Cloud network-based edge end heat supply system, collaborative architecture and construction method of collaborative architecture

Abstract

The embodiment of the invention provides an edge end heat supply system based on a cloud network, a collaborative architecture and a construction method of the collaborative architecture, and belongs to the technical field of intelligent heat supply. The construction method comprises the steps that a cloud computing platform of the heat supply system is built, and the cloud computing platform comprises a data storage and management module and a data analysis and decision support module; building a backbone network and a wireless network in the heat supply system; acquiring positions of key nodes of a heat exchange station and a heat supply pipe network in the heat supply system, and deploying edge computing nodes at the key nodes of the heat exchange station and the heat supply pipe network in the heat supply system; according to the method, a cloud network side-end collaborative cooperation mode is adopted, collaborative heat supply of the heat source plant, the heat supply pipe network, the heat exchange station and the user end is achieved, the heat supply collaboration and controllability are effectively improved, then the heat supply efficiency is improved, the overall monitoring effect on the heat supply system is better, the processing efficiency is high, and the effect is good.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent heating, and particularly to a cloud-network-edge-end based heating system, a collaborative architecture and a construction method thereof. Background Art

[0002] Heating is an important infrastructure service to ensure the comfort of residents' lives and the normal progress of industrial production. Especially in cold regions, its importance is self-evident. Traditional heating systems usually adopt a relatively decentralized management and operation mode, and the coordination among various links such as heat source plants, heat supply pipelines, heat exchange stations and user terminals is poor. For example, it is difficult for heat source plants to accurately adjust heating parameters according to real-time user demands, and they often can only carry out extensive heating according to preset modes; there are no effective real-time monitoring and control means during the operation of heat supply pipelines. Once a fault occurs (such as leakage, blockage, etc.), it is difficult to detect and handle in time; the degree of automation of heat exchange stations is also limited, and the distribution and adjustment of heat are not flexible enough; user terminals can only passively receive heating, the feedback channels for heating quality are not smooth, and they cannot form effective interaction with the front-end links of the heating system.

[0003] With the increasingly prominent problem of energy shortage and the continuous improvement of attention to environmental protection, the heating industry is facing great pressure to improve energy utilization efficiency and meet environmental protection standards. Under the traditional heating mode, each node is independent, and there are large losses in the process of energy transmission and conversion. For example, there are heat dissipation losses in heat supply pipelines and low energy conversion efficiency in heat exchange stations. At the same time, some heating methods (such as some heat source plants mainly fueled by coal) will also produce a large amount of pollutant emissions, causing pollution to the environment and not meeting the requirements of sustainable development. Generally speaking, the traditional heating mode has problems such as poor coordination among various links, low energy utilization efficiency, lag in fault monitoring and handling, and poor user experience.

[0004] The inventors of the present application found in the process of implementing the present invention that the above-mentioned solutions in the prior art have the defects of poor heating coordination and low efficiency. Summary of the Invention

[0005] An object of an embodiment of the present invention is to provide a cloud-network-edge-end based heating system, a collaborative architecture and a construction method thereof, and the cloud-network-edge-end based heating system, collaborative architecture and construction method thereof have the functions of good heating coordination and high efficiency.

[0006] To achieve the above object, on the one hand, an embodiment of the present invention provides a construction method for a collaborative architecture in a cloud-network-edge-end based heating system, including:

[0007] Building a cloud computing platform for the heating system, wherein the cloud computing platform includes a data storage and management module and a data analysis and decision support module;

[0008] Build a backbone network and a wireless network in the heating system;

[0009] Obtain the positions of the heat exchange stations and the key nodes of the heat supply pipeline network in the heating system, and deploy edge computing nodes at the key nodes of the heat exchange stations and the heat supply pipeline network in the heating system;

[0010] Install data acquisition modules on the heat source plant equipment, heat supply pipeline network monitoring equipment, heat exchange station equipment, and user end equipment in the heating system, and communicatively connect the data acquisition modules to the cloud computing platform and the edge computing nodes.

[0011] Optionally, the stored data of the data storage and management module includes the production data of the heat source plant, the operation data of the heat supply pipeline network, the heat exchange data of the heat exchange station, and the heat consumption feedback data of the user end.

[0012] Optionally, the data analysis and decision support module includes:

[0013] Obtain the historical heating data of the user end, and preprocess the historical heating data, where the historical heating data includes the heat supply amounts in different regions and different time periods;

[0014] Construct a heating demand prediction model;

[0015] Train the heating demand prediction model using the preprocessed historical heating data;

[0016] Obtain the real-time heating data of the current user end;

[0017] Input the real-time heating data into the heating demand prediction model to obtain the heating demand prediction value of the user end;

[0018] Determine the production plan of the heat source plant according to the heating demand prediction value.

