Intelligent heat supply system of heat exchange station

Abstract

The invention discloses a heat exchange station intelligent heat supply system, and relates to the field of heat exchange station intelligent heat supply, and the system comprises a stable heat supply module which is used for obtaining a preset target value for maintaining the total water supply temperature of a secondary network, and dynamically adjusting the total water supply temperature of the secondary network through a PID controller to maintain the preset target value; the model construction module is used for drawing a secondary network heat supply pipe network topological graph and constructing a heat supply pipe network real-time hydraulic model; the instruction generation module is used for predicting and generating a cooperative control instruction set of all the intelligent regulating valves in the whole network based on a genetic algorithm; the dynamic adjusting module is used for dynamically adjusting the opening degree of the adjusting valves according to the cooperative control instruction set of all the intelligent adjusting valves in the whole network; and the interaction platform module is used for building a user interaction platform and dynamically adjusting the opening degree of the adjusting valve according to feedback of the user interaction platform. The method has the advantages that by dynamically adjusting the opening degree of the adjusting valve, the heat supply comfort degree of a user is adapted in a personalized mode, and the problems of unified heat supply and unified pricing are avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of heat exchange station intelligent heating, in particular to a heat exchange station intelligent heating system. BACKGROUND

[0002] City central heating as an important infrastructure to protect the livelihood of the people in the northern region in winter and improve energy efficiency, its stable, efficient and fair operation is crucial, the traditional central heating system usually adopts a three-level architecture of "heat exchange station - building - user": high-temperature hot water (primary network) produced by heat source plant is heat exchanged by heat exchange station to transfer heat to low-temperature hot water (secondary network) to buildings, and finally releases heat to users through the radiator inside the building, in this mode, the control core of the system is long-term concentrated in the heat source and heat exchange station level, and the pursuit is to meet the stable output of regional overall heating load in extreme weather, its classic control strategy depends on "quality regulation" and "quantity regulation", that is, by monitoring the outdoor temperature, adjusting the secondary network water supply temperature or circulating water pump speed according to the preset heating curve, to realize the macro-level supply and demand rough balance.

[0003] In recent years, with the improvement of building energy-saving standards and the development of Internet of Things technology, household heat metering and simple temperature control valve have begun to be applied in user end, intending to stimulate users' energy-saving behavior and improve local comfort, however, this improvement has not fundamentally changed the control paradigm of the system, the heat exchange station still outputs hot water with uniform temperature and pressure, and the entire secondary network heating pipe network is still a strong coupling hydraulic system, its operation logic has not broken through the framework of "uniform supply, passive distribution", the user-side adjustment is regarded as a local disturbance, rather than a basis for the system to make accurate resource allocation, therefore, the current heating system is in the initial stage of transition from "station-level automation" to "house-level intelligence" in technology, and its underlying architecture is still difficult to support truly personalized and on-demand heating service, which leaves a huge innovation space and clear technical evolution direction for the development of new generation of intelligent heating technology.

[0004] Although the existing heating technology guarantees the basic heating demand in a certain period, its inherent technical defects are increasingly prominent with the increasing requirements of users on comfort and economy, the primary problem of which is the fundamental contradiction between the unified heating mode and individualized demand, the system is designed and operated according to the most unfavorable working condition, which leads to the fact that in most of the non-extreme weather, especially at the beginning and end of the heating season, the users close to the heat exchange station are generally overheated due to excessive pressure head, and have to open the window to dissipate heat, causing a huge waste of energy, while the users far away are under-heated due to insufficient pressure head, and the room temperature is difficult to meet the standard, the root cause of this uneven heating and cooling phenomenon lies in that the system cannot sense and respond to the real-time heat demand of each user, and carry out accurate hydraulic and thermal balance, which seriously affects the fairness and comfort of user experience, secondly, the existing system lacks a cooperative mechanism, and there is serious hydraulic coupling interference, when individual users try to adjust the room temperature through the temperature control valve, their actions will change the local pipe network resistance, causing the redistribution of system pressure and flow, causing uncontrollable interference to the flow of other users, leading to fluctuations in the room temperature, which makes simple household adjustment ineffective in collective heating systems, and may even cause systemic instability, in addition, the traditional area-based charging or simple household metering mode cannot realize real fair pricing, the area-based charging is disconnected with the actual heat consumption, and users lack the motivation to save energy, and even if a heat meter is installed, the metering result is often affected by the hydraulic imbalance, and it is difficult to accurately reflect the energy saving amount brought by the user's active energy saving behavior, weakening the adjustment effect of the price lever. SUMMARY

[0005] To solve the above technical problems, a heat exchange station intelligent heating system is provided, which solves the fundamental contradiction between the unified heating mode and individualized demand in the background art, which causes the system to be unable to sense and respond to the real-time heat demand of each user, and to carry out accurate hydraulic and thermal balance, which seriously affects the fairness and comfort of user experience, secondly, the existing system lacks a cooperative mechanism, and there is serious hydraulic coupling interference, which easily causes the redistribution of system pressure and flow, causing uncontrollable interference to the flow of other users, leading to fluctuations in the room temperature, and finally, it cannot realize real fair pricing, the area-based charging is disconnected with the actual heat consumption, and users lack the motivation to save energy.

