Heat supply flow adjusting method and device, storage medium and electronic device

Abstract

The invention discloses a heat supply flow adjusting method and device, a storage medium and an electronic device. The method comprises the steps that the estimated heat supply load of each single heat supply machine is determined through a pre-trained load prediction model, and the total estimated heat supply load of a heat supply unit is determined based on the estimated heat supply loads; wherein the pre-trained load prediction model is a model which is trained by using historical load data of each single heat supply machine through a machine learning or big data analysis technology and is used for predicting the output load of the heat supply machine; under the condition that the total demand load of the multiple target users is determined, multiple pieces of operation parameter information of heat sources corresponding to the multiple target users are collected; and the flow between the heat supply unit and the multiple target users is adjusted based on the total estimated heat supply load, the total demand load and the multiple pieces of operation parameter information. The problem that a traditional heat supply system cannot quickly adapt to load changes in heat distribution and parameter adjustment is solved.

Description

Technical Field

[0001] The present application relates to the field of heating technology, and in particular to a method and device for adjusting heating flow, a storage medium and an electronic device. Background Art

[0002] In traditional heating systems, the primary network variable temperature quality regulation operation mode is usually adopted, that is, the circulating water flow of the primary heating network remains unchanged, and the water supply temperature changes with the load. This operation mode is manifested in supply-type load scheduling in practice, that is, the "supply determines demand" and planned allocation control mode. Due to the lack of effective linkage between the heat source, the heat network and the thermal power station, the timeliness of parameter scheduling is often poor. For example: in the heat source load increase stage, the heating station at the front end of the heat network gets heat first, and the station at the end of the heat network gets heat later. The end of the pipe network is always in a relatively lagging state of operating parameters. Superimposed with the thermal inertia of the building, the heat supply of the terminal station and the weather conditions are often mismatched, which not only causes a waste of thermal energy resources, but also leads to a decrease in the satisfaction of heat users with the heating quota.

[0003] Regarding the problem in related technologies that traditional heating systems cannot quickly adapt to load changes in terms of heat distribution and parameter adjustment, 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 heat flow, a storage medium and an electronic device to at least solve the problem in the related art that the traditional heating system cannot quickly adapt to load changes in heat distribution and parameter adjustment.

[0006] According to one aspect of an embodiment of the present application, a method for adjusting heat flow is provided, comprising: determining an estimated heat load of each individual heat machine through a pre-trained load prediction model, and determining a total estimated heat load of the heat unit based on the estimated heat load; wherein the pre-trained load prediction model is a model for predicting the output load of the heat machine trained by machine learning or big data analysis technology using historical load data of each individual heat machine; when determining a total demand load of multiple target users, collecting multiple operating parameter information at heat sources corresponding to the multiple target users; adjusting the flow between the heat unit and the multiple target users based on the total estimated heat load, the total demand load, and the multiple operating parameter information.

[0007] In an exemplary embodiment, the flow between the heating unit and multiple target users is adjusted based on the total estimated heating load, the total demand load, and multiple operating parameter information, including: when the total demand load is less than the total estimated heating load, determining to reduce the operating load of the heating unit and to reduce the circulating water flow size in the multiple operating parameter information; when the total demand load is greater than the total estimated heating load, determining to increase the operating load of the heating unit and to increase the circulating water flow size in the multiple operating parameter information; when the total demand load is equal to the total estimated heating load, determining to keep the operating load of the heating unit unchanged within the target period.

[0008] In an exemplary embodiment, when determining the total demand load of multiple target users, before collecting multiple operating parameter information at the heat sources corresponding to the multiple target users, the above method also includes: obtaining the pipe loss heat and network heat storage between the heating unit and each target user, as well as the outdoor temperature of the target family area corresponding to each target user and the heating area of ​​the target family area; determining the first sub-demand load of the pipe network between the heating unit and each target user based on the pipe loss heat and network heat storage; determining the second sub-demand load of each target user based on the outdoor temperature and the heating area; determining the target demand load corresponding to each target user according to the first sub-demand load and the second sub-demand load; and summarizing the target demand loads corresponding to multiple target users to obtain the total demand load.

[0009] In an exemplary embodiment, after adjusting the flow between the heating unit and multiple target users based on the total estimated heating load, the total demand load, and multiple operating parameter information, the above method also includes: obtaining the adjusted target flow size of each of the multiple target users to obtain multiple target flow values; calculating the heating load corresponding to the target flow values ​​among the multiple target flow values ​​to obtain multiple target heating loads; comparing the target heating load corresponding to each target user with the target demand load to determine whether to perform a secondary flow adjustment on the current target user.

[0010] In an exemplary embodiment, the target heating load corresponding to each target user is compared with the target demand load to determine whether to perform secondary flow adjustment on the current target user, including: when the target heating load is greater than or equal to the target demand load, the current target user is identified as a stable heating user, and it is determined that no secondary flow adjustment is performed on the current target user; when the target heating load is less than the target demand load, the current target user is identified as an unstable heating user, and it is determined that secondary flow adjustment is performed on the current target user.

