Heat supply optimization method for heat supply system of thermal power plant and related device

Abstract

The invention belongs to an optimization adjustment method, and provides a thermal power plant heat supply system heat supply optimization method and a related device for solving the technical problems that a traditional thermal power plant heat supply system monitoring method is incomplete in monitoring and low in real-time performance and accuracy, and temperature data, pressure data and flow data of a thermal power plant heat supply system are monitored and obtained in real time; after correlation analysis and clustering analysis, system health state evaluation and fault type identification are carried out by means of a health state evaluation model and a system fault classification identification model, and a future state prediction result of the system is obtained by means of a system state prediction model; and according to a monitoring result, a future state prediction result, the processed state data and historical data, an optimization algorithm is used for dynamically adjusting a heat supply strategy. The heat supply efficiency can be improved, the energy consumption can be reduced, and personalized and efficient heat supply services can be provided for users.

Description

Technical Field

[0001] This application belongs to an optimization adjustment method, and specifically relates to a heating optimization method and related devices for a heating system in a thermal power plant. Background Art

[0002] As an important facility for centralized heating, the operating efficiency, stability, and safety of a thermal power plant are directly related to the urban energy supply and the living quality of residents.

[0003] Most traditional monitoring methods for heating systems in thermal power plants rely on manual inspections and simple instrument monitoring. This method is not only inefficient but also difficult to achieve comprehensive, real-time, and accurate monitoring of the heating system. The main problems are as follows: (1) The method of manual inspection is limited by the professional knowledge and experience of the inspection personnel, and it is difficult to detect potential and complex faults or performance bottlenecks; (2) Although simple instrument monitoring can reflect part of the operating state of the heating system, it cannot provide comprehensive and systematic data support, making it difficult to conduct in-depth analysis and optimization of the heating system; (3) Traditional methods lack intelligent early warning and scheduling mechanisms, making it difficult to give early warnings before faults occur and unable to dynamically adjust the heating strategy according to the real-time state, resulting in low heating efficiency and high energy consumption. Summary of the Invention

[0004] In view of the technical problems of incomplete monitoring and low real-time performance and accuracy in traditional monitoring methods for heating systems in thermal power plants, this application provides a heating optimization method and related devices for a heating system in a thermal power plant.

[0005] To achieve the above object, this application is implemented by adopting the following technical solutions: In the first aspect, this application proposes a heating optimization method for a heating system in a thermal power plant, including: Obtain temperature data, pressure data, and flow data of the heating system in the thermal power plant in real time to form state data; Conduct correlation analysis and clustering analysis on the state data respectively to obtain processed state data; Compare the processed state data with a health status assessment model for monitoring to determine whether the operating state of the heating system in the thermal power plant is abnormal. If it is abnormal, input the state data into a trained system fault classification and recognition model to determine the fault type. Otherwise, continue monitoring; the health status assessment model is constructed based on historical data and knowledge data; Input the state data into a trained system state prediction model to obtain a future state prediction result of the heating system in the thermal power plant; Dynamically adjust the heating strategy according to the monitoring results, future state prediction results, processed state data, and historical data by using an optimization algorithm.

[0006] Further, it also includes: encrypting and protecting the privacy of the transmission of status data and the transmitted processed status data, as well as the data transmission between the health status evaluation model, the system status prediction model, the system fault classification and identification model, and the optimization algorithm.

[0007] Further, after obtaining the processed status data, it also includes: Presenting the processed status data in a visual form.

[0008] Further, the method of dynamically adjusting the heating strategy according to the monitoring results, future status prediction results, processed status data, and historical data by using the optimization algorithm also includes: Dynamically adjusting the heating strategy by using the optimization algorithm in combination with user feedback.

[0009] Further, the sources of the temperature data include: temperature data at the heat source, temperature data along the pipeline, temperature data at the heat exchange station, and temperature data at the user terminal.

[0010] Further, the sources of the pressure data include: pressure data at the pump outlet, pressure data in front of the valve, and pressure data behind the valve.

[0011] Further, the sources of the flow rate data include: flow rate data at the pipeline inlet and flow rate data at the pipeline outlet.