[0006] To achieve the above purposes, the technical scheme adopted by the present application is:

[0007] A heat exchange station intelligent heating system, comprising:

[0008] A stable heating module, the stable heating module is used to obtain a preset target value of the total secondary network water supply temperature retention, and dynamically adjust the total secondary network water supply temperature retention to the preset target value through a PID controller;

[0009] a model construction module, configured to draw a topology graph of the secondary network of the heating pipe network, and construct a real-time hydraulic model of the heating pipe network based on a mass and energy conservation law;

[0010] an instruction generation module, configured to predict and generate a set of coordinated control instructions of all the intelligent regulating valves in the whole network based on a genetic algorithm, with the real-time hydraulic model of the heating pipe network as a physical constraint condition;

[0011] a dynamic adjustment module, configured to deploy an intelligent regulating valve at each user inlet at the end of the secondary network of the heating pipe network, and dynamically adjust the opening degree of the regulating valve according to the set of coordinated control instructions of all the intelligent regulating valves in the whole network;

[0012] an interactive platform module, configured to build a user interactive platform, support a user to adjust an indoor temperature value and set a leaving home / coming home scene mode, and dynamically adjust the opening degree of the regulating valve according to feedback of the user interactive platform.

[0013] Preferably, the obtaining of the preset target value of the total secondary network water supply temperature keeping and the dynamic adjustment of the preset target value of the total secondary network water supply temperature keeping by the PID controller specifically include:

[0014] a temperature sensor is arranged on the total secondary network water supply and return pipe to monitor the actual water supply temperature in real time;

[0015] the preset target value of the total secondary network water supply temperature keeping is obtained based on outdoor meteorological conditions and user heat demand characteristics;

[0016] the actual water supply temperature is compared with the preset target value to obtain a deviation value of the actual water supply temperature from the preset target value;

[0017] the deviation value of the actual water supply temperature from the preset target value is input into the PID controller to dynamically adjust the opening degree of the electric regulating valve of the primary network of the heating pipe network until the total secondary network water supply temperature keeps the preset target value.

[0018] Preferably, the model construction module specifically includes:

[0019] a topology graph unit, configured to abstract the secondary network of the heating pipe network into a topology graph composed of nodes and edges, wherein the nodes represent user inlets, pipe junctions and circulating water pumps, and the edges represent pipe sections and intelligent regulating valves;

[0020] an impedance characteristic unit, configured to assign an impedance characteristic coefficient to each edge, which is calculated based on pipe material, diameter, length and local resistance coefficient;

[0021] a demand heat unit for assigning a theoretical heat load demand to each user node, which is calculated based on its indoor temperature adjustment value and real-time outdoor temperature value;

[0022] a model building unit for building a real-time hydraulic model of the heating pipe network based on the mass and energy conservation law, with the impedance characteristic coefficients of the edges and the theoretical heat load demands of the nodes as inputs.

[0023] Preferably, the instruction generating module specifically comprises:

[0024] a heat load demand unit for predicting the heat load demand values of the user nodes in the next control period based on future short-term weather forecasts and user habits;

[0025] an optimization function unit for establishing a multi-objective optimization function with the goal of meeting the predicted heat load demands of all user nodes and the optimization goal of minimizing the total circulating water pump energy consumption of the entire network;

[0026] an expected opening unit for solving the multi-objective optimization function using a genetic algorithm with the real-time hydraulic model of the heating pipe network as a constraint condition, to obtain a set of expected openings of the regulating valves that can maintain hydraulic stability of the entire network;

[0027] an instruction generating unit for generating expected opening instructions corresponding to each intelligent regulating valve according to the expected openings of the regulating valves, to form the set of collaborative control instructions.

