communication efficiency and network performance; by analyzing the communication indicators of each link and the topological position of the nodes in the communication-topology graph structure, it is determined which devices need to adjust the grouping or role. For example, if it is found that the communication delay of the devices in a certain area is high, the devices in the area are regrouped and their communication routes are adjusted so that they transmit data through other links with better communication quality; or when the packet loss rate of a node is too high, consider reducing its data forwarding tasks in the group, and let the adjacent nodes with more stable communication quality take on part of the tasks, thereby realizing adaptive optimization of the entire heating network to better meet the monitoring and control needs of the heating system.

[0039] Furthermore, step S440 also includes step S441, reconstructing the edge weights of the communication-topology graph structure according to the topological adjacency and communication status; step S442, using the weighted graph partitioning channel to perform initial group division of the reconstructed communication-topology graph structure to establish an initial division result; step S443, adaptively adjusting the initial division result locally to complete the adaptive grouping reconstruction.

[0040] Preferably, topological adjacency refers to the adjacency relationship between nodes in a communication network, that is, which nodes are adjacent in physical location or network connection. For example, in a heating pipeline monitoring network, two terminal devices installed in adjacent pipeline sections are topologically adjacent nodes; the communication status reflects the quality and performance of the communication link between nodes, including indicators such as signal strength, packet loss rate, and round-trip delay. For example, if the signal strength between two nodes is strong, the packet loss rate is low, and the round-trip delay is small, the communication status is good; taking into account the topological adjacency and communication status, the weights of each edge in the communication-topology graph structure are recalculated and adjusted. The edge weight is used to indicate the quality of communication between nodes. The better the communication status, the higher the edge weight may be. Conversely, the worse the communication status, the lower the weight.

[0041] Preferably, the communication-topology graph structure is regarded as a weighted graph, and different communication channels (or subnetworks) are divided according to the size of the edge weights to obtain weighted graph division channels, wherein connections with higher edge weights may be divided into the same channel to form a relatively independent communication subnetwork with better communication quality. According to the weighted graph division channels, based on the communication quality and topological adjacency relationship between nodes, the power-saving devices of the reconstructed communication-topology graph structure are divided into several initial groups. For example, nodes with good communication quality and topologically adjacent nodes are divided into the same group to improve the group communication efficiency and collaboration ability, and then the initial division result, that is, the initial group division plan, is determined, and the group to which each node belongs and the node member information within the group are recorded.

[0042] Preferably, the initial division results are dynamically adjusted according to the real-time communication status and topology changes during the operation of the heating system. Specifically, when the communication status of a node deteriorates (such as a sudden increase in the packet loss rate) or the topological adjacency relationship changes (such as a device is temporarily offline due to maintenance), the changes are automatically detected and local adjustments are made to the groups where the relevant nodes are located, such as re-dividing nodes with poor communication quality into other groups with better communication quality, or adjusting the role division within the group, etc. Through continuous adaptive local adjustments, the adaptive grouping reconstruction of the entire communication-topology graph structure is eventually completed, so that the terminal devices in the heating system are always kept in the optimal grouping and communication state, improving the overall performance and reliability of the heating system, and better meeting the needs of remote heating management.

[0043] Furthermore, step S442 also includes step a, performing isolated node identification based on the initial division result to generate an isolated node identification result; step b, performing adsorption identification of adjacent groups based on the isolated node identification result, and establishing a first adjustment result based on the adsorption identification result; step c, identifying key edges within the group on the initial division result, performing communication stability analysis based on the key edge identification result within the group, and generating a second adjustment result based on the communication stability analysis result; step d, completing adaptive local adjustment based on the first adjustment result and the second adjustment result.