[0011] In an exemplary embodiment, after the current target user is identified as a non-stable heating user and it is determined that a secondary flow adjustment is to be performed on the current target user, the method further includes: sending an adjustment prompt message to a management object that controls the heating unit, wherein the adjustment prompt message is used to carry location information of the target user to be subjected to secondary flow adjustment, multiple real-time operating parameter information at the heat source, and information of the heating machine connected thereto; and recording the processing result of the management object on the adjustment prompt message.

[0012] According to another aspect of an embodiment of the present application, a device for adjusting heat flow is also provided, including: a determination module, used to determine the estimated heating load of each individual heating machine through a pre-trained load prediction model, and determine the total estimated heating load of the heating unit based on the estimated heating load; wherein the pre-trained load prediction model is a model for predicting the output load of the heating machine trained by machine learning or big data analysis technology using the historical load data of each individual heating machine; a collection module, used to collect multiple operating parameter information at the heat source corresponding to multiple target users when determining the total demand load of multiple target users; an adjustment module, used to adjust the flow between the heating unit and multiple target users based on the total estimated heating load, the total demand load, and multiple operating parameter information.

[0013] According to another aspect of the embodiment 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 flow rate 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 method for adjusting the heating flow rate 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 the above-mentioned method for adjusting the heating flow rate is implemented when the computer program is executed by a processor.

[0016] Through this application, the estimated heating load of each single heating machine is determined by a pre-trained load prediction model, and the total estimated heating load of the heating unit is determined based on the estimated heating load; wherein, the pre-trained load prediction model is a model for predicting the output load of the heating machine trained by machine learning or big data analysis technology using the historical load data of each single heating machine; in the case of determining the total demand load of multiple target users, multiple operating parameter information at the heat source corresponding to the multiple target users is collected; based on the total estimated heating load, the total demand load, and multiple operating parameter information, the flow between the heating unit and the multiple target users is adjusted. The above technical solution is adopted to solve the problem that the traditional heating system cannot quickly adapt to load changes in heat distribution and parameter adjustment. Furthermore, the load of the single heating machine is predicted by machine learning, the prediction results are summarized and combined with the total user demand and the heat source operating parameters, the flow in the heating system is intelligently adjusted to achieve accurate supply and demand matching. 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 flow rate according to an embodiment of the present application;

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

[0021] Figure 3 It is a network-source linkage intelligent operation framework diagram based on a constant temperature and variable flow condition according to an embodiment of the present application;

[0022] Figure 4 It is a flow chart of a network-source linkage intelligent operation method based on a constant temperature and variable flow condition according to an embodiment of the present application;

[0023] Figure 5 It is a schematic diagram of a correlation element system based on a constant temperature and variable flow condition according to an embodiment of the present application;

[0024] Figure 6 It is a structural block diagram of a heating flow 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 flow rate according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or N ( Figure 1 The computer terminal includes a processor 102 (only one is shown) (the processor 102 may include but is not limited to a central processing unit (CPU) or a programmable logic device (Field Programmable Gate Array, FPGA) and a memory 104 for storing data, wherein the computer terminal may also include a transmission device 106 and an input / 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 are 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 the heat flow rate is provided. Figure 2 is a flow chart of a method for adjusting a heating flow rate 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: determining the estimated heating load of each individual heating machine through a pre-trained load prediction model, and determining the total estimated heating load of the heating unit based on the estimated heating load; wherein the pre-trained load prediction model is a model for predicting the output load of the heating machine trained by machine learning or big data analysis technology using the historical load data of each individual heating machine;

[0032] Optionally, the above historical load data at least includes: temperature, pressure, circulation flow, heat, water replenishment volume, and electric control valve opening.

[0033] Step S204: when the total demand load of multiple target users is determined, multiple operating parameter information of the heat sources corresponding to the multiple target users is collected;

[0034] Step S206: adjusting the flow between the heating unit and multiple target users based on the total estimated heating load, the total demand load, and the multiple operating parameter information.

[0035] In the above steps, the estimated heating load of each single heating machine is determined by a pre-trained load prediction model, and the total estimated heating load of the heating unit is determined based on the estimated heating load; wherein the pre-trained load prediction model is a model for predicting the output load of the heating machine trained by machine learning or big data analysis technology using the historical load data of each single heating machine; in the case of determining the total demand load of multiple target users, multiple operating parameter information at the heat source corresponding to the multiple target users is collected; based on the total estimated heating load, the total demand load, and the multiple operating parameter information, the flow between the heating unit and the multiple target users is adjusted. The above technical solution solves the problem that the traditional heating system cannot quickly adapt to load changes in heat distribution and parameter adjustment. Furthermore, the load of the single heating machine is predicted by machine learning, the prediction results are summarized and combined with the total user demand and the heat source operating parameters, the flow in the heating system is intelligently adjusted to achieve accurate supply and demand matching.

