Heat supply data processing system and method of heat supply system

Abstract

The invention discloses a heat supply data processing system and method of a heat supply system, relates to the technical field of heat supply data processing, and solves the technical problems that it is difficult to classify and process data in nodes of the heat supply system after classifying and summarizing the data, and it is also difficult to establish connection between high-similarity node data. The data transmission channel between the edge data nodes is established through the wireless communication equipment, so that low-delay data transmission is conveniently realized, the system can respond to changes in real time, the data transmission channel between the adjacent edge nodes is established, the network flow load is effectively dispersed, and the stability of the whole network is improved. A data table is established at an edge data node at a heat source, so that centralized management of data from different terminals and nodes is facilitated, and data accessibility and management efficiency are improved. By associating the high-similarity data, the relationship of the high-similarity nodes can be conveniently mined by analyzing the data subsequently, and deeper-level information is provided for subsequent analysis.

Description

Technical Field

[0001] The present invention belongs to the field of heating data processing, and in particular relates to a heating data processing system and method for a heating system. Background Art

[0002] With the acceleration of urbanization and the improvement of people's living standards, the demand for heating is increasing, especially in cold areas, and the efficiency and reliability of heating systems are particularly important. The increasing global attention to reducing carbon emissions and improving energy efficiency has promoted the transformation and optimization of traditional heating systems. Through data analysis, energy use can be better monitored and managed, thereby reducing environmental impact. The development of information technology has enabled emerging technologies such as the Internet of Things (IoT), big data, and artificial intelligence (AI) to be applied to heating systems to achieve intelligent monitoring and management and improve the degree of automation of the system. The heat data processing system of the heating system is an important tool for realizing modern intelligent heating management. Through effective data processing, it is convenient for the analysis of later data.

[0003] In most heat data processing solutions for heating systems, it is difficult to classify and summarize the data in each node of the heating system and perform classification processing by performing overall statistical analysis on the data of each node, and it is also difficult to establish connections between highly similar node data. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a heating data processing system and method for a heating system, which is used to solve the technical problems that it is difficult to classify and summarize the data in each node of the heating system, and then classify and process them, and it is also difficult to establish connections between highly similar node data.

[0005] In order to solve the above problems, a first aspect of the present invention provides a heating data processing system for a heating system, comprising: Node setting module: edge data nodes are set at the transmission pipe network and heat source in the heating system. According to the grouping principle that terminals connected to the same transmission pipe network branch are grouped into the same terminal group, the terminals in the heating system are grouped, and data aggregation nodes are set on the same transmission pipe network branch connected to the terminals in each group; Transmission channel construction module: set up a data mirror warehouse and wireless communication equipment at each edge data node, number the edge data nodes, set data labels for the data according to the timestamp and node number of data collection, and establish data transmission channels between adjacent edge data nodes in sequence from the edge data node at the heat source to the data aggregation node; Terminal data aggregation module: The terminal data collected by each terminal in the terminal group is sent to the data aggregation node in the group. After the data aggregation node stores the data, it is packaged and sent to the nearest edge data node; Transmission management module: After the edge data node collects the data of the node, it stores the data of the node in the image warehouse, and packages the data of the node and the terminal data packet together and transmits them to the edge data node at the hot source through the data transmission channel between the edge data nodes; Data association module: The edge data node at the heat source standardizes the received data, establishes the node data table, and associates the highly similar data.

[0006] As a further solution of the present invention: a data mirror warehouse and a wireless communication device are set at each edge data node, and the edge data nodes are numbered, including the following steps: Wireless communication equipment and a data mirror warehouse are set up at each edge data node, where the data mirror warehouse is used to store copies of data collected by the edge node; select an appropriate data format, such as JSON, CSV or binary format, to facilitate subsequent processing and analysis.

[0007] The edge data node is equipped with a wireless communication device, which can use a Wi-Fi module suitable for short-distance, high-bandwidth data transmission, a LoRa module suitable for long-distance, low-power data transmission, or a Zigbee module suitable for low-power, short-distance network applications. The specific selection can be based on the network environment where the node is set.

[0008] Assign a unique identifier to each edge data node as the edge data node number, create a transmission network node management map, and record the transmission network location of each edge node, the node number, and the number of nodes with the least distance between the edge data node at the heat source.

[0009] As a further solution of the present invention: from the edge data node at the heat source to the data aggregation node, a data transmission channel between adjacent edge data nodes is established in sequence, including the following steps: From the edge data node at the heat source to the data aggregation node, according to the transmission network node management diagram, the data transmission channel between adjacent edge data nodes is established in sequence through the wireless communication equipment on the transmission network; The aggregation node establishes a data transmission channel with the nearest edge data node through the MQTT communication protocol.