[0012] In a second aspect, the present application proposes a heating optimization system for a thermal power plant heating system, including: A data module for real-time monitoring and obtaining temperature data, pressure data, and flow rate data of the thermal power plant heating system to form status data; A preprocessing module for respectively performing correlation analysis and clustering analysis on the status data to obtain processed status data; A monitoring module for comparing the processed status data with the health status evaluation model for monitoring to determine whether the operation status of the thermal power plant heating system is abnormal. If it is abnormal, the status data is respectively input into the trained system fault classification and identification model to determine the fault type. Otherwise, continuous monitoring is carried out; the health status evaluation model is constructed based on historical data and knowledge data; A prediction module for inputting the status data into the trained system status prediction model to obtain the future status prediction result of the thermal power plant heating system; An optimization module for dynamically adjusting the heating strategy according to the monitoring results, future status prediction results, processed status data, and historical data by using the optimization algorithm.

[0013] In a third aspect, the present application provides an electronic device, including: a memory and one or more processors; the memory is coupled to the processor; wherein, computer program code is stored in the memory, and the computer program code includes computer instructions. When the computer instructions are executed by the processor, the electronic device executes the steps of the above-mentioned heat supply optimization method for a thermal power plant heat supply system.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned heat supply optimization method for a thermal power plant heat supply system are realized.

[0015] Compared with the prior art, the present application has the following beneficial effects: The present application provides a heat supply optimization method for a thermal power plant heat supply system, which obtains temperature data, pressure data, and flow data of the thermal power plant heat supply system through real-time monitoring. After correlation analysis and clustering analysis, a system health status evaluation model and a system fault classification and identification model are respectively used to evaluate the system health status and identify the fault types, and a system state prediction model is used to obtain the future state prediction result of the system. Finally, according to the monitoring results, future state prediction results, processed state data, and historical data, an optimization algorithm is used to dynamically adjust the heat supply strategy. The present application uses artificial intelligence algorithms such as machine learning and deep learning to establish a heat supply system health status prediction model, realizing real-time monitoring and intelligent early warning of the heat supply system health status. It can also dynamically adjust the heat supply strategy according to the analysis results and user feedback, improve the heat supply efficiency, reduce energy consumption, and provide personalized and efficient heat supply services for users.

[0016] The present application also provides a heat supply optimization system for a thermal power plant heat supply system, an electronic device, and a computer storage medium, which have all the advantages of the above-mentioned heat supply optimization method for a thermal power plant heat supply system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic flow chart of a heat supply optimization method for a thermal power plant heat supply system of the present application; Figure 2 It is a schematic diagram of a heat supply optimization system for a thermal power plant heat supply system of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To make the objectives, technical solutions and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are some, but not all, of the embodiments of this application. The components of the embodiments of this application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of this application without creative efforts fall within the scope of protection of this application.

[0021] It should be noted that like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0022] In the description of the embodiments of this application, it should be noted that if terms such as "upper", "lower", "horizontal", "inner", etc. are used to indicate the orientation or positional relationship, it is based on the orientation or positional relationship shown in the drawings or the orientation or positional relationship in which the inventive product is usually placed during use. This is only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. In addition, terms such as "first", "second", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0023] In addition, if the term "horizontal" appears, it does not mean that the component is required to be absolutely horizontal, but it can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but it can be slightly inclined.

[0024] In the description of the embodiments of this application, it should also be noted that unless otherwise clearly specified and limited, if terms such as "set", "installed", "connected", "connected" are used, they should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0025] In modern urban construction and energy utilization, as an important facility for centralized heating, the operating efficiency, stability, and safety of thermal power plants are directly related to energy supply and the quality of life of residents. Most traditional monitoring methods for the heating systems of thermal power plants rely on manual inspections and simple instrument monitoring. This approach is not only inefficient but also difficult to achieve comprehensive, real-time, and accurate monitoring of the heating system. With the rapid development of information technology, especially the rise of the Internet of Things (IoT), big data, cloud computing, and artificial intelligence technologies, new possibilities have emerged for the intelligent monitoring of the heating systems of thermal power plants.

