Heating load adjusting method and device, storage medium and electronic device

Abstract

The invention discloses a heat supply load adjusting method and device, a storage medium and an electronic device.The method comprises the steps that system data of a heat supply system are obtained, and the system data comprise structure parameters of the heat supply system, design operation parameters of the heat supply system, actual operation data of the heat supply system and geographic information of the heat supply system; inputting the system data into the heat supply load prediction model to obtain target pipe network operation parameters corresponding to the heat supply system; wherein the heat supply load prediction model is trained by using multiple groups of data through machine learning, and each group of data in the multiple groups of data comprises historical system data and historical pipe network operation parameters corresponding to the historical system data; and determining a parameter comparison result between the target pipe network operation parameter and a current pipe network operation parameter, collected in real time, of the heat supply system, and determining an adjustment strategy of multiple heat supply loads associated with the heat supply system based on the parameter comparison result.

Description

Technical Field

[0001] The present application relates to the technical field of urban centralized heating systems and Internet of Things platforms, and specifically, to a method and device for adjusting heating load, a storage medium and an electronic device. Background Art

[0002] In traditional urban centralized heating systems, hydraulic simulation and management generally rely on manual experience and data list analysis. This method is difficult to comprehensively and accurately evaluate the layout and hydraulic characteristics of the pipe network when facing complex heating networks, and it is even more difficult to adjust in real time to adapt to the changing situation. For a long time, although static display methods such as water pressure diagrams can provide simulation results, they lack intuitive geographic information assistance, which causes technical managers to often need to perform cumbersome conversions between data analysis and experience judgment when optimizing the system, resulting in low decision-making efficiency and biased adjustment plans. With the rapid development of Geographic Information System (GIS) and big data analysis technology, heating system management has ushered in an opportunity for intelligent upgrading. However, there are still many challenges in effectively integrating these advanced technologies into hydraulic simulation and load forecasting of heating systems. On the one hand, how to accurately integrate the physical layout, facility attributes and geographic spatial information of the heating network to form a dynamically updated GIS model; on the other hand, how to use massive historical data and real-time parameters to efficiently predict the heating load through machine learning algorithms, and optimize the pipe network operation parameters accordingly to achieve real-time adjustment and intelligent decision-making.

[0003] Regarding the problem in related technologies that it is impossible to accurately predict the pipe network operating parameters of the heating system to achieve real-time adjustment of the heating load, no effective solution has been proposed so far.

[0004] Therefore, it is necessary to improve the existing related technologies to overcome the above-mentioned defects in the related technologies and meet the needs of current actual production. Summary of the invention

[0005] The embodiments of the present application provide a method and device for adjusting a heating load, a storage medium and an electronic device, so as to at least solve the problem in the related art that the pipe network operating parameters of a heating system cannot be accurately predicted to achieve real-time adjustment of the heating load.

[0006] According to one aspect of an embodiment of the present application, a method for adjusting a heating load is provided, comprising: obtaining system data of a heating system, wherein the system data comprises: structural parameters of the heating system, design operating parameters of the heating system, actual operating data of the heating system, and geographic information of the heating system; inputting the system data into a heating load prediction model to obtain target pipeline network operating parameters corresponding to the heating system; wherein the heating load prediction model is trained by machine learning using multiple sets of data, and each set of data in the multiple sets of data comprises: historical system data, and historical pipeline network operating parameters corresponding to the historical system data; determining a parameter comparison result between the target pipeline network operating parameters and the current pipeline network operating parameters of the heating system collected in real time, and determining an adjustment strategy for multiple heating loads associated with the heating system based on the parameter comparison result.

[0007] In an exemplary embodiment, a parameter comparison result between a target pipeline network operating parameter and a current pipeline network operating parameter of a heating system collected in real time is determined, and an adjustment strategy for a plurality of heating loads associated with the heating system is determined based on the parameter comparison result, including: comparing one by one the first value of each parameter in the target pipeline network operating parameter with the second value corresponding to each parameter in the current pipeline network operating parameter; counting the number of occurrences where the first value is greater than the second value; when the number of occurrences is greater than or equal to a preset threshold, using a first adjustment strategy, wherein the first adjustment strategy is to use the second value as a reference to adjust the first value; when the number of occurrences is less than the preset threshold, using a second adjustment strategy, wherein the second adjustment strategy is to use the adjustment instruction received by the heating system to adjust the pipeline network operating parameters.

[0008] In an exemplary embodiment, after determining the adjustment strategies of multiple heating loads associated with the heating system based on the parameter comparison results, the method further includes: obtaining updated operating parameters of the heating system after the adjustment is completed using the adjustment strategy; using the updated operating parameters to control the target operating time of the heating system, and collecting the temperature values ​​corresponding to different branches in the pipe network of the heating system after the target operating time; when the temperature value is greater than or equal to the preset heating temperature value, identifying the adjustment strategy as a valid adjustment strategy; when the temperature value is less than the preset heating temperature value, identifying the adjustment strategy as an invalid adjustment strategy.

[0009] In an exemplary embodiment, after collecting the temperature values ​​corresponding to different branches in the pipe network of the heating system after the target operating time, the above method also includes: when obtaining the visual display image corresponding to the heating system, marking the temperature value in the visual display image to obtain a heating temperature distribution image of the heating system; sending the heating temperature distribution image to the target terminal of the operation and maintenance object to instruct the operation and maintenance object to inspect the heating area with abnormal temperature distribution.