[0010] As a further solution of the present invention: after the edge data node at the heat source standardizes the received data, a data table of the node is established, and association is performed between highly similar data, including the following steps: The edge data node at the heat source aggregates the data sent by the edge data nodes and sends it together with the data of the node to the cloud management platform. The cloud management platform standardizes the data of each edge data node and establishes the terminal data table and the data table of the transmission pipeline network node; Based on the data collected by the heating system terminal and the node data collected by the edge data nodes on the transmission pipeline network, a similarity evaluation model between terminal nodes and a similarity evaluation model for transmission pipeline network nodes are constructed; The similarity between terminal nodes evaluated by the similarity evaluation model between terminal nodes, and the similarity between transmission network nodes evaluated by the transmission network node similarity evaluation model; The data of terminals with high similarity are associated with each other, and the data of transmission network nodes with high similarity are associated with each other.

[0011] As a further solution of the present invention: the cloud management platform standardizes the data of each edge data node and establishes a terminal data table and a data table of a transmission network node, including the following steps: Establish data standardization rules for edge data nodes to convert the data collected by each edge data node into the same format; Node data collected by edge data nodes include: flow data, heat data and sound data, wherein the heat data includes: internal temperature data of the pipeline and external temperature data of the pipeline, and the sound data includes external sound audio data of the pipeline and internal sound audio data of the pipeline; The edge data nodes at the heat source collect node data such as flow data, heat data and sound data at the heat source outlet and return water outlet; Establish data tables for transmission network nodes, including data lists for monitoring flow data, heat data, and sound data, and establish connections between the same node data in each data list based on the data labels of the node data in the data list; Set up terminal nodes at the heating system terminals to collect data, number the terminal nodes, and add data labels to the data of the terminal nodes according to the terminal node numbers and terminal groups; The data collected by the terminal node include: flow data, heat data, pressure data and geographic information data, wherein the heat data includes: the ambient temperature of the terminal and the temperature in the pipeline, and the geographic information data includes: the geographic coordinates and floor location of the terminal node; Establish a terminal data table, including: data lists of flow data, heat data, pressure data and geographic information data, and establish connections between the same terminal grouping data between various data lists based on the data labels of the node data in the data lists.

[0012] As a further solution of the present invention: constructing a similarity evaluation model between terminal nodes, comprising the following steps: According to the geographic information data of the terminal, the terminal pipeline topology data, the flow data, heat data, pressure data and geographic information data collected by the terminal node are obtained; wherein the terminal pipeline topology data includes: the connection pipeline and position of the terminal node in the transmission pipeline network node management diagram, and the number of nodes directly connected to the terminal node; Create a terminal pipeline topology data table and add it to the established terminal data table; Through deep learning algorithms, a comprehensive similarity recognition model for flow data and heat data is constructed; The comprehensive similarity recognition model of flow data and heat data is used to evaluate the comprehensive similarity of flow data and heat data between terminal nodes; Based on the comprehensive similarity evaluation results, the terminal pipeline topology data table, and the pressure data and geographic information data tables, a similarity evaluation model between terminal nodes is established using the following formula: Among them, S is the similarity evaluation value between terminal nodes, S1 is the similarity of terminal pipeline topology data between terminal nodes, S2 is the similarity of pressure data between terminal nodes, S3 is the similarity of geographic information data between terminal nodes, C is a constant set according to the comprehensive similarity evaluation result, w1, w2 and w3 are the weights of the similarity of terminal pipeline topology data between terminal nodes, the similarity of pressure data between terminal nodes and the similarity of geographic information data between terminal nodes respectively; g0 is the average number of nodes directly connected to the terminal nodes in the terminal group where the two terminal nodes are located, is the difference in the number of nodes directly connected to the two terminal nodes, Dg0 is the mean of the distances between the terminal nodes and the connecting pipelines in the transmission network node management diagram in the terminal group where the two terminal nodes are located, Dg1 is the distance between the terminal node and the connecting pipeline in the transmission network node management diagram, and Dg2 is the distance between the other terminal node and the connecting pipeline in the transmission network node management diagram; S0 is the mean of the variance of the terminal node pressure data in the terminal group where the two terminal nodes are located within a preset time period, is the difference in variance of the terminal node pressure data within the preset time period, is the mean value of the terminal node pressure data in the terminal group where the two terminal nodes are located, It is the difference between the mean values ​​of the terminal node pressure data within the preset time period; is the mean distance between the geographical coordinates of the two terminal nodes in the terminal group where the two terminal nodes are located, is the distance between the geographical coordinates of the terminal nodes, is the sum of the total number of floors where the two terminal nodes are located, is the difference in the number of floors where the terminal nodes are located.