[0036] In an exemplary embodiment, the flow between the heating unit and multiple target users is adjusted based on the total estimated heating load, the total demand load, and multiple operating parameter information, including: when the total demand load is less than the total estimated heating load, determining to reduce the operating load of the heating unit and to reduce the circulating water flow size in the multiple operating parameter information; when the total demand load is greater than the total estimated heating load, determining to increase the operating load of the heating unit and to increase the circulating water flow size in the multiple operating parameter information; when the total demand load is equal to the total estimated heating load, determining to keep the operating load of the heating unit unchanged within the target period.

[0037] Optionally, assuming that the estimated total heating load is 1800MW and the actual total demand load is 1700MW, the system automatically recognizes that the estimated heating load is higher than the actual demand load, and then starts the linkage strategy of "reducing the circulating water flow" and "reducing the operating load of the heating unit". If the outdoor temperature drops suddenly, the system will again recognize through real-time data feedback that the actual total demand load has increased to 1900MW, exceeding the estimated 1800MW. The intelligent operation method quickly adjusts the strategy, increases the operating frequency of the circulation pump from 60Hz to 65Hz, and increases the circulating water flow. If the outdoor temperature tends to stabilize, the actual total demand load stabilizes at 1800MW, which is equal to the estimated heating load. At this time, the system keeps the operating load of the heating unit unchanged and maintains the stable state of the circulating water flow, ensuring the economy and efficiency of the operation and avoiding unnecessary energy consumption fluctuations. Through this series of dynamic adjustments, not only the precise matching of heat supply and demand is achieved, but also energy waste is effectively reduced.

[0038] In an exemplary embodiment, when determining the total demand load of multiple target users, before collecting multiple operating parameter information at the heat sources corresponding to the multiple target users, the above method also includes: obtaining the pipe loss heat and network heat storage between the heating unit and each target user, as well as the outdoor temperature of the target family area corresponding to each target user and the heating area of ​​the target family area; determining the first sub-demand load of the pipe network between the heating unit and each target user based on the pipe loss heat and network heat storage; determining the second sub-demand load of each target user based on the outdoor temperature and the heating area; determining the target demand load corresponding to each target user according to the first sub-demand load and the second sub-demand load; and summarizing the target demand loads corresponding to multiple target users to obtain the total demand load.

[0039] Optionally, a heating system obtains the heat loss between the heating unit and each target user, as well as the heat storage information of the primary network. At the same time, the system obtains the real-time outdoor temperature of each target household area through the data interface of the weather station, and determines the heating area of ​​each area through the user registration information database. Taking heating unit 1 and users in area A as an example, the system is based on the real-time outdoor temperature of area A (assuming -10°C) and the heating area (assuming 20,000m 2 ), through the preset second sub-demand load calculation model, the second sub-demand load of area A is obtained to be 200MW. At the same time, the system calculates the pipe heat loss between the heating unit and area A (assuming it is 50MW) and the network heat storage of the primary network (assuming it is 30MW), and obtains that the first sub-demand load of area A is 80MW. Based on the first sub-demand load and the second sub-demand load, the system determines that the target demand load of area A is 280MW (ie 80MW+200MW). Similarly, the system estimates the demand loads of multiple other target user areas and summarizes the total demand load, assuming it is 1700MW. Through this series of data collection, calculation and summary, the heating system can accurately estimate the total demand load, guide the formulation of production scheduling plans on the heat source side, ensure a high degree of match between thermal energy supply and actual demand, and achieve efficient, energy-saving and stable heating operation. In actual operation, when the system estimates that the total demand load is 1700MW, the operating parameters of the heat source can be adjusted accordingly, such as increasing the fuel supply of the boiler and adjusting the operating conditions of the turbine to ensure that the total estimated heating load matches the total demand load, avoiding excessive or insufficient operation of the heat source equipment, thereby optimizing the production efficiency and energy utilization efficiency of the heat source. At the same time, through the intelligent control module, the circulating water flow of the primary network is adjusted in real time to ensure the accurate distribution of heat and improve the quality of heating services.

[0040] In an exemplary embodiment, after adjusting the flow between the heating unit and multiple target users based on the total estimated heating load, the total demand load, and multiple operating parameter information, the above method also includes: obtaining the adjusted target flow size of each of the multiple target users to obtain multiple target flow values; calculating the heating load corresponding to the target flow values ​​among the multiple target flow values ​​to obtain multiple target heating loads; comparing the target heating load corresponding to each target user with the target demand load to determine whether to perform a secondary flow adjustment on the current target user.

