A heat balance regulation method and device for a heating system and a medium

Abstract

This application discloses a method, equipment, and medium for heat balance control of a heating system, relating to the field of automatic control technology for centralized heating systems. The method includes: collecting heating operation data, heat exchange operation data from each heat exchange station, and room temperature data from user nodes based on preset time intervals to determine the current heating condition of the heating system; inputting the heat exchange operation data into a preset extended transfer function model, and calculating the core parameter set of each heat exchange station based on the identification frequency corresponding to the current heating condition; calculating the control parameters of the primary-side valves at each heat exchange station node to generate opening adjustment amounts; performing hierarchical coordination constraint verification on the control parameters and opening adjustment amounts to generate opening control commands for the primary-side valves; executing the opening control commands to adjust the opening of the corresponding primary-side valves, and collecting updated data after adjustment. Under the premise of ensuring that the heating quality for users continuously meets standards, the method achieves autonomous optimization and long-term stability of the heating system operation.

Description

Technical Field

[0001] This application relates to the field of automatic control technology for centralized heating systems, specifically to a method, equipment, and medium for regulating the heat balance of a heating system. Background Technology

[0002] Centralized heating systems have become a core component of urban infrastructure in northern cities. Tree-like heating networks, due to their simple structure and low deployment costs, are widely used in various centralized heating network structures. A tree-like heating network typically uses a single heat source as its core, forming a tree-like network through main pipes and branch pipes, connecting multiple heat exchange stations. Some heat exchange stations also divide the secondary side into high-zone and low-zone units according to building height, achieving precise heating coverage for users in different areas and at different heights. Therefore, in actual operation, the heat balance regulation of the tree-like heating network in the heating system is not only a core requirement for ensuring stable indoor temperatures for users, but also a key link in improving the energy utilization efficiency of the network and ensuring the safe and stable operation of the heat source units.

[0003] Traditional methods for heat balance control in heating systems centered on tree-like heat networks typically involve two approaches: first, constructing a hydraulic-thermal coupling model based on mechanistic parameters such as pipe length, material, insulation layer parameters, and network layout to calculate theoretical heat distribution requirements and guide valve regulation; and second, relying on the combined effects of proportional, integral, and differential parameters to provide feedback regulation of key parameters such as network temperature and flow rate.

[0004] However, these methods generally rely on fixed mechanistic parameters and rigid models, lacking the ability to describe the dynamic characteristics of the heating network during operation, such as thermal delay, thermal inertia, and network heat loss. Their dynamic adaptability is insufficient, making it difficult to achieve online identification of dynamic characteristics and real-time updates of model parameters. This leads to a gradual widening of the deviation between the model and actual operating conditions, and a continuous decline in control accuracy. Furthermore, since the secondary valves of most heat exchange stations are often in a fixed or manually adjustable state, it is difficult to achieve precise heat distribution across the entire network solely through primary-side control. This easily leads to hydraulic imbalance in the network, resulting in uneven heating and cooling, with overheating at near-end users and undercooling at far-end users. Simultaneously, the operating characteristics of the heat source units are often neglected during control, resulting in insufficient safety assurance and a lack of attention to fluctuations in heat source outlet flow and temperature. Frequent or large-scale adjustments can easily cause sudden load changes in the heat source units, affecting their safe and stable operation. Summary of the Invention

[0005] To address the aforementioned problems, this application proposes a method for regulating the heat balance of a heating system, comprising:

[0006] Based on a preset time interval, heating operation data of heat source stations, heat exchange operation data of each heat exchange station, and room temperature data of user nodes associated with the heat exchange stations are collected in the heating system. Based on the heating operation data, the current heating condition of the heating system is determined.

[0007] The heat exchange operation data is input into a preset extended transfer function model. Based on the identification frequency corresponding to the current heating condition, the core parameter set of each heat exchange station is calculated, and the core parameter set is corrected by the particle swarm optimization algorithm.

[0008] Based on the corrected core parameter set, the control parameters of the primary side valves of each heat exchange station node are calculated, and the opening adjustment amount of the primary side valves is generated based on the temperature deviation between the room temperature data and the preset standard room temperature data.