[0013] As a further solution of the present invention: constructing a transmission network node similarity evaluation model includes the following steps: Obtain flow data, heat data and sound data collected by edge data nodes on the transmission pipeline network; The comprehensive similarity recognition model of flow data and heat data is used to evaluate the comprehensive similarity of flow data and heat data between terminal nodes; Through the audio data of the transmission network nodes with high similarity in the transmission network in the historical data, the time domain features and frequency domain features are extracted. Through the time domain features and frequency domain features, the sound data recognition model is trained through the machine learning algorithm to identify whether the transmission network node is a high similarity node; The time domain features of the sound data audio of the pipeline are extracted, including mean, variance and peak value, and the time domain signal of the audio is converted into frequency domain representation through Fourier transform to extract frequency domain features, including frequency width and power spectrum density; The similarity of the transmission network node data is identified through the comprehensive similarity recognition model of flow data and heat data and the sound data recognition model: if the output data of the comprehensive similarity recognition model of flow data and heat data, and the output data of the sound data recognition model, are both identified as high-similarity node data, then the transmission network node is a high-similarity node; otherwise, it is a non-high-similarity node.

[0014] As a further solution of the present invention: a comprehensive similarity recognition model of flow data and heat data is constructed by a deep learning algorithm, including the following steps: Through the flow data and heat data of high-similarity nodes in the historical data, transmission pipeline network or terminal nodes, the data is organized into structured training data sets and verification data sets; Through the training data set and the verification data set, the recurrent neural network is trained, and a comprehensive similarity recognition model of flow data and heat data is established to identify whether the transmission pipeline network node is a high-similarity node.

[0015] As a further solution of the present invention: the similarity between terminal nodes evaluated by the similarity evaluation model between terminal nodes includes the following steps: Calculate the similarity evaluation value between terminal nodes through the similarity evaluation model between terminal nodes; If the similarity evaluation value between the terminal nodes is greater than a preset threshold, the terminal node is a high-similarity terminal, otherwise, it is not a high-similarity terminal.

[0016] As another aspect of the present invention: a method for processing heating data of a heating system, comprising the following steps: Edge data nodes are set at the transmission pipe network and heat source in the heating system. According to the grouping principle that terminals connected to the same transmission pipe network branch are grouped into the same terminal group, the terminals in the heating system are grouped, and data aggregation nodes are set on the same transmission pipe network branch connected to the terminals in each group. Set up a data mirror warehouse and wireless communication equipment at each edge data node, number the edge data nodes, set data labels for the data according to the timestamp and node number of data collection, and establish data transmission channels between adjacent edge data nodes in sequence from the edge data node at the heat source to the data aggregation node; The terminal data collected by each terminal in the terminal group is sent to the data aggregation node in the group. After the data aggregation node stores the data, it is packaged and sent to the nearest edge data node; After the edge data node collects the data of the node, it stores the data of the node in the image warehouse, and packages the data of the node and the terminal data packet together and transmits them to the edge data node at the hot source through the data transmission channel between the edge data nodes; The edge data node at the heat source standardizes the received data, establishes the node data table, and associates the highly similar data.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention realizes local storage and management of data by setting up a mirror warehouse at each edge data node, reduces dependence on the central server, and improves data access speed. In the event of a failure or network interruption, the edge node can use the local mirror warehouse to quickly restore service to ensure the high availability of the system. By establishing a data transmission channel between edge data nodes through wireless communication equipment, it is convenient to achieve low-latency data transmission, so that the system can respond to changes in real time. By adding a timestamp and node number to each data packet, the integrity and consistency of the data are ensured. This is for subsequent data analysis and tracking. Establishing a data transmission channel between adjacent edge nodes effectively disperses the network traffic load and improves the stability of the overall network.

[0018] The present invention ensures that all transmitted data adopts a consistent format through standardized processing, which is convenient for subsequent data analysis and processing. Establishing a data table at the edge data node at the heat source helps to centrally manage data from different terminals and nodes, improve data accessibility and management efficiency. Standardizing the received data helps to eliminate deviations caused by different sources or formats and improve data consistency and accuracy. By associating highly similar data, it is convenient to analyze the relationship between highly similar nodes in the subsequent data mining, providing deeper information for subsequent analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0021] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1 The first aspect of the present invention provides a heating data processing system and method for a heating system, including: Node setting module: edge data nodes are set at the transmission pipe network and heat source in the heating system. According to the grouping principle that terminals connected to the same transmission pipe network branch are grouped into the same terminal group, the terminals in the heating system are grouped, and data aggregation nodes are set on the same transmission pipe network branch connected to the terminals in each group; Transmission channel construction module: set up a data mirror warehouse and wireless communication equipment at each edge data node, number the edge data nodes, set data labels for the data according to the timestamp and node number of data collection, and establish data transmission channels between adjacent edge data nodes in sequence from the edge data node at the heat source to the data aggregation node; Terminal data aggregation module: The terminal data collected by each terminal in the terminal group is sent to the data aggregation node in the group. After the data aggregation node stores the data, it is packaged and sent to the nearest edge data node; Transmission management module: After the edge data node collects the data of the node, it stores the data of the node in the image warehouse, and packages the data of the node and the terminal data packet together and transmits them to the edge data node at the hot source through the data transmission channel between the edge data nodes; Data association module: The edge data node at the heat source standardizes the received data, establishes the node data table, and associates the highly similar data.