[0041] Optionally, in a heating system, there are three target user areas (A, B, C). The system first obtains the adjusted target flow size of each area through an intelligent optimization algorithm, which are: Area A: The target flow size is 150m 3 / h; Area B: target flow rate is 200m 3 / h; Area C: target flow rate is 100m 3 / h; Based on the above target flow value, the system uses the preset heating load calculation model to obtain the target heating load for each area. Assume that the calculation results are: Area A: target heating load is 250MW; Area B: target heating load is 350MW; Area C: target heating load is 150MW. The system compares the above target heating load with the target demand load previously determined by the load forecasting model to determine whether secondary flow adjustment is required. If the target demand load calculation result is: Area A: target demand load is 240MW; Area B: target demand load is 360MW; Area C: target demand load is 150MW. It can be seen that the target heating load (250MW) in area A is higher than the target demand load (240MW), the target heating load (350MW) in area B is slightly lower than the target demand load (360MW), and the target heating load (150MW) in area C is equal to the target demand load (150MW). Based on the above comparison results, the system automatically adjusts the flow rate in each area to meet the personalized heating needs of different areas in real time, effectively control energy consumption, and maintain the economy and stability of the heating system.

[0042] In an exemplary embodiment, the target heating load corresponding to each target user is compared with the target demand load to determine whether to perform secondary flow adjustment on the current target user, including: when the target heating load is greater than or equal to the target demand load, the current target user is identified as a stable heating user, and it is determined that no secondary flow adjustment is performed on the current target user; when the target heating load is less than the target demand load, the current target user is identified as an unstable heating user, and it is determined that secondary flow adjustment is performed on the current target user.

[0043] Optionally, in an intelligent heating network, the system serves three target users: user X, user Y, and user Z. The system monitors and calculates the target heating load and target demand load of each user in real time. Assume that the data obtained from real-time monitoring are: User X: target heating load is 150MW, target demand load is 145MW; User Y: target heating load is 200MW, target demand load is 210MW; User Z: target heating load is 180MW, target demand load is 180MW. For user X, since the target heating load (150MW) is greater than or equal to the target demand load (145MW), the system identifies it as a "stable heating user" and decides not to make secondary flow adjustments to user X. For user Y, since the target heating load (200MW) is less than the target demand load (210MW), the system identifies it as an "unstable heating user" and decides to make secondary flow adjustments to user Y. Specifically, the system changes the flow of user Y from the original value (assuming it is 250m 3 / h) increased to 280m 3 / h to increase the heating load to about 210MW to meet its additional heating needs. For user Z, since the target heating load (180MW) is equal to the target demand load (180MW), the system also identifies it as a "stable heating user" and decides not to make secondary flow adjustments to user Z. Through this process, the intelligent heating system can effectively identify and respond to fluctuations in user demand, increase flow in a timely manner for users with insufficient heating (such as user Y), and avoid unnecessary flow adjustments for users with excess or balanced heating (such as user X and user Z), thereby achieving efficient allocation of resources across the heating network, ensuring the stability and comfort of user heating, and reducing system energy consumption and operating costs.

[0044] In an exemplary embodiment, after the current target user is identified as a non-stable heating user and it is determined that a secondary flow adjustment is to be performed on the current target user, the method further includes: sending an adjustment prompt message to a management object that controls the heating unit, wherein the adjustment prompt message is used to carry location information of the target user to be subjected to secondary flow adjustment, multiple real-time operating parameter information at the heat source, and information of the heating machine connected thereto; and recording the processing result of the management object on the adjustment prompt message.

[0045] Optionally, in a certain intelligent central heating system, after the system detects that the heating demand of a target user F (residential area) suddenly increases on a cold night, it automatically analyzes the real-time operating parameters at the heat source, including the supply water temperature of 80°C, the return water temperature of 55°C, the supply water pressure of 4.5MPa, the return water pressure of 4.0MPa, and the instantaneous circulation flow rate of 3200m 3 / h, the total output power of the heat source is 120MW. At the same time, the system also obtains the information of the heating unit connected to user F, No. 003, the current operating status is 60% load, and the electric control valve opening is 40%. The system then generates adjustment prompt information, including the location information of user F (city center residential area, specific coordinates are longitude XX, latitude YY), the above real-time operating parameters and relevant information of heating unit No. 003, and sends this information to the management object of the heating system control center. After receiving the adjustment prompt information, the management object decides to make a secondary flow adjustment to heating unit No. 003 according to the current heat source and the operating status of the heating unit to meet the additional needs of user F. After the adjustment, the system records the processing results of the management object, including the adjusted electric control valve opening, circulating water flow changes and heating load changes of user F. Through this process of real-time monitoring, intelligent analysis, rapid adjustment and result feedback, the intelligent centralized heating system can flexibly respond to fluctuations in heating demand, realize accurate allocation of resources, improve service quality and reduce energy waste.

[0046] 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:

[0047] The optional embodiment of the present application provides a network-source linkage intelligent operation method based on constant temperature and variable flow conditions to solve the problem that traditional heating systems cannot quickly adapt to load changes in heat distribution and parameter adjustment. The method establishes an overall technical framework with modular thinking, designs independent modules such as heating unit load forecasting, network-wide heating load forecasting, heat source export operating parameter collection, network source heating load gap decision-making, heat source production scheduling plan, operating parameter intelligent optimization, and heat source operating condition optimization library, and forms functional linkage in a comprehensive data interactive manner. By integrating big data analysis, artificial intelligence control strategies, machine self-learning algorithms, and centralized heating operations, a feedback-type heat source and heat network linkage control method is constructed, thereby improving the operating efficiency of the heating system.