[0010] In an exemplary embodiment, after the adjustment strategy is identified as an invalid adjustment strategy, the method further includes: sending a prompt message to the management object of the heating load prediction model, wherein the prompt message is used to instruct the management object to retrain the heating load prediction model; obtaining a response result of the management object to the prompt message, and updating the version information of the heating load prediction model based on the response result.

[0011] In an exemplary embodiment, after inputting system data into a heating load prediction model to obtain the target pipeline network operating parameters corresponding to the heating system, the method further includes: obtaining hydraulic condition data in the heating system; when the hydraulic condition data is less than the preset stable condition data, determining that the target pipeline network operating parameters do not meet the application conditions, and sending an alarm message for adjusting the hydraulic power to the operation and maintenance object; when the hydraulic condition data is greater than or equal to the preset stable condition data, determining that the target pipeline network operating parameters meet the use conditions, and sending a non-alarm message indicating that no hydraulic adjustment is required to the operation and maintenance object.

[0012] According to another aspect of an embodiment of the present application, a heating load adjustment device is also provided, including: an acquisition module, used to acquire system data of a heating system, wherein the system data includes: structural parameters of the heating system, design operation parameters of the heating system, actual operation data of the heating system, and geographic information of the heating system; an input module, used to input the system data into a heating load prediction model to obtain target pipeline network operation parameters corresponding to the heating system; wherein the heating load prediction model is trained through machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: historical system data, and historical pipeline network operation parameters corresponding to the historical system data; a determination module, used to determine a parameter comparison result between the target pipeline network operation parameters and the current pipeline network operation parameters of the heating system collected in real time, and determine an adjustment strategy for multiple heating loads associated with the heating system based on the parameter comparison result.

[0013] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for adjusting the heating load when running.

[0014] According to another aspect of the embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the heating load adjustment method through the computer program.

[0015] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned method for adjusting the heating load is implemented.

[0016] Through this application, the system data of the heating system is obtained, wherein the system data includes: structural parameters of the heating system, design operating parameters of the heating system, actual operating data of the heating system, and geographic information of the heating system; the system data is input into the heating load prediction model to obtain the target pipe network operating parameters corresponding to the heating system; wherein the heating load prediction model is trained by machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: historical system data, and historical pipe network operating parameters corresponding to the historical system data; the parameter comparison results between the target pipe network operating parameters and the current pipe network operating parameters of the heating system collected in real time are determined, and the adjustment strategies of multiple heating loads associated with the heating system are determined based on the parameter comparison results. The above technical solution solves the problem of being unable to accurately predict the pipe network operating parameters of the heating system to achieve real-time adjustment of the heating load. Furthermore, by comprehensively collecting and analyzing the comprehensive data of the heating system, the optimal pipe network operating parameters are determined using the heating load prediction model to achieve real-time adjustment of the heating load. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal of a method for adjusting a heating load according to an embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for adjusting heating load according to an embodiment of the present application;

[0021] Figure 3 It is a structural schematic diagram of a visual hydraulic simulation system based on GIS and heating load prediction according to an embodiment of the present application;

[0022] Figure 4 is an operation flow chart of a static hydraulic simulation system according to an embodiment of the present application;

[0023] Figure 5 is an operation flow chart of a dynamic hydraulic simulation system according to an embodiment of the present application;

[0024] Figure 6 It is a structural block diagram of a heating load adjustment device according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal, a mobile terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure block diagram of a computer terminal according to a method for adjusting a heating load according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or N ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, CPU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0028] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the adjustment method of the monitoring network point in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or N magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0029] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0030] In this embodiment, a method for adjusting heating load is provided. Figure 2 is a flow chart of a method for adjusting heating load according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps S202-S206:

[0031] Step S202: acquiring system data of the heating system, wherein the system data includes: structural parameters of the heating system, design operation parameters of the heating system, actual operation data of the heating system, and geographic information of the heating system;

[0032] Step S204: inputting the system data into a heating load prediction model to obtain target pipe network operation parameters corresponding to the heating system; wherein the heating load prediction model is trained by machine learning using multiple sets of data, each set of data in the multiple sets of data includes: historical system data, and historical pipe network operation parameters corresponding to the historical system data;

[0033] Step S206: Determine a parameter comparison result between the target pipe network operating parameters and the current pipe network operating parameters of the heating system collected in real time, and determine an adjustment strategy for multiple heating loads associated with the heating system based on the parameter comparison result.

[0034] In the above steps, the system data of the heating system is obtained, wherein the system data includes: structural parameters of the heating system, design operating parameters of the heating system, actual operating data of the heating system, and geographic information of the heating system; the system data is input into the heating load prediction model to obtain the target pipe network operating parameters corresponding to the heating system; wherein the heating load prediction model is trained by machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: historical system data, and historical pipe network operating parameters corresponding to the historical system data; the parameter comparison result between the target pipe network operating parameters and the current pipe network operating parameters of the heating system collected in real time is determined, and the adjustment strategy of multiple heating loads associated with the heating system is determined based on the parameter comparison result. The above technical solution solves the problem of being unable to accurately predict the pipe network operating parameters of the heating system to achieve real-time adjustment of the heating load. Furthermore, by comprehensively collecting and analyzing the comprehensive data of the heating system, the optimal pipe network operating parameters are determined using the heating load prediction model to achieve real-time adjustment of the heating load.