[0019] Optionally, building a backbone network and a wireless network in the heating system includes:

[0020] Build an Ethernet network in the heat source plant, the heat supply pipeline network control center, and the heat exchange station;

[0021] Deploy a wireless network in the heat supply pipeline network, the heat exchange station, and the user end, where the wireless network includes 5G, Wi-Fi, and ZigBee networks;

[0022] Debug and test the network protocols of the Ethernet network and the wireless network.

[0023] Optionally, the edge computing node is used to execute:

[0024] Collect real-time parameters within the range of the edge computing node;

[0025] Determine whether the real-time parameter is within the corresponding preset threshold range;

[0026] In the case where it is determined that the real-time parameter is not within the corresponding preset threshold range, give an audible and visual alarm;

[0027] In the case where it is determined that the real-time parameter is within the corresponding preset threshold range, determine that the real-time parameter is normal.

[0028] Optionally, the real-time parameters include temperature, pressure, and flow rate.

[0029] On the other hand, the present invention also provides a collaborative architecture of a cloud-network-edge-end collaborative heating system constructed according to the construction method described in any one of the above, including:

[0030] A cloud computing platform, which includes a data storage and management module and a data analysis and decision support module;

[0031] A data acquisition module, which is respectively arranged on the heat source plant equipment, the heat pipe network monitoring equipment, the heat exchange station equipment, and the user terminal equipment for acquiring real-time parameters;

[0032] An edge computing module, which is arranged at key nodes of the heat exchange station and the heat pipe network, and is communicatively connected with the data acquisition module and the cloud computing platform.

[0033] Optionally, the data acquisition module includes a temperature sensor, a pressure sensor, a flow sensor, and an acoustic sensor.

[0034] Optionally, the edge computing module or the cloud computing platform is used to execute:

[0035] Obtain a real-time infrared image of the heat pipe network;

[0036] Perform recognition and diagnosis on the real-time infrared image to determine whether there is an abnormality in the real-time infrared image;

[0037] In the case where it is determined that there is an abnormality in the real-time infrared image, use an infrared monitor to measure the abnormal point multiple times to obtain multiple sets of acquisition data;

[0038] Judge whether there is an abnormality according to multiple sets of the acquisition data;

[0039] In the case where it is determined that the acquisition data is abnormal, arrange personnel for maintenance and repair.

[0040] On yet another aspect, the present invention also provides a cloud-network-edge-end heating system, including:

[0041] A heat source plant for producing heat supply;

[0042] A heat exchange station, which is connected to a heat source plant through a first heat pipeline network;

[0043] A user end, which is connected through a second heat pipeline network;

[0044] The collaborative architecture as described above, which is used to conduct collaborative monitoring and heating allocation for the heat source plant, the heat exchange station, and the user end.

[0045] Through the above technical solution, the cloud-network-edge-end heating system, collaborative architecture, and its construction method provided by the present invention first build a cloud computing platform for the heating system, and at the same time build a backbone network and a wireless network for the heating system, then deploy edge computing nodes at key nodes of the heat exchange station and the heat pipeline network, and make processing decisions on local data through the edge computing nodes. Finally, install data acquisition modules on the heat source plant equipment, heat pipeline network monitoring equipment, heat exchange station equipment, and user end equipment. The data acquisition modules perform real-time data acquisition and transmit the data to the cloud computing platform and the edge computing nodes to achieve collaborative heating of the heat source plant, heat pipeline network, heat exchange station, and user end, effectively improving the heating collaboration and controllability, thereby improving the heating efficiency, and having a better monitoring effect on the overall heating system, with high processing efficiency and good effect.

[0046] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification, and are used to explain the embodiments of the present invention together with the following specific implementation, but do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0048] Figure 1 is a flowchart of a method for constructing a collaborative architecture in a cloud-network-edge-end heating system according to an embodiment of the present invention;

[0049] Figure 2 is a flowchart of a method for obtaining a predicted value of heating demand in a method for constructing a collaborative architecture in a cloud-network-edge-end heating system according to an embodiment of the present invention;

[0050] Figure 3 is a flowchart of a method for building a network in a method for constructing a collaborative architecture in a cloud-network-edge-end heating system according to an embodiment of the present invention;

[0051] Figure 4 is a flowchart of real-time monitoring and diagnosis in a method for constructing a collaborative architecture in a cloud-network-edge-end heating system according to an embodiment of the present invention;

[0052] Figure 5 It is a schematic diagram of the working mechanism of the collaborative architecture in the cloud-network-edge-end heating system according to an embodiment of the present invention;

[0053] Figure 6 It is a flowchart of fault monitoring and processing of the collaborative architecture in the cloud-network-edge-end heating system according to an embodiment of the present invention. Specific embodiments

[0054] The following will describe in detail the specific embodiments of the embodiments of the present invention with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention.