[0028] Compared with the prior art, the present application has the following beneficial effects:

[0029] The application provides a heat exchange station intelligent heat supply system, which realizes accurate digital mapping of the hydraulic and thermal states of a secondary network heat supply pipe network through construction of a real-time hydraulic model of the heat supply pipe network, and generates a cooperative control instruction set of intelligent regulating valves in the whole network based on a genetic algorithm and real-time hydraulic model prediction, so that when a user adjusts the indoor temperature or sets a leaving home / coming home scene mode through an interactive platform, the system dynamically responds and reoptimizes the valve opening degree instruction, on the one hand, realizes accurate prediction and on-demand distribution of the heat supply load according to outdoor meteorological conditions and user individual preferences, and on the other hand, effectively suppresses the whole network hydraulic coupling interference caused by local regulation, and guarantees the hydraulic stability of the system in the dynamic adjustment process. The scheme realizes a fundamental change from "building level" unified extensive heating to "household level" accurate intelligent heat supply, dynamically adjusts the opening degree of the regulating valve of each user, individualizes the differentiated needs of users for indoor temperature, time and economy, completely avoids the comfort problems of near-end overheating, far-end insufficient heating and uneven room temperature caused by unified heat supply, at the same time, carries out household heat metering and pricing based on accurate household heat consumption data, fundamentally solves the problems of fairness and lack of energy-saving incentive caused by the unified pricing mode of "one size fits all", and further significantly improves the comprehensive energy efficiency, user comfort and fairness of the heat supply system. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 It is a structure block diagram of a heat exchange station intelligent heat supply system of the application.

[0031] Figure 2 It is a flowchart of constructing a real-time hydraulic model of a heat supply pipe network based on the law of conservation of mass and energy, and drawing a topology diagram of a secondary network heat supply pipe network.

[0032] Figure 3 It is a flowchart of predicting and generating a cooperative control instruction set of all intelligent regulating valves in the whole network based on a genetic algorithm. DETAILED DESCRIPTION

[0033] The following description is used to disclose the application so that those skilled in the art can implement the application. The preferred embodiments in the following description are only used as examples, and other obvious modifications can be thought of by those skilled in the art.

[0034] REFERENCE Figure 1 As shown in the figure, a heat exchange station intelligent heat supply system comprises:

[0035] A stable heat supply module is used to obtain a preset target value of total secondary network water supply temperature retention, and dynamically adjust the total secondary network water supply temperature retention to the preset target value through a PID controller.

[0036] A model construction module is configured to draw a topology map of the secondary network heating pipe network and construct a real-time hydraulic model of the heating pipe network based on the law of conservation of mass and energy;

[0037] An instruction generation module is configured to predict and generate a set of coordinated control instructions of all intelligent regulating valves in the network based on a genetic algorithm, with the real-time hydraulic model of the heating pipe network as a physical constraint condition;

[0038] A dynamic adjustment module is configured to deploy an intelligent regulating valve at each user inlet at the end of the secondary network heating pipe network and dynamically adjust the opening degree of the regulating valve according to the set of coordinated control instructions of all intelligent regulating valves in the network;

[0039] An interactive platform module is configured to build a user interactive platform, support the user to adjust an indoor temperature value and set a leaving home / coming home scene mode, and dynamically adjust the opening degree of the regulating valve according to feedback from the user interactive platform.

[0040] Referring to Figure 1 The model construction module specifically includes:

[0041] A topology map unit is configured to abstract the secondary network heating pipe network into a topology map composed of nodes and edges, wherein the nodes represent user inlets, pipe junctions and circulating water pumps, and the edges represent pipe sections and intelligent regulating valves;

[0042] An impedance characteristic unit is configured to assign an impedance characteristic coefficient to each edge, which is calculated based on pipe material, diameter, length and local resistance coefficient;

[0043] A heat demand unit is configured to assign a theoretical heat load demand to each user node, which is calculated based on an indoor temperature adjustment value and a real-time outdoor temperature value;

[0044] A model construction unit is configured to construct a real-time hydraulic model of the heating pipe network based on the law of conservation of mass and energy, with the impedance characteristic coefficients of the edges and the theoretical heat load demands of the nodes as inputs.

[0045] Referring to Figure 1 The instruction generation module specifically includes:

[0046] A heat load demand unit is configured to predict heat load demand values of each user node in a next control period based on future short-term weather forecasts and user habits;

[0047] An optimization function unit is configured to establish a multi-objective optimization function with the goal of meeting the predicted heat load demands of all user nodes and the optimization goal of minimizing the total circulating water pump energy consumption of the network;

[0048] An expected opening degree unit is configured to use a genetic algorithm to solve a multi-objective optimization function with the real-time hydraulic model of the heat supply pipe network as a constraint condition, to obtain a set of expected opening degrees of the regulating valves that can maintain the hydraulic stability of the entire network;

[0049] An instruction generation unit is configured to generate an expected opening degree instruction corresponding to each intelligent regulating valve according to the expected opening degree of the regulating valve, to form the set of collaborative control instructions.