[0035] In an exemplary embodiment, a parameter comparison result between a target pipeline network operating parameter and a current pipeline network operating parameter of a heating system collected in real time is determined, and an adjustment strategy for a plurality of heating loads associated with the heating system is determined based on the parameter comparison result, including: comparing one by one the first value of each parameter in the target pipeline network operating parameter with the second value corresponding to each parameter in the current pipeline network operating parameter; counting the number of occurrences where the first value is greater than the second value; when the number of occurrences is greater than or equal to a preset threshold, using a first adjustment strategy, wherein the first adjustment strategy is to use the second value as a reference to adjust the first value; when the number of occurrences is less than the preset threshold, using a second adjustment strategy, wherein the second adjustment strategy is to use the adjustment instruction received by the heating system to adjust the pipeline network operating parameters.

[0036] Optionally, a heating system sets the target network operation parameters as the supply and return water temperature difference ΔT = 25°C, the total supply and return water flow rate Q = 1000m3 / h, and the supply and return water pressure difference ΔP = 2bar. If the current network operation parameters are ΔT = 23°C, Q = 1020m3 / h, and ΔP = 1.8bar. By comparing the target parameters with the current network operation parameters one by one, it is found that the first value of the supply and return water temperature difference (25°C) is greater than the second value (23°C) and occurs once; the first value of the total supply and return water flow rate (1000m3 / h) is less than the second value (1020m3 / h) and is not included in the statistics; the first value of the supply and return water pressure difference (2bar) is greater than the second value (1.8bar) and occurs once. The statistical result is that the first value is greater than the second value twice, which exceeds the preset threshold (assuming it is 1). Since the statistical result is greater than or equal to the preset threshold, the system will adopt the first adjustment strategy. The target parameter (first value) is adjusted with reference to the current network operation parameter (second value). Similarly, if in the comparison result, the number of occurrences of the first value being greater than the second value is less than the preset threshold, the system will adopt the second adjustment strategy, that is, directly execute the adjustment instruction received by the heating system. The above implementation steps, through comparison and statistics, combined with flexible adjustment strategies, can ensure that the heating system effectively responds to user needs and changes in system status while maintaining the target operating state, thereby achieving efficient, stable and intelligent control of the heating system.

[0037] In an exemplary embodiment, after determining the adjustment strategies of multiple heating loads associated with the heating system based on the parameter comparison results, the method further includes: obtaining updated operating parameters of the heating system after the adjustment is completed using the adjustment strategy; using the updated operating parameters to control the target operating time of the heating system, and collecting the temperature values ​​corresponding to different branches in the pipe network of the heating system after the target operating time; when the temperature value is greater than or equal to the preset heating temperature value, identifying the adjustment strategy as a valid adjustment strategy; when the temperature value is less than the preset heating temperature value, identifying the adjustment strategy as an invalid adjustment strategy.

[0038] Optionally, the preset heating temperature value of a certain centralized heating system is 20°C to ensure the user's living comfort. After a series of adjustment strategies based on real-time data and predictions are executed, the system obtains new updated operating parameters including the supply and return water temperature difference adjusted to 25°C, the circulating water volume adjusted to 800m3 / h, and the system pressure adjusted to 3.5bar. The system runs for a preset target time under the new parameters, such as 1 hour. During this hour, the system automatically collects the temperature values ​​of different branches in the pipe network: Branch A: average temperature 22°C, Branch B: average temperature 19.5°C, Branch C: average temperature 21°C. For branches A and C, the temperature value is greater than or equal to the preset heating temperature value of 20°C, so the system marks this adjustment strategy as a valid adjustment strategy for these two branches. For branch B, the temperature value is less than the preset heating temperature value, so the system marks this adjustment strategy as an invalid adjustment strategy for branch B. In the above embodiment, by obtaining the adjusted updated operating parameters, controlling the operation of the heating system within the target time, and marking the effectiveness of the adjustment strategy based on temperature feedback, the system can achieve accurate and timely heating adjustment, improve user satisfaction, and promote the improvement of system operating efficiency and intelligence level.

[0039] In an exemplary embodiment, after collecting the temperature values ​​corresponding to different branches in the pipe network of the heating system after the target operating time, the above method also includes: when obtaining the visual display image corresponding to the heating system, marking the temperature value in the visual display image to obtain a heating temperature distribution image of the heating system; sending the heating temperature distribution image to the target terminal of the operation and maintenance object to instruct the operation and maintenance object to inspect the heating area with abnormal temperature distribution.

[0040] Optionally, the system combines the collected temperature data with the GIS heating network map to generate a heating temperature distribution image. In this image, the pipe network of the entire heating system is divided into multiple areas, and the color depth of each area represents the average heating temperature of the area. The darker the color, the higher the temperature; the lighter the color, the lower the temperature. In the generated heating temperature distribution image, the system automatically identifies and marks the area with abnormal temperature, such as a specific area in the eastern part of the city, whose color is significantly lighter than the surrounding area, which intuitively reflects the abnormal heating temperature. The system sends the heating temperature distribution image to the mobile phone application of the operation and maintenance personnel responsible for the area in real time. At the same time, the system also sends an alarm message of abnormal temperature, including the specific location and temperature value of the abnormal area, so that the operation and maintenance personnel can quickly understand the situation. The operation and maintenance personnel who receive the alarm directly locate the abnormal heating area in the eastern part of the city for inspection according to the mark in the heating temperature distribution image. In the above embodiment, the use of the heating temperature distribution image can efficiently and intuitively help the operation and maintenance personnel locate and solve problems in the heating system, greatly improving the operation and maintenance efficiency of the centralized heating system.