[0026] The limitations of traditional monitoring methods are mainly reflected in the following aspects: (1) Manual inspection dimension: Manual inspections highly rely on the professional knowledge reserve and past work experience of inspection personnel. This makes it difficult to promptly detect many potential and relatively complex faults, as well as performance bottlenecks in the heating system, resulting in a lag in fault discovery and affecting the stable operation of the heating system. (2) Instrument monitoring dimension: Monitoring the heating system solely through simple instruments can provide some feedback on the operating state information, but the data provided is neither comprehensive nor systematic. This makes it difficult for operation and maintenance personnel to conduct in-depth analysis and optimized control of the heating system based on the existing data, hindering the improvement of the operating efficiency of the heating system. (3) Early warning and dispatching dimension: Traditional monitoring methods have obvious deficiencies in intelligent early warning and dispatching mechanisms. They can neither effectively warn in advance of faults using intelligent means nor dynamically adjust the heating strategy based on the real-time operating state of the heating system. As a result, the heating efficiency is reduced, the energy consumption remains high, and the operating costs are further consumed.

[0027] In response to the above problems, a large number of studies and explorations have been carried out, proposing various intelligent monitoring methods based on IoT, big data, cloud computing, and artificial intelligence technologies. These methods deploy multiple sensors to collect multi-dimensional data of the heating system in real time, use big data processing and analysis technologies to deeply mine and integrate the data, apply artificial intelligence algorithms such as machine learning and deep learning to intelligently analyze the data, establish a prediction model for the health state of the heating system, monitor the health state of the heating system in real time, and intelligently warn of potential faults or abnormal situations. At the same time, according to the monitoring results and warning information, as well as the real-time state, historical data, and user feedback of the heating system, the heating strategy is dynamically adjusted to improve the heating efficiency, reduce energy consumption, and provide personalized heating services for users.

[0028] However, there are still some problems and challenges in the existing intelligent monitoring methods. For example, how to ensure the stability and accuracy of the sensor network, how to effectively process and analyze massive data, how to establish an accurate and reliable prediction model for the health status of the heating system, how to ensure the security and privacy of data during transmission, storage, and processing, etc. Therefore, researching a more efficient, accurate, and intelligent monitoring method and system for the heating system of a thermal power plant is of great significance for improving heating efficiency, reducing energy consumption, ensuring urban energy supply, and the quality of residents' lives.

[0029] Based on the above situation, this application proposes a heating optimization method and related devices for the heating system of a thermal power plant, realizing comprehensive, real-time, and intelligent monitoring and management of the heating system of the thermal power plant, improving heating efficiency, reducing energy consumption, and providing personalized and efficient heating services for users. The following will explain this application in detail with reference to the embodiments and drawings.

[0030] As Figure 1 shown, it is a schematic flow diagram of a heating optimization method for the heating system of a thermal power plant in this application, which may include: S101, obtaining temperature data, pressure data, and flow data of the heating system of the thermal power plant in real time to form status data.

[0031] It should be noted that temperature, pressure, and flow are key operating parameters in the heating system of a thermal power plant, directly reflecting the operating state of the system. By monitoring these data in real time, the working conditions of the system at each moment can be comprehensively and accurately understood.

[0032] In practical applications, temperature data at different locations (such as heat source temperature, transmission pipeline temperature, user end temperature, etc.) can reflect the generation, transmission, and consumption of heat. For example, changes in the heat source temperature may be related to the combustion efficiency of fuel and the operating conditions of equipment, while the user end temperature is directly related to the heating quality and user satisfaction. The pressure data in the system (such as steam pressure, water pressure, etc.) is crucial for ensuring the normal operation of the heating system. Appropriate pressure can ensure the stable flow of the medium in the pipeline, avoiding problems such as leakage and blockage. Excessive or too low pressure may affect the heating effect and even cause safety accidents. Flow data (such as steam flow, water flow, etc.) reflects the delivery volume of the heating medium and is closely related to the heating load. By monitoring the flow data, it can be understood whether the actual heating capacity of the system meets the user's needs and whether there are problems such as uneven flow. Real-time monitoring can be achieved by installing sensors such as temperature sensors, pressure sensors, and flow sensors at various key parts of the heating system of the thermal power plant. These sensors transmit the data collected in real time to the data acquisition system, and then form status data.

