An adaptive control method and system for a cold supply pipeline, a terminal and a storage medium

Abstract

This invention discloses an adaptive control method, system, terminal, and storage medium for cooling pipelines. The method includes: initializing a valve control strategy to obtain multiple preset discrete load intervals and preset valve control strategies; performing total load analysis based on cooling signals to obtain the total demand load; performing matching processing on the multiple preset discrete load intervals based on the total demand load; if a matching result is obtained, the preset valve control strategy is used as the actual valve control strategy; if no matching result is obtained, an adaptive strategy is constructed based on the total demand load to obtain the actual valve control strategy; controlling the cooling pipeline to supply cooling to users according to the actual valve control strategy, and performing performance monitoring based on real-time cooling data to obtain performance monitoring results. This invention adaptively selects the optimal valve control strategy based on the user's flow demand and controls the opening and closing of valves in each branch of the cooling pipeline, which can improve the metering accuracy of flow meters in each branch.

Description

Technical Field

[0001] This invention relates to the field of control technology, and in particular to an adaptive control method, system, terminal, and computer-readable storage medium for cooling pipelines. Background Technology

[0002] With increasingly stringent requirements for cold environment control precision in high-end manufacturing, data centers, and cutting-edge scientific research, the accuracy of cold energy metering has become a core indicator for measuring system performance. Every flow meter used for cold energy metering has an optimal measurement range, typically exhibiting the highest accuracy in the mid-to-high flow range, while accuracy drops significantly in the low flow range. Maximizing metering accuracy while maintaining the flow meter's optimal measurement range remains a common and challenging measurement problem.

[0003] The adaptive control method for cooling pipelines in the existing technology usually involves setting up multiple flow meters in the pipeline. A large-range flow meter is used for measurement when the flow rate is high, and a small-range flow meter is used for measurement when the flow rate is low. However, this adaptive control method for cooling pipelines only optimizes the flow measurement by switching between flow meters with different ranges. It cannot ensure that the flow meters of each branch always operate in the high-precision range, which can easily lead to large measurement errors.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this invention is to provide an adaptive control method, system, terminal, and computer-readable storage medium for cooling pipelines. This invention aims to solve the problem that existing adaptive control methods for cooling pipelines only optimize flow measurement by switching between flow meters with different ranges, which cannot ensure that each branch flow meter always operates in a high-precision range and is prone to large measurement errors.

[0006] To achieve the above objectives, the present invention provides an adaptive control method for cooling pipelines, comprising the following steps: Multiple preset discrete loads are obtained, and valve control strategies are initialized based on the multiple preset discrete loads to obtain multiple preset discrete load ranges and preset valve control strategies. Obtain the user's cooling demand signal, and perform total load analysis and processing based on the cooling demand signal to obtain the total demand load; The total demand load is matched within multiple preset discrete load intervals. If a matching result is obtained, the preset valve control strategy is used as the actual valve control strategy. If no matching result is obtained, an adaptive strategy is constructed based on the total demand load to obtain the actual valve control strategy. The cooling pipeline is controlled to provide cooling according to the actual valve control strategy, and real-time cooling data is collected. The performance monitoring data is then processed to obtain the performance monitoring results.

[0007] Optionally, the adaptive control method for the cooling pipeline, wherein obtaining multiple preset discrete loads and initializing the valve control strategy based on the multiple preset discrete loads to obtain multiple preset discrete load ranges and preset valve control strategies specifically includes: Multiple preset discrete loads and preset hysteresis intervals are obtained, and interval construction processing is performed based on the multiple preset discrete loads and preset hysteresis intervals to obtain multiple preset discrete load intervals; For each of the preset discrete load intervals, valve control strategy construction is performed to obtain multiple initial valve control strategies, and a preset valve control strategy is obtained based on the multiple initial valve control strategies.

[0008] Optionally, the adaptive control method for the cooling pipeline, wherein acquiring the user's cooling demand signal and performing total load analysis processing based on the cooling demand signal to obtain the total demand load specifically includes: Obtain the user's cooling usage signal, perform demand analysis processing based on the cooling usage signal, and obtain the demand level; Obtain the cooling season and cooling period, and calculate the cooling coefficient based on the cooling season and cooling period; The total load is calculated based on the demand level and the cooling coefficient to obtain the total demand load.

[0009] Optionally, the adaptive control method for the cooling pipeline, wherein the matching process is performed on multiple preset discrete load intervals based on the total demand load; if a matching result is obtained, the preset valve control strategy is used as the actual valve control strategy; if no matching result is obtained, an adaptive strategy construction process is performed based on the total demand load to obtain the actual valve control strategy, specifically including: Determine whether the total demand load falls within any one of the multiple preset discrete load intervals; If so, the preset valve control strategy will be used as the actual valve control strategy. If not, obtain the branch flow data, and perform adaptive strategy construction based on the branch flow data and the total demand load to obtain the actual valve control strategy.

[0010] Optionally, the adaptive control method for the cooling pipeline, wherein acquiring branch flow data and performing adaptive strategy construction processing based on the branch flow data and the total demand load to obtain the actual valve control strategy specifically includes: Multiple preset valve openings are obtained, and the flow rate of each branch of the cooling pipeline at each preset valve opening is measured and processed to obtain multiple branch flow rates. The multiple branch flow rates are used as branch flow rate data, and the flow coefficient is calculated based on the branch flow rate data to obtain flow coefficient data. Based on the flow coefficient data and the branch flow data, a coefficient curve is constructed to obtain the first flow curve; The readings of the cooling meter for each branch are collected and processed to obtain meter data. An error curve is constructed based on the meter data and the branch flow data to obtain a second flow curve. Multiple candidate control strategies and parallel equation sets are obtained. Based on the parallel equation sets, the total demand load, and the first flow curve, the multiple candidate control strategies are screened to obtain a target candidate strategy set. The accuracy of the target candidate strategies in the target candidate strategy set is evaluated based on the second flow curve to obtain the measurement accuracy of each target candidate strategy. The target candidate strategy with the highest measurement accuracy is then used as the actual valve control strategy.

