Hydraulic intelligent balance regulation and control method and platform for multiple systems of heat exchange station

Abstract

The invention discloses a hydraulic intelligent balance regulation and control method and platform for multiple systems of a heat exchange station, and relates to the technical field of hydraulic balance of central heating systems, and the method comprises the steps: obtaining the real-time operation data of multiple heating systems of the heat exchange station, calculating the heat consumption data of a unit area, analyzing the hydraulic unbalance, and obtaining a hydraulic supply state parameter set; temperature change parameters of the heat exchange station are monitored, and a climate compensation adjustment curve is constructed in combination with hydraulic unbalance indexes; collaboratively optimizing the two to generate a multi-system valve control instruction set; adjusting and monitoring according to the instruction set and then feeding back and backtracking, and constructing a hydraulic balance regulation and control strategy to realize intelligent control. The technical problems that due to the fact that regulation and control of the multiple heat supply systems of the heat exchange station are lack of pertinence and collaboration and inaccurate in state judgment, heat supply is uneven and energy consumption is too high are solved, and the technical effects that the operation states of the multiple systems of the heat exchange station are precisely controlled and cooperatively adjusted, heat supply balance is improved, and overall energy consumption is reduced are achieved.

Description

Technical Field

[0001] This invention relates to the field of hydraulic balance technology for centralized heating systems, and particularly to a method and platform for intelligent hydraulic balance control of multiple systems in heat exchange stations. Background Technology

[0002] The hydraulic balance of multiple systems in a heat exchange station directly affects heating quality and energy consumption, and its precise control is crucial for the efficient operation of the heating system. Existing technologies largely rely on manual experience or single-system regulation, lacking comprehensive analysis of multi-system operational data. Due to differences in the characteristics of each system's pipe network and fluctuations in outdoor climate, traditional methods cannot accurately identify whether the hydraulic imbalance is over- or under-supply. Regulation lacks coordination and climate adaptability, leading to incomplete data, blind regulation, and prominent problems of uneven heating and excessive energy consumption, making it difficult to meet the actual needs of precise hydraulic balance and energy-saving regulation of multiple systems. Summary of the Invention

[0003] This application provides a hydraulic intelligent balance control method and platform for multiple systems in a heat exchange station, which solves the technical problems of uneven heating and excessive energy consumption caused by the lack of targeted and coordinated control of multiple heating systems in a heat exchange station and inaccurate status judgment.

[0004] The first aspect of this application provides a hydraulic intelligent balance control method for multiple systems in a heat exchange station. The method includes: acquiring real-time operating data of multiple heating systems in the heat exchange station to calculate heat consumption per unit area; performing hydraulic balance analysis based on the heat consumption per unit area, setting hydraulic imbalance indices, and performing supply analysis based on the hydraulic imbalance indices to obtain a hydraulic supply state parameter set; monitoring temperature change parameters in the heat exchange station to obtain dynamic temperature parameters, and combining these with the hydraulic imbalance indices to perform climate compensation analysis and construct a climate compensation adjustment curve; performing multi-system collaborative optimization analysis based on the hydraulic supply state parameter set and the climate compensation adjustment curve to construct a multi-system valve control command set; monitoring and regulating the multi-system hydraulic balance according to the multi-system valve control command set to obtain multi-system operating status information; feeding back the multi-system operating status information to the multiple heating systems in the heat exchange station for retrospective optimization, and constructing a hydraulic balance control strategy for intelligent control.

[0005] The second aspect of this application provides a hydraulic intelligent balance control platform for multiple systems in a heat exchange station. The platform includes: a unit area heat consumption data acquisition module, used to acquire real-time operating data of multiple heating systems in the heat exchange station for heat consumption calculation, obtaining unit area heat consumption data; a hydraulic supply state parameter set acquisition module, used to perform hydraulic balance analysis based on the unit area heat consumption data, set hydraulic imbalance indicators, and perform supply analysis based on the hydraulic imbalance indicators to obtain a hydraulic supply state parameter set; a climate compensation adjustment curve construction module, used to monitor temperature change parameters in the heat exchange station, obtain dynamic temperature parameters, combine them with the hydraulic imbalance indicators, perform climate compensation analysis, and construct a climate compensation adjustment curve; a control command set construction module, used to perform multi-system collaborative optimization analysis based on the hydraulic supply state parameter set and the climate compensation adjustment curve, constructing a multi-system valve control command set; and a hydraulic balance control strategy execution module, used to perform multi-system hydraulic balance adjustment monitoring according to the multi-system valve control command set, obtain multi-system operating status information, feed back the multi-system operating status information to multiple heating systems in the heat exchange station for backtracking optimization, and construct a hydraulic balance control strategy for intelligent control.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application calculates the heat consumption per unit area by collecting operating data from multiple heating systems in a heat exchange station, sets imbalance indicators through hydraulic balance analysis and obtains supply status parameters, constructs a climate compensation adjustment curve by combining dynamic temperature parameters and imbalance indicators, and generates valve control commands through multi-system collaborative optimization. Through adjustment monitoring and retrospective optimization, it achieves precise control and collaborative adjustment of the operating status of multiple systems in the heat exchange station, improves the heat supply balance and reduces the overall energy consumption. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 This is a flowchart illustrating the intelligent hydraulic balance control method for multiple systems in a heat exchange station provided in this application embodiment.

[0009] Figure 2 This is a schematic diagram of the structure of the intelligent hydraulic balance control platform for multiple systems in a heat exchange station provided in the embodiments of this application.

[0010] Figure labeling: Module 1 for heat consumption per unit area, Module 2 for hydraulic supply state parameter set acquisition, Module 3 for climate compensation adjustment curve construction, Module 4 for control command set construction, and Module 5 for hydraulic balance control strategy execution. Detailed Implementation

[0011] This application provides a hydraulic intelligent balance control method and platform for multiple systems in a heat exchange station, which solves the technical problems of uneven heating and excessive energy consumption caused by the lack of targeted and coordinated control of multiple heating systems in a heat exchange station and inaccurate status judgment.

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0013] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0014] Example 1, as Figure 1 As shown, a hydraulic intelligent balance control method for multiple systems in a heat exchange station is provided, wherein the method includes: Real-time operating data of multiple heating systems in the heat exchange station are obtained to calculate heat consumption and obtain heat consumption data per unit area.

[0015] In this embodiment, the heat exchange station is a key hub in the centralized heating system, responsible for transferring heat from the heat source plant to the user-side pipe network through heat exchange equipment, and for realizing heat distribution and operation control. The heating system consists of a heat source, pipe network such as supply and return water pipes, circulating pumps, heat exchange equipment, and control devices, forming a complete system for delivering and providing heat to users in a specific area.

[0016] Specifically, by analyzing multiple heating systems within a heat exchange station, including secondary water supply pipes, return pipes, and circulating pump outlets, temperature and flow sensors are installed at corresponding locations to collect secondary supply and return water temperatures and system pump flow rate data, which are then correlated and stored to generate a dataset. Based on this dataset, the heat output of each system is calculated, and combined with the heating area parameters of the heat exchange station, the heat consumption per unit area is finally obtained.

[0017] Based on the heat consumption data per unit area, a hydraulic balance analysis is performed, a hydraulic imbalance index is set, and a supply analysis is conducted according to the hydraulic imbalance index to obtain a set of hydraulic supply state parameters.

[0018] In this embodiment of the application, hydraulic imbalance is the uneven distribution of water flow in each branch of the heating network, resulting in oversupply or undersupply of heat in some areas, which is either waste or insufficient heating, and the supply and demand cannot be matched.

[0019] Optionally, the heat consumption values ​​of multiple heating systems in the heat exchange station are first calculated, and the values ​​are then sorted in ascending order. The first heat consumption value is extracted as the target heat consumption value, and the benchmark heating system is determined accordingly. Next, using the benchmark system as a reference, the heat consumption deviation ratio of each system is calculated. Then, a critical deviation analysis is conducted using historical heat consumption deviation datasets, and hydraulic imbalance indicators are set accordingly.

[0020] Next, the hydraulic imbalance index includes a first and a second deviation threshold, as well as preset values. The first deviation threshold lies between the preset value and the second deviation threshold. Based on the range of the heat consumption deviation ratio, four corresponding hydraulic state parameters are generated and labeled as normal supply, slightly over-supply, severely over-supply, and under-supply, respectively. Finally, the four types of hydraulic state parameters are associated with their corresponding labels to form a hydraulic supply state parameter set.

[0021] By monitoring temperature change parameters at the heat exchange station and obtaining dynamic temperature parameters, climate compensation analysis is conducted in conjunction with the aforementioned hydraulic imbalance index to construct a climate compensation adjustment curve.

[0022] In one embodiment of this application, the temperature change parameters of the heat exchange station are first monitored according to the sampling frequency, and a dynamic temperature parameter sequence is constructed after interference elimination. Then, by combining the labels for slight oversupply, severe oversupply, and undersupply, and the corresponding hydraulic state parameters, a differentiated bias adjustment is made to this sequence. Based on the adjustment results, a basic water supply temperature setpoint is constructed, and an optimized compensation coefficient is generated by combining the hydraulic imbalance index. Finally, an initial water supply temperature target curve is constructed and corrected in segments to form a climate compensation adjustment curve.

[0023] Based on the set of hydraulic supply state parameters and the climate compensation adjustment curve, a multi-system collaborative optimization analysis is performed to construct a set of valve control commands for multiple systems.

[0024] Specifically, based on the set of hydraulic supply state parameters and the climate compensation regulation curve, the system first sets minimum energy consumption and hydraulic balance as dual objective parameters, constructs regulation influencing factors, and obtains dual objective optimization data through weighted summation. Then, it sets regulation critical constraints to form optimization constraints, and finally iteratively optimizes the climate compensation regulation curve according to these constraints to construct a multi-system valve control command set.