[0050] The preset target value of the total secondary network water supply temperature is obtained, and a PID controller is used to dynamically adjust the total secondary network water supply temperature to the preset target value.

[0051] A temperature sensor is arranged on the total secondary network supply and return pipe to monitor the actual water supply temperature in real time.

[0052] Based on the outdoor weather conditions and the user heat demand characteristics, a preset target value of the total secondary network water supply temperature is obtained.

[0053] The actual water supply temperature is compared with the preset target value to obtain a deviation value of the actual water supply temperature from the preset target value.

[0054] The deviation value of the actual water supply temperature from the preset target value is input into the PID controller to dynamically adjust the opening degree of the electric regulating valve of the primary network heat supply pipe network until the total secondary network water supply temperature is maintained at the preset target value.

[0055] It can be explained that in the heat exchange station intelligent heat supply system, stabilizing the total secondary network water supply temperature at the preset target value is the logical cornerstone and physical prerequisite for the entire system to realize "from centralized supply to precise distribution". The preset target value dynamically calculated based on the outdoor weather and user demand establishes a unified, stable, and adaptive preset target value benchmark for the entire network. PID closed-loop control based on this benchmark precisely adjusts the primary network heat medium flow, which provides a constant and reliable heat supply condition for subsequent collaborative control of the entire network based on the real-time hydraulic model of the heat supply pipe network and the genetic algorithm. The preset target value of the total secondary network water supply temperature is obtained as follows:

[0056] According to user questionnaire surveys or indoor design standards (such as the "Civil Building Energy Saving Design Standard"), the indoor pre-design temperature value is obtained.

[0057] The historical data of low-temperature heat supply in the region to be heated in recent years is obtained, and the daily average temperature of all years is sorted from small to large. According to the principle of "average of all years for 5 days" in the local heating design specification, the temperature value at the 5th position is taken as the outdoor design temperature standard value.

[0058] The adjustment coefficient is set according to the building's thermal inertia level, and the specific value is preferentially adopted from the recommended value given in the building energy conservation calculation report or heat load calculation report within the heating area of ​​the heat exchange station.

[0059] The real-time outdoor temperature is obtained by deploying radiation-proof temperature sensors on the roof of the heat exchange station.

[0060] Based on the indoor pre-design temperature value, the outdoor design temperature standard, the adjustment coefficient and the current outdoor real-time temperature, construct the preset target value expression for maintaining the total water supply temperature of the secondary network, and obtain the preset target value;

[0061] The preset target value expression for maintaining the total water supply temperature of the secondary network is:

[0062] T 预 =T 内 +K×(T 标 -T 测 )

[0063] In the formula, T 预 T is the preset target value for maintaining the total water supply temperature of the secondary network. 内 The pre-designed indoor temperature value for the user, K is the adjustment coefficient, and T 标 The outdoor design temperature standard value, T 测 This is the current outdoor real-time temperature;

[0064] Understandably, this expression introduces the outdoor temperature deviation T. 标 -T 测 To achieve feedforward compensation control of the total water supply temperature of the secondary network, in this expression, the user's indoor pre-design temperature value T 内 It is a fixed constant preset based on local user surveys or indoor design standards, providing a stable indoor temperature reference for the entire heating regulation system. Thus, through the PID controller, the opening of the electric regulating valve of the primary heating network is dynamically adjusted according to the deviation between the preset target value and the actual water supply temperature, that is, the flow rate of the high-temperature heat medium is changed, so that the total water supply temperature of the secondary network stably tracks the dynamically preset target temperature.

[0065] Reference Figure 2 As shown, the process of drawing the secondary heating network topology diagram and constructing a real-time hydraulic model of the heating network based on the laws of conservation of mass and energy specifically includes:

[0066] The secondary heating network is abstracted as a topology graph consisting of nodes and edges, where nodes represent user entrances, pipe junctions and circulating water pumps, and edges represent pipe segments and intelligent regulating valves.

[0067] Assign an impedance characteristic coefficient to each edge, calculated based on the pipe material, diameter, length, and local resistance coefficient;

[0068] assigning a theoretical heat load demand to each user node, which is calculated based on its indoor temperature adjustment value and real-time outdoor temperature value;

[0069] Taking the impedance characteristic coefficient of the edge and the theoretical heat load demand of the node as input, a real-time hydraulic model of the heating pipe network is constructed based on the law of conservation of mass and energy.

