Thermal management method based on thermal management platform, platform, medium and program product

Abstract

The invention discloses a thermal management method based on a thermal management platform, a platform, a medium and a program product, and relates to the field of data processing methods specially suitable for the management purpose, in the method, real-time heat transfer efficiency among nodes is calculated according to real-time temperature, pressure and flow parameters, detected by the thermal management platform, of the nodes of a heat supply pipe network; calculating an inertia response critical value of each node according to the thermal inertia prediction function; dividing the heat supply network into a plurality of inertia gradient areas based on the inertia response critical value and the real-time heat transfer efficiency; when sudden heat supply demand changes are detected, the inertia lag amount in the heat transfer process is calculated according to the product of the demand change amount and the thermal inertia prediction function; calculating a reverse compensation amount required for eliminating the inertia lag amount according to the inertia lag amount; and applying a reverse compensation amount through the thermal management platform in a progressive feedback mode. The method and the device are used for shortening the response time in the face of sudden heat supply demand change and improving the heat management efficiency.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of data processing methods specially applicable to management purposes, and particularly relates to a heat management method, platform, medium and program product based on a heat management platform. BACKGROUND

[0002] As an important part of urban infrastructure, the operation quality of the heat supply system is directly related to the quality of life of residents. In actual operation, the problem of uneven heat distribution caused by pipe network hydraulic imbalance is very common, resulting in uneven heat supply quality, with some users having high room temperature and some users having low room temperature, which not only affects the user's heat supply experience, but also causes a large amount of energy waste.

[0003] In related technologies, two-network balance adjustment technology can be used. This technology adjusts the resistance characteristics of each branch of the pipe network to ensure that heat is distributed as expected, mainly through the adjustment of flow distribution. Specifically, the system automatically adjusts the flow distribution of each pipe according to the real-time collected temperature, pressure and flow parameters, combined with the preset balance model, to realize the dynamic balance of the heat supply system.

[0004] However, when facing sudden changes in heat supply demand, the above related technology has the problem of long response time. For example, when a cold wave approaches or the regional heat use mode changes suddenly, it takes a long time to adjust because the system needs to complete a new round of data collection and analysis before determining the adjustment strategy. During this period, the heat supply system is difficult to maintain ideal heat supply effect, affecting the user's heat supply experience. SUMMARY

[0005] The application provides a heat management method, platform, medium and program product based on a heat management platform, for shortening the response time when facing sudden changes in heat supply demand and improving heat management efficiency.

[0006] In a first aspect, the application provides a heat management method based on a heat management platform, which calculates the real-time heat transfer efficiency between nodes according to the real-time temperature, pressure and flow parameters of each node of the heat supply pipe network detected by the heat management platform; calculates the inertia response critical value of each node according to the heat inertia prediction function, and the inertia response critical value is the inflection point value of the heat inertia prediction function from growth to oscillation; divides the heat supply pipe network into multiple inertia gradient zones based on the inertia response critical value and the real-time heat transfer efficiency; When a sudden change in heat supply demand is detected, the inertia lag amount in the heat transfer process is calculated according to the product of the demand change amount and the heat inertia prediction function; The reverse compensation amount required to eliminate the inertia lag amount is calculated according to the inertia lag amount; The thermal management platform applies the reverse compensation amount in a progressive feedback manner.

[0007] By using the above technical solution, by calculating the real-time heat transfer efficiency between nodes, the actual situation of heat transfer in the heat supply pipe network can be accurately mastered. Combined with the critical value of inertial response calculated by the thermal inertia prediction function, the response characteristics of different regions in the heat supply pipe network to heat changes can be identified. Based on these characteristics, the heat supply pipe network is divided into multiple inertia gradient zones, so that the thermal management platform can specifically deal with the thermal inertia problems of different regions. When a sudden change in heat supply demand occurs, the thermal management platform can quantify the delay effect caused by thermal inertia by calculating the inertia lag amount. By calculating the corresponding reverse compensation amount and applying the compensation in a progressive feedback manner, the thermal management platform can actively eliminate the lagging effect caused by thermal inertia. This zoning management and dynamic compensation method based on inertia characteristics can reduce the adjustment time of the heat supply thermal management platform, improve the response speed of the heat supply thermal management platform to sudden demand changes, ensure the smoothness of the heat transfer process, avoid oscillation of the heat supply thermal management platform due to improper compensation, and improve the thermal management efficiency.

[0008] In some embodiments according to the first aspect, in some embodiments, the real-time heat transfer efficiency between nodes is calculated according to the real-time temperature, pressure and flow parameters of each node of the heat supply pipe network detected by the thermal management platform, specifically comprising: The product of the real-time temperature, pressure and flow parameters of each node is calculated to obtain the real-time heat load of each node; The heat loss rate is calculated based on the real-time heat load of adjacent nodes, and the heat loss rate is the ratio of the difference between the real-time heat loads of adjacent nodes to the real-time heat load of the upstream node; The real-time heat transfer efficiency is calculated according to the heat loss rate, and the real-time heat transfer efficiency is 1 minus the heat loss rate.

[0009] By using the above technical solution, the real-time heat load is obtained by calculating the product of the real-time temperature, pressure and flow parameters of each node, which reflects the actual heat supply state of the node. The heat loss rate is calculated based on the real-time heat load of adjacent nodes, which can accurately quantify the loss of heat in the transmission process. The heat loss rate is converted into real-time heat transfer efficiency, so that the thermal management platform can intuitively evaluate the effectiveness of heat transfer. By establishing the correlation between heat load, heat loss rate and heat transfer efficiency, the scientificity and controllability of the operation of the thermal management platform are improved.

[0010] In some embodiments according to the first aspect, in some embodiments, the thermal inertia prediction function is: ; In the above function, T i is the temperature at node i, P i is the pressure at node i, Let be the fluid velocity at node i. For fluid density, For fluid dynamic viscosity, Specific heat capacity of the fluid Let be the volumetric flow rate at node i. The temperature diffusivity is... The convective heat transfer coefficient is... This is the Stefan-Boltzmann constant. For surface emissivity, For ambient temperature, The inner diameter of the pipe. The cross-sectional area of ​​the pipe. For the pipe inclination angle, As the friction factor, For time variables, For simplified coordinates along the pipe axis, It is the acceleration due to gravity. This is the maximum permissible flow rate.