[0011] Optionally, the adaptive control method for the cooling pipeline, wherein the step of performing accuracy evaluation processing on the target candidate strategies in the target candidate strategy set based on the second flow curve to obtain the metering accuracy of each target candidate strategy, and taking the target candidate strategy with the highest metering accuracy as the actual valve control strategy, specifically includes: The predicted flow of each target candidate strategy is calculated based on the parallel equation set to obtain multiple predicted flow sets; The relative error of each predicted flow group is calculated based on the second flow curve to obtain multiple predicted relative errors; Multiple weighting coefficients are obtained by weighting the multiple relative prediction errors and the total demand load; A weighted average is performed on the multiple prediction relative errors and the multiple weighting coefficients to obtain multiple measurement accuracies, and the measurement accuracies with the largest value are taken as the target accuracy. The target candidate strategy corresponding to the target accuracy is used as the actual valve control strategy.

[0012] Optionally, the adaptive control method for the cooling pipeline, wherein controlling the cooling pipeline to provide cooling according to the actual valve control strategy, collecting real-time cooling data, and performing performance monitoring processing based on the real-time cooling data to obtain performance monitoring results, specifically includes: A first mixing device is installed before the branch pipe of the cooling pipeline, a second mixing device is installed after the branch pipe and before the plate heat exchanger of the cooling pipeline, and a third mixing device is installed after the plate heat exchanger. When the cooling pipeline is controlled to provide cooling according to the actual valve control strategy, water is supplied according to the water supply path of the cold source, the first mixing device, the branch pipeline, the second mixing device, the plate heat exchanger, and the user end. Water is returned according to the return water path of the user terminal, the plate heat exchanger, the third mixing device, and the cold source; Collect real-time cooling data, perform branch fault diagnosis and processing based on the real-time cooling data, and obtain branch performance diagnosis results; Based on the real-time cooling data, instrument fault diagnosis is performed to obtain instrument performance diagnosis results; The performance monitoring results are obtained based on the branch performance diagnosis results and the instrument performance diagnosis results.

[0013] Furthermore, to achieve the above objectives, the present invention also provides an adaptive control system for a cooling pipeline, wherein the adaptive control system for the cooling pipeline includes: The strategy initialization module is used to acquire multiple preset discrete loads, initialize the valve control strategy according to the multiple preset discrete loads, and obtain multiple preset discrete load ranges and preset valve control strategies. The demand analysis module is used to acquire users' cooling signals, perform total load analysis based on the cooling signals, and obtain the total demand load. The strategy construction module is used to perform matching processing in multiple preset discrete load intervals according to the total demand load. If a matching result is obtained, the preset valve control strategy is used as the actual valve control strategy. If no matching result is obtained, adaptive strategy construction processing is performed according to the total demand load to obtain the actual valve control strategy. The cooling control module is used to control the cooling pipeline to provide cooling according to the actual valve control strategy, collect real-time cooling data, perform performance monitoring processing based on the real-time cooling data, and obtain performance monitoring results.

[0014] In addition, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and an adaptive control program for a cooling pipeline stored in the memory and executable on the processor, wherein when the adaptive control program for the cooling pipeline is executed by the processor, it implements the steps of the adaptive control method for the cooling pipeline as described above.

[0015] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores an adaptive control program for a cooling pipeline, which, when executed by a processor, implements the steps of the adaptive control method for a cooling pipeline as described above.

[0016] In this invention, multiple preset discrete loads are acquired, and valve control strategies are initialized based on these loads to obtain multiple preset discrete load intervals and preset valve control strategies. User cooling demand signals are acquired, and total load analysis is performed based on these signals to obtain the total demand load. Matching is then performed across the multiple preset discrete load intervals based on the total demand load. If a match is found, the preset valve control strategy is used as the actual valve control strategy; otherwise, an adaptive strategy is constructed based on the total demand load to obtain the actual valve control strategy. The cooling pipelines are controlled to provide cooling according to the actual valve control strategy, and real-time cooling data is collected. Performance monitoring is then performed based on the real-time cooling data to obtain performance monitoring results. This invention adaptively selects the optimal valve control strategy based on the user's flow demand and controls the opening and closing of valves on each branch of the cooling pipeline according to the optimal valve control strategy. This ensures that the flow meters on each branch always operate within a high-precision range, improving metering accuracy. Attached Figure Description

[0017] Figure 1 This is a flowchart of a preferred embodiment of the adaptive control method for cooling pipelines of the present invention; Figure 2 This is a system architecture design diagram of the adaptive control method for cooling pipelines of the present invention; Figure 3 This is a flowchart of the adaptive control method for cooling pipelines according to the present invention. Figure 4 This is a flowchart of the pipeline performance monitoring process for the adaptive control method of the cooling pipeline of the present invention; Figure 5 This is a structural diagram of a preferred embodiment of the adaptive control system for cooling pipelines of the present invention; Figure 6 This is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] With increasingly stringent requirements for cold environment control precision in high-end manufacturing, data centers, and cutting-edge scientific research, the accuracy of cold energy metering has become a core indicator for measuring system performance. Every flow meter used for cold energy metering has an optimal measurement range, typically exhibiting the highest accuracy in the mid-to-high flow range, while accuracy drops significantly in the low flow range. Maximizing metering accuracy while maintaining the flow meter's optimal measurement range remains a common and challenging measurement problem.

[0020] The adaptive control method for cooling pipelines in the existing technology usually involves setting up multiple flow meters in the pipeline. A large-range flow meter is used for measurement when the flow rate is high, and a small-range flow meter is used for measurement when the flow rate is low. However, this adaptive control method for cooling pipelines only optimizes the flow measurement by switching between flow meters with different ranges. It cannot ensure that the flow meters of each branch always operate in the high-precision range, which can easily lead to large measurement errors.