[0055] It should be noted that the acquisition, transmission, storage, use, processing, etc. of data in the technical solution of this application all comply with the relevant regulations of national laws and regulations. In the embodiments of this application, some existing industry solutions such as certain software, components, models, etc. may be mentioned. They should be regarded as exemplary, and their purpose is only to illustrate the feasibility in the implementation of the technical solution of this application, but it does not mean that the applicant has already or necessarily used this solution.

[0056] Figure 1 It is a flowchart of a method for constructing a collaborative architecture in the cloud-network-edge-end heating system according to an embodiment of the present invention. In Figure 1 it, the construction method may include:

[0057] In step S10, a cloud computing platform for the heating system is built. Among them, the cloud computing platform includes a data storage and management module and a data analysis and decision support module. Specifically, the cloud computing platform has the ability to store a large amount of data. The data storage and management module is used to store the data of the entire process of the heating system, including: production data of the heat source plant, such as fuel consumption, heating power, etc.; operation data of the heat pipe network, such as monitoring data of temperature, pressure, flow rate, etc.; heat exchange data of the heat exchange station, such as inlet and outlet water temperatures, heat exchange volume, etc.; heat consumption feedback data of the user side, such as indoor temperature, heat consumption, etc. Specifically, the data storage and management module adopts distributed storage technology to ensure the security, reliability and high availability of the data. The data analysis and decision support module uses technologies such as big data analysis, machine learning, and artificial intelligence to deeply mine and analyze the stored large amount of data. By analyzing historical data and real-time data, a heating demand prediction model is established to predict the heating demand in different regions and different time periods in advance, so that the heat source plant can reasonably arrange production; according to the pipe network operation data and user side feedback, optimize the heating parameter settings to achieve accurate heating. At the same time, based on the data analysis results, decision support is provided for the operation and management of the heating system, such as determining the best heating strategy, equipment maintenance plan, etc. Specifically, the specific steps for the data analysis and decision support module to perform heating prediction can be asFigure 2 As shown. Specifically, in Figure 2 , the heat supply prediction step may include:

[0058] In step S100, historical heat supply data of the user side is obtained, and the historical heat supply data is preprocessed. Among them, the historical heat supply data includes heat supply amounts in different regions and different time periods, etc. Specifically, the preprocessing steps may include normalization, interpolation processing, etc.

[0059] In step S101, a heat demand prediction model is constructed. Among them, the heat demand prediction model may include a neural network model known to those skilled in the art.

[0060] In step S102, the preprocessed historical heat supply data is used to train the heat demand prediction model.

[0061] In step S103, real-time heat supply data of the current user side is obtained. Among them, the real-time heat supply data may also be the real-time heat supply data of the heat exchange station. Specifically, the heat supply prediction data can be set according to the actual situation. Using the heat supply data of the user side for prediction has a large amount of data and high prediction accuracy. Using the heat supply data of the heat exchange station for prediction has a small amount of data and poor prediction accuracy. Therefore, in this embodiment of the present invention, the heat supply prediction model can also be deployed in each edge computing node to improve the prediction accuracy and prediction complexity.

[0062] In step S104, the real-time heat supply data is input into the heat demand prediction model to obtain the heat demand prediction value of the user side.

[0063] In step S105, a production plan of the heat source plant is determined according to the heat demand prediction value. Among them, after obtaining the heat demand prediction value, it is convenient for the heat source plant to reasonably arrange production to achieve intelligent, efficient and reliable heat supply.

[0064] In steps S100 to S105, first, historical heat supply data of the user side, that is, historical heat supply amounts, is obtained, and the historical heat supply data is subjected to normalization, interpolation or standardization processing. Then, a neural network model for heat demand prediction is constructed, and the preprocessed historical data is divided into a training set and a validation set to train and validate the heat demand prediction model, and thus a complete prediction model can be obtained, and real-time data is continuously uploaded for training and optimizing the model in subsequent use. The real-time heat supply data of the current user side is input into the trained prediction model, and the heat demand prediction value for a future period of time can be obtained. According to this heat demand prediction value, it is convenient for the heat source plant to arrange and formulate a production plan. In addition, the heat consumption in a future period of time can also be predicted according to the historical heat supply data and weather change conditions in previous years, and the output power of the heat source can be adjusted in advance to achieve accurate heat supply and avoid waste of energy.