[0025] Multi-system hydraulic balance regulation and monitoring are performed according to the set of multi-system valve control commands to obtain multi-system operating status information. The multi-system operating status information is then fed back to multiple heating systems in the heat exchange station for retrospective optimization, and a hydraulic balance regulation strategy is constructed for intelligent control.

[0026] Specifically, the process begins by parsing and issuing control commands for multiple system valves, driving the primary-side electric regulating valves to adjust their openings and generating adjustment information. A monitoring cycle is set to collect operating status information from multiple systems, constructing and comparing the desired optimization target with baseline operating status information. Based on the comparison results, feedback and backtracking optimization are performed in three scenarios: secondary regulation, two-round backtracking verification, or control allocation, ultimately constructing a hydraulic balance control strategy.

[0027] After the hydraulic balance control strategy is constructed, the corresponding multi-system valve control commands are parsed and sent to the primary-side electric regulating valve actuators of each heating system through the intelligent hydraulic balance control platform, driving the valves to adjust their openings. Operating data such as secondary supply water temperature, return water temperature, flow rate, and pressure are collected and integrated at fixed monitoring cycles to form multi-system operating status information, which is compared with the baseline operating status and the optimized target status. If the target is not met, the deviation is recalculated or the fault is verified, and the commands are optimized for secondary adjustment. If the target is met, the strategy is solidified and continuously executed, achieving closed-loop intelligent control of the multi-system hydraulic balance.

[0028] Furthermore, the method provided in this application embodiment includes: Multiple heating systems are analyzed, including secondary network supply pipes, secondary network return pipes, and secondary network circulation pump outlets. A first temperature sensor is installed on the secondary network supply pipe to collect secondary supply water temperature data; a second temperature sensor is installed on the secondary network return pipe to collect secondary return water temperature data; and a flow sensor is installed at the outlet of the secondary network circulation pump to collect system pump flow rate data. The secondary supply water temperature data, the secondary return water temperature data, and the system pump flow rate data are correlated and stored to generate a correlated dataset. Based on the correlated dataset, the heating capacity of the multiple systems is calculated to obtain the heating capacity of the multiple systems. Introducing the heating area parameter of the heat exchange station, the heat consumption per unit area is calculated based on the heating capacity of the multiple systems and the heating area parameter.

[0029] In this embodiment of the application, the secondary pipeline network is the general term for the supply and return water pipeline network and supporting valves, circulating pumps and other auxiliary facilities in the centralized heating system that connect the heat exchange station and the user end, and are used to transport hot water after heat exchange treatment and realize heat distribution.

[0030] Specifically, the first step is to analyze the multiple heating systems of the heat exchange station. Those skilled in the art can identify the components of each heating system by reviewing the design and construction drawings of the heat exchange station and combining this with on-site surveys of the pipeline connections. The key monitoring points to be identified are the secondary network supply pipe, the secondary network return pipe, and the outlet of the secondary network circulating pump, ensuring the relevance and accuracy of subsequent data collection.

[0031] Subsequently, sensors were deployed and real-time data acquisition was conducted. On the secondary water supply pipe, a straight section away from elbows, valves, and other local resistance components was selected. The first temperature sensor was installed using a threaded connection, employing a commonly used industrial PT100 platinum resistance sensor. Its measurement range is suitable for the heating system water temperature, converting the physical quantity of temperature into a 4-20mA standard electrical signal to acquire secondary water supply temperature data in real time. A second temperature sensor was installed on the corresponding straight section of the secondary return water pipe using the same installation method to acquire secondary return water temperature data. On the straight section at the outlet of the secondary circulation pump, ensuring an upstream diameter of at least 10 times the pipe diameter and a downstream diameter of at least 5 times the pipe diameter, an ultrasonic flow sensor was deployed. The system pump flow rate data was obtained by detecting the water flow velocity. The acquisition frequency was set to once per minute to ensure real-time data transmission.

[0032] After data acquisition, the data is associated and stored. Using the industrially common OPCDataLogger tool, the electrical signals output by the three sensors are converted into digital signals. The data is paired according to a fixed format of "acquisition timestamp + secondary water supply temperature + secondary return water temperature + system pump flow rate". Through the cross-protocol compatibility function of this tool, the scattered sensor data is integrated and stably stored in a MySQL database to form an associated dataset, avoiding data loss or misalignment and ensuring data integrity and time synchronization.

[0033] Then, the heating capacity was calculated based on the associated dataset. Using the general phaseless heat exchange formula for heating systems, the specific heat capacity and density of water were obtained. First, the system pump flow rate data (volume flow rate) was multiplied by the density of water to obtain the mass flow rate. Then, the difference between the secondary supply water temperature and the secondary return water temperature was calculated, i.e., the temperature difference. Finally, the mass flow rate, specific heat capacity and temperature difference were multiplied to obtain the heating capacity of each heating system.

[0034] Finally, the heating area parameters of the heat exchange station are used to calculate the heat consumption per unit area. These parameters can be obtained from heating contract filing documents, community building planning documents, or verified through on-site measurements of the heating area. The heat output of each heating system is compared to its corresponding heating area; that is, the heat consumption per unit area equals the heat output divided by the heating area. This yields the heat consumption data per unit area for each heating system. The calculation process can be directly implemented using a PLC controller or computer software.

[0035] By employing sensor acquisition technology, industrial data storage methods, and thermal calculation principles, accurate and reliable acquisition of heat consumption data per unit area for multiple heating systems in the heat exchange station has been achieved.

[0036] Furthermore, the method provided in this application embodiment includes: Multiple heating systems in the heat exchange station are traversed for heat consumption calculations to obtain multiple heat consumption values. These multiple heat consumption values ​​are then sorted in ascending order to construct a heat consumption value sequence. The first heat consumption value in the sequence is extracted as the target heat consumption value. The target heat consumption value is used as an index to retrieve multiple heating systems, and the target heating system is selected as the benchmark heating system. Heat consumption deviation is calculated for multiple heating systems according to the benchmark heating system to obtain a heat consumption deviation ratio. Based on the historical heat consumption deviation dataset and the heat consumption deviation ratio, a deviation criticality analysis is performed to set the hydraulic imbalance index.

[0037] Optionally, firstly, all heating systems in the heat exchange station are traversed to obtain the heat consumption data per unit area for each system. Using the MySQL database built in the preceding steps, data query commands are used to retrieve the stored heat consumption data per unit area for each heating system, ensuring complete coverage of all heating systems in the heat exchange station without omitting any branch system. Finally, multiple heat consumption values ​​are aggregated to provide basic data support for subsequent balance analysis.

[0038] Next, the bubble sort algorithm is used to sort the summarized heat loss values. Specifically, all heat loss values ​​are input into a computer program. The program repeatedly traverses the heat loss value sequence, comparing adjacent values ​​one by one. If the preceding value is greater than the following value, their positions are swapped. This iteration continues until the entire sequence is arranged in ascending order, forming a heat loss value sequence. After sorting, the first value in the sequence, i.e., the smallest heat loss value, is extracted and determined as the target heat loss value.

[0039] Then, a linear search method is used to retrieve the benchmark heating system. Based on the defined target heat consumption value, the heat consumption value of each heating system is retrieved one by one from the MySQL database storing information on each system, and compared with the target heat consumption value. When a heating system is found to have a heat consumption value that perfectly matches the target heat consumption value, the search stops, and that heating system is designated as the benchmark heating system, serving as the reference standard for subsequent deviation calculations.

[0040] Next, the heat consumption deviation ratio of each heating system is calculated. A relative deviation calculation method is used, with the heat consumption value of the benchmark heating system as the denominator and the difference between the heat consumption value of each system to be calculated and the benchmark system's heat consumption value as the numerator. The calculation formula is: Heat consumption deviation ratio = (Heat consumption value of the system to be calculated - Heat consumption value of the benchmark system) / Heat consumption value of the benchmark system. Data from each system is input into an Excel spreadsheet for calculation to obtain the corresponding heat consumption deviation ratio for each heating system, which is used to quantify the difference in heat consumption between each heating system and the benchmark heating system.

[0041] Finally, a critical deviation analysis was conducted using historical data to set a hydraulic imbalance index. Heat consumption deviation data from the past complete heating season was collected to construct a historical heat consumption deviation dataset, which was then statistically analyzed using the percentile method. The historical dataset was first sorted in ascending order, and the value corresponding to the 75th percentile was calculated as the first deviation threshold, and the value corresponding to the 90th percentile as the second deviation threshold. Simultaneously, a preset value was set as the minimum reasonable heat consumption deviation ratio to meet users' basic heating needs. The currently calculated heat consumption deviation ratio was combined with the statistical results of the historical dataset to comprehensively determine the reasonable critical range of the deviation, ultimately setting a hydraulic imbalance index that includes the first deviation threshold, the second threshold, and the preset value.

[0042] By employing data retrieval, bubble sorting, linear search, relative deviation calculation, and percentile statistical analysis, the benchmark heating system was gradually determined and the heat consumption deviation was quantified. Finally, a scientific and reasonable hydraulic imbalance index was set, providing a quantitative basis for subsequent accurate judgment of the heating system's supply status.