[0023] Specifically, in this embodiment, by setting edge data nodes at the transmission pipeline network and heat source in the heating system, the terminals in the heating system are grouped according to the grouping principle of grouping terminals connected to the same transmission pipeline network branch into the same terminal group, and a data aggregation node is set on the same transmission pipeline network branch connected to the terminals in each group; by setting a data aggregation node on the same transmission pipeline network branch, it is convenient to effectively concentrate data from multiple terminals, reducing the complexity and bandwidth requirements of data transmission. The data aggregation node realizes real-time processing of terminal data, facilitates centralized monitoring of all terminals in the terminal group, and improves the reliability of the system. The centrally processed data provides a basis for subsequent data analysis and facilitates the aggregation of data collected by the aggregation node; by avoiding each node sending data to the central server separately, the network pressure can be reduced.

[0024] Set up a data mirror warehouse and wireless communication equipment at each edge data node, number the edge data nodes, set data labels for the data according to the timestamp and node number of data collection, and establish data transmission channels between adjacent edge data nodes in sequence from the edge data node at the heat source to the data aggregation node; By setting up a mirror warehouse at each edge data node, local storage and management of data can be achieved, reducing dependence on central servers and improving data access speed. In the event of a failure or network interruption, the edge node can use the local mirror warehouse to quickly restore service and ensure high availability of the system. The data transmission channel between edge data nodes is established through wireless communication devices, which facilitates low-latency data transmission and enables the system to respond to changes in real time.

[0025] By adding timestamps and node numbers to each data packet, the integrity and consistency of the data is ensured. This is very useful for subsequent data analysis and tracking. Data transmission channels between adjacent edge nodes are established to effectively disperse network traffic load and improve the stability of the overall network.

[0026] The data of the node is packaged together with the terminal data packet and transmitted to the edge data node at the heat source through the data transmission channel between edge data nodes; the edge data node at the heat source standardizes the received data, establishes the node data table, and associates the highly similar data.

[0027] Through standardization, all transmitted data is ensured to be in a consistent format, which is convenient for subsequent data analysis and processing. Establishing a data table at the edge data node at the hot source helps to centrally manage data from different terminals and nodes, improve data accessibility and management efficiency. Standardizing the received data helps to eliminate deviations caused by different sources or formats and improve data consistency and accuracy. By associating highly similar data, it is convenient to analyze the relationship between highly similar nodes in the subsequent data analysis, providing deeper information for subsequent analysis.

[0028] In one embodiment of the present invention, a data mirror warehouse and a wireless communication device are set at each edge data node, and the edge data nodes are numbered, including the following steps: Wireless communication equipment and a data mirror warehouse are set up at each edge data node, where the data mirror warehouse is used to store copies of data collected by the edge node; select an appropriate data format, such as JSON, CSV or binary format, to facilitate subsequent processing and analysis.

[0029] The edge data node is equipped with a wireless communication device, which can use a Wi-Fi module suitable for short-distance, high-bandwidth data transmission, a LoRa module suitable for long-distance, low-power data transmission, or a Zigbee module suitable for low-power, short-distance network applications. The specific selection can be based on the network environment where the node is set.

[0030] Assign a unique identifier to each edge data node as the edge data node number, create a transmission network node management map, and record the transmission network location of each edge node, the node number, and the number of nodes with the least distance between the edge data node at the heat source.

[0031] Numbering format: A unified numbering format can be used, for example: Node_001, Node_002, and so on.

[0032] Nodes can be classified according to their geographical location or function, for example: ZoneA_Node_001.

[0033] In one embodiment of the present invention, from the edge data node at the heat source to the data aggregation node, a data transmission channel between adjacent edge data nodes is established in sequence, including the following steps: From the edge data node at the heat source to the data aggregation node, according to the transmission network node management diagram, the data transmission channel between adjacent edge data nodes is established in sequence through the wireless communication equipment on the transmission network; The aggregation node establishes a data transmission channel with the nearest edge data node through the MQTT communication protocol.