[0048] As an optional implementation, Figure 3 It is a network-source linkage intelligent operation framework diagram based on constant temperature and variable flow conditions according to an embodiment of the present application, and the framework includes: a heating unit load prediction self-learning module 32, a full network heating load prediction module 34, a heat source outlet operating parameter acquisition module 36, a load gap decision module 38, an operating parameter intelligent optimization module 40, a heat source production scheduling plan module 42, and a heat source operating condition optimization library 44.

[0049] Optionally, the heating unit load prediction self-learning module 32 establishes a load prediction model and operating parameter control logic for a single heating unit through the historical operating parameters of the heating unit (including but not limited to temperature, pressure, circulation flow, heat, make-up water volume, electric regulating valve opening, etc.), laying the foundation for the heating load prediction of the whole network.

[0050] Optionally, the whole network heating load prediction module 34. Based on the load prediction of each individual heating unit, the heating transmission pipe loss, the heat storage capacity of the primary network, the outdoor temperature, the heating area and other information are comprehensively considered to carry out model prediction of the heating load demanded by the heating network.

[0051] Optionally, the heat source outlet operation parameter collection module 36 comprehensively collects and stores the main operation parameter information of the heat source outlet (including: primary network supply and return water temperature, primary network supply and return water pressure, primary network instantaneous circulation flow, primary network water replenishment volume, primary network operation load, etc.), and provides data support for the load gap decision module.

[0052] Optional, load gap decision module 38. As a leading processing flow of the operation parameter intelligent optimization adjustment and heat source production scheduling planning module, this module compares the whole network predicted heating load with the heat source export supply load parameter, determines the load gap interval, and forms subsequent regulation and heat source production scheduling allocation plan.

[0053] Optionally, the operating parameter intelligent optimization module 40 performs intelligent optimization on the primary network circulation flow and the corresponding parameters such as the number of circulating pump starts and the operating frequency based on the processing results of the load gap decision module, so as to provide intelligent guidance for operation regulation.

[0054] Optionally, the heat source production scheduling module 42 makes optimization decisions on large-scale load scheduling plans and heat source production scheduling plans (including but not limited to turbine operating load, boiler operating load, boiler startup plan, power generation plan, etc.) based on the processing results of the load gap decision module, and provides intelligent guidance for production scheduling on the heat source side.

[0055] Optionally, the heat source operation condition optimization library 44 identifies and backs up the heat source production configuration plan and primary network operation parameters corresponding to good heating effect and economic operation conditions, so as to facilitate quick call under similar conditions and improve the decision-making efficiency of heat source and heat network operation scheduling.

[0056] Optional, Figure 4 It is a flow chart of a network-source linkage intelligent operation method based on a constant temperature and variable flow condition according to an embodiment of the present application, which specifically includes the following steps:

[0057] Step 1: System construction. Build a network-source linkage intelligent operation and control system based on constant temperature and variable flow conditions, including building the heat source DCS system, heat network SCADA monitoring system, coal transportation program control system, primary network circulation pump control system, and primary network water supply pump control system into a unified body, realizing real-time data call and command transmission on the basis of network information security, and using digital operation to provide the management system with remote centralized control of discretely distributed heat sources and heat network heating hardware equipment.

[0058] Step 2: Data collection and preprocessing. Comprehensively apply bus communication methods (RS485, RS232), dedicated line communication methods (TCP / IP), IoT wireless communication methods (4G, 5G, NB, LoRa, etc.), and API interface methods to remotely collect the heat source and heat network operating parameters involved in the present invention. After data cleaning, noise reduction filtering, integration and storage, the collected data is transmitted to the corresponding function management module in real time for big data analysis, artificial intelligence control strategy verification, and machine self-learning algorithm optimization, providing real-time and accurate data support for system operation.

[0059] Step 3: Load prediction and smoothness optimization. The load prediction of the optional embodiment of the present application takes the single heating unit as the basic unit, combines the corresponding heating area information and heating degree days information, and uses the LSTM model to dynamically fit and iterate the heating demand load, so as to continuously improve the accuracy of the heating demand load prediction of the single heating unit under different weather conditions. The prediction of the total load of the heating network heating demand is based on the load prediction of the single heating unit, and at the same time, the intelligent prediction model is established by considering the operating characteristics of the primary heating network (such as heat transmission pipe loss, total heat storage of the network, etc.). In order to ensure the stability of the load scheduling on the heat source side, it is necessary to optimize the smoothness of the heating load, and eliminate the adverse effects of the large-scale fluctuations of the heating load during the day and night and the severe fluctuations of the weather in the short term on the heat source and the heat network regulation in a "peak shaving and valley filling" manner. The heating load smoothness optimization needs to optimize the calculation of the weather forecast information for the past 3 days in a moving weighted average manner, and at the same time, according to the actual operation effect evaluation, the load flat arc optimization model is continuously improved.