[0043] Furthermore, the method provided in this application embodiment includes: The hydraulic imbalance index includes a first deviation threshold and a second deviation threshold, wherein the first deviation threshold is greater than a preset value and less than the second deviation threshold; when the heat consumption deviation ratio is greater than a preset value and less than or equal to the first deviation threshold, a first hydraulic state parameter is generated; a supply label is generated based on the first hydraulic state parameter; when the heat consumption deviation ratio is greater than the first deviation threshold and less than or equal to the second deviation threshold, a second hydraulic state parameter is generated; a supply label is generated based on the second hydraulic state parameter; when the heat consumption deviation ratio is greater than the first deviation threshold and less than or equal to the second deviation threshold, a slight oversupply label is generated; when the heat consumption deviation ratio is greater than the first deviation threshold and less than or equal to the second deviation threshold, a second hydraulic state parameter is generated; a slight oversupply label is generated based on the second hydraulic state parameter; when the heat consumption deviation ratio is greater than the first deviation threshold and less than or equal to the second deviation threshold, a slight oversupply label is generated .... When the difference ratio is greater than the second deviation threshold, a third hydraulic state parameter is generated; a severe oversupply tag is generated based on the third hydraulic state parameter; when the heat consumption deviation ratio is less than a preset value, a fourth hydraulic state parameter is generated; a short supply tag is generated based on the fourth hydraulic state parameter; the first hydraulic state parameter, the second hydraulic state parameter, the third hydraulic state parameter, the fourth hydraulic state parameter, and the normal supply tag, the slight oversupply tag, the severe oversupply tag, and the short supply tag are associated to obtain the hydraulic supply state parameter set.

[0044] Specifically, firstly, the preset values ​​for the hydraulic imbalance index are determined. The core function of these preset values ​​is to define the minimum heat consumption deviation threshold required to meet users' basic heating needs. The specific implementation method is as follows: Step a: Referring to the heat consumption index requirements for achieving indoor temperature standards in the "Design Standard for Urban Heating Pipeline Networks," and considering the building insulation level (e.g., energy-saving or non-energy-saving buildings) of the service area of ​​the heat exchange station, as well as the local winter outdoor design temperature, the average extreme minimum winter temperature of the past 10 years can be obtained from the meteorological department. The minimum heat consumption standard per unit area required to guarantee an indoor temperature of 18℃ for users can be calculated and denoted as... .

[0045] Step b: Collect heat consumption data per unit area for all heating systems during periods when the outdoor temperature is close to the design temperature in the past three complete heating seasons at the heat exchange station, and filter out the heat consumption data that meets the indoor temperature standard, i.e., greater than or equal to the design temperature. The heat consumption data is denoted as .

[0046] Step c: Calculate the ratio of the deviations between these data and the baseline heating system heat consumption values, using the following formula: Construct a dataset of heat consumption deviations to meet standards, where the heat consumption value of the benchmark heating system is the target heat consumption value obtained in the preceding steps, denoted as . .

[0047] Step d: Use the MIN function in Excel to extract the minimum value of the target heat consumption deviation dataset and set it as the preset value. For example, if the minimum value of the target heat consumption deviation dataset is 6%, then the preset value is 6%. This means that when the heat consumption deviation ratio of a certain system is <6%, even if its heat consumption value is higher than the minimum heat consumption value, it indicates that its heat consumption has not reached the minimum standard required by the user to achieve the target temperature, and it is in a state of undersupply.

[0048] Next, the composition and threshold relationship of the hydraulic imbalance index are clarified. The hydraulic imbalance index includes a first deviation threshold and a second deviation threshold set by the percentile method in the previous steps. The first deviation threshold is greater than a preset value and less than the second deviation threshold, which satisfies the logical closed loop of preset value < first deviation threshold < second deviation threshold. The three together constitute the judgment range of heat consumption deviation ratio, providing a quantitative standard for subsequent supply status classification.

[0049] Then, based on the comparison results between the heat consumption deviation ratio and each threshold, corresponding hydraulic state parameters are generated. The conditional judgment logic in the computer program is used to process the heat consumption deviation ratio of each heating system one by one. When the heat consumption deviation ratio is greater than a preset value and less than or equal to the first deviation threshold, the program automatically generates the first hydraulic state parameter; when the heat consumption deviation ratio is greater than the first deviation threshold and less than or equal to the second deviation threshold, the second hydraulic state parameter is generated; when the heat consumption deviation ratio is greater than the second deviation threshold, the third hydraulic state parameter is generated; and when the heat consumption deviation ratio is less than a preset value, the fourth hydraulic state parameter is generated, achieving precise quantification of different supply states.

[0050] Next, corresponding supply tags are added to each hydraulic state parameter. A key-value pair mapping method is used to establish the correspondence between hydraulic state parameters and supply tags in the program: the first hydraulic state parameter is mapped to the normal supply tag, the second hydraulic state parameter is mapped to the slightly over-supply tag, the third hydraulic state parameter is mapped to the severely over-supply tag, and the fourth hydraulic state parameter is mapped to the under-supply tag. This tagging process makes the supply status more intuitive and easier to understand.

[0051] Finally, the hydraulic state parameters are associated with their corresponding supply tags and stored to form a hydraulic supply state parameter set. A related table containing hydraulic state parameters, supply tags, and corresponding heat consumption deviation ranges is created using a MySQL database. The four types of hydraulic state parameters and their corresponding supply tags and heat consumption deviation ranges are entered into the table one by one. Database indexing ensures the accuracy of data association and the efficiency of data retrieval.

[0052] By using statistical analysis to determine preset values, conditional judgment logic to generate state parameters, key-value pair mapping to add labels, and database association storage, a set of hydraulic supply state parameters with accurate classification and complete structure was obtained, providing a clear state basis for subsequent multi-system coordinated control.

[0053] Furthermore, the method provided in this application embodiment includes: The temperature change parameters of the heat exchange station are continuously monitored according to the sampling frequency. Interference is eliminated to construct a temperature dynamic parameter sequence. Based on the slight oversupply label and the second hydraulic state parameter, the temperature dynamic parameter sequence is differentially biased and adjusted to obtain a first adjustment result. Based on the severe oversupply label and the third hydraulic state parameter, the temperature dynamic parameter sequence is differentially biased and adjusted to obtain a second adjustment result. Based on the undersupply label and the fourth hydraulic state parameter, the temperature dynamic parameter sequence is differentially biased and adjusted to obtain a third adjustment result. The first, second, and third adjustment results are analyzed to construct a basic water supply temperature setpoint. Climate compensation analysis is performed on the basic water supply temperature setpoint according to the hydraulic imbalance index to generate an optimized compensation coefficient. An initial water supply temperature target curve is constructed, and the initial water supply temperature target curve is segmented and corrected according to the optimized compensation coefficient to construct the climate compensation adjustment curve.

[0054] Specifically, firstly, continuous monitoring and interference elimination of temperature change parameters at the heat exchange station are implemented. Those skilled in the art can use an industrial-grade PT1000 temperature sensor, installed in an open, unobstructed outdoor location away from heat source exhaust outlets to ensure the collected temperature data accurately reflects the ambient temperature. A sampling frequency of once per minute is set, which meets real-time monitoring requirements without causing data redundancy. After the sensor converts the temperature physical quantity into a digital signal, it is uploaded to the data processing module in real time via wired transmission. To address potential random interference in the collected data, such as outliers caused by instantaneous airflow or sensor fluctuations, a moving average method is used for interference elimination. Specifically, five consecutive sampling points are selected as a sliding window, and the arithmetic mean of the data within each window is calculated. This average value is then used to replace the data at the intermediate sampling point within the window. This process is repeated for all sampling data to ultimately construct a smooth and stable sequence of dynamic temperature parameters.

[0055] Next, differentiated bias adjustments are made based on different hydraulic supply states. First, tags and corresponding hydraulic state parameters for each heating system are extracted from the hydraulic supply state parameter set, establishing a "tag-parameter" mapping table. A fixed bias coefficient method is used for adjustment, consistent with actual industrial application scenarios. Based on practical experience in the heating industry, three types of bias adjustment coefficients are preset: For example, for systems with a slight oversupply tag and a second hydraulic state parameter, the bias adjustment coefficient is set to -0.5℃, meaning 0.5℃ is subtracted from each data point of the corresponding system in the temperature dynamic parameter sequence, resulting in the first adjustment result, achieving a slight cooling to reduce heat waste; for systems with a severe oversupply tag and a third hydraulic state parameter, the bias adjustment coefficient is set to -1.2℃, subtracting 1.2℃ from the corresponding data point, resulting in the second adjustment result, increasing the cooling amplitude to curb severe waste; for systems with an undersupply tag and a fourth hydraulic state parameter, the bias adjustment coefficient is set to +0.8℃, adding 0.8℃ to the corresponding data point, resulting in the third adjustment result, supplementing heat by increasing the temperature to meet user needs.

[0056] Then, the three adjustment results are integrated to construct the basic water supply temperature setpoint. A weighted average method is used to merge the first, second, and third adjustment results. Weights are assigned according to heating priority: undersupply directly affects the user's heating experience, with a weight of 0.4; slight oversupply requires a balance between energy saving and comfort, with a weight of 0.3; severe oversupply prioritizes energy saving, with a weight of 0.3. In the specific calculation, the average of the three adjustment results is first calculated separately. Then, the average of the first adjustment result is multiplied by 0.3, the average of the second adjustment result by 0.3, and the average of the third adjustment result by 0.4. The three products are then added together, and the sum is the basic water supply temperature setpoint. This value comprehensively considers the control needs of different supply states, and those skilled in the art can adjust it according to actual needs.