[0065] In step S11, a backbone network and a wireless network are built in the heating system. The network building may include a wired network and a wireless network. The specific building steps may be as follows: Figure 3 Specifically, Figure 3 In the example, the network building steps may include:

[0066] In step S110, Ethernet is built in the heat source plant, the heat pipe network control center and the heat exchange station. Among them, a high-speed and stable wired network can be used as the backbone network of the heating system, such as Ethernet. The backbone network can ensure data transmission between major nodes such as the heat source plant, the heat pipe network control center, and the heat exchange station. Specifically, the backbone network has the characteristics of high bandwidth and low latency, and can meet the transmission requirements of a large amount of real-time data.

[0067] In step S111, wireless networks are deployed in the heat pipe network, heat exchange station and user end, wherein the wireless networks include 5G, Wi-Fi, and ZigBee networks. Among them, wireless networks, such as 5G, Wi-Fi, ZigBee and other wireless communication technologies, can be deployed in areas such as the heat pipe network, the converter station and the user end. The 5G network is used to achieve high-speed data transmission between key nodes of the heating system (such as the heat exchange station and the cloud, the heat pipe network monitoring equipment and the edge computing equipment, etc.), and its advantages of low latency and high bandwidth are used to ensure the rapid upload of real-time monitoring data and the rapid issuance of control instructions. The Wi-Fi network can provide a convenient local network connection for user-end devices, making it convenient for users to interact with the heating system (such as viewing heat information, feedback on heat problems, etc.); wireless sensor networks such as ZigBee are used to build local sensor networks inside the heat pipe network and the heat exchange station to achieve real-time monitoring of equipment operating status and environmental parameters. Its low power consumption and self-organization characteristics are suitable for long-term and stable data collection.

[0068] In step S112, the network protocols of Ethernet and wireless networks are debugged and tested. For the above networks, a unified network protocol can be used to ensure that data between different devices and different levels can be accurately and smoothly transmitted. At the same time, attention is paid to network security protection, and security measures such as firewalls and encrypted transmission are set to prevent security issues such as data leakage and network attacks, and to ensure the integrity and confidentiality of the heating system data.

[0069] In steps S110 to S112, first establish a stable wired network among the heat source plant, the heat pipe network control center, and the heat exchange station. At the same time, deploy a wireless network among the heat pipe network, the heat exchange station, and the user side, and adopt the same network protocol to ensure the stability and security of data transmission between different devices and different levels. The management personnel of the heating system can remotely monitor the operation status of the heating system in real time through the network, including the operation of the heat source, the pressure and temperature changes of the pipe network, the working status of the heat exchange station, and the heat consumption of the user side, etc. No matter where the management personnel are, they can timely understand the operation of the system and achieve all-round and all-weather monitoring of the heating system. When an abnormal situation occurs, the system can issue an alarm in time, and the management personnel can quickly take corresponding measures to handle it, improving the reliability and security of the system.

[0070] In step S12, obtain the positions of the key nodes of the heat exchange stations and the heat pipe network in the heating system, and deploy edge computing nodes at the key nodes of the heat exchange stations and the heat pipe network in the heating system. Among them, the deployment of edge computing nodes is generally to deploy edge computing nodes at the key nodes of the heat exchange station and the heat pipe network (such as branch pipe joints, pressure monitoring points, etc.) and other positions close to the data source. These edge computing nodes are equipped with certain computing power and storage capacity and can quickly process the locally collected data. Specifically, the edge computing nodes have the ability of local data processing and decision-making. Specifically, the local data processing and decision-making steps can be as Figure 4 shown. Specifically, in Figure 4 , this local data processing and decision-making step can include:

[0071] In step S120, collect the real-time parameters within the range of the edge computing nodes. Among them, the real-time parameters can include the temperature, pressure, flow rate, etc. of the heating.

[0072] In step S121, determine whether the real-time parameters are within the corresponding preset threshold range.