[0070] It can be explained that the present scheme abstracts the heating pipe network into a topological graph, and assigns precise impedance characteristics and dynamic heat load to it, thereby constructing a mathematical model that can truly reflect the hydraulic and thermal state of the pipe network. Through this model, the complex physical laws of mass conservation, Kirchhoff's pressure, and energy conservation are coded into calculable mathematical constraints, accurately describing the internal relationship between flow, pressure, and heat, thereby providing indispensable physical constraint conditions for subsequent prediction and generation of the coordinated control instruction set of all intelligent regulating valves in the entire heating pipe network, ensuring that the expected opening of the regulating valves searched by the subsequent intelligent algorithm is not only mathematically optimal, but also physically feasible, capable of achieving and maintaining hydraulic stability in the actual pipe network.

[0071] The impedance characteristic coefficient expression is:

[0072]

[0073] In the formula, S is the impedance characteristic coefficient of the pipe segment, ρ is the density of water, L is the length of the pipe segment, D is the inner diameter of the pipe, λ is the along-path resistance coefficient of the pipe, and ∑ζ is the sum of the resistance coefficients of all local resistance components on the pipe segment.

[0074] The theoretical heat load demand is solved based on the basic formula for heat load estimation, and the specific expression is:

[0075] Q i = K Q,i · A i · (T set,i -T 测 )

[0076] In the formula, Q i is the theoretical heat load demand of user node i, A i is the building area of user node i, K Q,i is the comprehensive heat transfer coefficient of the building where user node i is located, T set,i is the indoor adjustment temperature value of user node i, and T 测 is the current real-time outdoor temperature.

[0077] The real-time hydraulic model of the heating pipe network specifically includes:

[0078] Based on the law of conservation of mass, for any node i in the topological graph, the sum of the mass flows into that node is equal to the sum of the mass flows out of that node, i.e., ∑m in,i =∑m out,i , where m is the mass flow rate;

[0079] Based on Kirchhoff's pressure law, for any closed loop in the topology diagram, the algebraic sum of the pressure drops of all pipe segments in the loop is zero, i.e. Among them, S j and m j These are the impedance characteristic coefficient and mass flow rate of a certain pipe segment in loop j, respectively.

[0080] Based on the law of conservation of energy, for any user node i, the actual heat received from the heating network, calculated through simulation, is... in, For the actual heat obtained by user node i based on the law of conservation of energy, m i Let c be the quality traffic of user node i. p T is the specific heat capacity of water. r,i Let i be the return water temperature of user node i;

[0081] This scheme clearly distinguishes between two different types of heat calculation:

[0082] The actual heat received by a user is a calculated value based on actual system measurement data. It is calculated by monitoring the mass flow rate and return water temperature flowing into the user's heating system and based on the law of conservation of energy. It directly reflects the actual heat received by the user from the pipe network.

[0083] The theoretical heat load demand is a predicted value based on the target and the environment. It is calculated based on the user's personalized desired indoor temperature, current outdoor weather conditions, and the building's thermal characteristics. It represents the theoretical heat value required by the user to achieve and maintain their ideal room temperature. With the theoretical heat load demand as the target, forward simulation and optimization calculations are performed through a real-time hydraulic model of the heating network. The control parameters of mass flow rate and return water temperature are dynamically adjusted to drive the actual heat gain to infinitely approach the theoretical heat load demand, thereby achieving a dynamic and precise balance of "heating on demand".

[0084] Reference Figure 3 As shown, the method of predicting and generating a collaborative control instruction set for all intelligent regulating valves in the entire network based on a genetic algorithm specifically includes:

[0085] Based on future short-term weather forecasts and user habits, predict the heat load demand of each user node in the next control cycle;

[0086] To meet the predicted heat load demand of all user nodes and to minimize the total energy consumption of the circulating water pumps in the entire network, a multi-objective optimization function is established.

[0087] The real-time hydraulic model of the heat supply pipe network is taken as a constraint condition, a genetic algorithm is used to solve a multi-objective optimization function, and a set of expected opening degrees of the regulating valves capable of maintaining hydraulic stability of the entire network is obtained.

[0088] According to the expected opening degrees of the regulating valves, expected opening degree instructions corresponding to each intelligent regulating valve are generated to form the set of cooperative control instructions.

[0089] It can be explained that the prediction of the heat load demand value of each user node in the next control period based on the future short-term weather forecast and user habits specifically includes:

[0090] The historical indoor temperature data, heat load value data and corresponding outdoor weather data of each user node in the heating season are retrieved from the system database, and the indoor temperature data includes user indoor temperature adjustment values and preset away / home scene modes.