[0011] By adopting the above technical solution, this thermodynamic inertial prediction function establishes a complete dynamic characteristic description model of the thermal management platform through the coupling of the governing equations of the temperature field, pressure field, and flow field. The function considers multiple heat transfer methods such as heat conduction, convection, and radiation, and also includes factors such as pressure loss, gravity effects, and velocity limitations in fluid flow, enabling the model to comprehensively reflect the physical characteristics of the heating thermal management platform. By solving this function, the thermal management platform can accurately predict temperature changes, pressure distribution, and velocity changes during heat transfer. This prediction function mathematizes complex thermodynamic processes, enabling the thermal management platform to quantitatively analyze thermodynamic inertial characteristics and providing a theoretical basis for calculating the critical value of inertial response. This prediction method based on multi-physics coupling improves the accuracy of the thermal management platform's prediction of thermal processes, making thermal regulation more targeted.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, a reverse compensation amount is applied through a thermal management platform using a progressive feedback method, specifically including: The inertial gradient regions are prioritized based on their critical inertial response values ​​and real-time heat transfer efficiency. The higher the critical inertial response value and the lower the real-time heat transfer efficiency, the higher the priority of the inertial gradient region. The compensation timing sequence for each inertial gradient region is determined based on the thermo-inertial prediction function, and the time interval for applying reverse compensation in each inertial gradient region is calculated. The thermal management platform applies reverse compensation in order of priority and at time intervals, and collects temperature, pressure and flow parameters of nodes in each inertial gradient region in real time. The real-time heat transfer efficiency after compensation is calculated based on temperature, pressure and flow parameters, and then compared with the preset target heat transfer efficiency. When the real-time heat transfer efficiency after compensation in any inertial gradient region reaches the preset target heat transfer efficiency, the compensation for the inertial gradient region is stopped, and the reverse compensation amount is applied to other inertial gradient regions.

[0013] By adopting the above technical solutions and prioritizing the inertial gradient regions, the thermal management platform establishes a scientific compensation sequence, ensuring the rational allocation of compensation resources. The compensation timing is determined based on the thermal inertial prediction function, giving the compensation process a clear time rhythm. By collecting parameters in real time and calculating the heat transfer efficiency after compensation, the thermal management platform can dynamically evaluate the compensation effect. Compensation is stopped promptly when the preset target is reached, avoiding over-compensation. This progressive feedback compensation method achieves refined control of the compensation process, enabling the thermal management platform to maintain operational stability while ensuring compensation effectiveness. The tiered implementation and effect evaluation mechanism of the compensation process improves the accuracy and controllability of the thermal management platform's compensation and reduces interference with the normal operation of the thermal management platform during the compensation process.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, after applying the reverse compensation amount via a progressive feedback method through a thermal management platform, the method further includes: Real-time detection of temperature fluctuation frequency and amplitude at nodes within each inertial gradient region; Calculate the temperature gradient difference coefficient between adjacent nodes. The temperature gradient difference coefficient is the deviation rate between the node temperature and the average temperature of adjacent nodes. When the temperature gradient difference coefficient of any node exceeds a preset threshold, the compensation anomaly region where the node is located is determined. The direction of the deviation of heating parameters at each node in the statistical compensation abnormal area; Based on the direction of the heating parameter deviation, the abnormal compensation area is divided into an over-compensation area and an under-compensation area. The compensation amount is adjusted by decreasing the compensation method for over-compensated regions and by increasing the compensation method for under-compensated regions, thus obtaining the compensation parameters. Calculate the temperature uniformity index of each node after correction; When the temperature uniformity index meets the preset conditions, the compensation parameters are recorded as the optimization parameters under the current operating conditions.

[0015] By adopting the above technical solution, and through real-time detection of temperature fluctuation characteristics of nodes within the inertial gradient region and calculation of the temperature gradient difference coefficient, the thermal management platform can accurately identify compensation anomaly areas. Based on the direction of heating parameter deviation, the compensation anomaly areas are further subdivided into over-compensation and under-compensation areas, and corresponding incremental or decremental correction methods are used for compensation adjustment. This allows the thermal management platform to precisely control different types of compensation anomalies. During the correction process, temperature uniformity indicators are used for evaluation and recording, ensuring both the quantifiable measurement of the compensation effect and establishing an optimized parameter library corresponding to specific operating conditions. This regional and type-based compensation correction mechanism improves the thermal management platform's ability to correct local thermal imbalances, reduces oscillations and fluctuations during the compensation process, and makes the temperature distribution of the entire heating thermal management platform more uniform and stable. Simultaneously, the accumulated records of optimized parameters provide a reference for the thermal management platform's rapid response under similar operating conditions, enhancing the stability and adaptability of the thermal management platform's operation.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the direction of the deviation of heating parameters at each node within the abnormal compensation area is statistically compensated, specifically including: Acquire real-time temperature, pressure, and flow data of each node within the compensation anomaly area before and after applying reverse compensation; Calculate the rate of change of heating parameters at each node before and after applying the reverse compensation. The rate of change of heating parameters includes the rate of change of temperature, the rate of change of pressure, and the rate of change of flow rate. Based on preset parameter weighting coefficients, the temperature change rate, pressure change rate, and flow rate change rate are weighted and combined to obtain the comprehensive change rate. When the overall rate of change is greater than the average overall rate of change in the inertial gradient region where the node is located, the direction of the deviation of the heating parameters of each node in the compensation anomaly region is determined to be a positive deviation; when the overall rate of change is not greater than the average overall rate of change, the direction of the deviation of the heating parameters of each node in the compensation anomaly region is determined to be a negative deviation.

[0017] By employing the above technical solution, and through multi-dimensional comparative analysis of temperature, pressure, and flow data of nodes within the compensation anomaly area before and after compensation, the rate of change of each parameter is calculated, and a pre-set weighting coefficient is introduced for weighted combination. By comparing the comprehensive rate of change of each node with the average level of its inertial gradient region, the direction of deviation of the node's heating parameters can be accurately determined, improving the accuracy and reliability of determining the direction of heating parameter deviation.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the temperature uniformity index of each node after correction is calculated, specifically including: The temperature fluctuation uniformity is obtained by statistically analyzing the standard deviation of the temperature gradient difference coefficient within the abnormal compensation area. The temperature distribution uniformity is obtained by calculating the ratio of the maximum temperature deviation to the average temperature deviation of each node within the compensation anomaly area. The temperature uniformity index is obtained by calculating a weighted average based on the temperature fluctuation uniformity and the temperature distribution uniformity.