[0044] Preferably, isolated nodes are identified based on the initial partitioning results, i.e., nodes that cannot communicate normally with other nodes or have serious communication problems with other nodes in the group (such as communication interruption, extremely weak signal, etc.) are identified, and isolated nodes are marked to generate isolated node identification results. For example, if the packet loss rate of the communication link between a node and all other nodes in the initial partitioned group exceeds a certain threshold, or if all communications with other nodes in the group are interrupted, it can be determined as an isolated node and identified. Then, based on the isolated node identification results, adjacent group adsorption discrimination is performed. Specifically, for the identified isolated node, it is determined whether there are adjacent communication groups around it, and the communication quality between the isolated node and the adjacent group is evaluated (such as whether the signal strength is sufficient, whether the packet loss rate is within an acceptable range, etc.). If the communication quality meets certain conditions, it is considered that the isolated node can be adsorbed by the adjacent group, and an adsorption discrimination result is obtained. Finally, the adsorbed isolated node is incorporated into the adjacent group to form a first adjustment result, which is used to make the first correction to the initial partitioning result, so that the isolated node is initially re-attributed, reducing isolated nodes and improving overall connectivity and communication efficiency.

[0045] Preferably, the initial division result is subjected to intra-group key edge identification, i.e., edges that play a key role in the stability of communication within the group (i.e., connection links between nodes) are identified, which are usually edges with good communication quality (such as high signal strength, low packet loss rate, short round-trip delay, etc.), carrying more communication traffic, or being on the critical path in the intra-group communication topology, to obtain the intra-group key edge identification result; then a communication stability analysis is performed, i.e., evaluating whether the communication quality of the key edges shows a downward trend (such as a gradual increase in packet loss rate, a decrease in signal strength, etc.); if so, it is considered that the communication stability within the group may be threatened, and the communication stability analysis result is obtained; then, based on the results of the communication stability analysis, the node structure within the group is adjusted to improve the communication stability, for example, nodes that rely on unstable key edges are reallocated to other groups with more stable communication quality, or the roles and communication paths of the nodes are adjusted within the group to optimize the load and communication quality of the key edges, thereby generating a second adjustment result and making a second correction to the initial division result. Finally, by combining the first and second adjustment results, the entire communication-topology structure is adaptively adjusted locally, and the initial division results are optimized and improved, so that the terminal equipment grouping in the heating system is more reasonable and the communication link is more stable and efficient, thereby realizing adaptive grouping reconstruction and ensuring the stable operation and efficient management of the heating system.

[0046] Furthermore, step S400 also includes step S450, establishing a node role set, wherein the node role set includes a group leader node, a sentinel node, an auxiliary node, and a redundant node; step S460, respectively calculating the node capability characteristic indicators within each reconstructed group, wherein the capability characteristic indicators include a communication quality indicator, a topology centrality indicator, and an energy stability indicator; step S470, performing an adaptation analysis of the node role set based on the node capability characteristic indicators, and completing the node role configuration within the group based on the adaptation analysis results.

[0047] Preferably, a group of node roles are defined, including a leader node, a sentinel node, an auxiliary node, and a redundant node, to obtain a node role set, wherein the leader node is responsible for coordinating intra-group communication, management, and decision-making, such as collecting intra-group data, assigning tasks, communicating with other groups, etc.; the sentinel node is responsible for monitoring the intra-group communication status and node operation status, promptly detecting anomalies and reporting them to the leader node; the auxiliary node assists the leader node in data transmission and processing, enhancing the intra-group communication capability and redundancy; the redundant node is used to replace the function of other nodes when they fail or communication is interrupted, thereby improving the reliability and availability of the system.