[0021] To address the aforementioned issues, this invention proposes an adaptive control method for cooling pipelines. This method adaptively selects the optimal valve control strategy based on the user's flow demand and controls the opening and closing of valves in each branch of the cooling pipeline according to the optimal valve control strategy. This ensures that the flow meters in each branch always operate within a high-precision range, thereby improving metering accuracy.

[0022] The adaptive control method for cooling pipelines described in the preferred embodiment of the present invention, such as... Figure 1 As shown, the adaptive control method for the cooling pipeline includes the following steps: Step S10: Obtain multiple preset discrete loads, initialize the valve control strategy based on the multiple preset discrete loads, and obtain multiple preset discrete load ranges and preset valve control strategies.

[0023] like Figure 2 As shown, this invention includes an execution layer with at least three or more independent metering branches of different diameters connected in parallel on the main cooling pipe. Each branch includes an electric regulating valve (D1, D2, ..., Dn) and a corresponding diameter cooling capacity meter (N1, N2, ..., Nn). A mixing device H1 is installed at the inlet of the branch to ensure the stability of flow distribution and medium temperature. Another mixing device H2 is installed on the user side to stabilize the hydraulic conditions input to the user. A mixing device H3 is installed on the main cooling return pipe after the user's plate heat exchanger.

[0024] This invention includes a data acquisition layer, specifically by installing pressure sensors, temperature sensors, and refrigeration meters on branch pipelines to collect the total flow rate (G) of the system. 总 The system includes an embedded edge computing unit for measuring the supply and return water temperature difference (ΔT) and pipeline pressure, as well as a servo module configured with an electric regulating valve. These components are responsible for real-time data acquisition and command execution.

[0025] The invention also includes a decision layer, which is composed of a programmable logic controller (PLC) and an adaptive algorithm module. The PLC is responsible for receiving real-time data from the data acquisition layer, running the core control logic (including reviewing requirements analysis, valve combination decision-making, optimization algorithm, fault diagnosis, etc.), and issuing precise instructions to the execution layer. All historical data of the operation is stored in the database.

[0026] Specifically, multiple preset discrete loads and preset hysteresis intervals are obtained, and interval construction processing is performed based on the multiple preset discrete loads and preset hysteresis intervals to obtain multiple preset discrete load intervals.

[0027] The system presets multiple discrete load points (e.g., 100%, 75%, 50%, 20%, where 100% indicates the equipment operates at full rated maximum power); it sets a preset hysteresis interval, for example, 5%, and for each discrete load point, a preset discrete load interval is constructed based on the preset hysteresis interval. For example, if the discrete load is 100% and the system's maximum load is Q... 总 At that time, the upper limit of the required cooling capacity is Q. i,high =1.05Q 总 Q i,low =0.95Q 总 When Q 总 In [0.95Q 总 1.05Q 总 When the internal fluctuation occurs, the system maintains the current branch valve opening combination and prohibits frequent switching.

[0028] Setting a preset hysteresis range can prevent valves from frequently starting and stopping when the load fluctuates slightly, thus protecting the equipment.

[0029] Furthermore, valve control strategy construction is performed on each of the preset discrete load intervals to obtain multiple initial valve control strategies, and a preset valve control strategy is obtained based on the multiple initial valve control strategies.

[0030] Based on the preset discrete load range, an optimal valve switching combination decision table is established for flow distribution. Large-diameter branch electric regulating valves are given priority to meet the basic load, while small-diameter branch electric regulating valves are used for "fine-tuning" and "ensuring accuracy".

[0031] For example, three branches are set up, with valves D1, D2, and D3, and flow meters N1, N2, and N3 respectively. When the analyzed demand load is within a load range consisting of 100% discrete load, the PLC system sends a control signal to the control module of the electrically controlled valves. At this time, all three electrically controlled valves (D1, D2, and D3) are 100% open, and the cooling capacity recorded in the control room is the cumulative cooling capacity of the N1, N2, and N3 cooling capacity meters. When the analyzed demand load is within a load range consisting of 75% discrete load, the PLC system sends a control signal... When the control module of the electric regulating valve is given a signal, electric regulating valves D1 and D2 open to the required opening degree according to the instruction, while electric regulating valve D3 closes. The cooling capacity counted in the control room is the cumulative cooling capacity of cooling capacity meters N1 and N2. When the analyzed load demand is within a load range consisting of 50% discrete load, the PLC system sends a control signal to the control module of the electric regulating valve. At this time, electric regulating valve D2 opens to the required opening degree according to the instruction, while electric regulating valves D1 and D3 close. The cooling capacity counted in the control room is the cumulative cooling capacity of cooling capacity meter N2.

[0032] Step S20: Obtain the user's cooling signal, and perform total load analysis processing based on the cooling signal to obtain the total demand load.

[0033] The self-mixing device connects to three main cooling pipes of different diameters: large, medium, and small. Electric regulating valves and cooling capacity meters of the corresponding pipe diameters are installed on the main cooling pipes for control and metering.

[0034] Specifically, the system acquires users' cooling demand signals, performs demand analysis based on the cooling demand signals to obtain demand levels, acquires the cooling season and cooling period, and obtains the cooling coefficient based on the cooling season and cooling period, and performs total load calculation based on the demand levels and the cooling coefficient to obtain the total demand load.

[0035] like Figure 3 As shown, the cooling signals from the user side are collected in real time to calculate the total demand load.

[0036] A level parameter is pre-set for each demand level. The water pump operation status signal is received from the user's secondary water pump and sent to the PLC control system. Here, the water pump operation status feedback the user's cooling demand, and the user's demand level is analyzed based on the water pump operation status signal.