[0041] In an exemplary embodiment, after the adjustment strategy is identified as an invalid adjustment strategy, the method further includes: sending a prompt message to the management object of the heating load prediction model, wherein the prompt message is used to instruct the management object to retrain the heating load prediction model; obtaining a response result of the management object to the prompt message, and updating the version information of the heating load prediction model based on the response result.

[0042] Optionally, in the above embodiment, when the adjustment strategy is identified as an invalid adjustment strategy, the system automatically sends a prompt message to the operation and maintenance personnel responsible for model maintenance, indicating that the heating load forecasting model needs to be retrained to adapt to the current operating environment. The prompt message includes the recommended time for retraining, the variable updates that need to be considered, and possible improvement points. After receiving the prompt message, the operation and maintenance personnel decide to retrain the model the next day based on the system's suggestions and current operating data. After the training is completed, the operation and maintenance personnel feed back the results to the system, including the detailed parameters of the model training, the selection of the training data set, and the verification results of the model performance. After receiving the response results from the operation and maintenance personnel, the system automatically updates the version information of the heating load forecasting model, records the date of this model training, the ID of the operation and maintenance personnel, the version number of the model parameters, and the performance indicators (such as the prediction accuracy). In the above embodiment, the sending of prompt information and the acquisition mechanism of the response results not only ensure that the heating load forecasting model can dynamically adapt to environmental changes and improve the accuracy of the prediction, but also enhance the maintainability and intelligence level of the system through model version control. This model update strategy based on user participation and data-driven effectively improves the stability and efficiency of system operation.

[0043] In an exemplary embodiment, after inputting system data into a heating load prediction model to obtain the target pipeline network operating parameters corresponding to the heating system, the method further includes: obtaining hydraulic condition data in the heating system; when the hydraulic condition data is less than the preset stable condition data, determining that the target pipeline network operating parameters do not meet the application conditions, and sending an alarm message for adjusting the hydraulic power to the operation and maintenance object; when the hydraulic condition data is greater than or equal to the preset stable condition data, determining that the target pipeline network operating parameters meet the use conditions, and sending a non-alarm message indicating that no hydraulic adjustment is required to the operation and maintenance object.

[0044] Optionally, a heating system is equipped with an intelligent hydraulic condition monitoring module, which can obtain key hydraulic condition data such as pressure, flow, temperature, etc. in the heating pipeline in real time. The intelligent monitoring module obtains hydraulic condition data from sensors at each key point every 15 minutes, including pipeline pressure, water flow and temperature. These hydraulic condition data are then sent to the central control system for real-time analysis and compared with the preset stable condition data. If the analysis result shows that the pipeline pressure is lower than 90% of the normal value, the system determines that the hydraulic condition data is less than the preset stable condition data. At this time, the system will automatically identify that the target pipeline network operating parameters do not meet the use conditions, and immediately send an alarm message to the operation and maintenance object to adjust the hydraulic power. The alarm message will include specific parameter abnormalities and recommended adjustment measures. On the contrary, if the hydraulic condition data such as pipeline pressure, water flow and temperature are all within the preset stable range, the system determines that the target pipeline network operating parameters meet the use conditions, and sends a non-alarm message to the operation and maintenance object that no hydraulic adjustment is required. The non-alarm message will inform the operation and maintenance personnel that the current system status is normal and no special measures need to be taken, and provide the current condition data for reference. The above-mentioned embodiment, by real-time monitoring of hydraulic condition data and intelligently sending alarm or non-alarm information based on preset conditions, can not only ensure that the heating system operates in the best state, but also effectively prevent potential failures and improve operation and maintenance efficiency.

[0045] Obviously, the embodiments described above are only some embodiments of the present application, not all embodiments. In order to better understand the method, the above process is described below in conjunction with the embodiments, but it is not intended to limit the technical solutions of the embodiments of the present application, specifically:

[0046] The optional embodiment of the present application provides a visual hydraulic simulation system based on GIS and heating load prediction. The system collects the structural parameters, design operation parameters, actual operation data, geographic information and other elements of the heating system, establishes a hydraulic simulation model based on the principles of fluid mechanics and the actual structure of the heating system, combines an independent load prediction algorithm, and relies on the GIS system to dynamically visualize the parameters of the hydraulic conditions of the heating pipe network. Through intelligent simulation of the hydraulic conditions of the heating system, a scientific basis is provided for the optimal design and operation adjustment of the system. The systematic human-computer interaction interface facilitates the operation supervisor to set parameters, query and analyze the working conditions, realizes real-time tracking and monitoring of the hydraulic conditions of the heating system, and improves the intelligent management level of the heating system.

[0047] Optional, Figure 3 It is a structural schematic diagram of a visual hydraulic simulation system based on GIS and heating load prediction according to an embodiment of the present application. The above system at least includes: a GIS system modeling module 32, a data acquisition module 34, a hydraulic simulation and parameter optimization module 36, a heating load prediction module 38, a decision optimization module 40, and a data visualization presentation module 42.

[0048] Optionally, the above-mentioned GIS system modeling module 32 is mainly responsible for physical modeling of the heating pipe network system on the GIS application platform. By integrating geographic spatial data and pipe network facility attribute data, the discretely distributed heating system is constructed into a complete information structure with GIS as the link, and the system data can be updated and the model reconstructed according to changes or overdue renovation plans.