[0048] Preferably, three types of capability characteristic indicators are calculated for each node in the reconstructed grouping to quantitatively evaluate the capabilities of the nodes, including communication quality indicators, topological centrality indicators, and energy stability indicators. Specifically, the communication quality indicators are calculated through signal strength, packet loss rate, round-trip delay, etc., such as calculating the average signal strength, packet loss rate and round-trip delay of the node over a period of time, and performing weighted calculation to obtain a communication quality score, which reflects the reliability of the node's communication with other nodes; the topological centrality indicator is calculated based on the position of the node in the network topology, that is, the topological centrality indicator is evaluated using betweenness centrality (measuring the frequency of the node as an intermediary of the shortest path between other pairs of nodes) or closeness centrality (measuring the inverse of the average shortest path length from the node to other nodes), which reflects the connection importance and information dissemination capability of the node in the network; the energy stability indicator is calculated based on the energy consumption and remaining energy of the node, such as statistically analyzing the energy consumption rate of the node over a period of time, and combining the current remaining energy to evaluate its energy stability and sustainable working time, reflecting the energy usage status of the node.

[0049] Preferably, an adaptability analysis is performed on the node roles based on the node's capability characteristic indicators to obtain the adaptation analysis results. Based on the adaptation analysis results, the node roles within the group are configured. For example, nodes with good communication quality, high topological centrality, and high energy stability are assigned as group leader nodes to ensure efficient communication. Nodes with good communication quality and important topological locations are assigned as sentinel nodes to better coordinate communication and monitor the network. Nodes with good communication quality and high energy stability are assigned as auxiliary nodes. Nodes with high energy stability and acceptable communication quality are assigned as redundant nodes to ensure they can operate for a long time when needed. By rationally configuring node roles, intra-group communication and management are optimized, and the overall performance and reliability of the heating system are improved.

[0050] Furthermore, step S470 also includes step S471, establishing a verification cycle, performing health status verification of the leader node in each reconstructed group within the verification cycle, and generating a health status verification result; step S472, if the health status verification result is a verification failure result, generating a switching instruction, and replacing and updating the redundant node with the leader node according to the switching instruction.

[0051] Preferably, a fixed time period (i.e., verification period) is set, and the health status of the leader node in the reconstructed grouping is verified during the verification period, and dual verification of the threshold and the reduction ratio is performed to ensure the normal operation and reliability of the leader node. Specifically, the preset minimum thresholds of key performance indicators (communication quality indicators, energy stability indicators, response time for processing tasks, etc.) are configured. During the verification period, it is checked whether various indicators of the leader node are higher than or equal to the preset thresholds. For example, the preset minimum thresholds of the communication quality indicators are set to signal strength -70dBm, packet loss rate less than 5%, round-trip delay less than 100ms, etc. If the actual communication quality of the leader node is less than 70dBm, the packet loss rate is less than 5%, the round-trip delay is less than 100ms, etc. If the communication quality indicator is lower than the preset minimum threshold, it is considered that there is a problem with its communication function; at the same time, the changing trend of various performance indicators of the group leader node during the verification period is analyzed, and the proportion of its performance degradation is calculated. If the performance degradation ratio exceeds the ratio threshold (such as the communication quality indicator drops by more than 30% within a week), it is considered that there may be potential problems with the health status of the group leader node; the results of the threshold verification and the reduction ratio verification are combined to obtain the health status verification result of the group leader node. If all indicators of the group leader node pass the threshold verification and the performance degradation ratio is within an acceptable range, the verification result is passed; otherwise, as long as one verification fails, the verification result is failed.

[0052] Preferably, if the health status verification result is a verification failure result, a switching instruction is automatically triggered and generated, which includes operation information for replacing and updating the redundant node with the group leader node, that is, according to the switching instruction, a suitable redundant node is selected (the optimal redundant node is selected based on the capability characteristic indicators of the redundant node, such as communication quality, energy stability, etc.) to replace the current group leader node and take over the responsibilities of the group leader node, including coordinating intra-group communication, management and decision-making tasks, so as to ensure the continuity and reliability of intra-group communication and management, and maintain the stable and reliable operation of the heating system.

[0053] Step S500: After calling the monitoring data of the terminal device, the monitoring data anomaly recognition based on the node role in the group is performed using the reconstruction grouping, and a local anomaly is reported.