[0034] In one embodiment of the present invention, the edge data node at the heat source normalizes the received data, establishes a data table of the node, and associates the highly similar data, including the following steps: The edge data node at the heat source aggregates the data sent by the edge data nodes and sends it together with the data of the node to the cloud management platform. The cloud management platform standardizes the data of each edge data node and establishes the terminal data table and the data table of the transmission pipeline network node; Based on the data collected by the heating system terminal and the node data collected by the edge data nodes on the transmission pipeline network, a similarity evaluation model between terminal nodes and a similarity evaluation model for transmission pipeline network nodes are constructed; The similarity between terminal nodes evaluated by the similarity evaluation model between terminal nodes, and the similarity between transmission network nodes evaluated by the transmission network node similarity evaluation model; The data of terminals with high similarity are associated with each other, and the data of transmission network nodes with high similarity are associated with each other.

[0035] In one embodiment of the present invention, the cloud management platform standardizes the data of each edge data node and establishes a terminal data table and a data table of a transmission network node, including the following steps: Establish data standardization rules for edge data nodes to convert the data collected by each edge data node into the same format; Node data collected by edge data nodes include: flow data, heat data and sound data, wherein the heat data includes: internal temperature data of the pipeline and external temperature data of the pipeline, and the sound data includes external sound audio data of the pipeline and internal sound audio data of the pipeline; The edge data nodes at the heat source collect node data such as flow data, heat data and sound data at the heat source outlet and return water outlet; Establish data tables for transmission network nodes, including data lists for monitoring flow data, heat data, and sound data, and establish connections between the same node data in each data list based on the data labels of the node data in the data list; Set up terminal nodes at the heating system terminals to collect data, number the terminal nodes, and add data labels to the data of the terminal nodes according to the terminal node numbers and terminal groups; The data collected by the terminal node include: flow data, heat data, pressure data and geographic information data, wherein the heat data includes: the ambient temperature of the terminal and the temperature in the pipeline, and the geographic information data includes: the geographic coordinates and floor location of the terminal node; Establish a terminal data table, including: data lists of flow data, heat data, pressure data and geographic information data, and establish connections between the same terminal grouping data between various data lists based on the data labels of the node data in the data lists.

[0036] In one embodiment of the present invention, constructing a similarity evaluation model between terminal nodes includes the following steps: According to the geographic information data of the terminal, the terminal pipeline topology data, the flow data, heat data, pressure data and geographic information data collected by the terminal node are obtained; wherein the terminal pipeline topology data includes: the connection pipeline and position of the terminal node in the transmission pipeline network node management diagram, and the number of nodes directly connected to the terminal node; Create a terminal pipeline topology data table and add it to the established terminal data table; Through deep learning algorithms, a comprehensive similarity recognition model for flow data and heat data is constructed; The comprehensive similarity recognition model of flow data and heat data is used to evaluate the comprehensive similarity of flow data and heat data between terminal nodes; Based on the comprehensive similarity evaluation results, the terminal pipeline topology data table, and the pressure data and geographic information data tables, a similarity evaluation model between terminal nodes is established using the following formula: Among them, S is the similarity evaluation value between terminal nodes, S1 is the similarity of terminal pipeline topology data between terminal nodes, S2 is the similarity of pressure data between terminal nodes, S3 is the similarity of geographic information data between terminal nodes, C is a constant set according to the comprehensive similarity evaluation result, w1, w2 and w3 are the weights of the similarity of terminal pipeline topology data between terminal nodes, the similarity of pressure data between terminal nodes and the similarity of geographic information data between terminal nodes respectively; g0 is the average number of nodes directly connected to the terminal nodes in the terminal group where the two terminal nodes are located, is the difference in the number of nodes directly connected to the two terminal nodes, Dg0 is the mean of the distances between the terminal nodes and the connecting pipelines in the transmission network node management diagram in the terminal group where the two terminal nodes are located, Dg1 is the distance between the terminal node and the connecting pipeline in the transmission network node management diagram, and Dg2 is the distance between the other terminal node and the connecting pipeline in the transmission network node management diagram, in km; S0 is the mean of the variance of the terminal node pressure data in the terminal group where the two terminal nodes are located within the preset time period, is the difference in variance of the terminal node pressure data within the preset time period, is the mean value of the terminal node pressure data in the terminal group where the two terminal nodes are located, in MPa. It is the difference between the mean values ​​of the terminal node pressure data within the preset time period; is the mean distance between the geographical coordinates of the two terminal nodes in the terminal group where the two terminal nodes are located, is the distance between the geographical coordinates of the terminal nodes, in km. is the sum of the total number of floors where the two terminal nodes are located, is the difference in the number of floors where the terminal nodes are located.

