A heat supply network control method, a heat supply system, a control device, and a medium

Abstract

The application discloses a heat supply pipe network control method, a heat supply system, a control device and a medium, and relates to the technical field of heat supply pipe network control. The method comprises the following steps: acquiring pipe network structure information, wherein the pipe network structure information comprises the pipe connection relationship of a steam heat supply pipe network, the positions of various connection nodes in the steam heat supply pipe network, and the pipe parameters between the various connection nodes; acquiring fluid detection information; calculating the fluid characteristic parameters of at least part of the positions in the steam heat supply pipe network based on a fluid dynamic model according to the fluid detection information and the pipe network structure information, and formulating a control strategy according to the fluid characteristic parameters; estimating the fluid performance change parameters according to the control strategy based on the fluid dynamic model, and comparing the fluid performance change parameters with fluid performance threshold values; and if the fluid performance change parameters meet the fluid performance threshold values, outputting the control strategy to an operation module of the steam heat supply pipe network to execute actions. The design improves the control rationality, operation efficiency and safety of the steam heat supply pipe network.

Description

Technical Field

[0001] This invention relates to the field of computer control technology for heating pipeline networks, and particularly to a heating pipeline network control method, heating system, control device, and medium. Background Technology

[0002] Steam heating networks are a widely used method of heat energy transmission in industry, civil use, and agriculture. They utilize steam as a heat carrier, delivering heat from boilers or other heat sources to users through pipelines. Steam heating networks offer advantages such as high heat transfer efficiency, controllable temperature, and energy conservation and environmental friendliness. However, they also have some drawbacks, such as complex network structure, variable operating parameters, equipment aging, and heat loss. These issues can affect the network's operational efficiency and safety.

[0003] Existing steam heating control systems all involve installing sensors at various locations in the steam heating network to detect various parameters. The sensor values ​​are then manually judged, and when the sensor values ​​exceed preset thresholds, human experience is used to provide corresponding control strategies. This method is inefficient, has poor control effects, and is prone to errors that can damage the steam heating network. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a heating network control method, as well as a heating system, control device, and medium, to improve the control rationality, operational efficiency, and safety of steam heating networks.

[0005] A heating network control method according to a first aspect of the present invention includes: acquiring network structure information, wherein the network structure information includes the pipe connection relationship of the steam heating network, the location of each connection node in the steam heating network, and the pipe parameters between each connection node; acquiring fluid detection information, wherein the fluid detection sensing information is data detected by each sensor installed in the steam heating network; calculating fluid characteristic parameters at at least some locations in the steam heating network based on the fluid detection information and the network structure information using a fluid dynamic model, and formulating a control strategy based on the fluid characteristic parameters; predicting fluid performance change parameters based on the fluid dynamic model according to the control strategy, and comparing the fluid performance change parameters with a fluid performance threshold; if the fluid performance change parameters meet the fluid performance threshold, then outputting the control strategy to the operation module of the steam heating network for execution.

[0006] A heating network control method according to an embodiment of the present invention has at least the following beneficial effects:

[0007] This invention provides a heating network control method. Knowing the network structure information, the method correlates the data detected by various sensors based on pipe connections and the locations of connection nodes. By combining the pipe parameters between connection nodes, fluid characteristic parameters at at least some locations within the steam heating network can be calculated. A control strategy is then formulated, and the fluid performance changes resulting from the control strategy are predicted. By comparing fluid performance thresholds, the applicability of the control strategy to the steam heating network is determined, and the appropriate control strategy is selected to control the operating module to execute actions. This design improves the control rationality, operational efficiency, and safety of the steam heating network.

[0008] According to some embodiments of the present invention, in the process of estimating fluid performance change parameters based on the fluid dynamic model according to the control strategy and comparing the fluid performance change parameters with the fluid performance threshold, if the fluid performance change parameters do not meet the fluid performance threshold, the control strategy is re-formulated based on the fluid characteristic parameters, and the process of estimating fluid performance change parameters based on the fluid dynamic model according to the control strategy and comparing the fluid performance change parameters with the fluid performance threshold is executed again until the fluid performance change parameters meet the fluid performance threshold.