[0091] For each user node, a heat load prediction model is independently trained, and the model can be selected from a long short-term memory (LSTM) model or a seasonal autoregressive integrated moving average (SARIMA) model. At least one set of historical data in the heating season is used as a training set, historical weather data, date types, user set temperature adjustment values and scene modes are used as input features, and historical actual heat consumption or calculated heat load values of the user are used as output labels for supervised training of the model.

[0092] When each control cycle is started, the prepared future weather forecast data and user setting data are input into the trained heat load prediction model of each user, and the dynamic heat load demand prediction value of each user node in sequence with time in multiple future control cycles is output.

[0093] The multi-objective optimization function is established by taking the satisfaction of the predicted heat load demands of all user nodes as the target and taking the minimization of the total circulating water pump energy consumption of the entire network as the optimization target, and the multi-objective optimization function specifically includes:

[0094] The supply-demand target optimization function is constructed by taking the satisfaction of all user predicted heat load demands as the target and quantifying it as the minimization of the square sum of the heat supply-demand deviations of all user nodes.

[0095] The heat supply-demand deviation refers to the difference between the heat load demand heat obtained by forward simulation calculation of the real-time hydraulic model of the heat supply pipe network and the heat load demand prediction value output by the heat load prediction model for any given combination of intelligent regulating valve opening degrees.

[0096] The energy consumption optimization function is constructed based on the mean square error formula, wherein the total circulating water pump energy consumption of the whole network is obtained by substituting the mass flow calculated by the real-time hydraulic model of the heating pipe network and the head provided by the pump to overcome the total resistance of the system into the pump power calculation formula;

[0097] The real-time hydraulic model of the heating pipe network is used as a constraint condition, and a genetic algorithm is used to solve the multi-objective optimization function to obtain a group of expected opening degrees of the regulating valves that can maintain the hydraulic stability of the whole network, and the expected opening degrees of the regulating valves specifically include:

[0098] The opening degrees of all intelligent regulating valves in the whole network are arranged in order to form a chromosome individual, and each gene position represents a specific opening degree of a valve.

[0099] The supply-demand target optimization function and the energy consumption target optimization function are combined by a weighted summation method to construct the fitness function of the genetic algorithm, and the higher the fitness value is, the better the comprehensive performance of the valve opening degree combination in meeting the heat demand and reducing the pump energy consumption is.

[0100] In each generation evolution, the real-time hydraulic model of the heating pipe network needs to be called once for complete forward simulation calculation of each valve opening degree combination in the population, which takes the current valve opening degree combination as input, iteratively solves the real-time hydraulic model of the heating pipe network, outputs the return water temperature, pipe section pressure drop and mass flow parameters of the whole network, and updates the fitness of the chromosome individual based on the parameters.

[0101] The expected opening degrees of the regulating valves generate expected opening degree instructions corresponding to each intelligent regulating valve to form the collaborative control instruction set, and the expected opening degrees of the regulating valves specifically include:

[0102] The obtained expected opening degrees of the regulating valves are parsed and paired according to the unique physical address code of the regulating valves.

[0103] A control instruction containing the expected opening degree and execution timestamp of each regulating valve is generated.

[0104] All valve control instructions are summarized to form a structured and complete collaborative control instruction set.

[0105] Before the instructions are issued, a global check is performed to ensure that there is no illegal instruction exceeding the physical limit of the valve, and then the instruction set is concurrently issued to the corresponding intelligent regulating valve microprocessor units in the whole network through the communication network.

[0106] The intelligent regulating valves are deployed at each user inlet at the end of the secondary network heating pipe network, and the opening degrees of the regulating valves are dynamically adjusted according to the collaborative control instruction set of all intelligent regulating valves in the whole network, and the dynamic adjustment of the opening degrees of the regulating valves specifically includes:

[0107] A high-precision, linearly regulated electric regulating valve is arranged at the inlet of each user at the end of the secondary network heating pipe network;

[0108] A microprocessor unit integrated in the valve body is used to receive control instructions, control the position of the valve core, and collect local instantaneous flow and supply and return water pressure data;

[0109] A low-power wide-area network communication module based on NB-IoT or LoRaWAN is connected to the microprocessor unit for bidirectional data communication between the microprocessor unit and the central control system of the heat exchange station, receiving instructions and uploading state data;

[0110] A backup power unit integrated in the microprocessor unit or arranged adjacent to it, preferably a super capacitor or rechargeable battery, is used to provide emergency power for the microprocessor unit and the communication module in the event of a power outage;

[0111] The microprocessor unit receives the control instructions of the regulating valve issued by the heat exchange station control system and synchronously adjusts to the expected opening within the preset time window;

[0112] The feedback pressure data is compared with the real-time hydraulic model pipe segment pressure of the heating pipe network, and if the deviation exceeds the allowed threshold, a data update process is triggered.