[0009] Based on the tree-like heating network topology of the heating system, hierarchical coordination constraint verification is performed on the control parameters and the opening adjustment amount to generate the opening control command of the primary side valve.

[0010] The opening control command is executed to adjust the opening of the corresponding primary valve, and the updated data of the heating system after adjustment is collected to trigger the iterative update of the core parameter set and the control parameters.

[0011] On the other hand, this application also proposes a heat balance control device for a heating system, comprising:

[0012] At least one processor; and,

[0013] A memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform a heat balance control method for a heating system as described in the above example.

[0015] On the other hand, this application also proposes a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured as: a heat balance control method for a heating system as described in the above example.

[0016] The heat balance control method for a heating system proposed in this application can bring the following beneficial effects:

[0017] Through data-driven system identification and optimization, the real-time characteristics of each heat exchange station can be accurately captured and dynamically tracked, providing a highly realistic and time-varying model foundation for controller design. Simultaneously, the structural constraints of the tree-like pipe network are transformed into hierarchical coordination verification rules and deeply integrated with heat source stability requirements. This ensures that while pursuing precise local room temperature regulation, the primary-side valves strictly guarantee the overall hydraulic balance of the entire network and the overall stability of the heat source outlet conditions, fundamentally solving the industry challenge of balancing hydraulic imbalance and stability control.

[0018] Furthermore, by directly applying it to most existing tree-like heating networks, it significantly reduces the threshold and cost of upgrading and renovation. It automatically adjusts the parameter learning rhythm according to the operating conditions and intelligently triggers iterative updates based on the control effect. Thus, it has a strong adaptability to different seasons, different weather conditions, and the aging changes of the pipeline itself, improving the universality, adaptability, and long-term reliability of the intelligent control of the heating system. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 This is a schematic flowchart of a heat balance control method for a heating system according to an embodiment of this application;

[0021] Figure 2 This is a schematic diagram of a heat balance control device for a heating system in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0024] like Figure 1 As shown in the figure, this application provides a method for regulating the heat balance of a heating system, including:

[0025] S101. Based on a preset time interval, collect heating operation data of heat source stations, heat exchange operation data of each heat exchange station, and room temperature data of user nodes associated with the heat exchange stations in the heating system. Based on the heating operation data, determine the current heating condition of the heating system.

[0026] Specifically, a sensor network deployed in the heat source, primary pipeline, each heat exchange station, and representative user rooms synchronously collects three types of key operational data, with the collection action triggered once at a preset time interval.

[0027] Collect heating operation data from heat source sites, specifically the total circulation flow, total supply water temperature, and total return water temperature at the heat source outlet. This data is then processed using formulas. Calculate the total heat supply of the heat source station in real time, where, For total heat supply, The specific heat capacity of water, The density of water, The total circulation flow rate of the heat source. The total water supply temperature of the heat source. This is the total return water temperature of the heat source.

[0028] Heat exchange operation data of each heat exchange station is collected. For each heat exchange station, the real-time opening degree of its primary side regulating valve, the temperature and flow rate on the primary side supply water pipe, and the temperature on the primary side return water pipe are collected. These data directly represent the real-time status and regulation intention of heat transfer from the main pipeline network to each branch station.

[0029] Collect room temperature data from user nodes. Install room temperature acquisition devices at typical locations associated with the heating area served by each heat exchange station and report indoor ambient temperature data regularly.

[0030] It should be noted that the preset time interval for synchronous data acquisition ensures that data from all spatial locations have a unified time reference. The reason for calculating the total heat supply of the heat source instead of directly using a single parameter such as flow rate or temperature is that heat supply is a direct reflection of load demand, and by integrating flow rate and temperature difference information, it can more comprehensively characterize the output energy of the heat source.