[0054] Step S500 further includes step S510, using the terminal device to perform periodic collection of temperature data, valve control data, vibration data, node communication data, and power consumption data to establish the monitoring data; step S520, performing independent node abnormality verification based on the monitoring data for each node in the reconstructed group to generate an independent abnormality verification result; step S530, performing collaborative analysis of the independent abnormality verification results according to the role of the node in the group within the reconstructed group to establish a local abnormality.

[0055] Preferably, the terminal device is used to perform periodic data collection, including temperature data, valve control data, vibration data, node communication data, and power consumption data. Specifically, the temperature sensor integrated in the terminal device measures the temperature of hot water or steam in the heating pipe at set time intervals (such as every hour, every half hour, etc.), and records the temperature value to form a temperature data sequence; the valve control sensor in the terminal device is responsible for monitoring the opening and closing status of the valve and the execution of the control instructions, such as the valve opening percentage, the number and time of valve opening and closing, etc., for analyzing the working status of the valve and the regulation of the heating flow; the vibration sensor is used to detect the vibration of the heating pipe, and the frequency and intensity of the vibration are recorded at a set period to determine whether there is abnormal vibration in the pipe, such as water hammer phenomenon, loose connection, etc.; the terminal device records the communication between itself and other nodes, including the frequency of communication, the sending and receiving time of data packets, the signal strength change, etc., for evaluating the communication quality and network performance between nodes; the energy consumption of the terminal device during operation is monitored, such as the battery level, the power usage of the device, etc., and the power consumption data is recorded periodically to understand the energy efficiency and remaining energy of the device. Integrate the collected data to form monitoring data of the terminal device.

[0056] Preferably, the monitoring data of the terminal device is called to perform independent abnormality verification on each node in the reconstructed group, that is, for each node in the reconstructed group, an abnormality judgment is made based on the monitoring data, for example, checking whether the temperature data exceeds the normal heating temperature range (such as the designed heating temperature is 70-80 degrees Celsius, and the actual monitoring temperature is continuously lower than 60 degrees Celsius or higher than 90 degrees Celsius, it may be abnormal), whether the valve control data shows that the valve is stuck (unable to open and close normally for a long time), whether the vibration data shows an abnormal vibration pattern (such as a sudden and significant increase in vibration frequency), whether the node communication data has frequent packet loss or communication interruption, whether the power consumption data is abnormally increased (which may indicate equipment hardware failure or energy management problems), etc.; and then generating an independent abnormality verification result for each node. If the monitoring data of the node is abnormal in one or more aspects, the node is marked as abnormal in this aspect; if all monitoring data are within the normal range, it is marked as normal.

[0057] Preferably, a collaborative analysis of independent anomaly verification results is performed based on the roles of the nodes within the reconstructed formation. Specifically, the roles and responsibilities of different nodes within the group are taken into consideration, and a comprehensive analysis is performed on the independent anomaly verification results of each node. For example, the leader node is responsible for coordinating communications and management. If its communication data is abnormal, it may affect the communication of the entire group; the sentinel node is responsible for monitoring the status within the group. If its data is abnormal, it may indicate that the monitoring function has failed; anomalies of auxiliary nodes and redundant nodes may also affect the execution and reliability of tasks within the group to varying degrees; then, a collaborative analysis is performed to determine whether there are local anomalies within the group. For example, if the leader node has communication anomalies and multiple auxiliary nodes have data transmission anomalies at the same time, it can be comprehensively judged that there are local communication anomalies within the group; if multiple nodes in a certain area (such as several topologically adjacent nodes) have temperature anomalies at the same time, it can be judged that there are heating temperature anomalies in the area; or if the equipment of a certain section of the heating pipeline is dismantled, a local anomaly is eventually established and reported, which helps to quickly locate the abnormal area and type, thereby ensuring accurate early warning of the heating management system.

[0058] Step S600: After uploading the local anomaly, a secondary verification of the local anomaly is performed to generate a heat supply management warning.