[0009] According to some embodiments of the present invention, the step of calculating fluid characteristic parameters at at least some locations in the steam heating pipeline network based on fluid detection information and pipeline structure information using a fluid dynamic model, and formulating a control strategy based on the fluid characteristic parameters, includes: the fluid dynamic model including a hydraulic dynamic model and a thermal dynamic model, and the fluid detection information including the flow rate G of each pipe segment. k The inlet temperature T of pipe section k fk The external temperature T of pipe section k 0k The pipeline network structure information includes the friction coefficient λ of pipe segment k. k The average density ρ of pipe segment k k The diameter D of pipe section k k The length L of pipe segment k k The average isobaric specific heat capacity C of pipe section k pk The overall heat transfer coefficient K of pipe section k k The density ρ of node i i The gas constant R of the vapor and the temperature T of node i i The hydraulic dynamic model calculates the pressure drop Δp of each pipe segment k based on fluid detection information and pipe network structure information. k and the water pressure p at each node i The thermodynamic model calculates the temperature drop ΔT in pipe segment k based on fluid detection information and pipe network structure information. k The pressure drop Δp in each pipe section k kWater pressure p at each node i And the temperature drop ΔT in pipe section k k As a fluid characteristic parameter; based on the pressure drop Δp of each pipe section k. k Water pressure p at each node i And the temperature drop ΔT in pipe section k k Control strategies are developed based on neural network models.

[0010] According to some embodiments of the present invention, the hydraulic dynamic model includes a pipe section pressure drop model:

[0011]

[0012] The thermodynamic model includes a pipe section temperature drop model:

[0013]

[0014] The hydraulic dynamic model also includes a nodal water pressure model:

[0015] p i =ρ i RT i .

[0016] According to some embodiments of the present invention, the step of estimating fluid performance change parameters based on a fluid dynamic model according to a control strategy and comparing the fluid performance change parameters with a fluid performance threshold includes: estimating the flow rate G of each pipe segment after the operation module is controlled by the control strategy according to the control strategy. k and the inlet temperature T of pipe section k fk Based on the estimated flow rate G of each pipe section k and the inlet temperature T of pipe section k fk Calculate the pressure drop Δp for each pipe section k. k Water pressure p at each node i And the temperature drop ΔT in pipe section k k The change in fluid performance is used as a parameter for fluid performance change; the fluid performance change parameter is compared with the fluid performance threshold for judgment.

[0017] According to some embodiments of the present invention, the process of formulating a control strategy based on fluid characteristic parameters includes: formulating multiple control strategies based on a neural network model according to the fluid characteristic parameters; and in the process of predicting fluid performance change parameters based on the control strategies and the fluid performance change parameters are compared with fluid performance thresholds, the process includes: each control strategy predicts a corresponding fluid performance change parameter based on the fluid dynamic model, and each fluid performance change parameter is compared with a fluid performance threshold; if multiple fluid performance change parameters meet the fluid performance threshold, the control strategy corresponding to the fluid performance change parameter with the largest difference exceeding the fluid performance threshold is selected to control the operating module.

[0018] According to some embodiments of the present invention, the control strategy includes valve opening and closing control signals, pump power control signals, and heating element power control signals at at least some nodes.

[0019] A heating system according to a second aspect of the present invention includes: a steam heating network including multiple connection nodes and multiple pipes, wherein adjacent pipes are connected through the connection nodes; an operation module disposed in the steam heating network, the operation module being used to supply steam to the steam heating network, provide heat to the steam, and regulate the flow rate of the steam; multiple sensors disposed in the steam heating network, the sensors being used to detect fluid detection information; and a control module connected to the operation module and the sensors respectively to execute a heating network control method including any of the above embodiments.

[0020] The heating system according to embodiments of the present invention has at least the following beneficial effects:

[0021] The heating system of the present invention uses the heating network control method disclosed in any of the above embodiments to control the operation module of the steam heating network. This design improves the control rationality, operation efficiency and safety of the steam heating network.

[0022] According to a third aspect of the present invention, a control device includes: one or more memories; one or more processors, configured to execute one or more computer programs stored in the one or more memories, and further configured to execute a heating network control method disclosed in any of the above embodiments.

[0023] A computer-readable storage medium according to a fourth aspect of the present invention includes instructions that, when executed on a computer, cause the computer to perform a heating network control method disclosed in any of the above embodiments.

[0024] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0025] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0026] Figure 1 This is a schematic diagram of the heating system of the present invention, according to one embodiment.