[0037] Specifically, in this embodiment, through summarizing a large amount of experimental data, w1 is set to 0.4, w2 is set to 0.4, w3 is set to 0.2, and the constant is set according to the comprehensive similarity evaluation result. If the comprehensive similarity evaluation result is a high similarity node, C is set to 2.5. If the comprehensive similarity evaluation result is a non-high similarity node, C is set to 0.

[0038] In one embodiment of the present invention, constructing a transmission network node similarity evaluation model includes the following steps: Obtain flow data, heat data and sound data collected by edge data nodes on the transmission pipeline network; The comprehensive similarity recognition model of flow data and heat data is used to evaluate the comprehensive similarity of flow data and heat data between terminal nodes; Through the audio data of the transmission network nodes with high similarity in the transmission network in the historical data, the time domain features and frequency domain features are extracted. Through the time domain features and frequency domain features, the sound data recognition model is trained through the machine learning algorithm to identify whether the transmission network node is a high similarity node; The time domain features of the sound data audio of the pipeline are extracted, including mean, variance and peak value, and the time domain signal of the audio is converted into frequency domain representation through Fourier transform to extract frequency domain features, including frequency width and power spectrum density; The similarity of the transmission network node data is identified through the comprehensive similarity recognition model of flow data and heat data and the sound data recognition model: if the output data of the comprehensive similarity recognition model of flow data and heat data, and the output data of the sound data recognition model, are both identified as high-similarity node data, then the transmission network node is a high-similarity node; otherwise, it is a non-high-similarity node.

[0039] In one embodiment of the present invention, a comprehensive similarity recognition model of flow data and heat data is constructed by a deep learning algorithm, including the following steps: Through the flow data and heat data of high-similarity nodes in the historical data, transmission pipeline network or terminal nodes, the data is organized into structured training data sets and verification data sets; Through the training data set and the verification data set, the recurrent neural network is trained, and a comprehensive similarity recognition model of flow data and heat data is established to identify whether the transmission pipeline network node is a high-similarity node.

[0040] In one embodiment of the present invention, the similarity between terminal nodes evaluated by the similarity evaluation model between terminal nodes includes the following steps: Calculate the similarity evaluation value between terminal nodes through the similarity evaluation model between terminal nodes; If the similarity evaluation value between the terminal nodes is greater than a preset threshold, the terminal node is a high-similarity terminal, otherwise, it is not a high-similarity terminal.

[0041] Specifically, in this embodiment, if the similarity evaluation value between terminal nodes is greater than 5, the terminal node is a high-similarity terminal, otherwise, it is not a high-similarity terminal.

[0042] In an embodiment of another aspect of the present invention, a method for processing heating data of a heating system is provided, comprising the following steps: Edge data nodes are set at the transmission pipe network and heat source in the heating system. According to the grouping principle that terminals connected to the same transmission pipe network branch are grouped into the same terminal group, the terminals in the heating system are grouped, and data aggregation nodes are set on the same transmission pipe network branch connected to the terminals in each group. Set up a data mirror warehouse and wireless communication equipment at each edge data node, number the edge data nodes, set data labels for the data according to the timestamp and node number of data collection, and establish data transmission channels between adjacent edge data nodes in sequence from the edge data node at the heat source to the data aggregation node; The terminal data collected by each terminal in the terminal group is sent to the data aggregation node in the group. After the data aggregation node stores the data, it is packaged and sent to the nearest edge data node; After the edge data node collects the data of the node, it stores the data of the node in the image warehouse, and packages the data of the node and the terminal data packet together and transmits them to the edge data node at the hot source through the data transmission channel between the edge data nodes; The edge data node at the heat source standardizes the received data, establishes the node data table, and associates the highly similar data.

[0043] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A heating data processing system for a heating system, characterized in that: include: Node setting module: edge data nodes are set at the transmission pipe network and heat source in the heating system. According to the grouping principle that terminals connected to the same transmission pipe network branch are grouped into the same terminal group, the terminals in the heating system are grouped, and data aggregation nodes are set on the same transmission pipe network branch connected to the terminals in each group; Transmission channel construction module: set up a data mirror warehouse and wireless communication equipment at each edge data node, number the edge data nodes, set data labels for the data according to the timestamp and node number of data collection, and establish data transmission channels between adjacent edge data nodes in sequence from the edge data node at the heat source to the data aggregation node; Terminal data aggregation module: The terminal data collected by each terminal in the terminal group is sent to the data aggregation node in the group. After the data aggregation node stores the data, it is packaged and sent to the nearest edge data node; Transmission management module: After the edge data node collects the data of the node, it stores the data of the node in the image warehouse, and packages the data of the node and the terminal data packet together and transmits them to the edge data node at the hot source through the data transmission channel between the edge data nodes; Data association module: The edge data node at the heat source standardizes the received data, establishes the node data table, and associates the highly similar data.