[0057] Next, optimized compensation coefficients are generated based on the hydraulic imbalance index. A piecewise linear interpolation method is used to quickly map the thresholds to the coefficients. First, the specific values ​​of the preset value, the first deviation threshold, and the second deviation threshold in the hydraulic imbalance index are defined, dividing the system into four intervals: a heat consumption deviation ratio < preset value indicates an undersupply interval; a preset value ≤ heat consumption deviation ratio ≤ first deviation threshold indicates a normal interval; a first deviation threshold < heat consumption deviation ratio ≤ second deviation threshold indicates a slightly oversupply interval; and a heat consumption deviation ratio > second deviation threshold indicates a severely oversupply interval. Based on the principle of climate compensation, preset benchmark values ​​for the compensation coefficients corresponding to each interval are established: for example, the compensation coefficient for the undersupply interval is 1.3, used to enhance temperature compensation; the compensation coefficient for the normal interval is 1.0, maintaining the basic water supply temperature; the compensation coefficient for the slightly oversupply interval is 0.8, moderately reducing the water supply temperature; and the compensation coefficient for the severely oversupply interval is 0.6, significantly reducing the water supply temperature. The threshold values ​​and corresponding benchmark values ​​of each interval are input into the interpolation calculation model. The model automatically performs linear transition calculations on the coefficients of adjacent intervals to generate optimized compensation coefficients that cover the entire deviation range, ensuring that the compensation effect is continuous and smooth.

[0058] Finally, the operating time series of multiple heating systems in the heat exchange station are extracted as the horizontal axis, and the basic water supply temperature setpoint is used as the vertical axis. A discrete data point sequence is plotted in conjunction with the temperature dynamic parameter sequence. This sequence is then connected using linear interpolation to construct the initial water supply temperature target curve. Subsequently, the temperature dynamic parameter sequence is analyzed to construct a temperature change trend graph and divide it into multiple temperature feature segments. These feature segments are traversed and matched with the optimized compensation coefficient to generate temperature-compensation data pairs. The initial water supply temperature target curve is then corrected according to these data pairs, resulting in multiple corrected curve segments. Finally, the corrected curve segments are smoothly connected to construct the climate compensation regulation curve. This step will be explained in detail later.

[0059] By employing methods such as moving average, fixed bias coefficient, weighted average, and piecewise linear interpolation, the temperature data processing, differential adjustment, baseline value construction, and compensation coefficient generation were gradually completed. This enabled the accurate calculation of climate compensation parameters based on the hydraulic imbalance state, providing reliable data support for the subsequent construction of climate compensation adjustment curves.

[0060] Furthermore, the method provided in this application embodiment includes: The operating time series of multiple heating systems in the heat exchange station are extracted. Using the operating time series as the horizontal axis and the basic water supply temperature setpoint as the vertical axis, a discrete data point sequence is plotted based on the temperature dynamic parameter sequence. Linear interpolation is used to connect the discrete data point sequence to construct the initial water supply temperature target curve. Change analysis is performed based on the temperature dynamic parameter sequence to construct a temperature change trend map for feature segmentation, identifying multiple temperature feature segments. These multiple temperature feature segments are traversed and matched with the optimized compensation coefficient to generate temperature-compensation data pairs. The initial water supply temperature target curve is corrected according to the temperature-compensation data pairs, generating multiple corrected curve segments. These multiple corrected curve segments are then smoothly connected to construct the climate compensation adjustment curve.

[0061] Specifically, firstly, the operating time series of multiple heating systems in the heat exchange station are extracted and a discrete data point sequence is plotted. Those skilled in the art can obtain the continuous operating time records of each heating system from the operating log of the PLC control system of the heat exchange station, divide it into 15-minute intervals, construct an operating time series, and use this series as the horizontal axis. Simultaneously, the previously calculated basic water supply temperature setpoint is retrieved and used as the vertical axis. For the temperature dynamic parameter sequence, the average temperature corresponding to each time point is calculated. Each time point, the basic water supply temperature setpoint for that point, and the corresponding average temperature dynamic parameter are correlated to form time-temperature coordinate pairs, such as (08:00, 52℃) and (08:15, 51.8℃). Based on all coordinate pairs, a discrete data point sequence is plotted in a two-dimensional coordinate system to ensure that the data points accurately reflect the temperature correlation status at different times.

[0062] Next, linear interpolation is used to connect the discrete data point sequence to construct the initial target curve for the water supply temperature. Specifically, for two adjacent discrete data points, let the coordinates of the previous point be... The coordinates of the next point are ( , ), by calculating the linear equation between two points The slope ,exist to Four interpolation points are selected evenly within a time interval. The temperature value corresponding to each interpolation point is calculated according to the linear equation. Then, the original discrete data points and the interpolation points are connected sequentially in time order to form a continuous initial water supply temperature target curve, so that the curve can initially reflect the trend of water supply temperature change over time.

[0063] Then, based on the temperature dynamic parameter sequence, a change analysis was performed to divide the temperature into characteristic segments. Using the moving average trend analysis method, the average value of the temperature dynamic parameters was calculated for six consecutive time points. By comparing the changes in adjacent average values, the temperature change trend was determined to be rising, stable, or falling. Excel software was used to map the temperature dynamic parameter sequence to the running time sequence and plot a line graph of the temperature change trend. A temperature change rate threshold of 0.5℃ per 15 minutes was set. When the temperature change rate at adjacent time points was greater than 0.5℃ and lasted for more than two consecutive points, it was determined to be an rising or falling segment; when the temperature change rate was less than or equal to 0.5℃, it was determined to be a stable segment. The entire temperature change trend graph was traversed, and multiple temperature characteristic segments were divided according to the above rules, such as a morning temperature falling segment, a midday temperature stable segment, and a nighttime temperature rising segment.

[0064] Subsequently, temperature characteristic segments are traversed and matched with optimized compensation coefficients to generate temperature-compensation data pairs. First, all divided temperature characteristic segments are organized, clarifying the time range, trend (rising, stable, falling), and corresponding hydraulic imbalance distribution for each segment, such as the proportion of under-supply and over-supply systems within that segment. The previously generated optimized compensation coefficients are retrieved, and matching rules are established based on heating system operation experience: higher compensation coefficients are matched when the temperature is falling and the proportion of under-supply systems is high; standard compensation coefficients are matched when the temperature is stable and most systems are supplying normally; and lower compensation coefficients are matched when the temperature is rising and the proportion of over-supply systems is high. Each temperature characteristic segment is paired with its corresponding optimized compensation coefficient according to these rules; for example, a compensation coefficient of 1.0 is matched for the midday stable temperature segment, and a compensation coefficient of 1.2 is matched for the morning temperature falling segment, forming multiple temperature-compensation data pairs.

[0065] Next, for each temperature-compensation data pair and its corresponding time interval, a segment of the curve within that interval is extracted from the initial target water supply temperature curve. A coefficient multiplication correction method is used to multiply the temperature value at each point on this curve segment by the corresponding optimized compensation coefficient to obtain the corrected temperature value. For example, if the temperature at a point on the initial curve segment corresponding to a certain temperature characteristic segment is 50℃, and the matched optimized compensation coefficient is 1.2, then the corrected temperature at that point is 50 × 1.2 = 60℃; if the compensation coefficient is 0.9, then the corrected temperature is 50 × 0.9 = 45℃. This method is used to correct the initial curve segment corresponding to each temperature characteristic segment, generating corrected curve segments that correspond one-to-one with each temperature characteristic segment.

[0066] Finally, a quadratic function transition method is used to achieve a smooth connection between multiple correction curve segments. For two adjacent correction curve segments, let the coordinates of the endpoint of the previous segment be... The starting coordinates of the second segment of the curve are Constructing a quadratic function Substituting the coordinates of the two points and the rate of temperature change at the endpoints of the two curve segments, the function coefficients a, b, and c are calculated using the slopes of adjacent points at the endpoints of the curves, thus obtaining the equation of the transition curve. Based on this equation, the following calculations can be performed: to The temperature values ​​at multiple transition points within a time interval are used to connect the transition points in chronological order to form a smooth transition curve. Then, all the correction curve segments are sequentially connected to the transition curve to finally construct a continuous, smooth climate compensation adjustment curve that fits the characteristics of temperature changes and compensation needs.

[0067] By employing methods such as time series extraction, linear interpolation, trend division, coefficient matching, piecewise correction, and quadratic function smoothing connection, the climate compensation regulation curve was accurately constructed, providing a scientific and reliable basis for the intelligent control of multiple system valves.

[0068] Furthermore, the method provided in this application embodiment includes: A feasibility analysis is conducted to minimize the total energy consumption parameters of the heat exchange station, setting a first objective parameter. Based on the hydraulic supply state parameters, a balance calculation is performed to obtain the hydraulic balance state parameters of the heat exchange station, setting a second objective parameter. The first and second objective parameters are analyzed for their regulatory impact, constructing a regulatory impact factor. Based on the regulatory impact factor, a weighted sum of the first and second objective parameters is performed to construct dual-objective optimization data. Based on the dual-objective optimization data, critical constraints are applied to the multiple systems of the heat exchange station, constructing optimization constraints. According to the optimization constraints and the dual-objective optimization data, the climate compensation regulation curve is iteratively optimized and solved to construct the set of valve control commands for the multiple systems.

[0069] In one embodiment, firstly, the total energy consumption parameters of the heat exchange station are introduced and a minimization feasibility analysis is performed to set a first target parameter. Those skilled in the art can collect total station energy consumption data using energy metering devices already deployed in the heat exchange station, such as electricity meters and heat meters. The collected parameters include electricity consumption and heat consumption per unit time, with a collection frequency set to once per hour to ensure the data reflects real-time energy consumption levels. A statistical analysis method is used for minimization feasibility analysis, collecting total station energy consumption data from the past three complete heating seasons. The minimum energy consumption under different outdoor temperatures and operating loads is selected to construct a minimum energy consumption dataset. Linear regression analysis is performed on this dataset using Excel to fit the correlation equation between energy consumption and outdoor temperature and operating load. Based on this equation, the minimum feasible energy consumption value under the current operating conditions (combining real-time outdoor temperature and user-side heat demand) is determined, and this value is set as the first target parameter, i.e., the total station energy consumption minimization target.