[0049] Optionally, the GIS system is developed using open source GIS software and is equipped with a powerful front-end human-computer interaction interface to facilitate system users to perform real-time maintenance and correction of basic network information. At the same time, the GIS system satellite base map is updated annually to ensure the accuracy and timeliness of geospatial data.

[0050] Optionally, the above-mentioned pipeline facility attribute data includes but is not limited to: pipeline length, pipe diameter, valve well, compensator well, pipeline components, branch pipelines, construction year, terrain elevation and other information.

[0051] Optionally, the data acquisition module 34 is mainly responsible for collecting operating data related to the heating system, including meteorological information, real-time operating parameter information, historical operating parameter information, heating area information, building maintenance structure information, etc., and integrating and storing these data on the basis of data cleaning, noise reduction and filtering, to provide a high-quality data source for the simulation of hydraulic conditions.

[0052] Specifically, the above-mentioned data acquisition module 34 is compatible with bus data acquisition, Internet of Things data acquisition and API interface data acquisition methods, connects discrete heating system data into a whole, uses programming languages ​​such as Python and Java to perform data cleaning and noise reduction, and integrates and stores errors and invalid data after processing.

[0053] Optionally, the hydraulic simulation and parameter optimization module 36 is mainly responsible for performing hydraulic simulation using a computational fluid dynamics (CFD) model according to real-time load demand and heating system operating parameters, diagnosing potential problems in system operation by simulating system performance under different operating conditions, and proposing corresponding optimization solutions. According to different application conditions of hydraulic simulation, the module is divided into two major functional units, namely: static hydraulic simulation for evaluating the rationality of pipeline network construction and dynamic hydraulic simulation for evaluating the rationality of real-time operating conditions.

[0054] Specifically, the hydraulic simulation module uses CFD software (such as ANSYS Fluent, Flora, or other independently developed hydraulic simulation software systems) to perform comprehensive simulation of the heating pipe network system, and ultimately outputs comprehensive simulation results including information on water flow direction, pressure distribution, flow distribution, flow velocity, etc.

[0055] Optional, Figure 4 This is an operation flow chart of a static hydraulic simulation system according to an embodiment of the present application, which specifically includes the following steps:

[0056] Step 1: GIS geographic information modeling. In this initial stage, the system models the heating system by importing the geospatial data of the heating network and the attribute data of the network facilities. The data includes but is not limited to the length, diameter, valve well, compensator well, pipeline components, branch pipelines, construction years, terrain elevation and other information of the network. The purpose is to build a GIS model that can reflect the actual layout and attributes of the heating system. The model needs to have the ability to be updated in real time to adapt to changes in the network.

[0057] Step 2: Static hydraulic calculation of the heating network. Once the GIS model is established, the system will use the principles of fluid mechanics to perform static hydraulic calculations to evaluate the hydraulic characteristics of the network under the current design and operating parameters. This includes the application of computational fluid dynamics (CFD) models to simulate the flow state of supply and return water in the network and analyze key hydraulic parameters such as pressure distribution, flow distribution, and flow rate. The purpose of the calculation is to identify potential hydraulic bottlenecks or unreasonable configurations to provide a basis for subsequent analysis and optimization.

[0058] Step 3: Hydraulic condition evaluation and improvement. The system evaluates whether the hydraulic condition of the current heating system is good based on the static hydraulic calculation results. If the hydraulic condition is found to be defective, the system will automatically enter the improvement plan formulation stage and use the preset optimization algorithm to make adjustment suggestions. Improvement measures include pipe network structure optimization, operation parameter adjustment, etc., with the aim of improving the hydraulic efficiency and balance of the system.

[0059] Step 4: Optimize operating parameters and adjust the system. Based on the improvement plan, the system will guide the optimization of operating parameters, such as adjusting the pump speed and valve opening, to improve the hydraulic conditions. The adjusted parameters will be applied to the GIS model for a new round of hydraulic simulation and calculation to verify the optimization effect. This step is the key to ensure that the heating system can operate stably and efficiently after the improvement.

[0060] Step 5: GIS visualized hydraulic simulation presentation. After the optimization and adjustment is completed, the system will be visualized through the GIS platform for hydraulic simulation presentation, comparing the optimized system status with the original status to intuitively display the improvement effect. The visualization presentation uses color scale diagrams and other forms to clearly mark the pressure, temperature, flow and other parameters of each point in the pipe network, which is convenient for operation management personnel to understand and analyze.

[0061] Optional, Figure 5 This is an operation flow chart of a dynamic hydraulic simulation system according to an embodiment of the present application, which specifically includes the following steps:

[0062] Step 1: GIS geographic information modeling. The heating network is physically modeled on the GIS platform, integrating the network's geographic spatial data, facility attribute data (including pipe length, pipe diameter, etc.), and current outdoor temperature data. The GIS system integrates all the attributes and geographic information of the heating network into a digital model to form a comprehensive information structure of the heating network.

[0063] Step 2: Real-time operation data and load forecasting. The system collects the operation data of the heating system in real time (such as pump speed, valve opening, pressure, temperature, flow rate at each point of the pipe network, etc.), and collects outdoor temperature and current operation load data. Based on historical operation data and meteorological information, machine learning algorithms (such as random forest, LSTM, etc.) are used to predict the heating load and predict the heat load under future weather conditions, providing a basis for setting target operating conditions.