[0019] By employing the above technical solution, the temperature fluctuation uniformity is obtained by calculating the standard deviation of the temperature gradient difference coefficient within the compensated abnormal area, reflecting the dispersion of temperature changes within the area. The temperature distribution uniformity is obtained by calculating the ratio of the maximum temperature deviation to the average deviation, reflecting the concentration of temperature distribution within the area. The temperature uniformity index obtained by weighted averaging these two indicators considers both the dynamic fluctuation characteristics of temperature and the static distribution characteristics, improving the accuracy of the thermal management platform's assessment of temperature uniformity. This makes the evaluation of compensation effects more objective and comprehensive, helping the thermal management platform to promptly identify and resolve temperature unevenness issues and ensure the operational quality of the heating management platform.

[0020] In a second aspect, embodiments of this application provide a thermal management platform, which includes: one or more processors and a memory; the memory is coupled to one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the thermal management platform to perform the methods described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a thermal management platform, cause the thermal management platform to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer program product that, when run on a thermal management platform, causes the thermal management platform to execute the method described in any possible implementation of the first aspect.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. This application provides a thermal management method based on a thermal management platform. By calculating the real-time heat transfer efficiency between nodes, the actual heat transfer status in the heating network can be accurately grasped. Combined with the critical value of inertial response calculated by the thermal inertia prediction function, the response characteristics of different areas in the heating network to heat changes can be identified. Based on these characteristics, the heating network is divided into multiple inertial gradient zones, enabling the thermal management platform to address the thermal inertia problems of different areas in a targeted manner. When a sudden change in heating demand occurs, the thermal management platform can quantify the delay effect caused by thermal inertia by calculating the inertial hysteresis. By calculating the corresponding reverse compensation amount and applying compensation using a progressive feedback method, the thermal management platform can proactively eliminate the hysteresis effect caused by thermal inertia. This zoned management and dynamic compensation method based on inertial characteristics can reduce the adjustment time of the thermal management platform, improve the response speed of the thermal management platform to sudden changes in demand, ensure the stability of the heat transfer process, avoid oscillations of the thermal management platform due to improper compensation, and improve thermal management efficiency.

[0024] 2. This application provides a thermal management method based on a thermal management platform. By prioritizing the inertial gradient region, the thermal management platform establishes a scientific compensation sequence, ensuring the rational allocation of compensation resources. The compensation timing is determined based on a thermal inertial prediction function, giving the compensation process a clear time rhythm. By acquiring parameters in real time and calculating the heat transfer efficiency after compensation, the thermal management platform can dynamically evaluate the compensation effect. Compensation is stopped promptly when the preset target is reached, avoiding over-compensation. This progressive feedback compensation method achieves refined control of the compensation process, enabling the thermal management platform to maintain operational stability while ensuring compensation effectiveness. The hierarchical implementation and effect evaluation mechanism of the compensation process improves the accuracy and controllability of the thermal management platform's compensation and reduces interference from the compensation process on the normal operation of the thermal management platform.

[0025] 3. This application provides a thermal management method based on a thermal management platform. By real-time detection of temperature fluctuation characteristics of nodes within the inertial gradient region and calculation of the temperature gradient difference coefficient, the thermal management platform can accurately identify compensation anomaly areas. Based on the direction of heating parameter deviation, the compensation anomaly areas are further subdivided into over-compensation and under-compensation areas, and corresponding incremental or decremental correction methods are used for compensation adjustment. This allows the thermal management platform to precisely control different types of compensation anomalies. During the correction process, temperature uniformity indicators are used for evaluation and recording, ensuring both the quantifiable measurement of the compensation effect and establishing an optimized parameter library corresponding to specific operating conditions. This regional and type-based compensation correction mechanism improves the thermal management platform's ability to correct local thermal imbalances, reduces oscillations and fluctuations during the compensation process, and makes the temperature distribution more uniform and stable. Simultaneously, the accumulated recording of optimized parameters provides a reference for the thermal management platform's rapid response under similar operating conditions, enhancing the stability and adaptability of the thermal management platform's operation. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a thermal management method based on a thermal management platform in an embodiment of this application.

[0027] Figure 2 This is a flowchart illustrating a compensation effect evaluation and optimization method in an embodiment of this application.

[0028] Figure 3 This is a schematic diagram of the physical device structure of a thermal management platform provided in an embodiment of this application. Detailed Implementation

[0029] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0030] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0031] The thermal management method provided in this application is implemented based on a thermal management platform. This platform provides users with a visual window that reflects the balance of the pipe network by displaying return water temperature data for each unit. It can also display a list of return water temperatures for each unit in each community. Furthermore, it can display real-time data on community heat consumption, electricity consumption, and circulating water volume; and analyze data such as heat consumption per square meter and electricity consumption based on outdoor temperature, indoor temperature, and heat consumption. It also features room temperature management functionality. Based on the actual room temperature data acquisition devices installed in each community, management is implemented according to temperature distributions below 18℃, 18~20℃, 20~22℃, 22~24℃, and above 24℃.

[0032] This heating management platform features hydraulic calculation capabilities, automatically calculating valve openings based on the pressure difference between the supply and return water at the inlet of each unit. It also provides guidance for on-site balancing adjustments. Furthermore, it offers basic information query and display functions for the residential community, allowing users to input information such as the area (total area, actual heating area), insulation status, and occupancy rate of each building and unit. The platform also features tiered access control, with different account management permissions set according to the organizational management levels of the group company, grassroots heating companies, and central stations. Under different access accounts, information such as network imbalances, room temperature distribution, and energy consumption for each community within their respective areas can be displayed.

[0033] The following example is used in conjunction with Figure 1 The following describes a thermal management method based on a thermal management platform in an embodiment of this application: Please see Figure 1 This is a flowchart illustrating a thermal management method based on a thermal management platform in an embodiment of this application.

[0034] S101. Calculate the real-time heat transfer efficiency between nodes based on the real-time temperature, pressure and flow parameters of each node in the heating network detected by the heat management platform. The real-time heat transfer efficiency between nodes is calculated based on the real-time temperature, pressure, and flow parameters of each node in the heating network detected by the thermal management platform. Specifically, this includes: calculating the product of the real-time temperature, pressure, and flow parameters of each node to obtain the real-time heat load of each node; calculating the heat loss rate based on the real-time heat load of adjacent nodes, where the heat loss rate is the ratio of the difference in real-time heat load of adjacent nodes to the real-time heat load of the upstream node; and calculating the real-time heat transfer efficiency based on the heat loss rate, where the real-time heat transfer efficiency is 1 minus the heat loss rate.