[0073] In step S122, in the case where it is determined that the real-time parameters are not within the corresponding preset threshold range, issue an audible and visual alarm. Among them, if the real-time parameters are not within the corresponding preset threshold range, it can include too high or too low temperature, abnormal pressure fluctuation, etc. When parameter anomalies are found, some preliminary treatment measures can be taken locally, such as adjusting the valve opening of the heat exchange station, issuing a local audible and visual alarm, etc. And it is not necessary to upload all data to the cloud, thus reducing the network transmission burden and improving the response speed to local problems. At the same time, the edge computing nodes can also optimize and adjust the local operation of the heating system according to the local data and the instructions issued by the cloud, such as reasonably adjusting the heat exchange efficiency of the heat exchange station according to the local flow rate.

[0074] In step S123, when it is determined that the real-time parameter is within the corresponding preset threshold range, the real-time parameter is determined to be normal.

[0075] In steps S120 to S123, the real-time parameters within the monitoring range of the edge computing node are acquired, and the real-time parameters are compared with the corresponding preset threshold ranges. If the real-time parameter is within the corresponding preset threshold range, it indicates that the real-time parameter is normal; otherwise, it is abnormal, and the valve opening degree, etc. needs to be adjusted and an audible and visual alarm is issued.

[0076] In step S13, data acquisition modules are respectively installed on the heat source plant equipment, heat supply network monitoring equipment, heat exchange station equipment, and user-side equipment in the heating system, and the data acquisition modules are communicatively connected to the cloud computing platform and the edge computing node. Among them, the data acquisition module has different forms and combinations according to the installed equipment. Specifically, for the heating equipment in the heat source plant (such as boilers, steam turbines, etc.), the data acquisition module needs to collect its own operating status data, such as equipment temperature, pressure, rotational speed, etc., and transmit this data to the cloud or the edge computing node through network communication technology. At the same time, these devices can receive control instructions from the cloud or the edge computing node to achieve remote control and automated operation, such as automatically adjusting the heating power according to the heating demand. For the heat supply network, the data acquisition module can include temperature sensors, pressure sensors, flow sensors, acoustic sensors, etc. These monitoring devices can collect data such as the temperature, pressure, flow, and abnormal sounds of the pipeline network in real time and transmit them to the edge computing node or directly upload them to the cloud. By analyzing these data, the operating status of the pipeline network can be monitored in real time, and faults such as leaks and blockages can be detected in a timely manner. For the heat exchange station equipment (heat exchangers, water pumps, valves, etc.), the data acquisition module needs to collect its own operating data, such as the inlet water temperature, flow rate, valve opening, etc., and make adjustments according to the instructions of the cloud or the edge computing node. For example, according to the heat consumption demand of the user side and the operating conditions of the pipeline network, adjust the heat exchange efficiency of the heat exchanger, the flow rate of the water pump, and the opening of the valve, etc., to achieve precise heat distribution and efficient heat exchange. For the user side, the data acquisition module can include intelligent heat metering equipment and interactive equipment. The intelligent heat metering equipment can accurately measure the heat consumption of users (such as heat consumption, heat consumption time, etc.) and transmit this data to the cloud. The intelligent interactive equipment (such as intelligent thermostats, mobile phone APPs, etc.) allows users to conveniently view their own heat consumption information, feedback heat consumption problems, and at the same time can also receive heating adjustment notifications from the cloud, so that users can make corresponding preparations in advance, such as adjusting the indoor temperature setting, etc. In addition, in the heating system, various devices such as sensors and intelligent meters are widely distributed in various links such as the heat source, pipeline network, heat exchange station, and user side, and a large amount of multi-source data such as temperature, pressure, and flow are collected in real time. Through the cloud-network-edge collaborative architecture, these scattered data can be quickly and accurately collected and integrated to form a comprehensive and detailed operating data set of the heating system, providing a solid data foundation for subsequent analysis and decision-making. For example, during the peak winter heating period, the heat consumption demand data of different regions and different users can be obtained in real time to timely adjust the heating parameters to meet the needs of users.

[0077] In steps S10 to S13, first, a cloud computing platform for the heating system is built. At the same time, a backbone network and a wireless network of the heating system are built, so that there is a stable communication connection among the heat source plant, the converter station, the heat pipe network, and the user side. Then, edge computing nodes are deployed at the key nodes of the heat exchange station and the heat pipe network to make local data processing decisions for the key nodes of the heat exchange station and the heat pipe network. Finally, data acquisition modules are installed on the heat source plant equipment, the heat pipe network monitoring equipment, the heat exchange station equipment, and the user side equipment, and are communicatively connected to the cloud computing platform and the edge computing nodes, so that the cloud computing platform can perform heating prediction based on real-time data and generate decision-making schemes such as heating production and heating regulation, thereby realizing efficient, intelligent, and reliable heating.