[0113] It can be explained that this scheme is the key link from algorithm optimization to physical execution. The collaborative control instruction set generated by the optimization algorithm is accurately mapped to the intelligent regulating valve of each end user in the physical world. Through the deployment of intelligent valve units integrated with sensing, decision-making, communication, and execution capabilities, the system can not only collaboratively and synchronously regulate the flow distribution of the whole network to achieve precise control of on-demand heating, but also form a closed-loop adaptive system through continuous data collection and feedback. The microprocessor unit uploads the periodically collected supply and return water pressure data to the central control system through the communication module. The central control system compares the received pressure data of each node in the whole network with the theoretical pressure data calculated by the real-time hydraulic model of the heating pipe network. If the deviation between the measured pressure and the theoretical pressure of a specific node or region continuously exceeds the preset threshold, it is determined that the pipe network hydraulic condition may be abnormal or the model may be inaccurate. At this time, the central control system automatically triggers a model calibration and control instruction re-planning process: using the latest network pressure and flow measurement data, the key parameters (such as local resistance coefficient) of the real-time hydraulic model are corrected, and a new set of collaborative control instructions that better meet the current physical state of the pipe network is generated based on the corrected model. The system is executed immediately to form a perception-decision-execution-feedback closed-loop control, ensuring that the system always operates in a hydraulically stable and efficient state.

[0114] The building user interaction platform supports user adjustment of indoor temperature value, setting of home / away scene mode, and dynamic adjustment of the regulating valve opening degree based on user interaction platform feedback, and specifically comprises:

[0115] The building user interaction platform provides an indoor temperature setting interface and a home / away scene mode setting function for the user.

[0116] The user-set indoor temperature adjustment value is received as an input for the real-time hydraulic model of the heating pipe network.

[0117] Based on the instantaneous mass flow data collected by the intelligent regulating valve deployed at each user inlet and the user return water temperature data, the cumulative heat consumption of each user in a billing period is obtained based on the energy conservation law in the real-time hydraulic model of the heating pipe network.

[0118] Based on the calculated cumulative heat consumption of each user and the preset heat price per unit, a household heat metering bill is generated and displayed on the user interaction platform.

[0119] It can be explained that the user interaction platform interface is an important tool for users to make personalized settings based on their own usage, and is an important management platform for intelligent control like an air conditioner. The user-set indoor temperature adjustment value is used as an input for the real-time hydraulic model of the heating pipe network, thereby effectively realizing personalized and intelligent control of heating, and making the user clear about the price transparency brought by intelligent control through the generation of a household heat metering bill, thereby avoiding the problem of one-size-fits-all heating prices and temperatures. In the home / away scene mode, if the home scene is set, the default is the user's indoor preset temperature value if no adjustment temperature is set, or the set adjustment temperature is used as the input if the adjustment temperature is set. If the away scene is set, the inlet intelligent regulating valve performs a close or small opening operation to save energy.

[0120] In summary, the advantages of the present application are that by dynamically adjusting the regulating valve opening degree, the user's heating comfort is personalized and adapted, and the problems of unified heating and unified pricing are avoided.

[0121] The basic principles, main features, and advantages of the present application have been shown and described. Those skilled in the art should understand that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection claimed by the present application is defined by the appended claims and their equivalents.

Claims

1. A heat exchange station intelligent heat supply system, characterized in that, include: A stable heating module is used to obtain a preset target value for maintaining the total water supply temperature of the secondary network, and dynamically adjust the total water supply temperature of the secondary network to maintain the preset target value through a PID controller. The model building module is used to draw the topology of the secondary heating network and build a real-time hydraulic model of the heating network based on the laws of conservation of mass and energy. The instruction generation module is used to predict and generate a set of coordinated control instructions for all intelligent regulating valves in the entire network based on a genetic algorithm, using a real-time hydraulic model of the heating network as a physical constraint. The dynamic adjustment module is used to deploy intelligent regulating valves at each user inlet at the end of the secondary heating network and dynamically adjust the opening degree of the regulating valves according to the coordinated control instruction set of all intelligent regulating valves in the network. The interactive platform module is used to build a user interaction platform, which supports users to adjust the indoor temperature value, set the leaving / returning home scene mode, and dynamically adjust the opening of the regulating valve based on the feedback from the user interaction platform.