[0060] Step 4: Variable flow regulation and network source linkage. Details are as follows:

[0061] (1) According to the changes in the heat load demand of the entire network, the circulating water flow of the primary heating network is adjusted in real time. When the heat load demand of the entire network decreases, the operating load of the heat source heat generating equipment is reduced, the circulating water flow is reduced, and the energy consumption of the water pump is reduced; when the heat load demand of the entire network increases, the operating load of the heat source heat generating equipment is increased, the circulating water flow is increased, and the supply demand is guaranteed.

[0062] (2) Establish a linkage mechanism between the heating network and the heat source. When the pressure, flow and other parameters of the heating network change, timely adjust the heat supply and circulating water flow of the heat source to ensure the stable operation of the heating system. When there is a large gap between the heat source load supply and the heat network load demand, batch optimization and adjustment should be carried out on the heat network side while ensuring the steady regulation of the heat source and the good hydraulic condition of the primary network, so as to avoid improper operation or uncontrollable risks caused by the mismatch of the network source regulation amplitude and regulation rate.

[0063] Step 5: Intelligent control and optimized operation. Use artificial intelligence control strategies to monitor and control the operation of the heating system in real time. The intelligent control module can automatically adjust the operating parameters of the heating system based on the collected data and preset control strategies to achieve a smooth transition of different working conditions. At the same time, the intelligent control strategy can be optimized and improved based on historical control result data, continuously improving the intelligence and rationality of network-source linkage regulation.

[0064] Optional, Figure 5 It is a schematic diagram of a system of associated elements based on a constant temperature and variable flow condition according to an embodiment of the present application; the above-mentioned system includes: a primary network flow meter and a heat meter 1, a heating heat source 2, a primary network circulation pump system 3, a primary network water supply tank 4, a primary network water supply pump 5, a primary network water supply flow meter 6, a primary network circulation bypass electric regulating valve 7, a primary network water supply pipeline 8, a primary network return pipeline 9, a primary network electric regulating valve 10 of a heating unit, a primary network heat meter 11 of a heating unit, and a primary network flow meter 12 of a heating unit.

[0065] Optional, Figure 5 The heating system consists of heating unit 1, heating unit 2 to heating unit n, and each heating unit is connected to the target user area through a specific circulation loop. These circulation loops constitute the key part of the heating system, through which the heating unit can supply heat to the target user. In each circulation loop, the pipe heat loss and network heat storage between the heating unit and the target user are continuously monitored and recorded to ensure the effectiveness and stability of the heat during the transmission process. For example, when heating unit 1, heating unit 2 to heating unit n supplies heat to the target user area, the heating unit will adjust its own operating status according to the real-time operating parameter information (such as supply and return water temperature, supply and return water pressure, instantaneous flow, make-up water flow) and the estimated heating load.

[0066] It should be noted that when integrating multiple advanced technologies, the above optional embodiments need to ensure the compatibility between the technologies and the standardization of data formats. Including all aspects of data collection, transmission, processing and storage, it is necessary to create a unified interface standard to promote seamless integration and data sharing between different systems. At the same time, it is necessary to pay attention to the impact of technology upgrades on existing infrastructure, formulate reasonable upgrade paths and plans, and reduce the risks and costs in the process of technology integration. At the same time, the establishment of the two-level prediction model should be based on high-quality historical data, and the overfitting problem should be paid attention to during the model training process to ensure that the model not only performs well on the training data, but also accurately predicts future load changes. The model should have sufficient generalization ability to adapt to complex working conditions such as seasonal changes and sudden weather changes. In addition, although the operation mode of constant temperature and flow regulation simplifies the control process, it is necessary to continuously monitor the hydraulic conditions of the pipeline network during implementation to ensure its stable operation and avoid abnormal pressure or thermal stress damage to the pipeline network caused by flow changes. At the same time, the operational safety under extreme weather conditions needs to be considered. For example, in extremely cold weather, the constant temperature and flow regulation method may be insufficient for heating in some areas, and there must be backup plans or emergency control measures to ensure that the heating needs of all users are met.

[0067] In summary, the optional embodiments of the present application optimize the scheduling and management process of the centralized heating system by integrating big data, artificial intelligence and self-learning technology. The bottom-up two-level prediction model is adopted to improve the accuracy of load estimation, and the integration of multiple communication technologies improves the efficiency of data collection. The constructed intelligent decision-making module replaces the traditional manual experience, simplifies the operation mode, and enhances the safety and response speed of the primary network system. While keeping the water supply temperature relatively constant, real-time flow regulation realizes the precise distribution of heat in the entire network, reduces the complexity of load scheduling, and improves the economy and reliability of system operation. In addition, by clarifying the parameter differences of each heating unit, it supports accurate heating regulation and energy consumption assessment, realizes the transformation from passive scheduling to active intelligent management, and comprehensively improves the operation efficiency and management level of the heating system.