[0078] In the traditional heating mode, each node is independent, and there are problems such as poor coordination among various links, low energy utilization efficiency, lag in fault monitoring and handling, and poor user experience. In this embodiment of the present invention, by leveraging the powerful data processing and analysis capabilities of cloud computing, the efficient data transmission ensured by network communication technology, the fast response processing of edge computing locally, and the data advantages of intelligent acquisition by terminal devices, the heat source plant, the heat pipe network, the heat exchange station, and the user side are closely integrated to achieve vertical integrated collaborative operation. By accurately analyzing the real-time data of each link, optimizing the heating strategy, improving the energy utilization efficiency, reducing energy loss and pollution emissions, the effects of energy conservation and environmental protection are achieved. At the same time, the operation status of the system can be monitored in real time, and various problems such as pipeline leakage and equipment failure can be quickly located and processed to ensure the stability and continuity of heating. And, according to the feedback data of the user side, the heating is flexibly adjusted to effectively improve the user's heating experience, providing a new, efficient, intelligent, and sustainable solution for the heating industry.

[0079] On the other hand, the present invention also provides a collaborative architecture for a cloud-network-edge-end collaborative heating system. Specifically, as Figure 5 shown, the collaborative architecture may include a cloud computing platform / cloud end, a data acquisition module, and an edge computing module. Specifically, the cloud computing platform may include a data storage and management module and a data analysis and decision support module.

[0080] The data acquisition module is respectively arranged on the heat source plant equipment, the heat pipe network monitoring equipment, the heat exchange station equipment, and the user side equipment for collecting real-time parameters. The edge computing module is arranged at the key nodes of the heat exchange station and the heat pipe network and is communicatively connected to the data acquisition module and the cloud computing platform.

[0081] Each terminal device (such as heat source plant equipment, heat supply network monitoring equipment, heat exchange station equipment, user-side equipment, etc.) collects its own operating status data and heat supply-related environmental parameters in real time according to the set time interval or event trigger mechanism, and uploads this data to the edge computing node or directly to the cloud through the corresponding network communication method (wired or wireless). The edge computing module / node performs preliminary processing and analysis on the locally collected data, judges whether there are local abnormal situations, and takes corresponding processing measures. At the same time, it uploads some of the processed data and complex data that cannot be processed locally to the cloud / cloud computing platform. After receiving the data from the edge computing node and directly uploaded, the cloud / cloud computing platform uses technologies such as big data analysis for in-depth mining and analysis, evaluates the operating status of the heat supply system from a global perspective, formulates the optimal heat supply strategy, and issues relevant instructions to the edge computing node and each terminal device. The cloud / cloud computing platform issues control instructions according to the data analysis results, and these instructions are transmitted to the edge computing node and each terminal device through the network communication method. After receiving the instructions, the edge computing node further refines and adjusts the instructions in combination with the local actual situation, and then executes the instructions. After receiving the instructions, each terminal device adjusts its own operating status according to the instruction requirements, such as the heat source plant equipment adjusting the heating power, the heat exchange station equipment adjusting the valve opening, etc., so as to realize the overall optimized operation of the heat supply system.

[0082] In this embodiment of the present invention, the data acquisition module has different forms and combinations according to the installed equipment. Specifically, for the heat supply equipment of the heat source plant (such as boilers, steam turbines, etc.), the data acquisition module may include temperature, pressure, speed sensors, etc.; for the heat supply network, the data acquisition module may include temperature sensors, pressure sensors, flow sensors, acoustic sensors, etc. These monitoring devices can collect data such as the temperature, pressure, flow, and abnormal sounds of the pipeline network in real time; for the heat exchange station equipment (heat exchangers, water pumps, valves, etc.), the data acquisition module may include temperature, flow, valve opening sensors, etc.; for the user side, the data acquisition module may include intelligent heat metering equipment and interaction equipment.

[0083] In this embodiment of the present invention, for the edge computing module or the cloud computing platform, the monitoring and handling of faults can be realized. Specifically, the monitoring and handling steps can be as Figure 6 shown. Specifically, in Figure 6 , the monitoring and handling of the fault may include:

[0084] In step S20, obtain the real-time infrared image of the heat supply network. Among them, for the monitoring of the operating status of the heat supply system, the key concerns are whether there are abnormal situations such as pipeline network leakage, blockage, and heat exchange station equipment failure. The real-time infrared image of the heat supply network can be obtained by using infrared thermal imaging equipment, drones, etc.