[0070] Next, equilibrium calculations are performed based on the hydraulic supply state parameters, and a second target parameter is set. Hydraulic state parameters and corresponding labels for each heating system are extracted from the hydraulic supply state parameter set to clarify the current supply status of each system, including normal, slightly oversupplied, severely oversupplied, and undersupplied. The flow balance calculation method is used as the core means of equilibrium calculation. First, real-time flow values ​​are obtained through flow sensor data of each system, and the average flow value of all heating systems is calculated. Then, the deviation rate between the real-time flow value and the average flow value of each system is used as the equilibrium evaluation index: flow deviation rate = (real-time flow - average flow) / average flow. Referring to the allowable deviation requirements for hydraulic balance in the "Design Standard for Urban Heating Pipeline Networks," the equilibrium compliance threshold is set at ±10%. The core requirement is that the flow deviation rate of all heating systems is within ±10%. Combined with the optimization expectations of the hydraulic state parameters of each system, the second target parameter, namely the multi-system hydraulic equilibrium state parameter of the heat exchange station, is set.

[0071] Then, an impact analysis of regulation was conducted. Actual values ​​of the first and second target parameters, along with corresponding valve regulation amplitude data, were collected for different operating periods during the past heating season to construct a correlation dataset of energy consumption, energy balance, and regulation amplitude. The correlation coefficient between the first target parameter and the regulation amplitude was calculated using SPSS statistical software. The correlation coefficient between the second target parameter and the control amplitude The larger the absolute value of the correlation coefficient, the more significant the influence of regulatory behavior on the target parameter. The regulatory influence factor is calculated based on the correlation coefficient using the following formula: First Regulatory Influence Factor Second regulatory influencing factor This ensures that the sum of the two influencing factors is 1 and that both are positive. For example, if r1=0.6 and r2=0.4, then the first regulatory influencing factor = 0.6 and the second regulatory influencing factor = 0.4, which directly reflects the importance of the two target parameters in the regulation process.

[0072] Subsequently, the first and second target parameters are weighted and summed based on the regulation influence factors. The first target parameter is multiplied by the corresponding first regulation influence factor, and the second target parameter is multiplied by the corresponding second regulation influence factor. The two products are then added together to obtain the dual-target optimization data. The specific calculation process can be implemented using an Excel spreadsheet. For example, if the first target parameter is 120kW and the first regulation influence factor is 0.6, and the second target parameter is 90% (equilibrium degree) and the second regulation influence factor is 0.4, the second target parameter is first standardized, converting 90% to 0.9. Then, the dual-target optimization data is calculated as 120 × 0.6 + 0.9 × 0.4 = 72.36. This data comprehensively reflects the dual optimization requirements of minimizing energy consumption and achieving hydraulic balance, providing a unified optimization target for subsequent regulation.

[0073] Subsequently, referring to the equipment operation parameter manuals and industry safety standards of the heat exchange station, the critical adjustment values ​​were determined: valve opening constraints were set to 0%-100% based on the technical parameters of the electric regulating valve to avoid over-range damage to the equipment; system pressure constraints were set based on the design pressure resistance values ​​of the secondary pipe network, with upper and lower limits for the supply and return water network pressures to prevent over-pressure or insufficient pressure in the network; and supply and return water temperature difference constraints were set to a range of 10℃-20℃, combined with heat exchange efficiency requirements, to ensure stable heat exchange performance. These critical values ​​were then combined with the dual-objective optimization data to clarify the core requirement of maximizing the dual-objective optimization data while satisfying the constraints of valve opening, system pressure, and supply and return water temperature difference. Optimization constraints were constructed to provide boundary limits for subsequent iterative optimization solutions.

[0074] Finally, based on the optimization constraints and dual-objective optimization data, the climate compensation adjustment curve is iteratively optimized and solved. Candidate solutions for valve opening are generated by calculation, simulation is used to obtain the system simulation energy consumption and hydraulic simulation equilibrium parameters, the first fitness value is evaluated and matched iteratively, and the instructions are converted after the preset number of iterations is reached. Finally, a set of multi-system valve control instructions is generated. This step will be explained in detail in the following content.

[0075] By employing steps such as statistical analysis, flow balance calculation, correlation analysis, linear weighted summation, and critical value setting, the target parameter setting, influencing factor construction, optimized data integration, and constraint condition establishment are gradually completed, providing a scientific and feasible optimization basis and boundary reference for the construction of a multi-system valve control command set.

[0076] Furthermore, the method provided in this application embodiment includes: Based on the optimization constraints and the dual-objective optimization data, the valve opening of the climate compensation adjustment curve is calculated, and candidate solutions for valve opening are randomly generated. Simulation control is performed according to the candidate solutions for valve opening to generate simulation control parameters, which include system simulation energy consumption parameters and hydraulic simulation equilibrium parameters. Fitness evaluation is performed based on the system simulation energy consumption parameters and hydraulic simulation equilibrium parameters to calculate a first fitness value. The first fitness value is matched with the candidate solutions for valve opening, and the matching result is mapped to the solution space for iteration. When the number of iterations reaches a preset number, the iteration is terminated. Instruction conversion is performed based on the solution space to generate the multi-system valve control instruction set.

[0077] Optionally, firstly, based on the optimization constraints and dual-objective optimization data, the valve opening degree of the climate compensation regulation curve is calculated, and candidate valve opening solutions are randomly generated. First, the boundary parameters in the optimization constraints, such as the valve opening degree range of 0%-100%, the upper and lower limits of system pressure, and the range of supply and return water temperature difference, are defined. Then, the target supply water temperature at each time node in the climate compensation regulation curve is extracted. A proportional conversion method is used to calculate the valve opening degree, based on the difference between the target supply water temperature and the current actual supply water temperature, to determine the opening degree calculation interval within the constraints. Using a uniform random number generation method, a valve opening degree value is randomly generated for each heating system within this calculation interval. The opening values ​​of all systems are combined to form a set of candidate valve opening solutions. This process is repeated to generate 50 sets of candidate solutions, ensuring that the diversity of solutions covers the feasible interval.

[0078] Next, simulation control was performed according to the candidate solutions for valve opening, generating simulation control parameters. Professional hydraulic system simulation software was selected, and preset information such as the heat exchange station's pipe network structure, equipment parameters, and optimization constraints were entered into the simulation model. Each set of candidate solutions for valve opening was input into the model one by one to simulate actual operating conditions. The simulation software simulated the water flow state and energy loss within the pipe network using computational fluid dynamics principles, outputting the system simulation energy consumption parameters and hydraulic simulation equilibrium parameters corresponding to each set of candidate solutions. The system simulation energy consumption parameters represent the total power and heat consumption per unit time, and the hydraulic simulation equilibrium parameters represent the average flow deviation rate of each system.

[0079] Then, fitness evaluation is performed based on simulation control parameters, and the first fitness value is calculated. A weighted summation method is used to construct the fitness evaluation model. Referring to industry practice, the weight of the system simulation energy consumption parameter is set to 0.4, and the weight of the hydraulic simulation equilibrium parameter is set to 0.6. The system simulation energy consumption parameter is normalized, and the calculation formula is (maximum allowable energy consumption - simulation energy consumption) / (maximum allowable energy consumption - minimum feasible energy consumption) to obtain the normalized energy consumption value. The hydraulic simulation equilibrium parameter is normalized, and the calculation formula is 1 - average flow deviation rate to obtain the normalized equilibrium value. The two normalized values ​​are multiplied by their corresponding weights and then summed. The result is the first fitness value of the candidate solution. The higher the fitness value, the more the candidate solution meets the dual objective optimization requirements.

[0080] Finally, the first fitness value is matched with the candidate valve opening solutions, mapped to the solution space for iteration, and after termination, a multi-system valve control command set is generated. A relationship is established between candidate solutions and their corresponding first fitness values, mapping all candidate solutions and their fitness values ​​to a preset solution space containing all valve opening combinations that satisfy the optimization constraints. A greedy iterative strategy is adopted, retaining the top 20% of candidate solutions by fitness value in each iteration. Based on the opening values ​​of these optimal solutions, new candidate solutions are generated through small-amplitude random perturbations, and the new candidate solutions must satisfy the optimization constraints. The preset number of iterations is 50. When the preset number of iterations is reached, the iteration terminates, and the candidate solution with the largest fitness value in the solution space is selected. The valve opening values ​​of each system in this candidate solution are converted into industrial standard electric regulating valve control commands, forming a multi-system valve control command set.

[0081] By employing steps such as proportional conversion, uniform random number generation, hydraulic system simulation, weighted summation evaluation, and greedy iteration, the generation of candidate solutions for valve opening, simulation verification, fitness evaluation, and iterative optimization are gradually completed. Finally, a set of multi-system valve control commands that meets the dual objective optimization requirements is generated, ensuring the scientific validity and feasibility of the control commands.

[0082] Furthermore, the method provided in this application embodiment includes: The set of multi-system valve control commands is parsed and sent to the primary-side electric regulating valve actuators of the multiple systems in the heat exchange station to drive the valves to adjust their openings, generating valve adjustment information. A fixed-duration monitoring cycle is set, and the monitoring cycle is started based on the valve adjustment information. Data from multiple heating systems is collected and integrated according to the monitoring cycle to obtain multi-system operating status information. Optimization expectation calculation is performed based on the set of multi-system valve control commands to construct the optimization expectation target status information. Baseline operating status information of multiple heating systems is extracted, and the multi-system operating status information is compared with the baseline operating status information and the optimization expectation target status information. When the multi-system operating status information reaches the baseline operating status information but does not reach the optimization expectation target status information, the multi-system operating status information is fed back to the multiple heating systems in the heat exchange station for backtracking. The control deviation is calculated, and the control command set of the multi-system valves is optimized based on the operating deviation value to construct a multi-system valve optimized control command set for secondary adjustment and monitoring. When the operating status information of the multi-system does not reach the baseline operating status information and does not reach the optimized expected target status information, the operating status information of the multi-system is fed back to multiple heating systems in the heat exchange station for a first round of backtracking verification. Based on the first verification result, the valve control command set of the multi-system is subjected to a second round of backtracking verification. Based on the second verification result, multiple heating systems in the heat exchange station are subjected to secondary adjustment and monitoring. When the operating status information of the multi-system reaches the baseline operating status information and reaches the optimized expected target status information, the operating status information of the multi-system is fed back to multiple heating systems in the heat exchange station for control and allocation, thus constructing the hydraulic balance control strategy.