[0035] In this step, the real-time temperature, pressure, and flow rate of each node in the heating network are monitored through a thermal management platform, and these parameters are used to calculate the real-time heat transfer efficiency between adjacent nodes. Specifically, the real-time heat load of each node is first calculated, which is the product of its temperature, pressure, and flow rate. Then, the heat loss rate is calculated based on the real-time heat load of adjacent nodes, and finally, the real-time heat transfer efficiency is obtained based on the heat loss rate. Besides the specific calculation method used in this embodiment, other methods can also be used to calculate the real-time heat transfer efficiency, such as calculating the real-time heat transfer efficiency based on the supply and return water temperature difference and flow rate.

[0036] In practical applications, temperature sensors, pressure sensors, and flow meters can be installed at various nodes of the heating network to collect temperature, pressure, and flow data at each node in real time and transmit the data to the thermal management platform. After receiving the data, the thermal management platform can use the calculation method provided in this embodiment to obtain the real-time heat transfer efficiency between adjacent nodes.

[0037] S102. Calculate the critical value of the inertial response of each node based on the thermo-inertial prediction function; The critical values ​​of the inertial response at each node are calculated based on the thermo-inertial prediction function. The critical value of the inertial response is the inflection point where the thermo-inertial prediction function changes from growth to oscillation. The thermo-inertial prediction function is: ; In the above function, Let i be the temperature at node i. Let be the pressure at node i. Let be the fluid velocity at node i. For fluid density, For fluid dynamic viscosity, Specific heat capacity of the fluid Let be the volumetric flow rate at node i. The temperature diffusivity is... The convective heat transfer coefficient is... This is the Stefan-Boltzmann constant. For surface emissivity, For ambient temperature, The inner diameter of the pipe. The cross-sectional area of ​​the pipe. For the pipe inclination angle, As the friction factor, For time variables, For simplified coordinates along the pipe axis, It is the acceleration due to gravity. This is the maximum permissible flow rate.

[0038] In this step, the critical inertial response values ​​of each node in the heating network are calculated using the given thermodynamic inertial prediction function. The thermodynamic inertial prediction function is a system of partial differential equations describing the variations of parameters such as temperature, pressure, and flow velocity with time and spatial location. The critical inertial response value refers to the value of the independent variable corresponding to the inflection point where the function curve changes from monotonically increasing to oscillating in the solution of the thermodynamic inertial prediction function. The prediction function can be solved numerically to obtain the critical inertial response values ​​at each node. The critical inertial response value reflects the inertial hysteresis characteristics in the heat transfer process and is an important basis for defining the inertial gradient region.

[0039] Solving the thermodynamic inertial prediction function requires multiple input conditions, such as pipe dimensions and fluid properties. These conditions can be obtained by consulting design documents or through actual measurements. Furthermore, solving the partial differential equations requires appropriate boundary and initial conditions. Boundary conditions such as inlet temperature, pressure, and flow rate can be reasonably set according to actual operating conditions. For initial conditions, real-time data collected from the pipeline network can be used, or the pipeline network can be assumed to be initially in a uniform temperature field. When solving the partial differential equations, numerical methods such as the finite difference method and the finite element method can be used, and a calculation program can be written to obtain the critical values ​​of the inertial response at each node.

[0040] S103. Based on the critical value of inertial response and real-time heat transfer efficiency, the heating network is divided into multiple inertial gradient zones. This step utilizes the real-time heat transfer efficiency calculated in S101 and the critical inertial response calculated in S102 to partition the heating network into several inertial gradient zones. Within each inertial gradient zone, the gradient of change in the critical inertial response value and real-time heat transfer efficiency of the nodes is relatively gentle. However, there are significant differences in inertial response characteristics and heat transfer efficiency between different inertial gradient zones. By dividing the network into inertial gradient zones, regional control measures can be applied, improving the control accuracy and efficiency of the entire thermal management platform.

[0041] In practical implementation, cluster analysis can be used to group nodes. Using the critical value of inertial response and real-time heat transfer efficiency as clustering features, clustering algorithms such as K-means and DBSCAN are used to divide nodes into several categories, each corresponding to an inertial gradient region. During the division process, the number of categories can be set according to factors such as the scale and complexity of the pipeline network. To make the clustering results more reasonable, the feature data can be standardized before clustering to eliminate the influence of dimensional differences. After obtaining the clustering results, further fine-tuning can be performed, re-dividing nodes located at cluster boundaries or exhibiting drastic feature changes to ensure the consistency of node characteristics within each inertial gradient region.

[0042] S104. When a sudden change in heating demand is detected, the inertial lag in the heat transfer process is calculated based on the product of the change in demand and the thermal inertia prediction function. In actual heating processes, sudden changes in heating demand may occur, such as a sharp increase in the number of heat users or a sudden drop in temperature, leading to a rapid increase in heating demand in local areas. At this time, due to the thermal inertia effect of the pipe network, the increase in heat supply cannot be immediately transferred to the user end, resulting in a heat supply lag. To quantify this lag effect, it is necessary to calculate the inertial lag in the heat transfer process.

[0043] In specific calculations, the sudden change in heating demand, ΔQ, can be obtained first based on information such as user feedback or weather forecasts. Then, ΔQ is substituted into the thermal inertia prediction function in S102, and the function is numerically solved to obtain the temperature response curves at various moments and nodes during the heat transfer process. Ideally, if there is no inertial lag effect, changes in heating supply should immediately cause synchronous changes in user-end temperature. However, in reality, the temperature response curve exhibits a certain lag characteristic, with the temperature rise lagging behind the increase in heating supply. The inertial lag can be defined as the difference between the time required for the temperature response curve to reach a specified temperature rise threshold (such as the room temperature required by the heating standard) and the time when the heating supply changes.

[0044] The key to calculating inertial hysteresis lies in solving the thermodynamic inertial prediction function. Due to the introduction of sudden changes in heating demand, the boundary conditions of the function change, and the original numerical solution may no longer be applicable. Therefore, it is necessary to re-solve the prediction function based on the new boundary conditions. In practical programming implementation, ΔQ can be used as an input parameter, automatically triggering the modification of boundary conditions and the re-solving of the function when a sudden change in heating demand is detected. Furthermore, since calculating inertial hysteresis requires extensive numerical simulations and demands high computational efficiency, acceleration algorithms such as parallel computing and GPU acceleration can be employed to improve computational speed.