[0064] Step 3: Target operating condition setting and hydraulic simulation. Set the target operating condition based on the predicted heat load and the current outdoor temperature. Then, perform GIS visualized hydraulic simulation to evaluate whether the current operating condition meets the optimization target. If the hydraulic condition is not good (bad hydraulic condition), the system will generate an optimization plan; if the hydraulic condition is good, the system will directly enter the GIS visualized hydraulic simulation presentation stage.

[0065] Step 4: Generate and execute the optimization plan. In case of poor hydraulic conditions, the system generates a scheduling optimization plan, including but not limited to adjusting operating parameters such as pump speed and valve opening to improve hydraulic conditions. After the optimization plan is executed, the system performs hydraulic simulation again to ensure that the optimization effect achieves the expected goal.

[0066] Step 5: GIS visualization and comparison of optimization results. After the optimization plan is executed, the system will compare the hydraulic conditions before and after the optimization in a color scale diagram on the GIS platform. Operation managers can intuitively understand the optimization effect through comparison, identify unfavorable points in the pipe network conditions, and conduct targeted management. In addition, the system will form an optimized set of optimized operating parameters to facilitate calls under similar conditions and improve scheduling efficiency.

[0067] Optionally, the main function of the above-mentioned heating load prediction module 38 is to dynamically simulate the load demand of the heating system based on good hydraulic simulation conditions, so as to clarify the target operating condition point for adjusting the operating parameters and the corresponding pipeline supply and return water temperature, pressure and circulation flow parameters.

[0068] Specifically, in the above-mentioned heating load prediction module 38, an algorithm model based on machine learning (such as random forest, LSTM, etc.) is selected, and static hydraulic working condition simulation is carried out through historical operation data and meteorological information to identify the physical weaknesses of the heating system network, guide the network system transformation and operation parameter setting, so as to ensure the rationality of the heating network system, and on this basis, conduct basic model training for a reasonable heating system.

[0069] Optionally, the decision optimization module 40 is mainly responsible for achieving the optimal decision of operating parameters and the setting of control steps based on the heating load prediction module and the improvement target of the hydraulic conditions of the heating system, promoting the transformation of hydraulic condition adjustment from manual adjustment to systematic automatic adjustment, and improving the scientificity and timeliness of the adjustment of the heating pipe network system.

[0070] Specifically, the decision optimization module 40 continuously improves the system's operating strategy through regular data analysis and feedback mechanisms, enhances the system's automation and intelligent control capabilities, and forms an optimized set of operating parameters under typical working conditions to facilitate data calls under similar working conditions.

[0071] Optionally, the data visualization presentation module 42 is based on the GIS system, divides the heating area associated with the heating network into independent blocks, uses real-time monitoring data such as temperature, pressure, and flow to analyze and simulate the operating status of the heating system, and intuitively presents the hydraulic conditions in the form of high-recognition color screenshots. This module needs to realize the current operating hydraulic conditions and the target optimized hydraulic conditions, and establish real-time comparative tracking between the two.

[0072] Specifically, the above-mentioned visualization analysis module 42 adopts D3.js front-end development technology to display data in a graphical and color-scaled manner to assist operation management personnel to quickly grasp the hydraulic operating information of the heating system and identify related situations of unfavorable points in the pipeline network operating conditions.

[0073] It should be noted that data collection needs to ensure the comprehensiveness and accuracy of the collected heating system structural parameters, design and operation parameters, actual operation data and geographic information. The structural parameters and geographic information must be accurate to each element of the pipe network and its spatial position, and the design and operation parameters and actual operation data must cover all factors that may affect the hydraulic conditions, including but not limited to water supply temperature, return water temperature, pressure, flow, ambient temperature, etc. Data islands should be avoided during data collection to ensure data interconnection and interoperability for subsequent data integration and analysis. In addition, when establishing a hydraulic simulation model, the unique fluid properties of the heating system must be considered, such as the effect of water viscosity and density changes with temperature, and the thermal conductivity characteristics of pipe networks of different materials. Ensure the efficiency, accuracy and safety of the visual hydraulic simulation system based on GIS and heating load prediction during the establishment and operation process, and provide a solid technical foundation for the optimal design and operation regulation of the heating system.

[0074] In summary, the above-mentioned scheme of the optional embodiment of the present application innovatively combines GIS technology with heating load prediction and visual hydraulic simulation, and uses the powerful spatial analysis and visual presentation capabilities of GIS to provide intuitive human-computer interaction for hydraulic simulation and load prediction of the heating system. This integration breaks the limitations of a single technology in traditional heating system analysis and realizes multi-dimensional, comprehensive system analysis and presentation; and adopts machine learning algorithms and big data analysis technology to deeply mine and analyze multi-source data such as historical heating parameters, meteorological information, and pipe network information. The system can automatically learn the complex relationship between different factors and heating loads, establish a dynamic connection between future heating loads and hydraulic condition simulation, significantly improve the prediction accuracy, and meet the system users' needs for real-time tracking of operating conditions; in addition, the system presents the differences in heating pipe networks, operating parameters, and hydraulic conditions in a visual, highly recognizable color scale diagram, and users can intuitively observe hydraulic parameters such as the flow direction, pressure distribution, and temperature changes of the pipe network water flow, which greatly improves the recognition and decision-making efficiency of operation management personnel for the operating status of the heating system, and helps users quickly identify potential problem areas and conduct targeted management tracking.