[0027] Figure 2 This is a flowchart of one embodiment of the heating network control method of the present invention;

[0028] Figure 3 This is a schematic diagram of the control device of the present invention in one embodiment.

[0029] Figure label:

[0030] Steam heating network 100; operation module 200; sensor 300; control module 400; processor 610; memory 620; input / output interface 630; communication interface 640; bus 650. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0032] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0034] This application is applied to the steam heating pipeline network 100, such as Figure 1As shown, in the steam heating network 100, the steam heating network 100 includes multiple connection nodes, multiple pipes, an operation module 200, multiple sensors 300, and a control module 400. Adjacent pipes are connected through the connection nodes. The operation module 200 includes a steam output source, an air pump, etc. The operation module 200 is used to supply steam to the steam heating network 100, provide heat to the supplied steam, and regulate the flow rate of the supplied steam. The sensors 300 include a temperature sensor 300, a flow sensor 300, etc. The temperature sensor 300 is used to detect the steam temperature at a location, and the flow sensor 300 is used to detect the steam flow rate at a location. The control module 400 is connected to the sensors 300 and the operation module 200 respectively to execute the heating network control method.

[0035] Specifically, the sensors include multiple (201, 202, ... 20m) distributed at various nodes of the steam heating pipeline network. Each sensor may include a temperature sensor, a pressure sensor, a flow sensor, a battery, a wireless communication module, and a microcontroller.

[0036] like Figure 1 , 2 As shown, a heating network control method according to a first aspect embodiment of the present invention includes:

[0037] S510. Obtain pipeline network structure information, including the pipeline connection relationship of the steam heating pipeline network, the location of each connection node in the steam heating pipeline network, and the pipeline parameters between each connection node.

[0038] S520. Obtain fluid detection information, wherein the fluid detection sensing information is data detected by various sensors installed in the steam heating pipeline network;

[0039] S530. Based on the fluid detection information and pipeline structure information, calculate the fluid characteristic parameters at at least some locations in the steam heating pipeline network using a fluid dynamic model, and formulate a control strategy based on the fluid characteristic parameters.

[0040] S540. Based on the control strategy and the fluid dynamics model, the fluid performance change parameters are estimated, and the fluid performance change parameters are compared with the fluid performance threshold to make a judgment.

[0041] S550. If the fluid performance change parameters meet the fluid performance threshold, the control strategy will be output to the operation module of the steam heating network for execution.

[0042] The pipeline connection relationship and the location of each connection node in the steam heating pipeline network include: dividing the pipeline network into several nodes and pipe segments, and setting geometric parameters such as node number, pipe segment number, pipe segment length, and pipe segment diameter for each node and pipe segment.

[0043] Since steam flows in the pipes, dynamic fluid characteristic parameters in the steam heating network can be calculated based on fluid detection information and pipeline structure information using a fluid dynamic model.

[0044] The fluid performance threshold can be a value set by the staff. The operation of the module is controlled by the control strategy to change the fluid performance in the steam heating network. Only when the fluid performance change parameter exceeds the fluid performance threshold can the user's requirements be met.

[0045] This invention provides a heating network control method. Knowing the network structure information, the method correlates the data detected by various sensors based on pipe connections and the locations of connection nodes. By combining the pipe parameters between connection nodes, fluid characteristic parameters at at least some locations within the steam heating network can be calculated. A control strategy is then formulated, and the fluid performance changes resulting from the control strategy are predicted. By comparing fluid performance thresholds, the applicability of the control strategy to the steam heating network is determined, and the appropriate control strategy is selected to control the operating module to execute actions. This design improves the control rationality, operational efficiency, and safety of the steam heating network.

[0046] In some embodiments of the present invention, in the process of estimating fluid performance change parameters based on the fluid dynamic model according to the control strategy and comparing the fluid performance change parameters with the fluid performance threshold, if the fluid performance change parameters do not meet the fluid performance threshold, the control strategy is re-formulated based on the fluid characteristic parameters, and the process of estimating fluid performance change parameters based on the fluid dynamic model according to the control strategy and comparing the fluid performance change parameters with the fluid performance threshold is executed again until the fluid performance change parameters meet the fluid performance threshold.

[0047] This means that if the control strategy formulated for the fluid characteristic parameters fails to meet the fluid performance threshold, steps S530 and S540 are repeated until the fluid performance change parameters can meet the fluid performance threshold. This design automatically generates a control strategy and controls the operating module.