[0037] Automatic cooling system with different load configurations according to different cooling seasons and cooling periods; specifically, a cooling coefficient is set in advance for each cooling season and cooling period, the current cooling season and cooling time are obtained, and the corresponding cooling coefficient is obtained according to the pre-set values.

[0038] Based on the level parameters and cooling coefficient corresponding to the current demand level, multiply by the system's maximum load to obtain the user's total demand load.

[0039] Step S30: Matching is performed on multiple preset discrete load intervals according to the total demand load. If a matching result is obtained, the preset valve control strategy is used as the actual valve control strategy. If no matching result is obtained, an adaptive strategy construction process is performed according to the total demand load to obtain the actual valve control strategy.

[0040] Specifically, it is determined whether the total demand load is within any of the multiple preset discrete load intervals. If so, the preset valve control strategy is used as the actual valve control strategy.

[0041] like Figure 3 As shown, the continuous load is mapped to a preset discrete load range. It is determined whether the hysteresis range is exceeded. If not, the current valve combination remains inactive. If so, the optimal valve switching combination for the corresponding load range is matched.

[0042] The system analyzes the user's cooling demand in real time and maps the continuous total demand load to preset discrete load intervals. Specifically, it compares the user's total demand load with each preset discrete load interval. If the user's total demand load is within any preset discrete load interval, the preset valve opening and closing strategy corresponding to that preset discrete load interval is used to control the valves of each branch, so that the overall accuracy of the online cooling load meter reaches the highest level.

[0043] Furthermore, if not, multiple preset valve openings are obtained, and the flow rate of each branch of the cooling pipeline at each preset valve opening is measured and processed to obtain multiple branch flow rates. The multiple branch flow rates are used as branch flow rate data, and the flow coefficient is calculated based on the branch flow rate data to obtain flow coefficient data.

[0044] like Figure 3 As shown, if the user's total demand load is not within the preset discrete load range, the adaptive algorithm is called to calculate all possible valve opening combinations in real time and predict the corresponding branch flow. The algorithm evaluates the flow meter prediction accuracy of all open branches under each combination and selects the combination with the highest overall accuracy.

[0045] First, a high-precision portable reference instrument is introduced for short-term parallel comparison. Through multiple calibrations, an initial database considering factors such as flow rate and temperature is established. The "valve opening degree - flow coefficient" curve and "flow rate - relative error" curve of each refrigeration meter are digitized and stored in the PLC or host computer.

[0046] Based on the characteristics of parallel pipe networks, the pressure drop ΔP of each branch is equal, and the sum of the flow rates meets the demand. The flow distribution follows the valve flow characteristic formula: ; in, Indicates the valve flow coefficient. This indicates the pressure drop across the valve. Indicates flow rate.

[0047] For any metering branch i in this system, its flow rate It can be represented as: ; in, This represents the flow coefficient of branch i. This indicates the relative opening degree of the valve.

[0048] Open each branch i (i=1, 2, 3, ...) in turn (close all other branches), and measure and record the actual flow rate and pressure difference between the two ends of the branch at multiple different valve opening degrees li. Calculate the actual flow coefficient of the branch at the opening degree according to the formula above.

[0049] Further, coefficient curves are constructed based on the flow coefficient data and the branch flow data to obtain a first flow curve; the readings of the cooling meter for each branch are collected and processed to obtain meter data, and error curves are constructed based on the meter data and the branch flow data to obtain a second flow curve.

[0050] Based on the actual flow coefficient data and the actual flow data of the corresponding branch, a curve is constructed to obtain the "valve opening degree - flow coefficient" characteristic curve of each branch.

[0051] Record the reading Q of the branch cooling meter N. i The reading of the cooling meter was compared with the actual flow rate G measured by the reference instrument. i初 Compare the results. Calculate the cooling capacity at that flow point and the relative error. Finally, a "flow rate-relative error" characteristic curve is established for each refrigeration meter.

[0052] Furthermore, multiple candidate control strategies and parallel equation sets are obtained. Based on the parallel equation sets, the total demand load, and the first flow curve, the multiple candidate control strategies are screened to obtain a target candidate strategy set.

[0053] Candidate control strategies include valve opening / closing combinations and the relative opening degree of each valve. Specifically, a list of all valve combinations is first generated. For example, for valves D1, D2, and D3, combinations such as [D1, D2, D3] and [D1, D2] are generated, and the valves in these combinations are designated as the open valves. For each valve combination, multiple different combinations of relative valve opening degrees are set. For example, for the relative opening degrees of three valves... , , Set separately =1、 =1、 =1, or =0.8、 =0.6、 =0, where an opening of 1 indicates fully open and an opening of 0 indicates closed.

[0054] According to the principle of parallel connection, when there are three pipe branches, the following system of equations is satisfied: ; in Indicates total demand load. , , These represent the flow rates of the three branches, , , These represent the flow coefficients of the three branches, , , These represent the relative opening degrees of the three valves.

[0055] For each candidate control strategy, the flow coefficient value is obtained based on the valve opening degree and the flow coefficient characteristic curve. Substituting the flow coefficient value and the user's total demand load into the equation system, the predicted valve flow rate for each candidate control strategy is solved. and system pressure difference .

[0056] Check whether the predicted flow rate is within the effective range (demand load plus or minus hysteresis interval) and whether it exceeds the safe adjustment range of the valve. If the above constraints are not met, remove the combination that does not meet the constraints from the effective combination.

[0057] Further, the predicted flow rate for each target candidate strategy is calculated based on the parallel equation set to obtain multiple predicted flow rate groups; the relative error for each predicted flow rate group is calculated based on the second flow rate curve to obtain multiple predicted relative errors; weight calculation is performed on the multiple predicted relative errors and the total demand load to obtain multiple weight coefficients; a weighted average is performed on the multiple predicted relative errors and the multiple weight coefficients to obtain multiple metering accuracies, and the metering accuracies with the largest value are taken as the target accuracies; the target candidate strategy corresponding to the target accuracies is taken as the actual valve control strategy.