[0033] It should be noted that the data acquisition location, i.e., the installation location of the sensor, can be determined according to the optimization requirements.

[0034] S102, perform correlation analysis and clustering analysis on the status data respectively to obtain the processed status data.

[0035] It should be noted that through correlation analysis, the internal relationships and mutual influences between different parameters can be found. For example, when analyzing the relationship between temperature and pressure, when the temperature rises, the pressure may change accordingly under certain conditions. Or, when analyzing the relationship between flow rate and temperature, the change in flow rate may affect the heat dissipation of the medium in the pipeline, resulting in a change in temperature. In practical applications, statistical analysis methods can be used to determine the correlation coefficient between different parameters and judge the degree of their correlation. Data mining techniques can also be used to discover hidden correlation patterns between parameters, providing a basis for a deeper understanding of the system operation mechanism.

[0036] Through clustering analysis, the status data can be classified according to similarity to identify different operation modes or working conditions. For example, according to the combined characteristics of temperature, pressure, and flow rate, the operation status of the system can be divided into high-load operation mode, low-load operation mode, stable operation mode, etc. In practical applications, common clustering algorithms include the K-Means algorithm, hierarchical clustering algorithm, etc. Through clustering analysis, the system performance under different working conditions can be compared and analyzed, providing a reference for subsequent operation optimization.

[0037] After correlation analysis and clustering analysis, the status data becomes more orderly and regular, and can more clearly reflect the operation characteristics of the system, providing more valuable information for subsequent monitoring, evaluation, and optimization.

[0038] S103, compare the processed status data with the health status evaluation model for monitoring to determine whether the operation status of the thermal power plant heating system is abnormal. If it is abnormal, input the status data into the trained system fault classification and identification model respectively to determine the fault type. Otherwise, continue to monitor; the health status evaluation model is constructed based on historical data and knowledge data.

[0039] Historical data records the past operation conditions of the system, including various parameter data during normal operation and fault operation. Knowledge data contains professional knowledge about the thermal power plant heating system, such as the normal operation parameter range of equipment, the causes and mechanisms of faults, etc. Using these data, machine learning algorithms or traditional statistical methods can be used to construct a health status evaluation model. The training process of the model is to establish a mapping relationship between the status data and the system health status through learning the historical data.

[0040] Input the processed status data into the health status assessment model. The model determines whether the current operating status of the system is within the normal range based on the pre-learned knowledge and rules. If the model determines that the system operating status is abnormal, it indicates that there may be faults or potential problems. When a fault occurs in the system, collect the status data at the time of the fault and label the fault type. Use this labeled data to train the system fault classification and recognition model so that it can accurately determine the fault type based on the input status data.

[0041] When the health status assessment model determines that the system is operating abnormally, input the status data into the trained system fault classification and recognition model. The model outputs the fault type, providing a basis for fault diagnosis and repair.

[0042] S104, Input the status data into the trained system status prediction model to obtain the future status prediction result of the thermal power plant heating system.

[0043] In practical applications, prediction models for future states can be established based on methods such as time series analysis and machine learning, using historical status data and current status data. For example, algorithms such as autoregressive integrated moving average models and long short-term memory networks can be used. By learning from a large amount of historical data, the parameters of the model can be adjusted so that it can accurately predict the future state of the system. The obtained future state prediction result can help the thermal power plant take preventive measures in advance. For example, if it is predicted that the system load will increase in a certain future period, the fuel supply can be adjusted in advance, standby equipment can be started, etc. to ensure that the heating system can meet user needs and avoid system failures caused by sudden load increases.

[0044] S105, Dynamically adjust the heating strategy according to the monitoring results, future state prediction results, processed status data, and historical data using an optimization algorithm.

[0045] It should be noted that the monitoring results reflect the current operating status of the system, the future state prediction results provide a prediction of the future development trend of the system, the processed status data contains the characteristic information of the system operation, and the historical data records the past operation experience of the system. Combining these data can comprehensively understand the operation situation and change trend of the system.