[0048] In some embodiments of the present invention, the step of calculating fluid characteristic parameters at at least some locations in the steam heating pipeline network based on fluid detection information and pipeline structure information using a fluid dynamic model, and formulating a control strategy based on the fluid characteristic parameters, includes:

[0049] The fluid dynamic model includes a hydraulic dynamic model and a thermodynamic dynamic model, and the fluid detection information includes the flow rate G of each pipe section. k The inlet temperature T of pipe section k fk The external temperature T of pipe section k 0kThe pipeline network structure information includes the friction coefficient λ of pipe segment k. k The average density ρ of pipe segment k k The diameter D of pipe section k k The length L of pipe segment k k The average isobaric specific heat capacity C of pipe section k pk The overall heat transfer coefficient K of pipe section k k The density ρ of node i i The gas constant R of the vapor and the temperature T of node i i ;

[0050] The hydraulic dynamic model calculates the pressure drop Δp of each pipe segment k based on fluid detection information and pipe network structure information. k and the water pressure p at each node i The thermodynamic model calculates the temperature drop ΔT in pipe segment k based on fluid detection information and pipe network structure information. k The pressure drop Δp in each pipe section k k Water pressure p at each node i And the temperature drop ΔT in pipe section k k As a fluid characteristic parameter;

[0051] Based on the pressure drop Δp of each pipe section k k Water pressure p at each node i And the temperature drop ΔT in pipe section k k Control strategies are developed based on neural network models.

[0052] This design employs an adaptive learning algorithm that runs a neural network model to formulate control strategies. The neural network model is trained based on historical data collected by sensors and experience from previous control strategies. Furthermore, the neural network model is dynamically adjusted and optimized using real-time fluid detection information collected by sensors to address simulation accuracy issues caused by equipment degradation and performance aging in the heating pipeline network.

[0053] Specifically, the mathematical expression for a deep neural network model can be represented as:

[0054] y = f(x; W, b);

[0055] Where x represents input data, such as the pressure, temperature, and flow rate of the pipeline network; y represents output data, such as parameters that the model needs to adjust and optimize, such as the friction coefficient, heat loss coefficient, and specific heat capacity at constant pressure of the pipe section; W and b are the weights and biases of the network; and f is the function of the network, consisting of multiple layers, each layer consisting of a linear transformation and a nonlinear activation function. For example, for a deep neural network with L hidden layers, f can be expressed as:

[0056] f(x; W, b) = W L σL (W L-1 σ L-1 (...σ1(W1x+b1)...)+b L-1 )+b L ;

[0057] Among them, W L and b L These are the weights and biases of layer I; σ L It is the activation function of the I-th layer, such as the ReLU function, sigmoid function, tanh function, etc.

[0058] To train a deep neural network model, an objective function and an optimization algorithm need to be defined. The objective function is a metric that measures the consistency between the model and reality, such as minimizing the mean squared error between the model output and the sensor data.

[0059]

[0060] Where N is the size of the dataset; x i and y i It represents the input and output of the i-th data point.

[0061] Optimization algorithms are methods used to update and optimize model parameters, such as gradient descent, stochastic gradient descent, and Adam. The basic idea of ​​gradient descent is to update the parameters along the negative gradient of the objective function to reach a local minimum. The update formula for gradient descent is:

[0062]

[0063]

[0064] Where α is the learning rate; and These are the partial derivatives of the objective function with respect to the weights and biases. To calculate these partial derivatives, the backpropagation algorithm can be used, which utilizes the chain rule to calculate the gradient layer by layer from the output layer to the input layer.

[0065] Specifically, the hydraulic dynamic model includes a pipe section pressure drop model:

[0066]

[0067] The thermodynamic model includes a pipe section temperature drop model:

[0068]

[0069] The hydraulic dynamic model also includes a nodal water pressure model:

[0070] p i=ρ i RT i .

[0071] In addition, the thermal dynamics model can also include a pipe temperature flow cytometer model. For any pipe segment k, the pipe temperature flow cytometer model is as follows:

[0072] q 1k L k (1+η k ) = 1000G k C pk ΔT k ;

[0073] Where, q 1k L is the heat dissipation per unit length of pipe segment k. k It is the correction factor for local heat dissipation loss.