[0058] After obtaining the predicted traffic, the predicted traffic of branch i is... Substitute the "flow rate - relative error" characteristic curve of the corresponding cooling meter into the curve to obtain the predicted relative error value of the cooling meter on the corresponding branch under the predicted operating condition. The smaller the error, the more accurate the measurement.

[0059] Based on the predicted flow of each branch and total demand flow Calculate the weighting coefficient w for each branch. i This represents the proportion of the predicted flow of this branch to the total demand flow: .

[0060] The measurement accuracy of a candidate strategy is obtained by weighting the weight coefficient of each branch and the relative error of each branch.

[0061] Traversing all valid combinations, the theoretical range of measurement accuracy is [0, 1]. The closer to 1, the higher the overall measurement accuracy of the system under that combination; the closer to 0, the lower the accuracy. The combination that makes the measurement accuracy closest to 1 is selected as the current total load Q. 总 The optimal valve control scheme is determined by identifying the opening degree of the target valves in each branch and the branches that should be opened.

[0062] like Figure 3 As shown, after determining the optimal valve control strategy, it is determined whether to enable pump feedforward control. If so, the PLC is instructed to control the pump frequency to increase or decrease before the valve operates. If not, the current pump power is maintained. The valve switching action is executed and completed. The pump operating frequency is finely adjusted according to the terminal differential pressure sensor. Finally, the system enters a stable flow cycle.

[0063] Step S40: Control the cooling pipeline to provide cooling according to the actual valve control strategy, collect real-time cooling data, perform performance monitoring processing based on the real-time cooling data, and obtain performance monitoring results.

[0064] This invention possesses advanced self-adaptation and fault tolerance capabilities. During operation, real-time data is compared with initial data to achieve dynamic error compensation and instrument health monitoring. Simultaneously, the control system can continuously learn and accumulate verification pattern data and patterns from different seasons and time periods, thereby enabling the prediction of demand in the near future and the fine-tuning of operating parameters in advance, allowing the system to operate stably and efficiently for extended periods.

[0065] Specifically, a first mixing device is installed before the branch pipe of the cooling supply pipeline, a second mixing device is installed after the branch pipe and before the plate heat exchanger of the cooling supply pipeline, and a third mixing device is installed after the plate heat exchanger. When the cooling supply pipeline is controlled to supply cooling according to the actual valve control strategy, water is supplied according to the water supply path of the cold source, the first mixing device, the branch pipe, the second mixing device, the plate heat exchanger, and the user end; water is returned according to the return path of the user end, the plate heat exchanger, the third mixing device, and the cold source.

[0066] This invention sets up multiple mixing devices at the branch inlet and the user side to break up the fluid stratification in the pipeline by forcibly disturbing it, especially in large-diameter, low-flow-rate cooling pipelines. This eliminates the temperature gradient and instantaneous temperature difference fluctuations in the pipeline, which can significantly reduce the metering error caused by the temperature difference between the supply and return water, and greatly improve the long-term comprehensive metering accuracy of the cooling meter under complex operating conditions such as actual variable operating conditions, especially the response mechanism under low flow conditions.

[0067] The number of mixing devices in this invention can be set arbitrarily. For example, three mixing devices H1, H2, and H3 can be set, with H1 being the first mixing device, H2 the second mixing device, and H3 the third mixing device. After the cold source is connected, the water is mixed by the mixing device H1, making the supply water temperature more stable. A mixing device H2 (or a parallel pipe) is set after the junction of each branch and before the user's plate heat exchanger to stabilize the supply water temperature entering the user. A mixing device H3 is set on the main cooling return pipe after the user's plate heat exchanger to make the return water temperature more stable. This will significantly reduce the metering error caused by instantaneous fluctuations in supply and return water temperatures, creating stable temperature conditions for the cold water meter's measurement.

[0068] During cooling, the chilled water starts from the cold source and passes through the mixing device H1, branch pipes, mixing device H2, and plate heat exchanger to reach the user end for water supply; during return, the return water starts from the user end and passes through the plate heat exchanger and mixing device H3 to return to the cold source.

[0069] Furthermore, real-time cooling data is collected, and branch fault diagnosis is performed based on the real-time cooling data to obtain branch performance diagnosis results.

[0070] The system performs dynamic data acquisition during cooling, collecting not only conventional supply and return water temperatures, flow rates, and pressures, but also continuously collecting parameters related to the system's dynamic characteristics, such as the rate of flow change (instantaneous fluctuations) and the temporal characteristics of water temperature changes. These data together constitute the real-time data of the current operating status.

[0071] like Figure 4 As shown, the system collects data during real-time cooling, compares the real-time data with the initial baseline data, and performs dynamic error compensation calculations and joint verification of multi-parameter consistency.

[0072] Based on the current real-time feedback valve opening degree l, query the pre-calibrated "valve opening degree - flow coefficient" characteristic curve (or function) of the current valve to obtain the theoretical flow coefficient of this branch at the current opening degree. Simultaneously, the flow rate is collected in real time by flow meters and differential pressure sensors installed on this branch. Readings and differential pressure Calculate the actual flow coefficient With valves, flow meters, pressure sensors, and other equipment all in good working order, Should be with The two are highly compatible. The system compares the relative deviations between the two. Make a judgment: .

[0073] Considering the impact of sensor measurement noise, initial calibration error, and flow fluctuations, this system employs a dynamic adaptive fault threshold instead of a fixed threshold. Real-time composite calculation based on the accuracy level of each sensor and the measurement uncertainty under the current operating conditions: ; in, This is the coverage factor, with a value of 2, corresponding to approximately a 95% confidence level. The threshold is relative to the standard uncertainty and can be dynamically adjusted. For example, under low flow (high uncertainty) conditions, the threshold will be automatically widened to avoid frequent false alarms due to low signal-to-noise ratio. Under high flow (low uncertainty) conditions, the threshold will be automatically tightened to achieve accurate diagnosis.