[0046] In practical applications, common optimization algorithms include genetic algorithms, particle swarm optimization algorithms, etc. These algorithms can, based on the above data, find the optimal heating strategy under certain constraint conditions, such as heating quality requirements and energy consumption limits. For example, the optimization algorithm can determine how to reasonably adjust the output power of the heat source, regulate the flow and pressure of the pipeline under different load conditions to achieve efficient energy utilization and maximize heating quality.

[0047] Since the operating conditions of the heat supply system in a thermal power plant are constantly changing, by dynamically adjusting the heat supply strategy, the system can always be in an optimal operating state, improving energy utilization efficiency, reducing operating costs, and ensuring the stability of heat supply quality.

[0048] The following are some specific embodiments of this application for further elaboration of this application.

[0049] 1. Data collection and preprocessing.

[0050] Obtain status data through real-time monitoring. Sensors can be deployed to obtain data at the required locations. The following specific solutions can be adopted: (1) Temperature sensors: Deploy temperature sensors at heat sources (such as boilers, steam turbines, etc.), along pipelines (especially branch points and turning points), heat exchange stations (including the inlets and outlets of the primary network and the secondary network), and user terminals (such as radiators, floor heating, etc.) to monitor the temperature distribution and changes in the heat supply system in real time.

[0051] (2) Pressure sensors: Install pressure sensors at key nodes of the pipeline (such as the pump outlet, before and after the valve, etc.) to monitor the pressure changes in the pipeline and ensure that the heat supply medium operates within a safe pressure range.

[0052] (3) Flow sensors: Set flow sensors at the inlet and outlet of the pipeline (especially the inlets and outlets of the heat exchange station) to measure the flow of the heat supply medium and ensure the accurate measurement and distribution of the heat supply.

[0053] In practical applications, other data can also be obtained for assistance. For example, vibration sensors can be installed near vulnerable components such as pumps and valves to monitor the vibration conditions during equipment operation, timely detect potential faults, and prevent equipment damage. Water quality monitoring sensors can be installed at the water source or in the circulating water of the heat supply system to monitor indicators such as impurities and pH value in the water to ensure that the water quality of the heat supply medium meets the standards. Humidity sensors, oxygen content sensors, etc. can also be added according to actual needs to comprehensively monitor the operating state and environmental conditions of the heat supply system.

[0054] In practical applications, according to the actual situation of the heat supply system in a thermal power plant, the layout and quantity of the sensor network can be reasonably planned to ensure the comprehensiveness and accuracy of data collection. Before system deployment, sufficient testing and verification should be carried out to ensure the stability and reliability of the system.

[0055] After collecting the above data, the data is transmitted and preprocessed: A data collector can be set up to receive the transmitted data. The data collector can be deployed at key locations such as thermal power plants or heat exchange stations, responsible for receiving data from the sensor network and performing preliminary data processing and packaging, such as data cleaning, format conversion, outlier handling, etc. When transmitting data, wireless communication technologies such as 4G / 5G, LoRa, NB-IoT, or wired communication technologies such as optical fiber and Ethernet can be used to achieve data transmission between the data collector and the cloud big data processing and analysis platform. According to the geographical location, network coverage, and data transmission requirements of the thermal power plant, select appropriate communication technologies and network architectures. Then, the collected data can be stored and computed in the cloud. Specifically, a highly available and scalable cloud storage and computing platform can be built to store a large amount of sensor data and provide API interfaces for data processing, analysis, and mining. In practical applications, the cloud platform should have data backup, recovery, and disaster tolerance capabilities to ensure the security and reliability of the data.

[0056] In practical applications, the cloud platform can receive and store sensor data, establish a data warehouse and a data lake for subsequent data processing and analysis. Data mining techniques can also be used to perform correlation analysis, clustering analysis, etc. on the sensor data to discover the operating rules and performance bottlenecks of the heating system.

[0057] In order to provide more basis for subsequent further processing, the association rule mining algorithm can be used to discover the association relationships between various parameters in the heating system, providing a basis for fault warning and performance optimization for correlation analysis. The sensor data can also be clustered to identify different operating states of the heating system, providing data support for subsequent optimized scheduling strategies.