[0059] Step S600 further includes step S610, locating the user according to the local anomaly and reading the user's historical behavior data; step S620, performing local anomaly authentication based on the historical behavior data and establishing a historical authentication result; step S630, executing data backtracking of the reconstructed grouping corresponding to the local anomaly and establishing a neighborhood data backtracking result; step S640, performing local anomaly authentication based on the neighborhood data backtracking result and establishing a neighborhood backtracking authentication result; step S650, completing secondary verification of the local anomaly based on the historical authentication result and the neighborhood backtracking authentication result.

[0060] Preferably, user positioning based on local anomalies means that when a local anomaly is detected, the location information of the terminal device at the location where the anomaly occurs is used to determine the specific user or user area corresponding to the anomaly, such as a certain area or household in a residential building, and then the behavior data of the user or users in the area in the past period of time is obtained, which may include the user's heating demand pattern (such as common temperature settings, usage time patterns, etc.), historical anomaly records, valve operation records, etc.; the historical behavior data is then compared and analyzed with the monitored local anomaly for authentication. For example, if the current anomaly is a temperature anomaly, check whether similar temperature fluctuations often appear in the user's historical data, or whether similar situations have occurred in a specific time period (such as extremely cold weather), and judge whether the current local anomaly is consistent with the user's historical behavior pattern. If it is consistent, it may be caused by normal fluctuations or habitual operations of the user, and it can be preliminarily considered to be a false alarm; if it is not consistent, it may be a real anomaly, and a historical authentication result is established.

[0061] Preferably, the historical data of the reconstructed formation where the local anomaly occurs is traced back, and the monitoring data of each node in the formation over the past period of time is collected, including temperature, valve control, vibration, communication, power consumption and other data. The data obtained by backtracking are sorted and analyzed, and a neighborhood data backtracking result is established to analyze whether there is an abnormal change trend or correlation in the data of each node in the formation before the current anomaly occurs; then the neighborhood data backtracking result is used to further authenticate the local anomaly, that is, to analyze the correlation and consistency between the historical data of each node in the formation and the current anomaly, such as whether similar anomalies occur at the same time in multiple nodes, or whether the anomaly of a node may trigger anomalies in other nodes, and then judge the impact range and severity of the current local anomaly in the formation based on the authentication result, and establish a neighborhood backtracking authentication result. If the historical data of multiple nodes show signs of abnormality and are related to the current anomaly, the credibility of the current anomaly is enhanced.

[0062] Preferably, the historical authentication results and the neighborhood backtracking authentication results are comprehensively analyzed. If the historical authentication results show that the current anomaly does not conform to the user's historical behavior pattern, and the neighborhood backtracking authentication results also show that there are related abnormal signs in multiple nodes within the group, it is judged that the current local anomaly is an anomaly that needs to be processed, and then the secondary verification of the local anomaly is completed, and the authenticity and severity of the anomaly are finally determined, and a heating management warning is generated to provide more accurate warning information, such as general warnings (local pipeline minor leaks, small temperature fluctuations, etc.), serious warnings (large-scale heating shutdowns, pipeline ruptures, etc.), and timely notification of heating management personnel so that corresponding measures can be taken quickly to deal with it, such as sending maintenance personnel to check and adjust heating parameters, etc., so as to ensure the stable and reliable operation of the heating system. Exemplary heating management warning data are shown in Table 2:

[0063] Table 2 Heating management early warning data table

[0064]

[0065] In the above, refer to Figure 1 The remote heating management method based on the Internet of Things according to an embodiment of the present invention is described in detail. Figure 2 A remote heating management system based on the Internet of Things according to an embodiment of the present invention is described.