2. The heating data processing system of a heating system according to claim 1, characterized in that: Setting up a data mirror warehouse and wireless communication equipment at each edge data node and numbering the edge data nodes includes the following steps: A wireless communication device and a data mirror warehouse are set up at each edge data node, wherein the data mirror warehouse is used to store copies of data collected by the edge node; Assign a unique identifier to each edge data node as the edge data node number, create a transmission network node management map, and record the transmission network location of each edge node, the node number, and the number of nodes with the least distance between the edge data node at the heat source.

3. The heating data processing system of a heating system according to claim 1, characterized in that: From the edge data node at the heat source to the data aggregation node, a data transmission channel between adjacent edge data nodes is established in sequence, including the following steps: From the edge data node at the heat source to the data aggregation node, according to the transmission network node management diagram, the data transmission channel between adjacent edge data nodes is established in sequence through the wireless communication equipment on the transmission network; The aggregation node establishes a data transmission channel with the nearest edge data node through the MQTT communication protocol.

4. The heating data processing system of a heating system according to claim 1, characterized in that: The edge data node at the heat source standardizes the received data, establishes a node data table, and associates the highly similar data, including the following steps: The edge data node at the heat source aggregates the data sent by the edge data nodes and sends it together with the data of the node to the cloud management platform. The cloud management platform standardizes the data of each edge data node and establishes the terminal data table and the data table of the transmission pipeline network node; Based on the data collected by the heating system terminal and the node data collected by the edge data nodes on the transmission pipeline network, a similarity evaluation model between terminal nodes and a similarity evaluation model for transmission pipeline network nodes are constructed; The similarity between terminal nodes evaluated by the similarity evaluation model between terminal nodes, and the similarity between transmission network nodes evaluated by the transmission network node similarity evaluation model; The data of terminals with high similarity are associated with each other, and the data of transmission network nodes with high similarity are associated with each other.

5. The heating data processing system of a heating system according to claim 4, characterized in that: The cloud management platform standardizes the data of each edge data node and establishes the terminal data table and the data table of the transmission network node, including the following steps: Establish data standardization rules for edge data nodes to convert the data collected by each edge data node into the same format; Node data collected by edge data nodes include: flow data, heat data and sound data, wherein the heat data includes: internal temperature data of the pipeline and external temperature data of the pipeline, and the sound data includes external sound audio data of the pipeline and internal sound audio data of the pipeline; The edge data nodes at the heat source collect node data such as flow data, heat data and sound data at the heat source outlet and return water outlet; Establish data tables for transmission network nodes, including data lists for monitoring flow data, heat data, and sound data, and establish connections between the same node data in each data list based on the data labels of the node data in the data list; Set up terminal nodes at the heating system terminals to collect data, number the terminal nodes, and add data labels to the data of the terminal nodes according to the terminal node numbers and terminal groups; The data collected by the terminal node include: flow data, heat data, pressure data and geographic information data, wherein the heat data includes: the ambient temperature of the terminal and the temperature in the pipeline, and the geographic information data includes: the geographic coordinates and floor location of the terminal node; Establish a terminal data table, including: data lists of flow data, heat data, pressure data and geographic information data, and establish connections between the same terminal grouping data between various data lists based on the data labels of the node data in the data lists.