[0058] In some embodiments of the present application, to ensure the security and privacy of sensor data and user information, the following data security and privacy protection measures can also be taken in the present application: ((1) Data encryption During the data transmission process, encryption technologies such as the SSL / TLS protocol are used to encrypt the transmitted data to ensure that the data is not stolen or tampered with during transmission. At the same time, when storing data, an encrypted storage method is also used to prevent the data from being illegally accessed.

[0059] ((2) Access control mechanism A strict access control mechanism can be established to authenticate the identities of system users and manage their permissions. Only authorized users can access the system data, and different users can only access the data and information they need according to their roles and permissions, which can effectively prevent data leakage and abuse.

[0060] ((3) Data desensitization Sensitive data, such as user personal information and key heating system parameters, can be processed using data desensitization technology. In practical applications, data desensitization technology can reduce the sensitivity of data through replacement, deletion, or generalization, thereby protecting user privacy while meeting the needs of data analysis.

[0061] (4) Security audit and monitoring Establish a security audit and monitoring system to monitor and record system access and operations in real time. Once abnormal access or operation behavior is detected, the system will immediately trigger an alarm mechanism and notify relevant personnel to handle it, helping to promptly identify and respond to potential security threats.

[0062] (5) Regular security assessment and upgrade Regularly conduct security assessments on the system to check its security and stability, and promptly identify and fix potential security vulnerabilities. At the same time, the system can be regularly upgraded and optimized based on technological developments and changes in user needs to ensure its advancement and security.

[0063] 2. Monitoring, prediction and optimization.

[0064] The processed status data can be compared with the health status assessment model for monitoring to determine whether the operating status of the thermal power plant heating system is abnormal. If abnormal, the status data will be input into the trained system fault classification and identification model to determine the fault type. Otherwise, continuous monitoring will be performed; the health status assessment model is constructed based on historical data and knowledge data.

[0065] Machine learning models can be trained based on historical data for fault warning, performance evaluation, and prediction. When monitoring the health of the heating system in real time, machine learning algorithms can be used for fault warning and performance evaluation. When an anomaly or potential fault is detected, a warning or alarm is automatically triggered, and fault location and preliminary treatment recommendations are provided. System fault classification and identification models can use classification algorithms, such as support vector machines and random forests, to identify fault types.

[0066] When making predictions, system state prediction models can employ regression algorithms, such as linear regression and decision tree regression, to predict the future state of the heating system. Deep learning neural network models, such as convolutional neural networks, can also be constructed for image recognition, such as pipeline leak detection. Prediction results can be used to optimize scheduling strategies and provide personalized services. Recurrent neural networks can also be employed for time series prediction, such as heating load forecasting. Deep learning networks can process complex data patterns and relationships, improving prediction and recognition accuracy.

[0067] Optimization algorithms, such as genetic algorithms and particle swarm optimization algorithms, can be used to optimize the scheduling strategy of the heating system. The optimization algorithm should be able to dynamically adjust key parameters such as heat source output, pipeline flow rate, and heat exchange station temperature according to real-time monitoring data and prediction results to maximize the heating efficiency. Specifically, according to the monitoring results and early warning information, as well as the real-time status, historical data, and user feedback of the heating system, the optimization algorithm is used to dynamically adjust the heating strategy, which may include heat source output adjustment, pipeline flow rate distribution, heat exchange station temperature setting, etc.

[0068] 3. User Interface and Feedback In practical applications, a monitoring interface can be set up. Design an intuitive and easy-to-use monitoring interface to display the operating status of the heating system in real time, including real-time data and historical trend charts of key parameters such as temperature, pressure, and flow rate. The interface can also have alarm and early warning functions according to the above methods. When abnormal or potential faults are detected, it can automatically trigger an alarm or early warning and provide fault location and preliminary handling suggestions by integrating knowledge data.

[0069] A user feedback mechanism can also be established to collect feedback opinions and complaint information from users about heating services. In practical applications, the user feedback information can be classified, statistically analyzed, and used to identify the hot issues in user feedback, providing a basis for optimizing the heating strategy and improving service quality.

[0070] As an optimization solution, personalized heating solutions and services can be provided according to the heat consumption needs and feedback opinions of users. The heating strategy can also be adjusted in combination with user preferences and living habits to improve user comfort and satisfaction. Then, based on user feedback and system operation data, the system can be continuously optimized and upgraded to improve the intelligent level and user experience.