[0075] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0076] In this embodiment, a heating load adjustment device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be repeated hereafter. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0077] Figure 6 : is a structural block diagram of a heating load adjustment device according to an embodiment of the present application, the device comprising:

[0078] An acquisition module 62 is used to acquire system data of the heating system, wherein the system data includes: structural parameters of the heating system, design operation parameters of the heating system, actual operation data of the heating system, and geographic information of the heating system;

[0079] An input module 64 is used to input the system data into a heating load prediction model to obtain target pipe network operation parameters corresponding to the heating system; wherein the heating load prediction model is trained by machine learning using multiple sets of data, each of the multiple sets of data including: historical system data, and historical pipe network operation parameters corresponding to the historical system data;

[0080] The determination module 66 is used to determine the parameter comparison result between the target pipeline network operation parameter and the current pipeline network operation parameter of the heating system collected in real time, and determine the adjustment strategy of multiple heating loads associated with the heating system based on the parameter comparison result.

[0081] The above device obtains system data of the heating system, wherein the system data includes: structural parameters of the heating system, design operating parameters of the heating system, actual operating data of the heating system, and geographic information of the heating system; the system data is input into the heating load prediction model to obtain the target pipe network operating parameters corresponding to the heating system; wherein the heating load prediction model is trained by machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: historical system data, and historical pipe network operating parameters corresponding to the historical system data; the parameter comparison result between the target pipe network operating parameters and the current pipe network operating parameters of the heating system collected in real time is determined, and the adjustment strategy of multiple heating loads associated with the heating system is determined based on the parameter comparison result. The above technical solution solves the problem that the pipe network operating parameters of the heating system cannot be accurately predicted to achieve real-time adjustment of the heating load. Furthermore, by comprehensively collecting and analyzing the comprehensive data of the heating system, the optimal pipe network operating parameters are determined using the heating load prediction model to achieve real-time adjustment of the heating load.

[0082] In an exemplary embodiment, the above-mentioned determination module is also used to compare the first value of each parameter in the target pipeline network operation parameters with the corresponding second value of each parameter in the current pipeline network operation parameters one by one; count the number of occurrences of the first value being greater than the second value; when the number of occurrences is greater than or equal to a preset threshold, use the first adjustment strategy, wherein the first adjustment strategy is to use the second value as a reference to adjust the first value; when the number of occurrences is less than the preset threshold, use the second adjustment strategy, wherein the second adjustment strategy is to use the adjustment instructions received by the heating system to adjust the pipeline network operation parameters.

[0083] In an exemplary embodiment, the above-mentioned device also includes: an acquisition module, which is used to determine the adjustment strategy of multiple heating loads associated with the heating system based on the parameter comparison result, and then obtain the updated operating parameters of the heating system after the adjustment is completed using the adjustment strategy; use the updated operating parameters to control the target operating time of the heating system, and collect the temperature values ​​corresponding to different branches in the pipeline network of the heating system after the target operating time; when the temperature value is greater than or equal to the preset heating temperature value, identify the adjustment strategy as a valid adjustment strategy; when the temperature value is less than the preset heating temperature value, identify the adjustment strategy as an invalid adjustment strategy.

[0084] In an exemplary embodiment, the above-mentioned acquisition module also includes: a first sending unit, which is used to collect the temperature values ​​corresponding to different branches in the pipe network of the heating system after the target operation time, and when the corresponding visual display image of the heating system is obtained, the temperature values ​​are marked in the visual display image to obtain a heating temperature distribution image of the heating system; the heating temperature distribution image is sent to the target terminal of the operation and maintenance object to instruct the operation and maintenance object to inspect the heating area with abnormal temperature distribution.

[0085] In an exemplary embodiment, the above-mentioned acquisition module also includes: a second acquisition unit, which is used to send a prompt message to the management object of the heating load prediction model after identifying the adjustment strategy as an invalid adjustment strategy, wherein the prompt message is used to instruct the management object to retrain the heating load prediction model; obtain the response result of the management object to the prompt message, and update the version information of the heating load prediction model based on the response result.

[0086] In an exemplary embodiment, the above-mentioned device also includes: a sending module, which is used to input system data into a heating load prediction model, obtain the target pipeline network operating parameters corresponding to the heating system, and then obtain the hydraulic condition data in the heating system; when the hydraulic condition data is less than the preset stable operating condition data, it is determined that the target pipeline network operating parameters do not meet the application conditions, and an alarm message for adjusting the hydraulic power is sent to the operation and maintenance object; when the hydraulic condition data is greater than or equal to the preset stable operating condition data, it is determined that the target pipeline network operating parameters meet the use conditions, and a non-alarm message indicating that no hydraulic adjustment is required is sent to the operation and maintenance object.

[0087] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0088] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0089] S1. Acquire system data of a heating system, wherein the system data includes: structural parameters of the heating system, design operation parameters of the heating system, actual operation data of the heating system, and geographic information of the heating system;

[0090] S2. Input the system data into a heating load prediction model to obtain target pipe network operation parameters corresponding to the heating system; wherein the heating load prediction model is trained by machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: historical system data, and historical pipe network operation parameters corresponding to the historical system data;

[0091] S3. Determine a parameter comparison result between the target pipe network operating parameters and the current pipe network operating parameters of the heating system collected in real time, and determine an adjustment strategy for multiple heating loads associated with the heating system based on the parameter comparison result.