[0083] In one embodiment, firstly, industrially common PLC programming technology is used to parse the set of valve control instructions for multiple systems. The instruction format is adapted to the communication protocol of the primary-side electric regulating valve actuator, and the MODBUS-RTU protocol is selected to achieve bidirectional communication between the PLC and the actuator. During the parsing process, the target opening value and adjustment rate parameters of each valve are extracted. The electric regulating valve actuator drives the valve core to move linearly according to the parsed instructions, completing the opening adjustment at the set adjustment rate. At the same time, the actual opening value, adjustment start time, and adjustment completion time are collected in real time through the actuator's feedback interface. This information is classified and recorded according to system number to generate valve adjustment information, which is stored in a local MySQL database for subsequent traceability and query.

[0084] Next, based on the response characteristics of the hydraulic conditions of the heating system, a fixed monitoring cycle of 30 minutes was set. This duration ensures that the system fully responds to valve adjustments while promptly capturing changes in operating status. The monitoring cycle starts with the adjustment completion time in the valve adjustment information. Within the cycle, secondary supply water temperature, return water temperature, flow rate, and pressure data are collected once per minute using PT100 platinum resistance temperature sensors, ultrasonic flow sensors, and pressure transmitters already deployed in each heating system. The collected data is transmitted and centrally integrated across devices in real time via an OPC server. The actual heat consumption of each system is calculated using the previously determined heat supply calculation formula: heat supply = mass flow rate × specific heat capacity × supply and return water temperature difference. The current hydraulic status is determined by the heat consumption deviation ratio, including normal, oversupply, and undersupply. The actual heat consumption, hydraulic status, and various raw data are summarized by system to form structured multi-system operating status information.

[0085] Then, based on the target opening degree of each valve in the multi-system valve control command set, and combined with the target supply water temperature corresponding to the climate compensation adjustment curve and the normal range requirements of the hydraulic imbalance index, the target value mapping method is used to construct the optimized expected target state information. Specifically, according to the expected flow range corresponding to the target valve opening degree, combined with the normal threshold of heat consumption per unit area, the reasonable range of supply and return water temperature difference, and the safe range of pipeline pressure, the expected heat consumption value, expected flow rate value, expected supply and return water temperature difference value, and expected pressure range of each system are determined. These parameters are then mapped one by one according to the system number to form standardized optimized expected target state information.

[0086] Subsequently, the average operating data for the 10 minutes prior to the execution of the multi-system valve control commands was extracted via database query commands. This included average heat consumption, average flow rate, average supply and return water temperature, and average pressure. This data was used as the baseline operating status information to ensure that the baseline data reflected the stable operating conditions before the command execution. A point-by-point comparison method was employed. Using Excel data processing tools, each actual parameter in the multi-system operating status information was compared one-to-one with the corresponding parameters in the baseline operating status information and the optimized target status information. A comparison result table was generated, containing "parameter name, actual value, baseline value, expected target value, and whether it meets the standard," clearly indicating whether each parameter met the baseline requirements and the expected target requirements.

[0087] When the comparison results show that the operating status information of multiple systems has reached the baseline operating status information but has not reached the optimized target status information, a deviation quantification calculation method is used to take the difference between the actual parameters of each system and the optimized target parameters as the control deviation value. Based on the magnitude of the deviation value, a proportional adjustment strategy is used to adjust the valve control commands. For example, if the actual heat consumption of a system is 10% higher than the expected target value, the target opening of the corresponding valve is reduced by 5%. Following this logic, the valve control parameters of each system are optimized one by one, constructing a set of optimized control commands for multiple systems. After being issued to the actuators for execution, a 30-minute monitoring cycle is restarted for secondary adjustment and monitoring.

[0088] When the comparison results show that the operating status information of the multiple systems has neither reached the baseline operating status information nor the optimized target status information, the first round of backtracking verification adopts the actuator status detection method. It uses the MODBUS-RTU protocol to query the power supply status, signal reception strength, and mechanical operation feedback signals of the electric control valve actuator to check whether the command was not effectively executed due to actuator failure, signal interference, etc. Based on the first verification result, if it is signal interference, the control command is resent; if it is actuator mechanical jamming, a maintenance prompt is issued. After correcting the problem, a second round of backtracking verification is performed, i.e., the corrected control command is reissued, the monitoring cycle is started to collect operating data, and the second verification result is used to determine whether further adjustment of control parameters or equipment maintenance is needed.

[0089] When the comparison results show that the multi-system operating status information meets both the baseline operating status information and the expected optimization target status information, a parameter solidification method is adopted to associate and store the current multi-system operating status parameters, valve control command parameters, and environmental parameters such as outdoor temperature. This data is then categorized and organized according to heating season and outdoor temperature range to form a standardized hydraulic balance control strategy library. This strategy can be directly invoked for control when encountering similar operating conditions in the future.

[0090] By employing a series of steps, including PLC instruction parsing, sensor data acquisition, target parameter mapping, item-by-item comparison, and scenario-based backtracking optimization, a complete closed-loop control process was constructed. This enabled precise and stable control of the hydraulic balance of multiple systems in the heat exchange station, ensuring the dual optimization of heating effect and operating efficiency.

[0091] In summary, the hydraulic intelligent balance control method for multiple systems in a heat exchange station provided in this application has the following technical effects: This application calculates the heat consumption per unit area by acquiring real-time operating data of multiple heating systems in a heat exchange station, analyzes and sets hydraulic imbalance indicators to obtain a set of supply state parameters, constructs a climate compensation adjustment curve by combining dynamic temperature parameters, generates valve control commands through multi-system collaborative optimization, monitors operating status feedback and backtracks for optimization, constructs a control strategy, realizes intelligent hydraulic balance of multiple systems in the heat exchange station, improves control accuracy and operating efficiency, achieves precise control and collaborative adjustment of the operating status of multiple systems in the heat exchange station, improves heating balance and reduces overall energy consumption.

[0092] Example 2, as Figure 2 As shown, based on the same inventive concept as in Embodiment 1 above, this application provides a hydraulic intelligent balance control platform for multiple systems in a heat exchange station, the platform comprising: Unit area heat consumption data acquisition module 1 is used to acquire real-time operating data of multiple heating systems in the heat exchange station to calculate heat consumption and obtain unit area heat consumption data.

[0093] The hydraulic supply state parameter set acquisition module 2 performs hydraulic balance analysis based on the heat consumption data per unit area, sets hydraulic imbalance indexes, performs supply analysis based on the hydraulic imbalance indexes, and obtains the hydraulic supply state parameter set.

[0094] Climate compensation and adjustment curve construction module 3 is used to monitor the temperature change parameters of the heat exchange station, obtain dynamic temperature parameters, and combine them with the hydraulic imbalance index to perform climate compensation analysis and construct a climate compensation and adjustment curve.

[0095] The control command set construction module 4 constructs a multi-system valve control command set based on the hydraulic supply state parameter set and the climate compensation adjustment curve through multi-system collaborative optimization analysis.

[0096] The hydraulic balance control strategy execution module 5 is used to monitor and regulate the hydraulic balance of multiple systems according to the set of valve control instructions of the multiple systems, obtain the operating status information of the multiple systems, feed back the operating status information of the multiple systems to the multiple heating systems of the heat exchange station for retrospective optimization, and construct a hydraulic balance control strategy for intelligent control.

[0097] Furthermore, the heat consumption data acquisition module 1 per unit area is used to perform the following steps: Multiple heating systems are analyzed, including secondary network supply pipes, secondary network return pipes, and secondary network circulation pump outlets. A first temperature sensor is installed on the secondary network supply pipe to collect secondary supply water temperature data; a second temperature sensor is installed on the secondary network return pipe to collect secondary return water temperature data; and a flow sensor is installed at the outlet of the secondary network circulation pump to collect system pump flow rate data. The secondary supply water temperature data, the secondary return water temperature data, and the system pump flow rate data are correlated and stored to generate a correlated dataset. Based on the correlated dataset, the heating capacity of the multiple systems is calculated to obtain the heating capacity of the multiple systems. Introducing the heating area parameter of the heat exchange station, the heat consumption per unit area is calculated based on the heating capacity of the multiple systems and the heating area parameter.

[0098] Furthermore, the hydraulic supply state parameter set acquisition module 2 is used to perform the following steps: Multiple heating systems in the heat exchange station are traversed for heat consumption calculations to obtain multiple heat consumption values. These multiple heat consumption values ​​are then sorted in ascending order to construct a heat consumption value sequence. The first heat consumption value in the sequence is extracted as the target heat consumption value. The target heat consumption value is used as an index to retrieve multiple heating systems, and the target heating system is selected as the benchmark heating system. Heat consumption deviation is calculated for multiple heating systems according to the benchmark heating system to obtain a heat consumption deviation ratio. Based on the historical heat consumption deviation dataset and the heat consumption deviation ratio, a deviation criticality analysis is performed to set the hydraulic imbalance index.