[0074] When relative deviation Continuously exceeding the dynamic threshold When this happens, the system determines that the physical relationship between "flow rate-opening degree-pressure" of the branch is out of tune, triggers a "hardware consistency failure" alarm for the branch, and marks the branch as faulty.

[0075] like Figure 4As shown, when an abnormal fault is detected in a branch, the faulty branch is automatically identified and logically isolated, the faulty branch is removed, and the normal branch cluster is reconstructed; when no branch fault is detected, all branches participate in load sharing normally.

[0076] When a branch is identified as faulty, the control strategy automatically excludes it from available resources and recalculates and switches to the optimal valve control combination among the remaining healthy branches. For example, when D2 fails, 50% of the load may be borne by D1.

[0077] like Figure 4 As shown, after branch fault diagnosis, the decision-making level recalculates the current cooling load demand and solves the optimal valve combination for the remaining branches; it outputs optimized valve opening commands to maintain uninterrupted cooling and metering; the system operation data is archived and stored, with accumulated load modes by season or time period; the algorithm continuously learns deep learning to explore the load change patterns and ensure high-precision continuous metering.

[0078] Furthermore, instrument fault diagnosis is performed based on the real-time cooling data to obtain instrument performance diagnosis results; performance monitoring results are obtained based on the branch performance diagnosis results and the instrument performance diagnosis results.

[0079] Real-time calculation of the instantaneous relative error ε of the cooling capacity meter under current operating conditions (such as specific flow rate and temperature). 实 The system then queries the initial database for historical "relative errors" under the same operating conditions. Instead of performing single-point error comparisons, the system uses a sliding time window to statistically analyze recent error sequences (e.g., the past N sampling periods) under similar operating conditions, calculating the real-time error mean and standard deviation within that window. The statistical characteristics of the real-time errors are then compared with historical benchmarks, and classic statistical tests (such as t-tests and F-tests) are used for significance analysis.

[0080] The t-test (Student's t-test) is used to determine whether the difference between two sample means is statistically significant, and to determine whether there is a significant deviation between the "current error mean" and the "historical benchmark mean" (i.e., whether there is systematic drift).

[0081] The F-test is used to compare whether the variances of two populations are equal. In this context, it is used to determine whether the dispersion (fluctuation) of the current error is significantly greater than the historical baseline (i.e., whether there is a sudden failure or a decrease in stability).

[0082] Sliding window is a commonly used method in real-time signal processing. By statistically analyzing recent data, it enables the system to remain responsive to dynamically changing conditions (such as load changes) rather than relying on a fixed global threshold.

[0083] After completing the above steps, if the detected real-time error characteristics deviate significantly from the baseline error characteristics in the initial database, the system determines that the performance of the cooling meter has degraded, triggers an "instrument performance warning," and indicates that calibration is required. At this point, the user is promptly notified to confirm the joint fault and perform online calibration of the cooling meter to prevent cooling disputes. If the deviation is extremely abnormal, it is directly escalated to a device failure fault.

[0084] The control system can learn the actual cooling patterns during the data accumulation process, thereby enabling it to fine-tune operating parameters in advance and make the system operate more smoothly within the efficient metering range.

[0085] This invention features a system adaptive calibration algorithm that can indirectly indicate potential faults such as sensor anomalies and provide calibration suggestions, thus realizing the transformation from "static calibration" to "dynamic lifelong learning calibration".

[0086] This invention adaptively selects the optimal valve control strategy based on the user's flow demand, and controls the opening and closing of valves in each cooling pipeline branch according to the optimal valve control strategy, which enables the flow meters in each branch to always operate in a high-precision range and improves metering accuracy.

[0087] Furthermore, such as Figure 5 As shown, based on the above-mentioned adaptive control method for cooling pipelines, the present invention also provides an adaptive control system for cooling pipelines, wherein the adaptive control system for cooling pipelines includes: The strategy initialization module 51 is used to acquire multiple preset discrete loads, initialize the valve control strategy according to the multiple preset discrete loads, and obtain multiple preset discrete load ranges and preset valve control strategies. Demand analysis module 52 is used to acquire users' cooling signals, perform total load analysis processing based on the cooling signals, and obtain the total demand load. The strategy construction module 53 is used to perform matching processing in multiple preset discrete load intervals according to the total demand load. If a matching result is obtained, the preset valve control strategy is used as the actual valve control strategy. If no matching result is obtained, adaptive strategy construction processing is performed according to the total demand load to obtain the actual valve control strategy. The cooling control module 54 is used to control the cooling pipeline to provide cooling according to the actual valve control strategy, collect real-time cooling data, perform performance monitoring processing based on the real-time cooling data, and obtain performance monitoring results.

[0088] Furthermore, such as Figure 6 As shown, based on the above-mentioned adaptive control method and system for cooling pipelines, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 6Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0089] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Further, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores an adaptive control program 40 for the cooling pipe, which can be executed by the processor 10 to implement the adaptive control method for the cooling pipe in this application.

[0090] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing the adaptive control method of the cooling pipe.

[0091] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface.

[0092] In one embodiment, when the processor 10 executes the adaptive control program 40 for the cooling pipes in the memory 20, the following steps are performed: Multiple preset discrete loads are obtained, and valve control strategies are initialized based on the multiple preset discrete loads to obtain multiple preset discrete load ranges and preset valve control strategies. Obtain the user's cooling demand signal, and perform total load analysis and processing based on the cooling demand signal to obtain the total demand load; The total demand load is matched within multiple preset discrete load intervals. If a matching result is obtained, the preset valve control strategy is used as the actual valve control strategy. If no matching result is obtained, an adaptive strategy is constructed based on the total demand load to obtain the actual valve control strategy. The cooling pipeline is controlled to provide cooling according to the actual valve control strategy, and real-time cooling data is collected. The performance monitoring data is then processed to obtain the performance monitoring results.