[0074] Taking one type of steam heating network as an example, the steam heating network consists of one boiler, four users, and six pipe sections. The parameters are shown in the table below:

[0075]

[0076]

[0077]

[0078] In some embodiments of the present invention, the step of estimating fluid performance change parameters based on a fluid dynamic model according to a control strategy and comparing the fluid performance change parameters with a fluid performance threshold includes: estimating the flow rate G of each pipe segment after the operation module is controlled by the control strategy according to the control strategy. k and the inlet temperature T of pipe section k fk Based on the estimated flow rate G of each pipe section k and the inlet temperature T of pipe section k fk Calculate the pressure drop Δp for each pipe section k. k Water pressure p at each node i And the temperature drop ΔT in pipe section k k The change in fluid performance is used as a parameter for fluid performance change; the fluid performance change parameter is compared with the fluid performance threshold for judgment.

[0079] In some embodiments of the present invention, the step of formulating a control strategy based on fluid characteristic parameters includes: formulating multiple control strategies based on a neural network model according to the fluid characteristic parameters;

[0080] The process of predicting fluid performance change parameters based on the fluid dynamic model according to the control strategy and comparing the fluid performance change parameters with the fluid performance threshold includes: each control strategy predicts the corresponding fluid performance change parameter based on the fluid dynamic model, and each fluid performance change parameter is compared with the fluid performance threshold; if multiple fluid performance change parameters meet the fluid performance threshold, the control strategy corresponding to the fluid performance change parameter with the largest difference exceeding the fluid performance threshold is selected to control the operating module.

[0081] This is equivalent to formulating multiple control strategies, using each strategy to estimate the corresponding fluid performance change parameters. Each fluid performance change parameter can characterize the effect of the steam heating network after being controlled by the control strategy. Therefore, by comparing with the fluid performance threshold, selecting the control strategy corresponding to the fluid performance change parameter with the largest difference exceeding the fluid performance threshold to control the operating module can achieve better control results.

[0082] In some embodiments of the present invention, the control strategy includes valve opening and closing control signals, pump power control signals, and heating element power control signals at at least some nodes.

[0083] A heating system according to a second aspect embodiment of the present invention, such as Figure 1 As shown, the system includes: a steam heating network 100, comprising multiple connection nodes and multiple pipes, with adjacent pipes connected through the connection nodes; an operation module 200, disposed within the steam heating network 100, used to supply steam to the steam heating network 100, provide heat to the supplied steam, and regulate the flow rate of the supplied steam; multiple sensors 300, disposed within the steam heating network 100, used to detect fluid detection information; and a control module 400, connected to the operation module 200 and the sensors 300 respectively to execute the heating network control method disclosed in any of the above embodiments. This invention's heating system applies the heating network control method disclosed in any of the above embodiments to control the operation module of the steam heating network. This design improves the control rationality, operating efficiency, and safety of the steam heating network.

[0084] According to a third aspect of the present invention, the control device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the heating network control method disclosed in any of the above embodiments.

[0085] The control device can be any intelligent terminal, including a central computer, a remote equipment terminal computer, or any other intelligent terminal.

[0086] like Figure 3 As shown, Figure 3The hardware structure of a control device according to another embodiment is also illustrated. The control device includes:

[0087] The processor 610 can be implemented using a general-purpose central processing unit (CPU), a microprocessor 610, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0088] The memory 620 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 620 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 620 and is called and executed by the processor 610 to execute the heating network control method of the embodiments of this application.

[0089] The input / output interface 630 is used to realize information input and output;

[0090] The communication interface 640 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0091] Bus 650 transmits information between various components of the device (e.g., processor 610, memory 620, input / output interface 630, and communication interface 640);

[0092] The processor 610, memory 620, input / output interface 630 and communication interface 640 are connected to each other within the device via bus 650.

[0093] According to a fourth aspect of the present invention, a computer-readable storage medium stores a computer program, characterized in that, when executed by a processor, the computer program implements the heating network control method disclosed in any of the above embodiments.