[0045] S105. Calculate the reverse compensation amount required to eliminate the inertial hysteresis based on the inertial hysteresis. After calculating the inertial hysteresis in the heat transfer process, a certain amount of reverse compensation needs to be applied to the thermal management platform to eliminate this hysteresis effect. This means appropriately increasing the heat source output based on the original heat supply to offset the heat loss caused by the inertial hysteresis. The magnitude of the reverse compensation is closely related to the inertial hysteresis and needs to be calculated based on the value of the hysteresis.

[0046] PID control algorithms can be used to calculate the reverse compensation amount. Using the inertial hysteresis as the controlled parameter and the heat supply as the controlled variable, a PID control system is established. The controller calculates the adjustment amount of the heat supply, i.e., the reverse compensation amount, based on the difference between the hysteresis and the target value (usually zero). The PID control algorithm includes three parts: proportional, integral, and derivative, which can respectively adjust the controller's response speed, steady-state error, and overshoot. By appropriately setting the PID parameters, fast and precise control of the hysteresis can be achieved.

[0047] In practical implementation, PID parameters can be tuned based on the Ziegler-Nichols method or other empirical methods. First, based on the dynamic characteristics of the pipeline network, parameters such as delay time and rise time are estimated. Then, the initial parameters of the PID controller are calculated according to the corresponding tuning formulas. During the operation of the thermal management platform, online adjustments to the parameters are necessary based on the actual control effect to adapt to changes in pipeline network characteristics. If intelligent control algorithms such as fuzzy PID control are used, a fuzzy rule base can be established by combining historical pipeline network operating data to achieve adaptive parameter tuning.

[0048] S106. Apply reverse compensation through the thermal management platform using a progressive feedback method.

[0049] The thermal management platform employs a progressive feedback approach to apply reverse compensation, specifically including: prioritizing inertial gradient regions based on their inertial response critical values ​​and real-time heat transfer efficiency, with higher priority given to inertial gradient regions that have larger inertial response critical values ​​and lower real-time heat transfer efficiency; determining the compensation sequence for each inertial gradient region based on the thermal inertial prediction function and calculating the time interval for applying reverse compensation to each inertial gradient region; applying reverse compensation step-by-step through the thermal management platform according to priority and time intervals, while simultaneously collecting real-time temperature, pressure, and flow parameters of nodes within each inertial gradient region; calculating the compensated real-time heat transfer efficiency based on the temperature, pressure, and flow parameters, and comparing the compensated real-time heat transfer efficiency with a preset target heat transfer efficiency; stopping compensation for any inertial gradient region when its compensated real-time heat transfer efficiency reaches the preset target heat transfer efficiency, and continuing to apply reverse compensation to other inertial gradient regions.

[0050] After obtaining the reverse compensation amount, it needs to be applied to the thermal management platform. Because the response characteristics differ across different inertial gradient regions, a progressive feedback approach is required when applying the compensation. This means first compensating regions with fast inertial response and high heat transfer efficiency, then gradually moving towards regions with slower response and lower efficiency. Simultaneously, the compensation strategy is dynamically adjusted based on feedback from the thermal management platform. This method can improve the overall response speed of the thermal management platform while ensuring a balanced heat supply across all regions.

[0051] In practical implementation, the thermal management platform needs to establish a comprehensive monitoring and feedback mechanism. For each inertial gradient zone, sensors for temperature, pressure, and flow rate need to be configured to collect the status parameters of each node within the zone in real time. Simultaneously, the thermal management platform also needs to communicate with actuators such as heat sources and circulating pumps, remotely adjusting the operating parameters of relevant equipment based on feedback information and compensation strategies. During the application of compensation, the management platform receives feedback data from each zone in real time, calculates the actual heat transfer efficiency, and compares it with the theoretical value. If the actual efficiency does not meet expectations, the cause needs to be analyzed promptly (such as pipe blockage, equipment failure, etc.), and countermeasures need to be taken, such as adjusting the compensation amount or repairing equipment. Once the compensation effect meets the requirements, the system switches to the next inertial gradient zone and repeats the above process.

[0052] In the above embodiments, by calculating the real-time heat transfer efficiency between each node, the actual heat transfer status in the heating network can be accurately grasped. Combined with the inertial response critical value calculated by the thermal inertia prediction function, the response characteristics of different areas in the heating network to heat changes can be identified. Based on these characteristics, the heating network is divided into multiple inertial gradient zones, enabling the thermal management platform to address the thermal inertia problems in different areas in a targeted manner. When a sudden change in heating demand occurs, the thermal management platform can quantify the delay effect caused by thermal inertia by calculating the inertial hysteresis. By calculating the corresponding reverse compensation amount and applying compensation using a progressive feedback method, the thermal management platform can proactively eliminate the hysteresis effect caused by thermal inertia. This zoned management and dynamic compensation method based on inertial characteristics can reduce the adjustment time of the thermal management platform, improve the response speed of the thermal management platform to sudden changes in demand, ensure the stability of the heat transfer process, avoid oscillations of the thermal management platform due to improper compensation, and improve thermal management efficiency.

[0053] To further improve the accuracy and adaptability of thermal management, this embodiment, after completing the aforementioned zonal management and dynamic compensation based on inertial characteristics, also provides a method for evaluating and optimizing the compensation effect. This method dynamically evaluates and corrects the compensation results by monitoring the operating status of the thermal management platform in real time, thereby achieving continuous optimization of the compensation parameters. The following section combines... Figure 2 The following describes a method for evaluating and optimizing compensation effects in an embodiment of this application: Please see Figure 2 This is a flowchart illustrating a compensation effect evaluation and optimization method in an embodiment of this application.

[0054] S201. Real-time detection of temperature fluctuation frequency and fluctuation amplitude at nodes within each inertial gradient region; In this step, the thermal management platform performs real-time detection and monitoring of the temperature at nodes within each inertial gradient region of the thermal pipeline network. By installing temperature sensors at each node, the thermal management platform can acquire temperature data from each node in real time and calculate the temperature fluctuation frequency and amplitude of each node based on the changes in temperature data. The temperature fluctuation frequency reflects the frequency of temperature changes at the node, while the fluctuation amplitude reflects the drastic nature of the temperature changes. The thermal management platform can also set different detection time intervals and data sampling frequencies according to actual needs to meet the temperature monitoring requirements under different operating conditions.