[0066] The remote heating management system based on the Internet of Things according to the embodiment of the present invention is used to solve the technical problems existing in the prior art, such as insufficient data collection dimensions, low network communication efficiency, poor anomaly detection reliability and real-time performance, and achieves the technical effects of achieving efficient and accurate monitoring, low-latency communication, and improving the reliability and real-time performance of heating early warning. Figure 2 As shown, the remote heating management system based on the Internet of Things includes: a terminal device installation module 10, a device information upload module 20, an edge communication networking module 30, an adaptive grouping reconstruction module 40, a monitoring data anomaly identification module 50, and a local anomaly secondary verification module 60.

[0067] The terminal device installation module 10 is used to install the terminal device on the user's heating pipe, and the terminal device is integrated with a temperature sensor, a vibration sensor, a valve control sensor and a position sensor; the device information uploading module 20 is used to upload the unique ID, location information and physical topology information of the terminal device after activating the terminal device; the edge communication networking module 30 is used to perform a proximity search of the terminal device, establish an initial handshake connection, and perform edge communication networking; the adaptive grouping reconstruction module 40 is used to perform adaptive grouping reconstruction according to the uploaded results and the edge communication networking, and configure the node roles within the group; the monitoring data anomaly identification module 50 is used to use the reconstructed grouping to perform monitoring data anomaly identification based on the node roles within the group after calling the monitoring data of the terminal device, and report local anomalies; the local anomaly secondary verification module 60 is used to perform secondary verification of the local anomaly after uploading the local anomaly, and generate a heating management warning.

[0068] The specific configuration of the adaptive grouping and reconfiguration module 40 will be described in detail below. The adaptive grouping and reconfiguration module 40 further includes: utilizing the edge communication network to conduct node communication testing and establish test indicators, including a signal strength indicator, a packet loss rate indicator, a round-trip delay indicator, and a communication stability duration indicator; after parsing the uploaded results, locating the node position based on the unique ID and location information, and establishing a node topology structure based on the physical topology information; establishing a communication-topology graph structure based on the node topology structure and the test indicators; and completing the adaptive grouping and reconfiguration according to the communication-topology graph structure.

[0069] The specific configuration of the adaptive grouping and reconstruction module 40 will be described in detail below. The adaptive grouping and reconstruction module 40 further includes: reconstructing the edge weights of the communication-topology graph structure based on topological adjacency and communication status; performing initial grouping of the reconstructed communication-topology graph structure using weighted graph partitioning channels to establish initial partitioning results; and performing adaptive local adjustments to the initial partitioning results to complete the adaptive grouping and reconstruction.

[0070] The specific configuration of the adaptive grouping and reconfiguration module 40 will be described in detail below. The adaptive grouping and reconfiguration module 40 further includes: performing isolated node identification based on the initial partitioning result to generate an isolated node identification result; performing adsorption identification of adjacent groups based on the isolated node identification result, and establishing a first adjustment result based on the adsorption identification result; identifying key edges within the group based on the initial partitioning result, performing communication stability analysis based on the key edge identification result, and generating a second adjustment result based on the communication stability analysis result; and performing adaptive local adjustment based on the first and second adjustment results.

[0071] The specific configuration of the adaptive grouping and reconfiguration module 40 will be described in detail below. The adaptive grouping and reconfiguration module 40 further includes: establishing a node role set, which includes a leader node, a sentinel node, an auxiliary node, and a redundant node; calculating node capability characteristic indicators within each reconfigured group, which include a communication quality indicator, a topology centrality indicator, and an energy stability indicator; performing an adaptive analysis of the node role set based on the node capability characteristic indicators, and completing the node role configuration within the group based on the adaptive analysis results.

[0072] The specific configuration of the adaptive grouping and reconfiguration module 40 will be described in detail below. The adaptive grouping and reconfiguration module 40 further includes: establishing a verification cycle, performing health status verification on the leader node in each reconfigured group within the verification cycle, and generating a health status verification result; if the health status verification result is a failure, generating a switching instruction, and replacing the redundant node with the leader node according to the switching instruction.