[0094] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0095] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0096] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0097] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0098] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0099] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0100] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A heating network control method, applied to a steam heating network system, characterized in that, include: Obtain pipeline network structure information, which includes the pipeline connection relationship of the steam heating pipeline network, the location of each connection node in the steam heating pipeline network, and the pipeline parameters between each connection node; Obtain fluid detection information, which is data detected by various sensors installed in the steam heating pipeline network; Based on fluid detection information and pipeline structure information, fluid characteristic parameters at at least some locations in the steam heating pipeline are calculated using a fluid dynamic model, and control strategies are formulated based on these fluid characteristic parameters. Based on the control strategy, fluid performance change parameters are predicted using the fluid dynamics model, and the fluid performance change parameters are compared with fluid performance thresholds for judgment. If the fluid performance change parameters meet the fluid performance threshold, the control strategy will be output to the operation module of the steam heating network for execution. The process of calculating fluid characteristic parameters at at least some locations in the steam heating pipeline network based on fluid detection information and pipeline structure information using a fluid dynamic model, and formulating a control strategy based on the fluid characteristic parameters, includes: The fluid dynamic model includes a hydraulic dynamic model and a thermodynamic dynamic model, and the fluid detection information includes the flow rate of each pipe section. Inlet temperature of pipe section k The external temperature of pipe section k The pipeline network structure information includes the friction coefficient of pipe segment k. Average density of pipe segment k Diameter of pipe section k Length of pipe segment k Average isobaric specific heat capacity of pipe section k The overall heat transfer coefficient of pipe section k Density of node i Gas constant of steam and the temperature of node i ; The hydraulic dynamic model calculates the pressure drop of each pipe segment k based on fluid detection information and pipeline structure information. and the water pressure at each node The thermodynamic model calculates the temperature drop of pipe segment k based on fluid detection information and pipeline structure information. Pressure drop in each pipe section k Water pressure at each node and the temperature drop of pipe section k As a fluid characteristic parameter; Based on the pressure drop of each pipe section k Water pressure at each node and the temperature drop of pipe section k Multiple control strategies are developed based on neural network models; The process of predicting fluid performance change parameters based on the fluid dynamics model according to the control strategy and comparing the fluid performance change parameters with fluid performance thresholds includes: Based on the control strategy, the flow rate of each pipe segment after the operating module is controlled by the control strategy is estimated. and the inlet temperature of pipe section k , Based on the estimated flow rates of each pipe section and the inlet temperature of pipe section k Calculate the pressure drop of each pipe section k. Water pressure at each node and the temperature drop of pipe section k The change in fluid performance is used as a parameter to measure the change in fluid properties. Judgment is made by comparing fluid performance change parameters with fluid performance thresholds, including: Each control strategy predicts the corresponding fluid performance change parameters based on the fluid dynamics model, and each fluid performance change parameter is compared with the fluid performance threshold for judgment. If multiple fluid performance change parameters meet the fluid performance threshold, the control strategy corresponding to the fluid performance change parameter with the largest difference exceeding the fluid performance threshold is selected to control the operating module.

2. The heating network control method according to claim 1, characterized in that, In the process of estimating fluid performance change parameters based on the fluid dynamic model according to the control strategy and comparing the fluid performance change parameters with the fluid performance threshold, if the fluid performance change parameters do not meet the fluid performance threshold, the control strategy is re-formulated based on the fluid characteristic parameters, and the process of estimating fluid performance change parameters based on the fluid dynamic model according to the control strategy and comparing the fluid performance change parameters with the fluid performance threshold is repeated until the fluid performance change parameters meet the fluid performance threshold.

3. The heating network control method according to claim 1, characterized in that, The hydraulic dynamic model includes a pipe section pressure drop model: ； The thermodynamic model includes a pipe section temperature drop model: ； The hydraulic dynamic model also includes a nodal water pressure model: 。 4. The heating network control method according to claim 1, characterized in that, The control strategy includes valve opening and closing control signals, pump power control signals, and heating element power control signals at at least some nodes.

5. A heating system, characterized in that, include: The steam heating network includes multiple connection nodes and multiple pipes, with adjacent pipes connected through the connection nodes; An operating module is installed in the steam heating network. The operating module is used to supply steam to the steam heating network, provide heat to the steam, and regulate the flow rate of the steam. Multiple sensors are installed in the steam heating network, and the sensors are used to detect fluid detection information; The control module is connected to the operating module and the sensor respectively to execute the heating network control method as described in any one of claims 1-4.

6. A control device, characterized in that, include: One or more memory units; One or more processors are configured to execute one or more computer programs stored in the one or more memories, and also to execute a heating network control method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, Includes instructions that, when executed on a computer, cause the computer to perform a heating network control method as described in any one of claims 1-4.

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