[0031] After obtaining heating operation data, the current macroscopic operating condition of the heating system is determined based on the fluctuation characteristics of the total heat supply from the heat source. In specific implementation, historical data of the total heat supply from the heat source collected and calculated within the most recent first preset time period are read, arranged in chronological order to form a heat supply sequence, the standard deviation of the heat supply sequence is calculated to obtain the fluctuation amplitude value, and the fluctuation amplitude value is compared with preset two-level thresholds. If the fluctuation amplitude is lower than the first preset heating threshold, it indicates that the heat source output changes very little during the observation period, and the heating system is in a highly stable operating state. Based on this, the heating system is currently in a steady-state heating condition. If the fluctuation amplitude is not lower than the first preset heating threshold but lower than the second preset heating threshold, it indicates that there is a continuous but controllable change in the heat source output, and the heating system is in a state of load adjustment or affected by slowly changing factors. Based on this, the system is in a dynamic heating condition. If the fluctuation amplitude is not lower than the second preset heating threshold, it indicates that the heat source output has changed drastically and rapidly, possibly due to a sudden change in external weather conditions, the start-up and shutdown of a large user cluster, or other major disturbances. Based on this, the heating system is in a sudden change condition.

[0032] S102. Input the heat exchange operation data into a preset extended transfer function model, calculate the core parameter set of each heat exchange station based on the identification frequency corresponding to the current heating condition, and correct the core parameter set through a particle swarm optimization algorithm.

[0033] Specifically, the activation frequency of parameter identification is adaptively adjusted based on the current heating conditions. Based on historical operating data, the system identification algorithm fits the core parameter set representing the dynamic characteristics of each heat exchange station to the preset extended transfer function model. The particle swarm optimization algorithm is then introduced to correct the physical rationality and smoothness of the parameter set.

[0034] In order to describe the dynamic relationship between changes in the opening degree of the primary valve and changes in the heat output of the heat exchange station, an extended transfer function model is preset for each heat exchange station. , .in, The gain coefficient represents the proportional relationship between the steady-state heat change caused by a unit change in valve opening. Pure thermal delay indicates the lag time between valve actuation and the onset of thermal response; The inertial time constant reflects the rate of change of heat in the system or the magnitude of inertia. is the heat loss factor, used to dynamically characterize the proportion of heat loss in the pipeline network during heat transfer; s is the Laplace transform variable, used in the transfer function of control theory to transform the dynamic system in the time domain to the complex frequency domain for analysis.

[0035] It should be noted that the function of 's' is: to interact with... Combine, This item precisely describes the dynamic delay process of the valve's response to room temperature adjustment; and Combination: The terms describe the slowly varying characteristics of the system; and Combine, The term describes the dynamic characteristics of heat loss. Simply put, 's' is the bridge connecting the parameter identification results and the dynamic response of the heating system, used to describe the dynamic changes in the heating system's delay, inertia, and heat loss, thereby supporting the dynamic control requirements of PID self-tuning. Furthermore, traditional transfer function models typically only include gain, delay, and inertia; this invention explicitly introduces a heat loss factor term multiplied by 's' into the model. This allows the model to dynamically describe the characteristics of heat loss fluctuating with changes in the state of the heating system, rather than a fixed proportion, thus improving the accuracy and physical rationality of the model when describing the controlled objects in the heating network.

[0036] Furthermore, the identification frequency is adaptively adjusted based on the current heating conditions of the heating system. If the current heating condition is steady-state, the dynamic characteristics of the heating system change slowly, so a lower first frequency is used for identification to avoid unnecessary computational overhead. If the current heating condition is dynamic, the characteristics of the heating system are constantly changing, so a higher second frequency is used to improve the model's ability to track changes in the real system. If the current heating condition is abrupt, the characteristics of the heating system may change rapidly and significantly, so the highest frequency is triggered to ensure that such abrupt changes can be quickly captured and adapted to.

[0037] When the preset identification time point for the current operating condition is reached, for each heat exchange station i, a data sequence within a predetermined time window is extracted from the historical data of heat exchange operation, including the valve opening change sequence. Supply and return water temperature sequence Supply and return water flow sequence and the actual heat value sequence of the heat exchange station. The valve opening change sequence serves as the input excitation sequence for the model, while the heat value sequence serves as the desired output sequence.