6. The heating data processing system of a heating system according to claim 4, characterized in that: Constructing a similarity evaluation model between terminal nodes includes the following steps: Obtain terminal pipeline topology data, flow data, heat data, pressure data and geographic information data collected by the terminal node according to the terminal's geographic information data; wherein the terminal pipeline topology data includes: the connection pipeline and position of the terminal node in the transmission pipeline network node management diagram, and the number of nodes directly connected to the terminal node; Create a terminal pipeline topology data table and add it to the established terminal data table; Through deep learning algorithms, a comprehensive similarity recognition model for flow data and heat data is constructed; The comprehensive similarity recognition model of flow data and heat data is used to evaluate the comprehensive similarity of flow data and heat data between terminal nodes; Based on the comprehensive similarity evaluation results, the terminal pipeline topology data table, and the pressure data and geographic information data tables, a similarity evaluation model between terminal nodes is established using the following formula: Among them, S is the similarity evaluation value between terminal nodes, S1 is the similarity of terminal pipeline topology data between terminal nodes, S2 is the similarity of pressure data between terminal nodes, S3 is the similarity of geographic information data between terminal nodes, C is a constant set according to the comprehensive similarity evaluation result, w1, w2 and w3 are the weights of the similarity of terminal pipeline topology data between terminal nodes, the similarity of pressure data between terminal nodes and the similarity of geographic information data between terminal nodes respectively; g0 is the average number of nodes directly connected to the terminal nodes in the terminal group where the two terminal nodes are located, is the difference in the number of nodes directly connected to the two terminal nodes, Dg0 is the mean of the distances between the terminal nodes and the connecting pipelines in the transmission network node management diagram in the terminal group where the two terminal nodes are located, Dg1 is the distance between the terminal node and the connecting pipeline in the transmission network node management diagram, and Dg2 is the distance between the other terminal node and the connecting pipeline in the transmission network node management diagram; S0 is the mean of the variance of the terminal node pressure data in the terminal group where the two terminal nodes are located within a preset time period, is the difference in variance of the terminal node pressure data within the preset time period, is the mean value of the terminal node pressure data in the terminal group where the two terminal nodes are located, It is the difference between the mean values ​​of the terminal node pressure data within the preset time period; is the mean distance between the geographical coordinates of the two terminal nodes in the terminal group where the two terminal nodes are located, is the distance between the geographical coordinates of the terminal nodes, is the sum of the total number of floors where the two terminal nodes are located, is the difference in the number of floors where the terminal nodes are located.

7. The heating data processing system of a heating system according to claim 4, characterized in that: Constructing a transmission network node similarity evaluation model includes the following steps: Obtain flow data, heat data and sound data collected by edge data nodes on the transmission pipeline network; The comprehensive similarity recognition model of flow data and heat data is used to evaluate the comprehensive similarity of flow data and heat data between terminal nodes; Through the audio data of the transmission network nodes with high similarity in the transmission network in the historical data, the time domain features and frequency domain features are extracted. Through the time domain features and frequency domain features, the sound data recognition model is trained through the machine learning algorithm to identify whether the transmission network node is a high similarity node; The time domain features of the sound data audio of the pipeline are extracted, including mean, variance and peak value, and the time domain signal of the audio is converted into frequency domain representation through Fourier transform to extract frequency domain features, including frequency width and power spectrum density; The similarity of the transmission network node data is identified through the comprehensive similarity recognition model of flow data and heat data and the sound data recognition model: if the output data of the comprehensive similarity recognition model of flow data and heat data, and the output data of the sound data recognition model, are both identified as high-similarity node data, then the transmission network node is a high-similarity node; otherwise, it is a non-high-similarity node.

8. The heating data processing system of a heating system according to claim 6, characterized in that: Through the deep learning algorithm, a comprehensive similarity recognition model for flow data and heat data is constructed, including the following steps: Through the flow data and heat data of high-similarity nodes in the historical data, transmission pipeline network or terminal nodes, the data is organized into structured training data sets and verification data sets; Through the training data set and the verification data set, the recurrent neural network is trained, and a comprehensive similarity recognition model of flow data and heat data is established to identify whether the transmission pipeline network node is a high-similarity node.

9. The heating data processing system of a heating system according to claim 4, characterized in that: The similarity between terminal nodes evaluated by the similarity evaluation model between terminal nodes includes the following steps: Calculate the similarity evaluation value between terminal nodes through the similarity evaluation model between terminal nodes; If the similarity evaluation value between the terminal nodes is greater than a preset threshold, the terminal node is a high-similarity terminal, otherwise, it is not a high-similarity terminal.

10. A method for processing heating data of a heating system, characterized in that: The following steps are involved: Edge data nodes are set at the transmission pipe network and heat source in the heating system. According to the grouping principle that terminals connected to the same transmission pipe network branch are grouped into the same terminal group, the terminals in the heating system are grouped, and data aggregation nodes are set on the same transmission pipe network branch connected to the terminals in each group; Set up a data mirror warehouse and wireless communication equipment at each edge data node, number the edge data nodes, set data labels for the data according to the timestamp and node number of the data collection, and establish data transmission channels between adjacent edge data nodes in sequence from the edge data node at the heat source to the data aggregation node; The terminal data collected by each terminal in the terminal group is sent to the data aggregation node in the group. After the data aggregation node stores the data, it is packaged and sent to the nearest edge data node; After the edge data node collects the data of the node, it stores the data of the node in the image warehouse, and packages the data of the node and the terminal data packet together and transmits them to the edge data node at the hot source through the data transmission channel between the edge data nodes; The edge data node at the heat source standardizes the received data, establishes the node data table, and associates the highly similar data.

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