[0055] Specifically, real-time temperature monitoring of each node can be achieved as follows: Temperature sensors, such as thermocouples and resistance temperature detectors (RTDs), are installed at each node and connected to a data acquisition unit; the data acquisition unit collects temperature data from each sensor in real time according to a preset time interval and sampling frequency, and transmits the data to a data processing unit; the data processing unit analyzes and calculates the received temperature data to determine the temperature fluctuation frequency and amplitude of each node. The temperature fluctuation frequency can be obtained by calculating the number of temperature changes per unit time, while the fluctuation amplitude can be obtained by calculating the difference between the maximum and minimum temperature changes per unit time.

[0056] S202, Calculate the temperature gradient difference coefficient between adjacent nodes; Calculate the temperature gradient difference coefficient between adjacent nodes. The temperature gradient difference coefficient is the deviation rate between the node temperature and the average temperature of adjacent nodes.

[0057] In this step, based on the acquired temperature data for each node, the temperature gradient difference coefficient between adjacent nodes is further calculated. The temperature gradient difference coefficient is an important indicator for evaluating the uniformity of node temperature distribution; it reflects the degree of deviation between a node's temperature and the temperatures of its neighboring nodes.

[0058] Specifically, the temperature gradient difference coefficient can be calculated using the following method: First, for each node, identify all its neighboring nodes and obtain their real-time temperature data. Then, calculate the arithmetic mean of the temperatures of the neighboring nodes as the average temperature of the node's neighboring nodes. Finally, calculate the deviation rate between the node's temperature and the average temperature of its neighboring nodes, which is the temperature gradient difference coefficient. The deviation rate can be expressed as a percentage or a decimal, reflecting the degree of deviation between the node's temperature and the temperature of its surrounding area. The larger the deviation rate, the greater the difference between the node's temperature and the temperature of its surrounding area, and the worse the uniformity of the temperature distribution.

[0059] S203. When the temperature gradient difference coefficient of any node exceeds the preset threshold, determine the compensation anomaly area where the node is located. After calculating the temperature gradient difference coefficient for each node, it is necessary to determine whether there are any anomalies in the node temperature distribution and identify the anomaly regions that require focused attention and optimization. In this step, the anomaly of the node temperature distribution is determined by comparing the node's temperature gradient difference coefficient with a preset threshold. When the temperature gradient difference coefficient of any node exceeds the preset threshold, that node is marked as an anomaly node, and its location is defined as an anomaly region.

[0060] The selection of a preset threshold requires comprehensive consideration of various factors, such as pipeline design parameters, operating conditions, and temperature control requirements. An initial threshold can be set based on historical operating data and expert experience, and then dynamically adjusted during actual operation to adapt to different operating conditions. When the temperature gradient difference coefficient of a node exceeds the preset threshold, it indicates a significant deviation in the temperature distribution at that node, necessitating temperature compensation to improve the uniformity of the temperature distribution.

[0061] S204. Statistical compensation of the direction of heating parameter deviations at each node within the abnormal area; The statistical analysis of the direction of heating parameter deviations at each node within the compensation anomaly area includes: acquiring real-time temperature, pressure, and flow data for each node before and after applying reverse compensation; calculating the rate of change of heating parameters at each node before and after applying reverse compensation, including the rate of change of temperature, pressure, and flow; weighting the rate of change of temperature, pressure, and flow according to preset parameter weighting coefficients to obtain a comprehensive rate of change; determining the direction of heating parameter deviation for each node within the compensation anomaly area as a positive deviation when the comprehensive rate of change is greater than the average comprehensive rate of change in the inertial gradient region where the node is located; and determining the direction of heating parameter deviation for each node within the compensation anomaly area as a negative deviation when the comprehensive rate of change is not greater than the average comprehensive rate of change.

[0062] In this step, the direction of heating parameter deviations at each node within the compensation anomaly area is statistically analyzed and determined. The direction of heating parameter deviation reflects the cause of the node's temperature anomaly, whether it is due to excessive or insufficient heating. By analyzing the changes in heating parameters before and after applying reverse compensation, the direction of heating parameter deviations at each node can be determined, providing a basis for subsequent optimization of the compensation amount.

[0063] Specifically, firstly, real-time temperature, pressure, and flow data of each node within the compensation anomaly area before and after applying reverse compensation are acquired as the basis for assessing changes in heating parameters. Then, the rate of change of heating parameters at each node before and after applying reverse compensation is calculated, including the rate of change of temperature, pressure, and flow. These rates of change reflect the magnitude and speed of change in the node's heating parameters. Next, based on preset parameter weighting coefficients, the rates of change of temperature, pressure, and flow are weighted and combined to obtain a comprehensive rate of change. The comprehensive rate of change reflects the overall trend and extent of changes in the node's heating parameters. Finally, the comprehensive rate of change is compared with the average comprehensive rate of change in the inertial gradient region where the node is located to determine the direction of the node's heating parameter deviation. When the comprehensive rate of change is greater than the average, it indicates that the node's heating parameter deviation is positive, meaning the heating supply is excessive; when the comprehensive rate of change is not greater than the average, it indicates that the node's heating parameter deviation is negative, meaning the heating supply is insufficient.

[0064] S205. Based on the direction of the deviation of heating parameters, the abnormal compensation area is divided into an over-compensation area and an under-compensation area. After determining the direction of heating parameter deviations at each node within the compensation anomaly area, it is necessary to further divide the compensation anomaly area into over-compensation and under-compensation areas to implement targeted compensation strategies. In this step, nodes with the same deviation direction are classified into the same type of compensation area based on their heating parameter deviation direction. Nodes with positive deviations are classified as over-compensation areas, indicating that their heating supply is too high and the compensation amount needs to be reduced; nodes with negative deviations are classified as under-compensation areas, indicating that their heating supply is insufficient and the compensation amount needs to be increased.

[0065] When dividing the compensation zone, the spatial distribution and connectivity of nodes within the zone also need to be considered. Nodes with the same deviation direction and spatial proximity can be grouped into the same compensation zone for unified compensation control. Graph theory algorithms, such as connected component analysis and minimum spanning trees, can be used to automatically divide the compensation zone. Furthermore, the compensation zone can be further optimized and adjusted by combining the pipeline topology and fluid flow characteristics to better match the actual physical area and control requirements.