[0093] Specifically, the step of acquiring multiple preset discrete loads and initializing the valve control strategy based on these preset discrete loads to obtain multiple preset discrete load ranges and a preset valve control strategy includes: Multiple preset discrete loads and preset hysteresis intervals are obtained, and interval construction processing is performed based on the multiple preset discrete loads and preset hysteresis intervals to obtain multiple preset discrete load intervals; For each of the preset discrete load intervals, valve control strategy construction is performed to obtain multiple initial valve control strategies, and a preset valve control strategy is obtained based on the multiple initial valve control strategies.

[0094] The step of acquiring the user's cooling signal and performing total load analysis based on the cooling signal to obtain the total demand load specifically includes: Obtain the user's cooling usage signal, perform demand analysis processing based on the cooling usage signal, and obtain the demand level; Obtain the cooling season and cooling period, and calculate the cooling coefficient based on the cooling season and cooling period; The total load is calculated based on the demand level and the cooling coefficient to obtain the total demand load.

[0095] Specifically, the matching process is performed on multiple preset discrete load intervals based on the total demand load. If a matching result is obtained, the preset valve control strategy is used as the actual valve control strategy. If no matching result is obtained, an adaptive strategy construction process is performed based on the total demand load to obtain the actual valve control strategy. This process includes: Determine whether the total demand load falls within any one of the multiple preset discrete load intervals; If so, the preset valve control strategy will be used as the actual valve control strategy. If not, obtain the branch flow data, and perform adaptive strategy construction based on the branch flow data and the total demand load to obtain the actual valve control strategy.

[0096] The step of acquiring branch flow data and performing adaptive strategy construction based on the branch flow data and the total demand load to obtain the actual valve control strategy specifically includes: Multiple preset valve openings are obtained, and the flow rate of each branch of the cooling pipeline at each preset valve opening is measured and processed to obtain multiple branch flow rates. The multiple branch flow rates are used as branch flow rate data, and the flow coefficient is calculated based on the branch flow rate data to obtain flow coefficient data. Based on the flow coefficient data and the branch flow data, a coefficient curve is constructed to obtain the first flow curve; The readings of the cooling meter for each branch are collected and processed to obtain meter data. An error curve is constructed based on the meter data and the branch flow data to obtain a second flow curve. Multiple candidate control strategies and parallel equation sets are obtained. Based on the parallel equation sets, the total demand load, and the first flow curve, the multiple candidate control strategies are screened to obtain a target candidate strategy set. The accuracy of the target candidate strategies in the target candidate strategy set is evaluated based on the second flow curve to obtain the measurement accuracy of each target candidate strategy. The target candidate strategy with the highest measurement accuracy is then used as the actual valve control strategy.

[0097] Specifically, the step of performing accuracy evaluation processing on the target candidate strategies in the target candidate strategy set based on the second flow curve to obtain the measurement accuracy of each target candidate strategy, and selecting the target candidate strategy with the highest measurement accuracy as the actual valve control strategy, includes: The predicted flow of each target candidate strategy is calculated based on the parallel equation set to obtain multiple predicted flow sets; The relative error of each predicted flow group is calculated based on the second flow curve to obtain multiple predicted relative errors; Multiple weighting coefficients are obtained by weighting the multiple relative prediction errors and the total demand load; A weighted average is performed on the multiple prediction relative errors and the multiple weighting coefficients to obtain multiple measurement accuracies, and the measurement accuracies with the largest value are taken as the target accuracy. The target candidate strategy corresponding to the target accuracy is used as the actual valve control strategy.

[0098] Specifically, the step of controlling the cooling pipeline to provide cooling according to the actual valve control strategy, collecting real-time cooling data, and performing performance monitoring processing based on the real-time cooling data to obtain performance monitoring results includes: A first mixing device is installed before the branch pipe of the cooling pipeline, a second mixing device is installed after the branch pipe and before the plate heat exchanger of the cooling pipeline, and a third mixing device is installed after the plate heat exchanger. When the cooling pipeline is controlled to provide cooling according to the actual valve control strategy, water is supplied according to the water supply path of the cold source, the first mixing device, the branch pipeline, the second mixing device, the plate heat exchanger, and the user end. Water is returned according to the return water path of the user terminal, the plate heat exchanger, the third mixing device, and the cold source; Collect real-time cooling data, perform branch fault diagnosis and processing based on the real-time cooling data, and obtain branch performance diagnosis results; Based on the real-time cooling data, instrument fault diagnosis is performed to obtain instrument performance diagnosis results; The performance monitoring results are obtained based on the branch performance diagnosis results and the instrument performance diagnosis results.

[0099] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores an adaptive control program for a cooling pipeline, which, when executed by a processor, implements the steps of the adaptive control method for the cooling pipeline as described above.

[0100] In summary, this invention provides an adaptive control method, system, and terminal for cooling pipelines. The method includes: acquiring multiple preset discrete loads; initializing valve control strategies based on the multiple preset discrete loads to obtain multiple preset discrete load intervals and preset valve control strategies; acquiring user cooling signals; performing total load analysis processing based on the cooling signals to obtain total demand load; performing matching processing on the multiple preset discrete load intervals based on the total demand load; if a matching result is obtained, the preset valve control strategy is used as the actual valve control strategy; if no matching result is obtained, adaptive strategy construction processing is performed based on the total demand load to obtain the actual valve control strategy; controlling the cooling pipeline to provide cooling according to the actual valve control strategy, and collecting real-time cooling data; performing performance monitoring processing based on the real-time cooling data to obtain performance monitoring results. This invention adaptively selects the optimal valve control strategy based on the user's flow demand and controls the opening and closing of valves in each branch of the cooling pipeline according to the optimal valve control strategy, enabling the flow meters in each branch to always operate in a high-precision range and improving metering accuracy.