[0038] Extract Input to the preset extended transfer function model In this process, the predicted heat value sequence is obtained through numerical calculation. Predicting heat value sequences using models Compared with the actual heat value sequence Minimizing the error is the optimization objective, and the objective function is: The least squares estimation algorithm is used to continuously reduce the objective function by iteratively adjusting the four parameters in the model. The value of . When the optimization algorithm converges, that is, when the value of is found. The algorithm stops when the set of parameters is minimized; the parameter values ​​obtained at this point are... This is the initial core parameter set of the heat exchange station.

[0039] The initial core parameter set is corrected using a particle swarm optimization algorithm. This algorithm takes the initial parameter set as a starting point and, within pre-defined physical constraints, searches for an optimal parameter solution that simultaneously satisfies the requirements of good fit to the current data, conforms to physical common sense, and smoothly transitions with historical parameters through simulated swarm iteration.

[0040] S103. Based on the corrected core parameter set, calculate the control parameters of the primary side valves of each heat exchange station node, and generate the opening adjustment amount of the primary side valves based on the temperature deviation between the room temperature data and the preset standard room temperature data.

[0041] Specifically, based on the revised core parameter set, the PID control parameters of the primary side valves of each heat exchange station are calculated using dedicated tuning rules. At the same time, combined with the deviation between the user's room temperature and the set target, the preliminary valve opening adjustment amount is calculated to prepare for the final safe and coordinated control.

[0042] In this approach, heat loss factor and pure heat delay are introduced as core compensation variables in the calculation. The controller parameters need to actively compensate for dynamic heat loss and response lag effects during the heat network transmission process. In specific implementation, based on the corrected heat loss factor and corrected pure heat delay, a first compensation factor, a second compensation factor, and a third compensation factor are calculated to quantify the specific impact of heat loss and delay on different PID parameter components.

[0043] The proportional gain coefficient is calculated based on the corrected inertial time constant, the corrected gain coefficient, the corrected pure thermal delay, and the first compensation factor. When the heat loss or delay is large, the calculated proportional gain coefficient will be appropriately increased through the effect of the first compensation factor, thereby enhancing the control action to overcome greater energy loss and response lag.

[0044] The integral time constant is calculated based on the corrected pure thermal delay and the second compensation factor. The logic is that when the delay time is long, the calculated integral time constant will be adjusted accordingly through the effect of the second compensation factor to prevent the integral term from accumulating too quickly before the feedback information arrives, which could lead to system overshoot and oscillation.

[0045] The differential time constant is calculated based on the corrected pure thermal delay and the third compensation factor to optimize the prediction and damping characteristics of the system.

[0046] The calculated proportional gain coefficient, integral time constant, and derivative time constant are combined to form the initial PID control parameters for the heat exchange station.

[0047] In this embodiment of the application, calculation is performed using a set of formulas. ,in, The gain coefficient is the result after particle swarm optimization correction. This is the optimized and corrected pure thermal delay. The optimized and corrected inertial time constant. This is the optimized and corrected heat loss factor. Constraints include: parameter adjustment constraints, i.e., PID parameters. , , The single adjustment range can be adjusted up or down to avoid heat source fluctuations caused by sudden parameter changes; parameter validity verification means that after the PID self-tuning receives the corrected parameters, it first verifies the physical rationality of the parameters. If the parameters are out of bounds, the calculation is rejected, the historical valid PID parameters are used and an alarm is triggered to ensure control safety; scenario adaptation optimization means that the PID parameter calculation results need to be associated with the corrected parameter characteristics.

[0048] Furthermore, to prevent unreasonable jumps in the calculated PID parameters, which could lead to drastic valve movements and threaten system stability, a safety check is performed on the initial PID control parameters. In practice, the current PID control parameters of the valves in the heat exchange station are obtained. The relative changes in the proportional gain coefficient, integral time constant, and derivative time constant of each parameter in the initial PID control parameters are calculated relative to the corresponding parameters in the current PID control parameters.

[0049] The relative change magnitude of each parameter is compared with the preset allowable change threshold for that type of parameter. If the relative change magnitude of a parameter exceeds its corresponding allowable change threshold, a limiting process is performed, correcting the parameter's adjustment value to the boundary value corresponding to that threshold. After all parameters have been verified and limited, the resulting parameter set is determined as the final control parameters applied to the primary side valves of the heat exchange station.