[0068] 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.

[0069] In this embodiment, a device for adjusting the heat flow is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and the descriptions that have been made will not be repeated. 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.

[0070] Figure 6 : is a structural block diagram of a device for adjusting heat flow according to an embodiment of the present application, the device comprising:

[0071] A determination module 62 is used to determine the estimated heating load of each individual heating machine through a pre-trained load prediction model, and determine the total estimated heating load of the heating unit based on the estimated heating load; wherein the pre-trained load prediction model is a model for predicting the output load of the heating machine trained by machine learning or big data analysis technology using historical load data of each individual heating machine;

[0072] A collection module 64 is used to collect a plurality of operating parameter information at the heat sources corresponding to the plurality of target users when the total demand load of the plurality of target users is determined;

[0073] The adjustment module 66 is used to adjust the flow between the heating unit and multiple target users based on the total estimated heating load, the total demand load, and the multiple operating parameter information.

[0074] The above-mentioned device determines the estimated heating load of each single heating machine through a pre-trained load prediction model, and determines the total estimated heating load of the heating unit based on the estimated heating load; wherein the pre-trained load prediction model is a model for predicting the output load of the heating machine trained by machine learning or big data analysis technology using the historical load data of each single heating machine; when the total demand load of multiple target users is determined, multiple operating parameter information at the heat source corresponding to the multiple target users is collected; based on the total estimated heating load, the total demand load, and the multiple operating parameter information, the flow between the heating unit and the multiple target users is adjusted. The above-mentioned technical solution solves the problem that the traditional heating system cannot quickly adapt to load changes in heat distribution and parameter adjustment. Furthermore, the load of the single heating machine is predicted through machine learning, the prediction results are summarized and combined with the total user demand and the heat source operating parameters, the flow in the heating system is intelligently adjusted to achieve accurate supply and demand matching.

[0075] In an exemplary embodiment, the above-mentioned adjustment module is also used to determine to reduce the operating load of the heating unit and adjust the circulating water flow rate in multiple operating parameter information when the total demand load is less than the total estimated heating load; determine to increase the operating load of the heating unit and adjust the circulating water flow rate in multiple operating parameter information when the total demand load is greater than the total estimated heating load; and determine to keep the operating load of the heating unit unchanged within the target period when the total demand load is equal to the total estimated heating load.

[0076] In an exemplary embodiment, the above-mentioned device also includes: a first acquisition module, which is used to obtain the pipe loss heat, network heat storage, and outdoor temperature of the target family area corresponding to each target user, and the heating area of ​​the target family area between the heating unit and each target user before collecting multiple operating parameter information at the heat source corresponding to the multiple target users when determining the total demand load of multiple target users; determine the first sub-demand load of the pipeline network between the heating unit and each target user based on the pipe loss heat and network heat storage; determine the second sub-demand load of each target user based on the outdoor temperature and the heating area; determine the target demand load corresponding to each target user according to the first sub-demand load and the second sub-demand load; summarize the target demand loads corresponding to multiple target users to obtain the total demand load.

[0077] In an exemplary embodiment, the above-mentioned device also includes: a second acquisition module, which is used to adjust the flow between the heating unit and multiple target users based on the total estimated heating load, the total demand load, and multiple operating parameter information, and then obtain the adjusted target flow size of each of the multiple target users to obtain multiple target flow values; calculate the heating load corresponding to the target flow value among the multiple target flow values ​​to obtain multiple target heating loads; compare the target heating load corresponding to each target user with the target demand load to determine whether to perform a secondary flow adjustment on the current target user.

[0078] In an exemplary embodiment, the above-mentioned second acquisition module is also used to identify the current target user as a stable heating user when the target heating load is greater than or equal to the target demand load, and determine that no secondary flow adjustment is performed on the current target user; when the target heating load is less than the target demand load, identify the current target user as an unstable heating user, and determine that secondary flow adjustment is performed on the current target user.

[0079] In an exemplary embodiment, the acquisition module further includes: a sending unit, used to identify the current target user as a non-stable heating user, and after determining to make a secondary flow adjustment for the current target user, send an adjustment prompt message to the management object that controls the heating unit, wherein the adjustment prompt message is used to carry the location information of the target user to be adjusted for secondary flow, multiple real-time operating parameter information at the heat source, and information of the heating machine connected thereto; and record the processing result of the management object on the adjustment prompt message.

[0080] 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.