2. The heat exchange station intelligent heating system according to claim 1, characterized in that, The process of obtaining a preset target value for maintaining the total water supply temperature of the secondary network, and dynamically adjusting the preset target value for maintaining the total water supply temperature of the secondary network through a PID controller, specifically includes: Temperature sensors are installed on the main supply and return water pipes of the secondary network to monitor the actual water supply temperature in real time. Based on outdoor meteorological conditions and user heat demand characteristics, obtain the preset target value for maintaining the total water supply temperature of the secondary network; The actual water supply temperature is compared with the preset target value to obtain the deviation value between the actual water supply temperature and the preset target value. The deviation between the actual water supply temperature and the preset target value is input into the PID controller, which dynamically adjusts the opening of the electric regulating valve of the primary heating network until the total water supply temperature of the secondary network is maintained at the preset target value.

3. The heat exchange station intelligent heating system according to claim 2, characterized in that, The model building module specifically includes: A topology graph unit, which is used to abstract the secondary heating network into a topology graph composed of nodes and edges; Among them, nodes represent user entrances, pipe junctions and circulating water pumps, and edges represent pipe segments and intelligent regulating valves; An impedance characteristic unit is used to assign an impedance characteristic coefficient to each edge based on the pipe material, diameter, length, and local resistance coefficient. The heat demand unit is used to assign each user node a theoretical heat load demand calculated based on its indoor temperature adjustment value and real-time outdoor temperature value. The model building unit is used to construct a real-time hydraulic model of the heating network based on the laws of conservation of mass and energy, using the impedance characteristic coefficients of the edges and the theoretical heat load requirements of the nodes as inputs.

4. The heat exchange station intelligent heating system according to claim 3, characterized in that, The instruction generation module specifically includes: The heat load demand unit is used to predict the heat load demand value of each user node in the next control cycle based on future short-term weather forecasts and user habits. An optimization function unit is established to meet the predicted heat load demand of all user nodes and to minimize the total energy consumption of the circulating water pumps in the entire network. The expected opening unit is used to take the real-time hydraulic model of the heating network as a constraint condition, and use a genetic algorithm to solve the multi-objective optimization function to obtain a set of expected openings of regulating valves that can maintain the hydraulic stability of the entire network. The instruction generation unit is used to generate an instruction corresponding to the expected opening degree of each intelligent control valve based on the expected opening degree of the control valve, thereby forming the collaborative control instruction set.

5. The heat exchange station intelligent heating system according to claim 4, characterized in that, The deployment of intelligent regulating valves at each user inlet at the end of the secondary heating network, and the dynamic adjustment of the valve opening based on the coordinated control command set of all intelligent regulating valves in the network, specifically includes: A high-precision, linearly adjustable electric regulating valve is deployed at the inlet of each user at the end of the secondary heating network. A microprocessor unit integrated on the valve body is used to receive control commands, control the valve core position, and collect local instantaneous flow rate and supply and return water pressure data; A low-power wide-area network communication module based on NB-IoT or LoRaWAN is connected to the microprocessor unit to realize bidirectional data communication between the microprocessor unit and the central control system of the heat exchange station, and to complete the command reception and status data upload. A backup power unit, integrated within or adjacent to the microprocessor unit, preferably a supercapacitor or a rechargeable battery, is provided to provide emergency power to the microprocessor unit and communication module in the event of an unexpected power outage. When the microprocessor unit receives the control command for the regulating valve issued by the heat exchange station control system, it synchronously adjusts the valve to the expected opening degree within a preset time window. The feedback pressure data is compared with the real-time hydraulic model of the heating network pipe section pressure. If the deviation exceeds the allowable threshold, a data update process is triggered.

6. The heat exchange station intelligent heating system according to claim 5, characterized in that, The aforementioned establishment of a user interaction platform, which supports users in adjusting indoor temperature values ​​and setting away / returning home scene modes, and dynamically adjusting the opening of the regulating valve based on feedback from the user interaction platform, specifically includes: Build a user interaction platform to provide users with an indoor temperature setting interface and the function of setting scene modes for leaving home / returning home; Receive the indoor temperature adjustment value set by the user and use it as the input for the real-time hydraulic model of the heating network; Based on the instantaneous mass flow data collected by the intelligent regulating valve deployed at each user's inlet, and the user's return water temperature data, and based on the law of conservation of energy in the real-time hydraulic model of the heating network, the cumulative heat consumption of each user in a billing cycle is obtained. Based on the calculated cumulative heat consumption of each user and the preset heat price per unit, individual heat metering bills are generated and displayed on the user interaction platform.

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