[0050] Furthermore, the calculation of the opening adjustment amount is performed in parallel while calculating the control parameters. In specific implementation, the temperature deviation is calculated for each heat exchange station based on the effective room temperature data of all associated user nodes, using the formula: Calculate the average room temperature. The real-time room temperature at the j-th measuring point of the i-th heat exchange station. The effective data is obtained after removing outliers below 5℃ and above 30℃ through moving average filtering.

[0051] The temperature deviation of the heat exchange station is obtained by comparing the average room temperature with the preset standard room temperature data, using the following formula: ,in, Set a default value for the secondary side room temperature (default 20℃, adjustable). This indicates the degree to which the room temperature deviates from the target value (a positive deviation indicates that the room temperature is too low and requires additional heating; a negative deviation indicates that the room temperature is too high and requires reduced heating). It should be noted that a temperature deviation greater than zero indicates that the room temperature is too low and requires increased heating; a deviation less than zero indicates that the room temperature is too high and requires reduced heating.

[0052] The valve opening adjustment amount is generated by taking the calculated temperature deviation as the input signal and, based on the final control parameters calculated for the heat exchange station, calculating a preliminary valve opening adjustment amount in real time according to the discrete calculation formula of the PID control law. The valve opening adjustment amount indicates the direction and magnitude of the valve opening change in order to eliminate the current room temperature deviation.

[0053] S104. Based on the tree-like heating network topology of the heating system, perform hierarchical coordination constraint verification on the control parameters and the opening adjustment amount, and generate the opening control command of the primary side valve.

[0054] Specifically, the pre-stored or online-identified tree-like heating network topology is read to clarify the physical connections of the heating system. This structure defines the hierarchy and connections from the heat source root node to each heat exchange station node. Based on this topology, the location of each heat exchange station in the tree network is analyzed and determined, especially its affiliation, i.e., identifying which heat exchange stations share the same upstream parent node, and grouping all child heat exchange stations belonging to the same parent node into a set. It should be noted that the hydraulic characteristics of the tree-like network dictate that any flow regulation of a child node will affect the available head and flow of its sibling nodes, and is ultimately limited by the total delivery capacity of its parent node.

[0055] For each identified parent node and its set of child nodes, calculate the total adjustment demand of the child nodes, obtain the initial opening adjustment amount of all sub-heat exchange stations within the set, sum the absolute values ​​of the adjustment amounts, and obtain the sum of absolute values. Compare the sum of absolute values ​​with the upper limit of valve adjustment capacity, where the upper limit of valve adjustment capacity is a pre-set or calculated engineering parameter corresponding to each parent node, representing the total flow or differential pressure change range that the parent node pipeline or equipment can safely withstand under this operating condition, caused by all its child nodes.

[0056] If the sum of absolute values ​​does not exceed the valve adjustment capacity limit, it means that the adjustment needs of all child nodes are within the capacity of the parent node, the verification passes, and the opening adjustment amount of each child node does not need to be modified. If the sum of absolute values ​​exceeds the valve adjustment capacity limit, it means that the adjustment demand is too large and must be coordinated and limited. Specifically, based on the proportion of the absolute value of the initial opening adjustment amount of each child node, the portion exceeding the limit is allocated and reduced. Specifically, the adjustment amount of each child node is reduced to a new value, ensuring that the sum of the absolute values ​​of the reduced adjustment amounts of all child nodes is exactly equal to the valve adjustment capacity limit of the parent node. It should be noted that during the calculation, the child node with greater demand will have a larger allocated adjustment amount, but all nodes will bear the reduction amount proportionally.

[0057] In this embodiment of the application, the modified heat loss factor is incorporated. The total unit heat load is inferred by using the limited room temperature measurement point deviation, while compensating for heat loss and heat attenuation caused by delay. The formula for inferring the total unit heat load is: ,in, The heat transfer coefficient of the heating network is calibrated through self-learning based on historical data; Let be the heating area of ​​the i-th heat exchange station; For the primary side supply and return temperature difference, For primary side heating, This is a single-sided temperature recovery; This refers to the runtime of the day.