[0073] The specific configuration of the monitoring data anomaly identification module 50 will be described in detail below. The monitoring data anomaly identification module 50 further includes: utilizing the terminal device to periodically collect temperature data, valve control data, vibration data, node communication data, and power consumption data to establish the monitoring data; performing independent node anomaly verification based on the monitoring data for each node within the reconstructed formation to generate an independent anomaly verification result; and performing collaborative analysis of the independent anomaly verification results based on the node roles within the reconstructed formation to establish local anomalies.

[0074] The specific configuration of the local anomaly secondary verification module 60 will be described in detail below. The local anomaly secondary verification module 60 further includes: locating the user based on the local anomaly and reading the user's historical behavior data; performing local anomaly authentication based on the historical behavior data and establishing a historical authentication result; performing data backtracking of the reconstructed group corresponding to the local anomaly and establishing a neighborhood data backtracking result; performing local anomaly authentication based on the neighborhood data backtracking result and establishing a neighborhood backtracking authentication result; and completing the secondary verification of the local anomaly based on the historical authentication result and the neighborhood backtracking authentication result.

[0075] The remote heating management system based on the Internet of Things provided by the embodiment of the present invention can execute the remote heating management method based on the Internet of Things provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0076] Figure 3 1 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. Figure 3 The electronic device shown is merely an example and should not limit the functionality and scope of use of the embodiments of the present invention. The electronic device is in the form of a general-purpose computing device, and its components may include, but are not limited to, an input device 401, a processor 402, a memory 403, and an output device 404. The processor 402 may be one or more; the memory 403 may include a computer-readable medium and at least one program product, which has a set (at least one) of program modules configured to perform the functions of the various embodiments of the present application.

[0077] The memory 403 shown in the embodiment of the present invention may adopt any combination of one or more computer-readable media; the computer-readable storage medium may be, but is not limited to, an infrared, semiconductor system, device or component, or any combination of the above, for storing software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the remote heating management method based on the Internet of Things in the embodiment of the present invention. The processor 402 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 403, thereby realizing the above-mentioned remote heating management method based on the Internet of Things.

[0078] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0079] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A remote heating management method based on the Internet of Things, characterized in that: The method comprises: Installing a terminal device on the user's heating pipe, wherein the terminal device is integrated with a temperature sensor, a vibration sensor, a valve control sensor and a position sensor; After activating the terminal device, upload the unique ID, location information, and physical topology information of the terminal device; Perform proximity search for terminal devices, establish initial handshake connections, and perform edge communication networking; Perform adaptive grouping reconstruction based on the uploaded results and the edge communication networking, and configure the node roles within the group; After calling the monitoring data of the terminal device, the reconstruction group is used to identify abnormalities in the monitoring data based on the node roles in the group and report local abnormalities; After uploading the local anomaly, a secondary verification of the local anomaly is performed to generate a heat supply management warning.

2. The remote heating management method based on the Internet of Things according to claim 1, characterized in that: The performing of adaptive grouping reconstruction according to the uploaded result and the edge communication networking includes: Utilize the edge communication network to conduct node communication testing and establish test indicators, wherein the test indicators include signal strength indicator, packet loss rate indicator, round-trip delay indicator, and communication stability duration indicator; After parsing the uploaded result, locating the node position according to the unique ID and location information, and establishing a node topology structure based on the physical topology information; Establishing a communication-topology graph structure for the node topology structure and the test indicators; Adaptive grouping reconstruction is completed according to the communication-topology graph structure.

3. The remote heating management method based on the Internet of Things according to claim 2, characterized in that: The adaptive grouping reconstruction is completed according to the communication-topology graph structure, including: Reconstruct the edge weights of the communication-topology graph structure based on topological adjacency and communication status; Using weighted graph partitioning channels to perform initial group partitioning of the reconstructed communication-topology graph structure, and establishing initial partitioning results; Adaptively locally adjust the initial division result to complete adaptive grouping reconstruction.