[0085] In step S21, the real-time infrared image is identified and diagnosed to determine whether there is any abnormality in the real-time infrared image. Among them, the judgment of the abnormality in the infrared image can be uploaded to the cloud computing platform, and infrared temperature field recognition algorithms, etc. can be used. In addition, the specific location of the pipeline network leakage or the specific component of the heat exchange station equipment failure can also be determined according to the change trends of data such as temperature, pressure, and flow rate, as well as the abnormal sounds captured by the acoustic sensors.

[0086] In step S22, when it is determined that there is an abnormality in the real-time infrared image, the infrared monitor is used to measure the abnormal point multiple times to obtain multiple sets of acquisition data. Among them, there may be errors in the recognition of the infrared image, and it is necessary to further arrange personnel to go to the abnormal point and use a handheld infrared monitor to collect data again. Specifically, data is collected through multiple measurements, uploaded to the cloud computing platform, and further identification and diagnosis are carried out using image recognition algorithms.

[0087] In step S23, it is determined whether there is any abnormality according to multiple sets of acquisition data.

[0088] In step S24, when it is determined that the acquisition data is abnormal, personnel are arranged for repair and maintenance. Among them, if there is still an abnormality after further diagnosis, it means that there is indeed a fault and timely repair and maintenance are required. For some local and minor faults, such as minor faults of heat exchange station equipment or local temperature abnormalities in the pipeline network, the edge computing node can take local treatment measures, such as adjusting the valve opening, repairing the equipment, etc. For relatively serious faults, such as large-area leakage of the pipeline network or major equipment failures of the heat exchange station, the cloud will issue instructions to coordinate relevant resources (such as maintenance personnel, emergency repair equipment, etc.) for handling, and at the same time notify the user side of the possible heating interruption situation so that users can make preparations in advance. Through the above cloud-network-edge-end collaborative architecture, the operation efficiency, energy utilization efficiency, and fault monitoring and handling capabilities of the heating system can be effectively improved, providing users with higher-quality and more stable heating services.

[0089] In steps S20 to S24, first, the real-time infrared image of the heat pipeline network is obtained, and then the infrared image is preliminarily identified and diagnosed to preliminarily determine whether there is any abnormality in the infrared image. If there is an abnormality in the infrared image, personnel are arranged to use an infrared monitor to measure the abnormal point multiple times to further verify the fault. If the fault is verified, repair and maintenance can be arranged.

[0090] On the other hand, the present invention also provides a heating system based on the cloud-network-edge architecture. The heating system may include a heat source plant, a heat exchange station, a user end, and the collaborative architecture as described above. Specifically, the heat source plant is used for heat production. The heat exchange station is connected to the heat source plant through a first heat pipe network, and the user end is connected through a second heat pipe network. The collaborative architecture is used for collaborative monitoring and heating allocation of the heat source plant, the heat exchange station, and the user end.

[0091] The heating system based on the cloud-network-edge collaborative architecture can achieve intelligent regulation. The edge computing node / module / device can initially process and analyze data locally, and according to preset algorithms and strategies, adjust the operating parameters of the heating equipment in real time, such as adjusting the opening degree of the valve, changing the rotation speed of the water pump, etc., to adapt to different heat consumption demands and working condition changes. At the same time, the cloud computing platform can optimize and adjust the regulation strategies of the edge computing node / module / device based on the global data analysis results, realizing more accurate and efficient heating control, improving energy utilization efficiency, and reducing operating costs. At the same time, through data collection and analysis of each link in the heating system, it is possible to accurately understand the heat consumption demands in different regions and different time periods, and thus reasonably allocate resources such as heat sources and pipe networks according to actual demands. For example, when the heat consumption demand is low at night, the output power of the heat source can be appropriately reduced to reduce energy consumption; during the peak heat consumption period during the day, increase the supply of the heat source to ensure the heating quality. In addition, according to the operating status and maintenance records of the equipment, the overhaul and maintenance plans of the equipment can be reasonably arranged to improve the utilization rate and service life of the equipment.

[0092] Through the above technical solutions, the heating system, collaborative architecture, and its construction method provided by the present invention first build the cloud computing platform of the heating system, and at the same time build the backbone network and wireless network of the heating system, then deploy edge computing nodes at the key nodes of the heat exchange station and the heat pipe network, make processing decisions on local data through the edge computing nodes, and finally install data collection modules on the heat source plant equipment, heat pipe network monitoring equipment, heat exchange station equipment, and user end equipment. The data collection modules collect data in real time and transmit it to the cloud computing platform and the edge computing nodes to achieve collaborative heating of the heat source plant, heat pipe network, heat exchange station, and user end, effectively improving the heating collaboration and controllability, thereby improving the heating efficiency, and having a better monitoring effect on the overall heating system, with high processing efficiency and good effect.