[0058] The formula for heat load heat loss-delay linkage correction is: , The longer the thermal delay, the more heat loss accumulates, and the larger the correction factor, ensuring sufficient heat supply to the far-end long-delay branches.

[0059] Furthermore, the final adjustment amount is combined with the control parameters using the formula: This generates specific opening control commands that can be issued to the primary-side regulating valves of the heat exchange station for execution. This represents the adjustment amount of the primary network valve opening at the i-th heat exchange station. A positive value indicates valve opening for heat replenishment, while a negative value indicates valve closing for heat reduction.

[0060] It should be noted that before the initially calculated control commands are sent to the physical valves, a round of safety and coordination checks is performed on the commands based on the inherent tree-like topology of the heating system. This ensures that the simultaneous adjustment of all valves will not exceed the delivery capacity of any parent pipeline, thereby achieving precise local regulation while maintaining the hydraulic balance and heat source stability of the entire network.

[0061] S105. Execute the opening control command to adjust the opening of the corresponding primary valve and collect the updated data of the heating system after adjustment, so as to trigger the iterative update of the core parameter set and the control parameters.

[0062] Specifically, the valve opening control command is sent to the primary-side regulating valve of the corresponding heat exchange station, driving the valve to change its opening to the target position. After the valve actuates, the hydraulic and thermal states of the pipeline network need a certain amount of time to reach a new equilibrium. After the feedback delay ends, a new round of data acquisition is initiated. The acquired updated data includes: the latest heat exchange operation data of each heat exchange station, the latest room temperature data of each user node, and optionally, the latest heating operation data of the heat source station, for auxiliary judgment.

[0063] Furthermore, based on the updated data, it is determined whether the preset parameter update trigger conditions are met. The first type of trigger condition is determined based on the latest room temperature data. In specific implementation, the deviation between the average room temperature of all users or the area under the jurisdiction of each heat exchange station and the target room temperature is calculated. If this deviation continuously exceeds a preset deviation threshold and reaches a first time threshold, it is determined that the trigger condition is met, that is, the control effect is not up to standard.

[0064] The second type of triggering condition is based on the latest heat exchange operation data. By analyzing the latest data sequence, the magnitude of change in the system characteristics reflected is calculated relative to the currently recorded characteristic values ​​in the parameter database. If this magnitude of change exceeds a preset threshold, the triggering condition is determined to be met, meaning that the characteristics of the heating system have changed significantly.

[0065] If no parameter update trigger condition is met, it indicates that the current control effect is acceptable and the characteristics of the heating system have not drifted significantly. The current set of core parameters and control parameters will continue to operate, and the heating system will enter a stable closed-loop control state until the next data collection and judgment cycle.

[0066] If any parameter update trigger condition is met, the iterative update process is immediately initiated. The current updated data is used as input for the new round of learning, and the processes of dynamic identification and optimization correction of core parameters and tuning of step control parameters are re-executed. Specifically: using the latest data and according to the frequency determined by the latest operating conditions, system identification and particle swarm optimization are re-performed to generate a new set of core parameters; based on the new set of core parameters, new control parameters are calculated through tuning rules and safety checks; the old parameters in memory or database are replaced with the new parameters, completing this iterative update.

[0067] This invention, through data-driven system identification and optimization, can accurately capture and dynamically track the real-time characteristics of each heat exchange station, thus providing a highly realistic and time-varying model foundation for controller design. Simultaneously, it transforms the structural constraints of the tree-like pipe network into hierarchical coordination verification rules and deeply integrates them with heat source stability control requirements. This ensures that while pursuing precise local room temperature regulation, the primary-side valves strictly guarantee the overall hydraulic balance of the entire network and the overall stability of the heat source outlet conditions, fundamentally solving the industry problem of balancing hydraulic imbalance and stability control.

[0068] Furthermore, by directly applying it to most existing tree-like heating networks, it significantly reduces the threshold and cost of upgrading and renovation. It automatically adjusts the parameter learning rhythm according to the operating conditions and intelligently triggers iterative updates based on the control effect. Thus, it has a strong adaptability to different seasons, different weather conditions, and the aging changes of the pipeline itself, improving the universality, adaptability, and long-term reliability of the intelligent control of the heating system.