[0099] Furthermore, the hydraulic supply state parameter set acquisition module 2 is used to perform the following steps: The hydraulic imbalance index includes a first deviation threshold and a second deviation threshold, wherein the first deviation threshold is greater than a preset value and less than the second deviation threshold; when the heat consumption deviation ratio is greater than a preset value and less than or equal to the first deviation threshold, a first hydraulic state parameter is generated; a supply label is generated based on the first hydraulic state parameter; when the heat consumption deviation ratio is greater than the first deviation threshold and less than or equal to the second deviation threshold, a second hydraulic state parameter is generated; a supply label is generated based on the second hydraulic state parameter; when the heat consumption deviation ratio is greater than the first deviation threshold and less than or equal to the second deviation threshold, a slight oversupply label is generated; when the heat consumption deviation ratio is greater than the first deviation threshold and less than or equal to the second deviation threshold, a second hydraulic state parameter is generated; a slight oversupply label is generated based on the second hydraulic state parameter; when the heat consumption deviation ratio is greater than the first deviation threshold and less than or equal to the second deviation threshold, a slight oversupply label is generated .... When the difference ratio is greater than the second deviation threshold, a third hydraulic state parameter is generated; a severe oversupply tag is generated based on the third hydraulic state parameter; when the heat consumption deviation ratio is less than a preset value, a fourth hydraulic state parameter is generated; a short supply tag is generated based on the fourth hydraulic state parameter; the first hydraulic state parameter, the second hydraulic state parameter, the third hydraulic state parameter, the fourth hydraulic state parameter, and the normal supply tag, the slight oversupply tag, the severe oversupply tag, and the short supply tag are associated to obtain the hydraulic supply state parameter set.

[0100] Furthermore, the climate compensation adjustment curve construction module 3 is used to perform the following steps: The temperature change parameters of the heat exchange station are continuously monitored according to the sampling frequency. Interference is eliminated to construct a temperature dynamic parameter sequence. Based on the slight oversupply label and the second hydraulic state parameter, the temperature dynamic parameter sequence is differentially biased and adjusted to obtain a first adjustment result. Based on the severe oversupply label and the third hydraulic state parameter, the temperature dynamic parameter sequence is differentially biased and adjusted to obtain a second adjustment result. Based on the undersupply label and the fourth hydraulic state parameter, the temperature dynamic parameter sequence is differentially biased and adjusted to obtain a third adjustment result. The first, second, and third adjustment results are analyzed to construct a basic water supply temperature setpoint. Climate compensation analysis is performed on the basic water supply temperature setpoint according to the hydraulic imbalance index to generate an optimized compensation coefficient. An initial water supply temperature target curve is constructed, and the initial water supply temperature target curve is segmented and corrected according to the optimized compensation coefficient to construct the climate compensation adjustment curve.

[0101] Furthermore, the climate compensation adjustment curve construction module 3 is used to perform the following steps: The operating time series of multiple heating systems in the heat exchange station are extracted. Using the operating time series as the horizontal axis and the basic water supply temperature setpoint as the vertical axis, a discrete data point sequence is plotted based on the temperature dynamic parameter sequence. Linear interpolation is used to connect the discrete data point sequence to construct the initial water supply temperature target curve. Change analysis is performed based on the temperature dynamic parameter sequence to construct a temperature change trend map for feature segmentation, identifying multiple temperature feature segments. These multiple temperature feature segments are traversed and matched with the optimized compensation coefficient to generate temperature-compensation data pairs. The initial water supply temperature target curve is corrected according to the temperature-compensation data pairs, generating multiple corrected curve segments. These multiple corrected curve segments are then smoothly connected to construct the climate compensation adjustment curve.

[0102] Furthermore, the control command set construction module 4 is used to perform the following steps: A feasibility analysis is conducted to minimize the total energy consumption parameters of the heat exchange station, setting a first objective parameter. Based on the hydraulic supply state parameters, a balance calculation is performed to obtain the hydraulic balance state parameters of the heat exchange station, setting a second objective parameter. The first and second objective parameters are analyzed for their regulatory impact, constructing a regulatory impact factor. Based on the regulatory impact factor, a weighted sum of the first and second objective parameters is performed to construct dual-objective optimization data. Based on the dual-objective optimization data, critical constraints are applied to the multiple systems of the heat exchange station, constructing optimization constraints. According to the optimization constraints and the dual-objective optimization data, the climate compensation regulation curve is iteratively optimized and solved to construct the set of valve control commands for the multiple systems.

[0103] Furthermore, the control command set construction module 4 is used to perform the following steps: Based on the optimization constraints and the dual-objective optimization data, the valve opening of the climate compensation adjustment curve is calculated, and candidate solutions for valve opening are randomly generated. Simulation control is performed according to the candidate solutions for valve opening to generate simulation control parameters, which include system simulation energy consumption parameters and hydraulic simulation equilibrium parameters. Fitness evaluation is performed based on the system simulation energy consumption parameters and hydraulic simulation equilibrium parameters to calculate a first fitness value. The first fitness value is matched with the candidate solutions for valve opening, and the matching result is mapped to the solution space for iteration. When the number of iterations reaches a preset number, the iteration is terminated. Instruction conversion is performed based on the solution space to generate the multi-system valve control instruction set.

[0104] Furthermore, the hydraulic balance control strategy execution module 5 is used to perform the following steps: The set of multi-system valve control commands is parsed and sent to the primary-side electric regulating valve actuators of the multiple systems in the heat exchange station to drive the valves to adjust their openings, generating valve adjustment information. A fixed-duration monitoring cycle is set, and the monitoring cycle is started based on the valve adjustment information. Data from multiple heating systems is collected and integrated according to the monitoring cycle to obtain multi-system operating status information. Optimization expectation calculation is performed based on the set of multi-system valve control commands to construct the optimization expectation target status information. Baseline operating status information of multiple heating systems is extracted, and the multi-system operating status information is compared with the baseline operating status information and the optimization expectation target status information. When the multi-system operating status information reaches the baseline operating status information but does not reach the optimization expectation target status information, the multi-system operating status information is fed back to the multiple heating systems in the heat exchange station for backtracking. The control deviation is calculated, and the control command set of the multi-system valves is optimized based on the operating deviation value to construct a multi-system valve optimized control command set for secondary adjustment and monitoring. When the operating status information of the multi-system does not reach the baseline operating status information and does not reach the optimized expected target status information, the operating status information of the multi-system is fed back to multiple heating systems in the heat exchange station for a first round of backtracking verification. Based on the first verification result, the valve control command set of the multi-system is subjected to a second round of backtracking verification. Based on the second verification result, multiple heating systems in the heat exchange station are subjected to secondary adjustment and monitoring. When the operating status information of the multi-system reaches the baseline operating status information and reaches the optimized expected target status information, the operating status information of the multi-system is fed back to multiple heating systems in the heat exchange station for control and allocation, thus constructing the hydraulic balance control strategy.

[0105] The hydraulic intelligent balance control platform for multiple systems in a heat exchange station provided in the embodiments of the present invention can execute the hydraulic intelligent balance control method for multiple systems in a heat exchange station provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0106] Although this application makes various references to certain modules in the platform according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0107] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A hydraulic intelligent balance control method for multiple systems in a heat exchange station, characterized in that, The method includes: Real-time operating data of multiple heating systems in the heat exchange station are obtained to calculate heat consumption and obtain heat consumption data per unit area. Based on the heat consumption data per unit area, hydraulic balance analysis is performed, hydraulic imbalance index is set, and supply analysis is performed according to the hydraulic imbalance index to obtain a set of hydraulic supply state parameters. Monitor the temperature change parameters of the heat exchange station, obtain dynamic temperature parameters, combine them with the hydraulic imbalance index to conduct climate compensation analysis, and construct a climate compensation adjustment curve. Based on the hydraulic supply state parameter set and the climate compensation adjustment curve, a multi-system collaborative optimization analysis is performed to construct a multi-system valve control command set. Multi-system hydraulic balance regulation and monitoring are performed according to the set of multi-system valve control commands to obtain multi-system operating status information. The multi-system operating status information is then fed back to multiple heating systems in the heat exchange station for retrospective optimization, and a hydraulic balance regulation strategy is constructed for intelligent control.

2. The hydraulic intelligent balance control method for multiple systems in a heat exchange station as described in claim 1, characterized in that, To obtain heat consumption data per unit area by acquiring real-time operating data from multiple heating systems at a heat exchange station and performing heat consumption calculations, the methods include: The analysis includes multiple heating systems, which include a secondary water supply network, a secondary water return network, and a secondary circulation pump outlet. A first temperature sensor is installed on the secondary water supply network to sense and collect secondary water supply temperature data. A second temperature sensor is installed on the secondary pipeline return water pipe to sense and collect secondary return water temperature data. A flow sensor is installed at the outlet of the secondary pipeline circulation pump to sense and collect system pump flow data. The secondary water supply temperature data, the secondary return water temperature data, and the system pump flow data are associated and stored to generate an associated dataset; Based on the associated dataset, heat supply is calculated to obtain the heat supply of multiple systems. The heating area parameter of the heat exchange station is introduced, and the heat consumption per unit area is calculated based on the heat supply of the multiple systems and the heating area parameter.

3. The hydraulic intelligent balance control method for multiple systems in a heat exchange station as described in claim 1, characterized in that, Based on the heat consumption per unit area data, a hydraulic balance analysis is performed, and a hydraulic imbalance index is set. The method includes: The heat consumption of multiple heating systems in the heat exchange station is calculated to obtain multiple heat consumption values. The multiple heat loss values ​​are arranged in ascending order to construct a heat loss value sequence, and the first heat loss value in the heat loss value sequence is extracted as the target heat loss value. The target heat consumption value is used as an index to search multiple heating systems, and the target heating system is used as the benchmark heating system. Heat consumption deviation is calculated for multiple heating systems based on the benchmark heating system to obtain the heat consumption deviation ratio; Based on the historical heat loss deviation dataset and the heat loss deviation ratio, a critical deviation analysis is performed to set the hydraulic imbalance index.