4. The remote heating management method based on the Internet of Things according to claim 3, characterized in that: The establishing of the initial partitioning result includes: Performing identification of isolated nodes based on the initial division result to generate an isolated node identification result; Performing adsorption discrimination of adjacent groups based on the isolated node identification result, and establishing a first adjustment result according to the adsorption discrimination result; Identifying key edges within the group on the initial division result, performing communication stability analysis based on the key edge identification result within the group, and generating a second adjustment result based on the communication stability analysis result; Adaptive local adjustment is performed based on the first adjustment result and the second adjustment result.

5. The remote heating management method based on the Internet of Things according to claim 1, characterized in that: The node roles in the configuration group include: Establishing a node role set, the node role set includes a leader node, a sentinel node, an auxiliary node, and a redundant node; Calculating node capability characteristic indicators in each reconfigured group, including communication quality indicator, topology centrality indicator, and energy stability indicator; An adaptation analysis of the node role set is performed according to the node capability characteristic indicators, and the node role configuration within the group is completed based on the adaptation analysis results.

6. The remote heating management method based on the Internet of Things according to claim 5, characterized in that: The step of completing the node role configuration within the group based on the adaptation analysis results includes: Establishing a verification cycle, performing health status verification of the leader node in each reconfigured group within the verification cycle, and generating a health status verification result; If the health status verification result is a verification failure result, a switching instruction is generated, and the redundant node is replaced and updated with the group leader node according to the switching instruction.

7. The remote heating management method based on the Internet of Things according to claim 1, characterized in that: After calling the monitoring data of the terminal device, using the reconstruction grouping to perform monitoring data anomaly identification based on the node role in the group and reporting local anomalies, including: Utilizing the terminal device to perform periodic collection of temperature data, valve control data, vibration data, node communication data, and power consumption data to establish the monitoring data; Perform independent abnormality verification on each node in the reconstructed formation based on monitoring data and generate independent abnormality verification results; The independent anomaly verification results are collaboratively analyzed according to the roles of the nodes within the reconstructed grouping to establish local anomalies.

8. The remote heating management method based on the Internet of Things according to claim 1, characterized in that: After uploading the local anomaly, performing secondary verification of the local anomaly includes: Locate the user based on the local anomaly and read the user's historical behavior data; Performing local anomaly authentication based on the historical behavior data and establishing historical authentication results; Perform data backtracking of the reconstruction group corresponding to the local anomaly and establish the neighborhood data backtracking results; Performing local anomaly authentication based on the neighborhood data backtracking result to establish a neighborhood backtracking authentication result; The secondary verification of the local anomaly is completed according to the historical authentication result and the neighborhood backtracking authentication result.

9. The remote heating management system based on the Internet of Things is characterized by: The system is used to implement the remote heating management method based on the Internet of Things according to any one of claims 1 to 8, and the system includes: A terminal device installation module is used to install a terminal device on the user's heating pipe. The terminal device is integrated with a temperature sensor, a vibration sensor, a valve control sensor, and a position sensor; A device information uploading module is used to upload the unique ID, location information, and physical topology information of the terminal device after activating the terminal device; Edge communication networking module, used to perform proximity search of terminal devices, establish initial handshake connection, and perform edge communication networking; An adaptive grouping and reconfiguration module, configured to perform adaptive grouping and reconfiguration according to the uploaded results and the edge communication networking, and configure the roles of nodes within the group; A monitoring data anomaly identification module is used to identify monitoring data anomalies based on the roles of nodes in the group by using the reconstruction group after calling the monitoring data of the terminal device, and report local anomalies; The local anomaly secondary verification module is used to perform secondary verification of the local anomaly after uploading the local anomaly and generate a heat supply management warning.

10. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable instructions; The processor is configured to implement the remote heating management method based on the Internet of Things as described in any one of claims 1 to 8 when executing the executable instructions stored in the memory.

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