[0092] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0093] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0094] An embodiment of the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are performed.

[0095] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0096] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0097] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0098] S1. Acquire system data of a heating system, wherein the system data includes: structural parameters of the heating system, design operation parameters of the heating system, actual operation data of the heating system, and geographic information of the heating system;

[0099] S2. Input the system data into a heating load prediction model to obtain target pipe network operation parameters corresponding to the heating system; wherein the heating load prediction model is trained by machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: historical system data, and historical pipe network operation parameters corresponding to the historical system data;

[0100] S3. Determine a parameter comparison result between the target pipe network operating parameters and the current pipe network operating parameters of the heating system collected in real time, and determine an adjustment strategy for multiple heating loads associated with the heating system based on the parameter comparison result.

[0101] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0102] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0103] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of N computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that here, or they can be made into individual integrated circuit modules, or N of the modules or steps can be made into a single integrated circuit module for implementation. In this way, the present application is not limited to any specific combination of hardware and software.

[0104] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for adjusting heating load, characterized in that: include: Acquire system data of the heating system, wherein the system data includes: structural parameters of the heating system, design operation parameters of the heating system, actual operation data of the heating system, and geographic information of the heating system; Input the system data into a heating load prediction model to obtain target pipe network operation parameters corresponding to the heating system; wherein the heating load prediction model is trained by machine learning using multiple sets of data, each set of data in the multiple sets of data includes: historical system data, and historical pipe network operation parameters corresponding to the historical system data; Determine a parameter comparison result between the target pipe network operating parameter and the current pipe network operating parameter of the heating system collected in real time, and determine an adjustment strategy for multiple heating loads associated with the heating system based on the parameter comparison result.

2. The method for adjusting the heating load according to claim 1, characterized in that: Determining a parameter comparison result between the target pipe network operation parameter and the current pipe network operation parameter of the heating system collected in real time, and determining an adjustment strategy for multiple heating loads associated with the heating system based on the parameter comparison result, including: Comparing the first value of each parameter in the target pipe network operation parameter with the second value corresponding to each parameter in the current pipe network operation parameter one by one; Counting the number of occurrences where the first value is greater than the second value; When the number of occurrences is greater than or equal to a preset threshold, a first adjustment strategy is used, wherein the first adjustment strategy is to use the second value as a reference to adjust the first value; When the number of occurrences is less than a preset threshold, a second adjustment strategy is used, wherein the second adjustment strategy is to use the adjustment instructions received by the heating system to adjust the pipe network operation parameters.

3. The method for adjusting the heating load according to claim 1, characterized in that: After determining the adjustment strategies of the multiple heating loads associated with the heating system based on the parameter comparison result, the method further comprises: obtaining updated operating parameters of the heating system after the adjustment is completed using the adjustment strategy; Using the updated operating parameters to control the target operating time of the heating system, and collecting the temperature values ​​corresponding to different branches in the pipe network of the heating system after the target operating time; In the case where the temperature value is greater than or equal to the preset heating temperature value, marking the adjustment strategy as a valid adjustment strategy; When the temperature value is less than the preset heating temperature value, the adjustment strategy is identified as an invalid adjustment strategy.

4. The method for adjusting the heating load according to claim 3, characterized in that: After collecting the temperature values ​​corresponding to different branches in the pipe network of the heating system after the target operation time, the method further includes: When a visualization display image corresponding to the heating system is obtained, the temperature value is marked in the visualization display image to obtain a heating temperature distribution image of the heating system; The heating temperature distribution image is sent to a target terminal of an operation and maintenance object to instruct the operation and maintenance object to check a heating area with abnormal temperature distribution.

5. The method for adjusting the heating load according to claim 3, characterized in that: After identifying the adjustment strategy as an invalid adjustment strategy, the method further includes: Sending a prompt message to a management object of the heating load prediction model, wherein the prompt message is used to instruct the management object to retrain the heating load prediction model; A response result of the management object to the prompt information is obtained, and version information of the heating load prediction model is updated based on the response result.

6. The method for adjusting the heating load according to claim 1, characterized in that: After inputting the system data into a heating load prediction model to obtain target pipe network operation parameters corresponding to the heating system, the method further includes: Acquiring hydraulic operating data in the heating system; When the hydraulic working condition data is less than the preset stable working condition data, it is determined that the target pipe network operation parameters do not meet the application conditions, and an alarm message for adjusting the hydraulic power is sent to the operation and maintenance object; When the hydraulic condition data is greater than or equal to the preset stable condition data, it is determined that the target pipe network operation parameters meet the use conditions, and a non-warning message indicating that no hydraulic adjustment is required is sent to the operation and maintenance object.

7. A heating load adjustment device, characterized in that: include: An acquisition module, used for acquiring system data of a heating system, wherein the system data includes: structural parameters of the heating system, design operation parameters of the heating system, actual operation data of the heating system, and geographic information of the heating system; An input module is used to input the system data into a heating load prediction model to obtain target pipe network operation parameters corresponding to the heating system; wherein the heating load prediction model is trained by machine learning using multiple sets of data, each set of data in the multiple sets of data includes: historical system data, and historical pipe network operation parameters corresponding to the historical system data; A determination module is used to determine a parameter comparison result between the target pipeline network operation parameter and the current pipeline network operation parameter of the heating system collected in real time, and determine an adjustment strategy for multiple heating loads associated with the heating system based on the parameter comparison result.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

Images