4. The hydraulic intelligent balance control method for multiple systems in a heat exchange station as described in claim 3, characterized in that, Based on the aforementioned hydraulic imbalance index, a hydraulic supply state parameter set is obtained through supply analysis, including the following methods: The hydraulic imbalance index includes a first deviation threshold and a second deviation threshold, wherein the first deviation threshold is greater than a preset value and less than the second deviation threshold. When the heat loss deviation ratio is greater than a preset value and less than or equal to the first deviation threshold, a first hydraulic state parameter is generated. Based on the first hydraulic state parameters, a supply identifier is generated, and a normal supply label is produced. When the heat loss deviation ratio is greater than the first deviation threshold and less than or equal to the second deviation threshold, a second hydraulic state parameter is generated. Based on the second hydraulic state parameter, a supply identification is performed to generate a slight oversupply tag; When the heat loss deviation ratio is greater than the second deviation threshold, a third hydraulic state parameter is generated; Based on the third hydraulic state parameter, a supply identification is performed, and a severe oversupply label is generated. When the heat loss deviation ratio is less than a preset value, a fourth hydraulic state parameter is generated; Based on the fourth hydraulic state parameter, a supply identification is performed, and a short supply tag is generated. The first hydraulic state parameter, the second hydraulic state parameter, the third hydraulic state parameter, and the fourth hydraulic state parameter are associated with the normal supply tag, the slightly over-supply tag, the severely over-supply tag, and the under-supply tag to obtain the hydraulic supply state parameter set.

5. The hydraulic intelligent balance control method for multiple systems in a heat exchange station as described in claim 4, characterized in that, Monitoring temperature change parameters at the heat exchange station, obtaining dynamic temperature parameters, and combining these with the hydraulic imbalance indicators for climate compensation analysis, constructing a climate compensation adjustment curve, the method includes: The temperature change parameters of the heat exchange station are continuously monitored according to the sampling frequency. The dynamic temperature parameters are obtained, interference is eliminated, and a dynamic temperature parameter sequence is constructed. Based on the mild oversupply label and the second hydraulic state parameter, the temperature dynamic parameter sequence is differentially biased and adjusted to obtain the first adjustment result; Based on the severe oversupply label and the third hydraulic state parameter, the temperature dynamic parameter sequence is differentially biased and adjusted to obtain a second adjustment result; Based on the undersupply tag and the fourth hydraulic state parameter, the temperature dynamic parameter sequence is differentially biased and adjusted to obtain the third adjustment result; Based on the analysis of the first adjustment result, the second adjustment result, and the third adjustment result, a basic water supply temperature setpoint is constructed. Based on the hydraulic imbalance index, a climate compensation analysis is performed on the basic water supply temperature setpoint to generate an optimized compensation coefficient. An initial water supply temperature target curve is constructed, and the initial water supply temperature target curve is segmented and corrected according to the optimized compensation coefficient to construct the climate compensation adjustment curve.

6. The hydraulic intelligent balance control method for multiple systems in a heat exchange station as described in claim 5, characterized in that, Constructing an initial target water supply temperature curve, and then segmentally correcting the initial target water supply temperature curve according to the optimized compensation coefficient to construct the climate compensation adjustment curve, the method includes: Extract the operating time series of multiple heating systems in the heat exchange station, use the operating time series as the horizontal axis and the basic water supply temperature setpoint as the vertical axis, and plot a discrete data point sequence based on the temperature dynamic parameter sequence. The discrete data point sequence is connected by linear interpolation to construct the initial water supply temperature target curve; Based on the temperature dynamic parameter sequence, change analysis is performed to construct a temperature change trend map for feature division and to determine multiple temperature feature segments. The multiple temperature feature segments are traversed and matched with the optimized compensation coefficient to generate temperature-compensation data pairs; Based on the temperature-compensation data, the initial water supply temperature target curve is corrected, and multiple correction curve segments are generated; The multiple modified curve segments are smoothly connected to construct the climate compensation adjustment curve.

7. The hydraulic intelligent balance control method for multiple systems in a heat exchange station as described in claim 1, characterized in that, Based on the hydraulic supply state parameter set and the climate compensation regulation curve, a multi-system collaborative optimization analysis is performed to construct a multi-system valve control command set. The method includes: A feasibility analysis was conducted to minimize the total energy consumption parameters of the heat exchange station, and the first objective parameter was set. Based on the hydraulic supply state parameters, a balance calculation is performed to obtain the hydraulic balance state parameters of the heat exchange station and set the second target parameters. An analysis of the regulatory impact on the first target parameter and the second target parameter was conducted to construct a regulatory impact factor. Based on the aforementioned regulatory influencing factors, the first target parameter and the second target parameter are weighted and summed to construct dual-objective optimized data; Based on the dual-objective optimization data, the critical constraints for adjusting multiple systems in the heat exchange station are determined, and optimization constraint conditions are constructed. Based on the optimization constraints, the climate compensation adjustment curve is iteratively optimized using the dual-objective optimization data to construct the multi-system valve control command set.

8. The hydraulic intelligent balance control method for multiple systems in a heat exchange station as described in claim 7, characterized in that, Based on the optimization constraints and the dual-objective optimization data, the climate compensation adjustment curve is iteratively optimized and solved to construct the multi-system valve control command set. The method includes: Based on the optimization constraints, the valve opening of the climate compensation adjustment curve is calculated according to the dual objective optimization data, and candidate solutions for valve opening are randomly generated. Simulation control is performed based on the candidate solutions for valve opening to generate simulation control parameters, which include system simulation energy consumption parameters and hydraulic simulation equilibrium parameters. Based on the system simulation energy consumption parameters and the hydraulic simulation equilibrium parameters, a fitness evaluation is performed, and a first fitness value is calculated. The first fitness value is matched with the candidate solutions for valve opening, and the matching results are mapped to the solution space for iteration. When the number of iterations reaches a preset number, the iteration is terminated, and instruction conversion is performed based on the solution space to generate the multi-system valve control instruction set.

9. The hydraulic intelligent balance control method for multiple systems in a heat exchange station as described in claim 1, characterized in that, Multi-system hydraulic balance regulation and monitoring are performed according to the aforementioned multi-system valve control command set to obtain multi-system operating status information. This multi-system operating status information is fed back to multiple heating systems in the heat exchange station for retrospective optimization, thereby constructing a hydraulic balance control strategy. The method includes: The set of valve control commands for the multi-system system is parsed and sent to the primary side electric regulating valve actuator of the multi-system system in the heat exchange station to drive the valve to adjust the opening degree and generate valve adjustment information. A fixed-duration monitoring cycle is set, and the monitoring cycle is started based on the valve adjustment information. Data is collected and integrated from multiple heating systems according to the monitoring cycle to obtain the operating status information of multiple systems. Based on the set of valve control commands from the multi-system system, the desired optimization state information is calculated and optimized target state information is constructed. Extract baseline operating status information from multiple heating systems, and compare the multi-system operating status information with the baseline operating status information and the optimized expected target status information; When the multi-system operating status information reaches the baseline operating status information, but the multi-system operating status information does not reach the optimized target status information, the multi-system operating status information is fed back to multiple heating systems in the heat exchange station for retrospective calculation of control deviation. Based on the operating deviation value, the multi-system valve control command set is optimized, and a multi-system valve optimized control command set is constructed for secondary adjustment and monitoring. When the multi-system operating status information does not reach the baseline operating status information and the multi-system operating status information does not reach the optimized expected target status information, the multi-system operating status information is fed back to the multiple heating systems of the heat exchange station for a first round of retrospective verification. Based on the first verification result, the multi-system valve control command set is subjected to a second round of retrospective verification. Based on the second verification result, the multiple heating systems of the heat exchange station are subjected to secondary adjustment and monitoring. When the multi-system operating status information reaches the baseline operating status information and the multi-system operating status information reaches the optimized target status information, the multi-system operating status information is fed back to the multiple heating systems of the heat exchange station for regulation and allocation, thus constructing the hydraulic balance regulation strategy.

10. A hydraulic intelligent balance control platform for multiple systems in a heat exchange station, characterized in that: The platform is used to implement the intelligent hydraulic balance control method for multiple systems in a heat exchange station as described in any one of claims 1-9, and the platform comprises: The heat consumption per unit area data acquisition module is used to acquire real-time operating data of multiple heating systems in the heat exchange station to calculate heat consumption and obtain heat consumption per unit area. The hydraulic supply state parameter set acquisition module performs hydraulic balance analysis based on the heat consumption data per unit area, sets hydraulic imbalance indexes, performs supply analysis based on the hydraulic imbalance indexes, and obtains the hydraulic supply state parameter set. The climate compensation adjustment curve construction module is used to monitor the temperature change parameters of the heat exchange station, obtain dynamic temperature parameters, combine them with the hydraulic imbalance index to perform climate compensation analysis, and construct the climate compensation adjustment curve. The control command set construction module performs multi-system collaborative optimization analysis based on the hydraulic supply state parameter set and the climate compensation adjustment curve to construct a multi-system valve control command set. The hydraulic balance control strategy execution module is used to monitor and regulate the hydraulic balance of multiple systems according to the set of valve control instructions of the multiple systems, obtain the operating status information of multiple systems, feed back the operating status information of multiple systems to multiple heating systems in the heat exchange station for retrospective optimization, and construct a hydraulic balance control strategy for intelligent control.

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