[0093] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0094] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the processes Figure 1 or blocks or combinations of blocks.

[0095] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the processes Figure 1 or blocks or combinations of blocks.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the processes Figure 1 or blocks or combinations of blocks.

[0097] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0098] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0099] A computer-readable medium includes permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transitory media that can be used to store information accessible by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0100] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0101] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for constructing a collaborative architecture in a cloud-network-edge heating system, characterized in that: include: Building a cloud computing platform for the heating system, wherein the cloud computing platform includes a data storage and management module and a data analysis and decision support module; Building a backbone network and a wireless network in the heating system; Obtaining the locations of key nodes of the heat exchange station and the heat pipe network in the heat supply system, and deploying edge computing nodes at the key nodes of the heat exchange station and the heat pipe network in the heat supply system; Data acquisition modules are respectively installed in the heat source plant equipment, thermal network monitoring equipment, heat exchange station equipment and user-end equipment in the heating system, and the data acquisition modules are communicatively connected with the cloud computing platform and the edge computing node.

2. The construction method according to claim 1, characterized in that: The data stored in the data storage and management module includes the production data of the heat source plant, the operation data of the thermal network, the heat exchange data of the heat exchange station and the heat feedback data of the user end.

3. The construction method according to claim 1, characterized in that: The data analysis and decision support module includes: Acquire historical heating data of the user end, and pre-process the historical heating data, wherein the historical heating data includes heating amounts in different regions and different time periods; Build a heating demand forecasting model; Using the pre-processed historical heating data to train the heating demand prediction model; Get the real-time heating data of the current user end; Inputting the real-time heating data into the heating demand prediction model to obtain a heating demand prediction value at the user end; The production plan of the heat source plant is determined according to the predicted value of heat demand.

4. The construction method according to claim 1, characterized in that: Building a backbone network and a wireless network in the heating system includes: Build Ethernet in heat source plants, heat pipe network control centers, and heat exchange stations; Deploy wireless networks in the heat pipe network, heat exchange station and user end, wherein the wireless networks include 5G, Wi-Fi and ZigBee networks; Debug and test the network protocols of the Ethernet and the wireless network.

5. The construction method according to claim 1, characterized in that: The edge computing node is used to perform: Collecting real-time parameters within the edge computing node; Determining whether the real-time parameter is within a corresponding preset threshold range; When it is determined that the real-time parameter is not within the corresponding preset threshold range, an audible and visual alarm is issued; When it is determined that the real-time parameter is within the corresponding preset threshold range, the real-time parameter is determined to be normal.

6. The construction method according to claim 5, characterized in that: The real-time parameters include temperature, pressure and flow.

7. A collaborative architecture based on a cloud-network-edge-end collaborative heating system constructed according to any one of the construction methods of claims 1-6, characterized in that: include: A cloud computing platform, comprising a data storage and management module and a data analysis and decision support module; Data acquisition modules are respectively installed on heat source plant equipment, heat pipe network monitoring equipment, heat exchange station equipment and user-end equipment to collect real-time parameters; The edge computing module is arranged at the key nodes of the heat exchange station and the thermal network, and is communicatively connected with the data acquisition module and the cloud computing platform.

8. The collaborative architecture according to claim 7, characterized in that: The data acquisition module includes a temperature sensor, a pressure sensor, a flow sensor and an acoustic sensor.

9. The collaborative architecture according to claim 7, characterized in that: The edge computing module or the cloud computing platform is used to perform: Acquiring a real-time infrared image of the thermal network; Performing identification and diagnosis on the real-time infrared image to determine whether there is an abnormality in the real-time infrared image; When it is determined that there is an abnormality in the real-time infrared image, an infrared monitor is used to measure the abnormal point multiple times to obtain multiple sets of collected data; Determining whether there is an abnormality based on the multiple sets of collected data; When it is determined that the collected data is abnormal, personnel are arranged to perform inspection and maintenance.

10. A cloud-network-based edge heating system, characterized in that: include: Heat source plants, used to produce heat; A heat exchange station, wherein the heat exchange station is connected to the heat source plant through a first heat pipe network; A user end, the user end is connected via a second heat pipe network; The collaborative architecture as described in any one of claims 7 to 9 is used to collaboratively monitor and allocate heat supply to the heat source plant, the heat exchange station and the user end.

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