[0069] like Figure 2 As shown in the embodiments of this application, a heat balance control device for a heating system is also proposed, comprising:

[0070] At least one processor; and,

[0071] A memory communicatively connected to the at least one processor; wherein,

[0072] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform a heat balance control method for a heating system as described in any of the above embodiments.

[0073] This application also provides a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured as: a heat balance control method for a heating system as described in any of the above embodiments.

[0074] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0075] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0076] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0078] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0080] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0081] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0082] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0083] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0084] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A heat balance regulation method for a heating system, characterized by, include: Based on a preset time interval, heating operation data of heat source stations, heat exchange operation data of each heat exchange station, and room temperature data of user nodes associated with the heat exchange stations are collected in the heating system. Based on the heating operation data, the current heating condition of the heating system is determined. The heat exchange operation data is input into a preset extended transfer function model. Based on the identification frequency corresponding to the current heating condition, the core parameter set of each heat exchange station is calculated, and the core parameter set is corrected by the particle swarm optimization algorithm. The extended transfer function model explicitly introduces a heat loss factor term multiplied by the Laplace transform variable s in the transfer function, which enables dynamic description of the characteristics of heat loss fluctuating with the state of the heating system. Among them, the steady-state heating condition corresponds to the first frequency, the dynamic heating condition corresponds to the second frequency which is higher than the first frequency, and the sudden change condition corresponds to the highest frequency; the core parameter set includes the gain coefficient, pure heat delay, inertial time constant, and heat loss factor. Based on the revised core parameter set, the control parameters of the primary side valves of each heat exchange station are calculated, and the opening adjustment amount of the primary side valves is generated based on the temperature deviation between the room temperature data and the preset standard room temperature data. Based on the tree-like heating network topology of the heating system, hierarchical coordination constraint verification is performed on the control parameters and the opening adjustment amount to generate the opening control command of the primary side valve. The opening control command is executed to adjust the opening of the corresponding primary valve, and the updated data of the heating system after adjustment is collected to trigger the iterative update of the core parameter set and the control parameters.

2. The heat balance control method for a heating system according to claim 1, characterized in that, Determining the current heating status of the heating system based on the heating operation data specifically includes: Obtain the heat supply sequence from the heating operation data within a first preset time period, calculate the standard deviation of the heat supply sequence, and obtain the fluctuation amplitude value of the heat source station; If the fluctuation amplitude value is lower than the first preset heating threshold, it is determined that the heating system is currently in a steady-state heating condition. If the fluctuation amplitude value is not lower than the first preset heating threshold and is lower than the second preset heating threshold, then the heating system is determined to be in dynamic heating condition. If the fluctuation amplitude value is not lower than the second preset heating threshold, the heating system is determined to be in a sudden change condition.

3. The heat balance control method for a heating system according to claim 2, characterized in that, The step of inputting the heat exchange operation data into a preset extended transfer function model, and calculating the core parameter set of each heat exchange station based on the identification frequency corresponding to the current heating condition, specifically includes: Determine the identification frequency corresponding to the current heating condition; Extract the valve opening change sequence within a predetermined time window from the heat exchange operation data, and calculate the actual heat value sequence corresponding to each heat exchange station based on the supply and return water temperature sequence and supply and return water flow sequence within the predetermined time window. When the preset time node of the identification frequency is reached, the valve opening change sequence corresponding to each heat exchange station is input into the preset extended transfer function model, and the core parameter set of the corresponding heat exchange station is calculated based on the actual heat value sequence.

4. The heat balance control method for a heating system according to claim 3, characterized in that, The step of inputting the valve opening change sequence corresponding to each heat exchange station into a preset extended transfer function model, and calculating the core parameter set of the corresponding heat exchange station based on the actual heat value sequence, specifically includes: Using the extended transfer function model, the valve opening change sequence within a predetermined time window is extracted from the heat exchange operation data, and the actual heat value sequence is calculated as the expected output sequence based on the supply and return water temperature sequence and supply and return water flow sequence within the predetermined time window. The valve opening change sequence is input into the extended transfer function model to obtain the predicted heat value sequence of the corresponding heat exchange station output by the extended transfer function model. With the optimization objective of minimizing the sum of squared errors between the predicted and actual heat value sequences, the least squares estimation algorithm is used to iteratively fit the model parameters in the extended transfer function model. The model parameters that achieve the optimization objective are determined as the core parameter set for the corresponding heat exchange station.