[0071] The method of this application realizes the comprehensive, real-time, and intelligent monitoring and management of the heating system by constructing a sensor network, a big data processing and analysis platform, an artificial intelligence algorithm application platform, and a user interface and feedback system. It can significantly improve the operating efficiency of the heating system, reduce energy consumption, enhance user satisfaction and service quality, and provide strong support for the intelligent transformation and sustainable development of thermal power plants.

[0072] By constructing a sensor network covering all key links of the heat supply system in a thermal power plant, this application realizes the comprehensive collection of multi-dimensional data of the heat supply system. Compared with traditional manual inspections and simple instrument monitoring, it provides more detailed and real-time system status information. The real-time transmission and processing of data ensure the immediate feedback of the heat supply system status, making rapid response and scheduling possible, and significantly improving the timeliness and accuracy of monitoring. By using big data processing and analysis technologies, the inherent laws and correlation relationships of the heat supply system data can be deeply explored, providing a scientific basis for fault warning, performance evaluation, and optimized scheduling. In addition, the introduction of artificial intelligence algorithms such as machine learning and deep learning not only improves the accuracy and timeliness of fault warning, but also can predict the future trends of the heat supply system based on historical data and real-time status, providing strong support for the formulation of intelligent scheduling strategies. Finally, by dynamically adjusting the heat supply strategy, this application can optimize key parameters such as heat source output and pipeline flow according to the user demand prediction and system health status prediction results, effectively reducing energy consumption and improving the heat supply efficiency. The provision of personalized heat supply solutions not only improves the comfort and satisfaction of users, but also further promotes the rational use of energy.

[0073] Furthermore, this application has good scalability and can easily add new sensors or optimize algorithms as needed to adapt to the development and changes of future heat supply systems. It has shown significant beneficial effects in terms of comprehensiveness and real-time, intelligent level, energy consumption and efficiency, data security and privacy protection, and ease of operation and easy expansion, providing new ideas and solutions for the intelligent monitoring and management of the heat supply system in thermal power plants.

[0074] As Figure 2 shown, it is a schematic diagram of a heat supply optimization system for the heat supply system of a thermal power plant in this application, which may include: A data module for real-time monitoring and obtaining temperature data, pressure data, and flow data of the heat supply system in a thermal power plant to form status data; A preprocessing module for performing correlation analysis and clustering analysis on the status data respectively to obtain processed status data; A monitoring module for comparing the processed status data with a health status evaluation model for monitoring to determine whether the operation status of the heat supply system in the thermal power plant is abnormal. If it is abnormal, the status data is respectively input into a trained system fault classification and recognition model to determine the fault type. Otherwise, continuous monitoring is carried out; the health status evaluation model is constructed based on historical data and knowledge data; A prediction module for inputting the status data into a trained system status prediction model to obtain a future status prediction result of the heat supply system in the thermal power plant; An optimization module for dynamically adjusting the heat supply strategy by using an optimization algorithm according to the monitoring results, future status prediction results, processed status data, and historical data.

[0075] It should be noted that in several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of each module is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules can be combined or integrated into another device, or some features can be ignored or not executed. The modules described as separate components may or may not be physically separated. The components shown as modules can be one physical unit or multiple physical units, that is, they can be located in one place or distributed to multiple different places. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0076] In addition, in each embodiment of the present invention, the modules can be integrated in a processing unit, or each module can exist physically alone, or two or more modules can be integrated in a unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0077] The embodiments of this application also provide an electronic device, which may include one or more processors, a memory, and a communication interface.

[0078] Among them, the memory and the communication interface are coupled to the processor. For example, the memory, the communication interface, and the processor can be coupled together through a bus.

[0079] Among them, the communication interface is used for data transmission with other devices. The memory stores computer program code. The computer program code includes computer instructions. When the computer instructions are executed by the processor, the electronic device executes the steps of the above-mentioned heat supply optimization method for the thermal power plant heat supply system.

[0080] Among them, the processor can be a processor or a controller. For example, it can be a Central Processing Unit (CPU), a general-purpose processor, a Digital Signal Processor (DSP), an Application-Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in connection with the present disclosure. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and so on. The processor can be used to support the electronic device in executing the method steps provided in the above embodiments.