[0066] S206. Adjust the compensation amount by decreasing the compensation method for over-compensated areas and by increasing the compensation amount for under-compensated areas to obtain the compensation parameters. After identifying over-compensated and under-compensated regions, appropriate compensation strategies need to be implemented for the nodes within these regions to improve the uniformity of node temperature distribution. In this step, a decreasing correction method is used to adjust the compensation amount of nodes in the over-compensated region, and an increasing correction method is used to adjust the compensation amount of nodes in the under-compensated region, ultimately obtaining the optimized compensation parameters.

[0067] For regions with excessive compensation, a gradual reduction method is used to progressively decrease the compensation amount at each node. Specifically, a reduction coefficient can be set, and within each control cycle, the compensation amount at each node is multiplied by the reduction coefficient to obtain the corrected compensation amount. The magnitude of the reduction coefficient can be adjusted according to the degree of node temperature deviation; the greater the deviation, the smaller the reduction coefficient, and the larger the adjustment range of the compensation amount. Through gradual reduction correction, the compensation amount at each node will gradually decrease until the node temperature returns to the normal range.

[0068] For areas with insufficient compensation, an incremental correction method is used to gradually increase the compensation amount at the nodes. Similar to the decremental correction, an increment coefficient can be set. In each control cycle, the compensation amount at the node is multiplied by the increment coefficient to obtain the corrected compensation amount. The magnitude of the increment coefficient can also be adjusted according to the degree of node temperature deviation; the greater the deviation, the larger the increment coefficient, and the greater the adjustment range of the compensation amount. Through incremental correction, the compensation amount at the nodes will gradually increase until the node temperature reaches the target value.

[0069] S207. Calculate the temperature uniformity index of each node after correction. The temperature uniformity index of each node after correction is calculated, specifically including: statistically analyzing the standard deviation of the temperature gradient difference coefficient in the compensation anomaly area to obtain the temperature fluctuation uniformity; calculating the ratio of the maximum deviation value to the average deviation value of the temperature of each node in the compensation anomaly area to obtain the temperature distribution uniformity; and calculating the weighted average value based on the temperature fluctuation uniformity and the temperature distribution uniformity to obtain the temperature uniformity index.

[0070] Step S207 primarily involves calculating the temperature uniformity index for each node after correcting for the compensation amount in the abnormal compensation area, in order to evaluate the effectiveness of the compensation correction. The temperature uniformity index reflects the uniformity of temperature distribution at each node in the thermal system and is an important indicator for measuring the stability of thermal operation and the quality of heating. In addition to calculating the temperature uniformity index, other indicators reflecting the operating status, such as pressure uniformity and flow uniformity, can also be calculated to comprehensively evaluate the compensation correction effect.

[0071] This step can be implemented as follows: First, calculate the standard deviation of the temperature gradient difference coefficient within the compensated anomaly area to obtain the temperature fluctuation uniformity. The smaller the standard deviation of the temperature gradient difference coefficient, the more uniform the temperature fluctuation of the nodes within the area. Then, calculate the ratio of the maximum deviation to the average deviation of the temperature at each node within the compensated anomaly area to obtain the temperature distribution uniformity. The closer this ratio is to 1, the more uniform the temperature distribution of the nodes within the area. Finally, calculate a weighted average based on the temperature fluctuation uniformity and the temperature distribution uniformity to obtain the temperature uniformity index. The weights can be set according to actual needs. The temperature uniformity index comprehensively considers both node temperature fluctuation and temperature distribution, and can more comprehensively reflect the compensation and correction effect.

[0072] S208. When the temperature uniformity index meets the preset conditions, record the compensation parameters as the optimization parameters under the current working conditions.

[0073] Step S208 primarily determines whether the temperature uniformity index meets preset conditions. If it does, the current compensation parameter is recorded as the optimization parameter. The preset conditions can be set according to actual needs, such as the temperature uniformity index reaching a certain threshold, or the improvement in temperature uniformity index being less than a certain threshold after multiple iterations. Meeting the preset conditions indicates that the current compensation parameter has reached a relatively optimal state and can be used as the optimization parameter under the current operating conditions.

[0074] In practice, the preset conditions can be set to multiple levels, each corresponding to a different temperature uniformity index threshold or improvement threshold. When a lower-level condition is met, the current compensation parameter can be recorded, and iterative optimization can continue; when a higher-level condition is met, the iteration terminates, and the current compensation parameter is taken as the optimal parameter. This multi-level preset condition can balance optimization effect and computational efficiency, avoiding resource waste caused by excessive iteration.

[0075] In the above embodiments, by real-time detection of temperature fluctuation characteristics of nodes within the inertial gradient region and calculation of the temperature gradient difference coefficient, compensation anomaly areas can be accurately identified. Based on the direction of heating parameter deviation, the compensation anomaly areas are further subdivided into over-compensation and under-compensation areas, and corresponding incremental or decremental correction methods are used for compensation adjustment, enabling precise control for different types of compensation anomalies. During the correction process, temperature uniformity indicators are used for evaluation and recording, ensuring both the quantifiable measurement of the compensation effect and establishing an optimized parameter library corresponding to specific operating conditions. This regional and type-based compensation correction mechanism improves the ability to correct local thermal imbalances, reduces oscillations and fluctuations during the compensation process, and makes the overall heating temperature distribution more uniform and stable. Simultaneously, the accumulated recording of optimized parameters provides a reference for rapid response under similar operating conditions, improving operational stability and adaptability.

[0076] The platform in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 3 This is a schematic diagram of the physical device structure of a thermal management platform provided in an embodiment of this application.

[0077] It should be noted that, Figure 3 The structure of the platform shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0078] like Figure 3 As shown, the platform includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 302 or programs loaded from storage section 308 into Random Access Memory (RAM) 303, such as executing the methods described in the above embodiments. Various programs and data required for platform operation are also stored in RAM 303. The CPU 301, ROM 302, and RAM 303 are interconnected via bus 304. Input / Output (I / O) interface 305 is also connected to bus 304.