5. The heat balance control method for a heating system according to claim 3, characterized in that, The calculation of the control parameters of the primary-side valves at each heat exchange station based on the corrected core parameter set specifically includes: The modified core parameter sets corresponding to each heat exchange station are input into the preset PID parameter tuning rule model. The initial PID control parameters for the corresponding heat exchange station are calculated and output through the PID parameter tuning rule model; the initial PID control parameters include the proportional gain coefficient, integral time constant, and derivative time constant. Obtain the current PID control parameters of the primary side valve of the corresponding heat exchange station, calculate the relative change amplitude of each parameter in the initial PID control parameters and the current PID control parameters, and compare the relative change amplitude with the allowable change threshold of the corresponding parameter. When the relative change of a parameter exceeds the corresponding allowable change threshold, the adjustment value of the parameter is corrected to the boundary value of the corresponding allowable change threshold, and the control parameters of the primary side valve of the corresponding heat exchange station are based on the corrected parameter value.

6. The heat balance control method for a heating system according to claim 5, characterized in that, The calculation and output of the initial PID control parameters for the corresponding heat exchange station through the PID parameter tuning rule model specifically includes: Calculate the first compensation factor, the second compensation factor, and the third compensation factor based on the corrected heat loss factor and the corrected pure heat delay. The proportional gain coefficient is calculated based on the corrected inertial time constant, the corrected gain coefficient, the corrected pure thermal delay, and the first compensation factor. Based on the corrected pure thermal delay and the second compensation factor, the integral time constant is calculated; The differential time constant is calculated based on the corrected pure thermal delay and the third compensation factor. The initial PID control parameters for the corresponding heat exchange station are formed based on the proportional gain coefficient, the integral time constant, and the derivative time constant.

7. The heat balance control method for a heating system according to claim 5, characterized in that, Based on the tree-like heating network topology of the heating system, hierarchical coordination constraint verification is performed on the control parameters and the opening adjustment amount to generate the opening control command for the primary side valve, specifically including: Based on the tree-like heat network topology of the heating system, the affiliation of each heat exchange station is determined, and based on the affiliation, multiple child heat exchange stations belonging to the same parent node are determined to form a set of child stations. Calculate the sum of the absolute values ​​of the opening adjustment amounts of all child heat exchange stations under the same parent node, and compare the absolute values ​​only with the upper limit of the valve adjustment capacity corresponding to the parent node; When the sum of the absolute values ​​exceeds the upper limit of the valve adjustment capacity of the parent node, the opening adjustment amount is reduced based on the ratio of the absolute value of the opening adjustment amount corresponding to each sub-heat exchange station, so that the sum of the reduced absolute values ​​is equal to the upper limit of the valve adjustment capacity. Based on the reduced opening adjustment of the primary valves at each heat exchange station, a corresponding primary valve opening control command is generated.

8. The heat balance control method for a heating system according to claim 1, characterized in that, The process of collecting updated data from the heating system after adjustment to trigger iterative updates of the core parameter set and the control parameters specifically includes: Collect the latest heating operation data of the heat source stations, the latest heat exchange operation data of each heat exchange station, and the latest room temperature data of the user nodes; Based on the updated data, it is determined whether the preset parameter update triggering conditions are met; wherein, the triggering conditions include: the deviation between the latest room temperature data and the target room temperature continuously exceeds the deviation threshold and reaches the first time threshold, or the change in system characteristics reflected by the latest heat exchange operation data exceeds the change threshold. If any of the parameter update trigger conditions are met, the core parameter set and the control parameters are recalculated based on the updated data to complete the iterative update.

9. A heat balance control device for a heating system, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform a heat balance control method for a heating system as described in any one of claims 1 to 8.

10. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are configured to execute a heat balance control method for a heating system as described in any one of claims 1 to 8.

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