[0081] Among them, the bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The above buses can be divided into an address bus, a data bus, a control bus, etc.

[0082] A computer-readable storage medium provided by an embodiment of the present application stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned heat supply optimization method for a thermal power plant heat supply system are implemented.

[0083] The computer-readable storage medium involved in the present application includes a Random Access Memory (RAM), an internal memory, a Read-Only Memory (ROM), an Electrically Programmable ROM, an Electrically Erasable Programmable ROM, a register, a hard disk, a removable disk, a CD ROM, or any other form of storage medium well-known in the technical field.

[0084] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing heat supply of a heat supply system in a thermal power plant, characterized in that, It includes: Obtain the temperature data, pressure data, and flow data of the heat supply system of the thermal power plant in real-time monitoring to form status data; Conduct correlation analysis and clustering analysis on the status data respectively to obtain the processed status data; Compare the processed status data with the health status assessment model for monitoring to determine whether the operation status of the heat supply system of the thermal power plant is abnormal. If it is abnormal, input the status data into the trained system fault classification and identification model respectively to determine the fault type. Otherwise, continue to monitor; The health status assessment model is constructed based on historical data and knowledge data; Input the status data into the trained system status prediction model to obtain the future status prediction result of the heat supply system of the thermal power plant; Dynamically adjust the heat supply strategy according to the monitoring results, future status prediction results, processed status data, and historical data by using the optimization algorithm.

2. The heat supply optimization method for the heat supply system of a thermal power plant according to claim 1, characterized in that, It also includes: encrypt and protect the privacy of the transmission of status data and the transmission of processed status data, as well as the data transmission between the health status assessment model, system status prediction model, system fault classification and identification model, and optimization algorithm.

3. The heat supply optimization method for the heat supply system of a thermal power plant according to claim 1, wherein After obtaining the processed status data, it also includes: Present the processed status data in a visual form.

4. The heat supply optimization method for the heat supply system of a thermal power plant according to claim 1, characterized in that, The method of dynamically adjusting the heat supply strategy according to the monitoring results, future status prediction results, processed status data, and historical data by using the optimization algorithm also includes: Dynamically adjust the heat supply strategy by using the optimization algorithm in combination with user feedback.

5. The heat supply optimization method for the heat supply system of a thermal power plant according to claim 1, characterized in that, The sources of the temperature data include: temperature data at the heat source, temperature data along the pipeline, temperature data at the heat exchange station, and temperature data at the user terminal.

6. The heat supply optimization method for the heat supply system of a thermal power plant according to claim 1, wherein The sources of the pressure data include: pressure data at the pump outlet, pressure data in front of the valve, and pressure data behind the valve.

7. The heat supply optimization method for the heat supply system of a thermal power plant according to claim 1, characterized in that The sources of the flow data include: flow data at the pipeline inlet and flow data at the pipeline outlet.

8. A heating optimization system for a heating system of a thermal power plant, characterized in that, It includes: A data module for obtaining the temperature data, pressure data, and flow data of the heat supply system of the thermal power plant in real-time monitoring to form status data; A preprocessing module for conducting correlation analysis and clustering analysis on the status data respectively to obtain the processed status data; A monitoring module for comparing the processed status data with the health status assessment model for monitoring to determine whether the operation status of the heat supply system of the thermal power plant is abnormal. If it is abnormal, input the status data into the trained system fault classification and identification model respectively to determine the fault type. Otherwise, continue to monitor; The health status assessment model is constructed based on historical data and knowledge data; A prediction module for inputting the status data into the trained system status prediction model to obtain the future status prediction result of the heat supply system of the thermal power plant; An optimization module for dynamically adjusting the heat supply strategy according to the monitoring results, future status prediction results, processed status data, and historical data by using the optimization algorithm.

9. An electronic device, characterized in that, It includes: A memory and one or more processors; the memory is coupled to the processor; wherein, computer program code is stored in the memory, the computer program code includes computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the steps of the heat supply optimization method for a thermal power plant heat supply system as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the heat supply optimization method for a thermal power plant heat supply system as described in any one of claims 1-7 are implemented.

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