[0079] The following components are connected to I / O interface 305: input section 306 including a camera, infrared sensor, etc.; output section 307 including a liquid crystal display (LCD) and speakers, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card and a modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0080] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0081] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor platform, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution platform, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. The transmitted data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0082] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of platforms, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based platform that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0083] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the platform described in the above embodiments; or it may exist independently and not assembled into the platform. The storage medium carries one or more computer programs that, when executed by a processor of a platform, cause the platform to implement the methods provided in the above embodiments.

[0084] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0085] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0086] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0087] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A thermal management method based on a thermal management platform, characterized in that, include: The real-time heat transfer efficiency between each node is calculated based on the real-time temperature, pressure, and flow parameters of each node in the heating network detected by the heat management platform. The critical value of the inertial response of each node is calculated based on the thermo-inertial prediction function. The critical value of the inertial response is the inflection point value at which the thermo-inertial prediction function changes from growth to oscillation. Based on the inertial response critical value and the real-time heat transfer efficiency, the heating network is divided into multiple inertial gradient regions. When a sudden change in heating demand is detected, the inertial lag in the heat transfer process is calculated based on the product of the change in demand and the thermal inertial prediction function. Calculate the reverse compensation amount required to eliminate the inertial hysteresis based on the inertial hysteresis; The reverse compensation amount is applied through the thermal management platform using a progressive feedback method.

2. The method according to claim 1, characterized in that, The calculation of the real-time heat transfer efficiency between each node based on the real-time temperature, pressure, and flow parameters of each node in the heating network detected by the heat management platform specifically includes: The real-time heat load of each node is obtained by multiplying the real-time temperature, pressure and flow parameters of each node. The heat loss rate is calculated based on the real-time heat load of the adjacent nodes, and the heat loss rate is the ratio of the difference in real-time heat load between the adjacent nodes to the real-time heat load of the upstream node. The real-time heat transfer efficiency is calculated based on the heat loss rate, where the real-time heat transfer efficiency is 1 minus the heat loss rate.

3. The method according to claim 1, characterized in that, The thermodynamic inertial prediction function is: ； In the above function, the The temperature at node i, the The pressure at node i, the The fluid velocity at node i is... For fluid density, the For fluid dynamic viscosity, the For the specific heat capacity of the fluid, the The volumetric flow rate at node i is... The temperature diffusivity is the coefficient of thermal diffusion. The convective heat transfer coefficient is... The Stefan-Boltzmann constant is mentioned. For surface emissivity, the The ambient temperature, the The inner diameter of the pipe, the The cross-sectional area of ​​the pipe is... The pipe inclination angle, the As the friction factor, the As a time variable, the For the simplified coordinates along the pipe axis, the For gravitational acceleration, the This is the maximum permissible flow rate.

4. The method according to claim 1, characterized in that, The application of the reverse compensation amount through the thermal management platform using a progressive feedback method specifically includes: The inertial gradient regions are prioritized based on their inertial response critical values ​​and real-time heat transfer efficiency. The higher the inertial response critical value and the lower the real-time heat transfer efficiency, the higher the priority of the inertial gradient region. The compensation timing sequence of each inertial gradient region is determined according to the thermodynamic inertial prediction function, and the time interval for applying the reverse compensation amount to each inertial gradient region is calculated. The thermal management platform applies the reverse compensation amount step by step according to the priority order and the time interval, and collects the temperature, pressure and flow parameters of each node in the inertial gradient region in real time. The real-time heat transfer efficiency after compensation is calculated based on the temperature, pressure and flow rate parameters, and the real-time heat transfer efficiency after compensation is compared with the preset target heat transfer efficiency. When the real-time heat transfer efficiency after compensation in any of the inertial gradient regions reaches the preset target heat transfer efficiency, the compensation for the inertial gradient region is stopped, and the reverse compensation amount is applied to other inertial gradient regions.

5. The method according to claim 1, characterized in that, After applying the reverse compensation amount via the thermal management platform using a progressive feedback method, the method further includes: Real-time detection of temperature fluctuation frequency and fluctuation amplitude at nodes within each inertial gradient region; Calculate the temperature gradient difference coefficient between adjacent nodes, where the temperature gradient difference coefficient is the deviation rate between the node temperature and the average temperature of adjacent nodes; When the temperature gradient difference coefficient of any of the nodes exceeds a preset threshold, the compensation anomaly region where the node is located is determined. The direction of the heating parameter deviation of each node in the compensation anomaly area is statistically analyzed; Based on the direction of the deviation of the heating parameters, the abnormal compensation area is divided into an over-compensation area and an under-compensation area. The compensation amount is adjusted by decreasing correction for the over-compensated region and by increasing correction for the under-compensated region to obtain the compensation parameters. Calculate the temperature uniformity index of each node after correction; When the temperature uniformity index meets the preset conditions, the compensation parameters are recorded as the optimization parameters under the current operating conditions.

6. The method according to claim 5, characterized in that, The statistical analysis of the direction of heating parameter deviations at each node within the compensation anomaly area specifically includes: Obtain real-time temperature, pressure, and flow data of each node within the compensation anomaly area before and after applying the reverse compensation amount; Calculate the rate of change of heating parameters for each node before and after applying the reverse compensation amount. The rate of change of heating parameters includes the rate of change of temperature, the rate of change of pressure, and the rate of change of flow rate. Based on preset parameter weighting coefficients, the temperature change rate, the pressure change rate, and the flow rate change rate are weighted and combined to obtain the comprehensive change rate; When the overall rate of change is greater than the average overall rate of change in the inertial gradient region where the node is located, the direction of the heating parameter deviation of each node in the compensation anomaly region is determined to be a positive deviation; when the overall rate of change is not greater than the average overall rate of change, the direction of the heating parameter deviation of each node in the compensation anomaly region is determined to be a negative deviation.

7. The method according to claim 5, characterized in that, The calculated and corrected temperature uniformity index for each node specifically includes: The temperature fluctuation uniformity is obtained by statistically analyzing the standard deviation of the temperature gradient difference coefficient within the compensated abnormal region. The temperature distribution uniformity is obtained by calculating the ratio of the maximum deviation to the average deviation of the temperature at each node within the compensated anomaly area. The temperature uniformity index is obtained by calculating a weighted average based on the temperature fluctuation uniformity and the temperature distribution uniformity.

8. A thermal management platform, characterized in that, The thermal management platform includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the thermal management platform to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the thermal management platform, the thermal management platform performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the thermal management platform, the thermal management platform performs the method as described in any one of claims 1-7.

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