[0081] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0082] S1. Determine the estimated heating load of each individual heating machine through a pre-trained load prediction model, and determine the total estimated heating load of the heating unit based on the estimated heating load; wherein the pre-trained load prediction model is a model for predicting the output load of the heating machine trained by machine learning or big data analysis technology using the historical load data of each individual heating machine;

[0083] S2. When the total demand load of multiple target users is determined, multiple operation parameter information of heat sources corresponding to the multiple target users is collected;

[0084] S3. Adjust the flow between the heating unit and multiple target users based on the total estimated heating load, the total demand load, and the multiple operating parameter information.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0091] S1. Determine the estimated heating load of each individual heating machine through a pre-trained load prediction model, and determine the total estimated heating load of the heating unit based on the estimated heating load; wherein the pre-trained load prediction model is a model for predicting the output load of the heating machine trained by machine learning or big data analysis technology using the historical load data of each individual heating machine;

[0092] S2. When the total demand load of multiple target users is determined, multiple operation parameter information of heat sources corresponding to the multiple target users is collected;

[0093] S3. Adjust the flow between the heating unit and multiple target users based on the total estimated heating load, the total demand load, and the multiple operating parameter information.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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 heat flow, characterized in that: include: Determine the estimated heating load of each individual heating machine through a pre-trained load prediction model, and determine the total estimated heating load of the heating unit based on the estimated heating load; wherein the pre-trained load prediction model is a model for predicting the output load of the heating machine trained by machine learning or big data analysis technology using historical load data of each individual heating machine; In the case of determining the total demand load of multiple target users, collecting multiple operating parameter information at the heat sources corresponding to the multiple target users; The flow between the heating unit and multiple target users is adjusted based on the total estimated heating load, the total demand load, and the multiple operating parameter information.

2. The method for adjusting the heat flow rate according to claim 1, characterized in that: Adjusting the flow between the heating unit and multiple target users based on the total estimated heating load, the total demand load, and the multiple operating parameter information includes: In the case where the total demand load is less than the total estimated heating load, determining to reduce the operating load of the heating unit, and adjusting the circulating water flow rate in the plurality of operating parameter information to be smaller; In the case where the total demand load is greater than the total estimated heating load, determining to increase the operating load of the heating unit, and increasing the circulating water flow rate in the plurality of operating parameter information; When the total demand load is equal to the total estimated heating load, it is determined to keep the operating load of the heating unit unchanged within a target period.

3. The method for adjusting the heat flow rate according to claim 1, characterized in that: In the case of determining the total demand load of multiple target users, before collecting multiple operation parameter information at the heat sources corresponding to the multiple target users, the method further includes: Obtain the pipe loss heat between the heating unit and each target user, the network heat storage, the outdoor temperature of the target family area corresponding to each target user, and the heating area of ​​the target family area; Determine the first sub-demand load of the pipe network between the heating unit and each target user based on the pipe loss heat and the network heat storage; Determine a second sub-demand load for each target user based on the outdoor temperature and the heating area; Determine a target demand load corresponding to each target user according to the first sub-demand load and the second sub-demand load; The target demand loads corresponding to the multiple target users are aggregated to obtain the total demand load.

4. The method for adjusting the heat flow rate according to claim 1, characterized in that: After adjusting the flow between the heating unit and multiple target users based on the total estimated heating load, the total demand load, and the multiple operating parameter information, the method further includes: Obtaining the adjusted target flow size of each of the multiple target users to obtain multiple target flow values; Calculating the heating load corresponding to the target flow value among the multiple target flow values ​​to obtain multiple target heating loads; Compare the target heating load and target demand load corresponding to each target user to determine whether to make secondary flow adjustment for the current target user.

5. The method for adjusting the heat flow rate according to claim 4, characterized in that: Compare the target heating load and target demand load corresponding to each target user to determine whether to make secondary flow adjustments for the current target user, including: When the target heating load is greater than or equal to the target demand load, the current target user is identified as a stable heating user, and it is determined that no secondary flow adjustment is performed on the current target user; In the case that the target heating load is less than the target demand load, the current target user is identified as an unstable heating user, and it is determined to perform a secondary flow adjustment on the current target user.

6. The method for adjusting the heat flow rate according to claim 5, characterized in that: After identifying the current target user as a non-stable heating user and determining to perform secondary flow adjustment on the current target user, the method further includes: Sending adjustment prompt information to the management object controlling the heating unit, wherein the adjustment prompt information is used to carry the location information of the target user to be adjusted for secondary flow, multiple real-time operating parameter information at the heat source, and information of the heating machine connected thereto; Record the processing result of the management object on the adjustment prompt information.

7. A device for adjusting heat flow, characterized in that: include: A determination module, used to determine the estimated heating load of each individual heating machine through a pre-trained load prediction model, and determine the total estimated heating load of the heating unit based on the estimated heating load; wherein the pre-trained load prediction model is a model for predicting the output load of the heating machine trained by machine learning or big data analysis technology using historical load data of each individual heating machine; A collection module, configured to collect a plurality of operating parameter information at heat sources corresponding to the plurality of target users when the total demand load of the plurality of target users is determined; An adjustment module is used to adjust the flow between the heating unit and multiple target users based on the total estimated heating load, the total demand load, and the multiple operating parameter information.

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.

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