[0101] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0102] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.). The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The computer-readable storage medium can be a memory, magnetic disk, optical disk, etc.

[0103] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. An adaptive control method for cooling pipelines, characterized in that, The adaptive control method for the cooling pipeline includes: Multiple preset discrete loads are obtained, and valve control strategies are initialized based on the multiple preset discrete loads to obtain multiple preset discrete load ranges and preset valve control strategies. Obtain the user's cooling demand signal, and perform total load analysis and processing based on the cooling demand signal to obtain the total demand load; The total demand load is matched within multiple preset discrete load intervals. If a matching result is obtained, the preset valve control strategy is used as the actual valve control strategy. If no matching result is obtained, an adaptive strategy is constructed based on the total demand load to obtain the actual valve control strategy. The cooling pipeline is controlled to provide cooling according to the actual valve control strategy, and real-time cooling data is collected. The performance monitoring data is then processed to obtain the performance monitoring results.

2. The adaptive control method for cooling pipelines according to claim 1, characterized in that, The process of acquiring multiple preset discrete loads and initializing the valve control strategy based on these preset discrete loads to obtain multiple preset discrete load ranges and a preset valve control strategy specifically includes: Multiple preset discrete loads and preset hysteresis intervals are obtained, and interval construction processing is performed based on the multiple preset discrete loads and preset hysteresis intervals to obtain multiple preset discrete load intervals; For each of the preset discrete load intervals, valve control strategy construction is performed to obtain multiple initial valve control strategies, and a preset valve control strategy is obtained based on the multiple initial valve control strategies.

3. The adaptive control method for cooling pipelines according to claim 1, characterized in that, The process of acquiring users' cooling demand signals and performing total load analysis based on these signals to obtain the total demand load specifically includes: Obtain the user's cooling usage signal, perform demand analysis processing based on the cooling usage signal, and obtain the demand level; Obtain the cooling season and cooling period, and calculate the cooling coefficient based on the cooling season and cooling period; The total load is calculated based on the demand level and the cooling coefficient to obtain the total demand load.

4. The adaptive control method for cooling pipelines according to claim 1, characterized in that, The process involves matching the total demand load across multiple preset discrete load intervals. If a matching result is obtained, the preset valve control strategy is used as the actual valve control strategy. If no matching result is found, an adaptive strategy construction process is performed based on the total demand load to obtain the actual valve control strategy. Specifically, this includes: Determine whether the total demand load falls within any one of the multiple preset discrete load intervals; If so, the preset valve control strategy will be used as the actual valve control strategy. If not, obtain the branch flow data, and perform adaptive strategy construction based on the branch flow data and the total demand load to obtain the actual valve control strategy.

5. The adaptive control method for cooling pipelines according to claim 4, characterized in that, The process of acquiring branch flow data and constructing an adaptive strategy based on the branch flow data and the total demand load to obtain the actual valve control strategy specifically includes: Multiple preset valve openings are obtained, and the flow rate of each branch of the cooling pipeline at each preset valve opening is measured and processed to obtain multiple branch flow rates. The multiple branch flow rates are used as branch flow rate data, and the flow coefficient is calculated based on the branch flow rate data to obtain flow coefficient data. Based on the flow coefficient data and the branch flow data, a coefficient curve is constructed to obtain the first flow curve; The readings of the cooling meter for each branch are collected and processed to obtain meter data. An error curve is constructed based on the meter data and the branch flow data to obtain a second flow curve. Multiple candidate control strategies and parallel equation sets are obtained. Based on the parallel equation sets, the total demand load, and the first flow curve, the multiple candidate control strategies are screened to obtain a target candidate strategy set. The accuracy of the target candidate strategies in the target candidate strategy set is evaluated based on the second flow curve to obtain the measurement accuracy of each target candidate strategy. The target candidate strategy with the highest measurement accuracy is then used as the actual valve control strategy.

6. The adaptive control method for cooling pipelines according to claim 5, characterized in that, The step of performing accuracy evaluation processing on the target candidate strategies in the target candidate strategy set based on the second flow curve to obtain the measurement accuracy of each target candidate strategy, and selecting the target candidate strategy with the highest measurement accuracy as the actual valve control strategy, specifically includes: The predicted flow of each target candidate strategy is calculated based on the parallel equation set to obtain multiple predicted flow sets; The relative error of each predicted flow group is calculated based on the second flow curve to obtain multiple predicted relative errors; Multiple weighting coefficients are obtained by weighting the multiple relative prediction errors and the total demand load; A weighted average is performed on the multiple prediction relative errors and the multiple weighting coefficients to obtain multiple measurement accuracies, and the measurement accuracies with the largest value are taken as the target accuracy. The target candidate strategy corresponding to the target accuracy is used as the actual valve control strategy.

7. The adaptive control method for cooling pipelines according to claim 1, characterized in that, The process involves controlling the cooling pipeline to provide cooling according to the actual valve control strategy, collecting real-time cooling data, and performing performance monitoring processing based on the real-time cooling data to obtain performance monitoring results. Specifically, this includes: A first mixing device is installed before the branch pipe of the cooling pipeline, a second mixing device is installed after the branch pipe and before the plate heat exchanger of the cooling pipeline, and a third mixing device is installed after the plate heat exchanger. When the cooling pipeline is controlled to provide cooling according to the actual valve control strategy, water is supplied according to the water supply path of the cold source, the first mixing device, the branch pipeline, the second mixing device, the plate heat exchanger, and the user end. Water is returned according to the return water path of the user terminal, the plate heat exchanger, the third mixing device, and the cold source; Collect real-time cooling data, perform branch fault diagnosis and processing based on the real-time cooling data, and obtain branch performance diagnosis results; Based on the real-time cooling data, instrument fault diagnosis is performed to obtain instrument performance diagnosis results; The performance monitoring results are obtained based on the branch performance diagnosis results and the instrument performance diagnosis results.

8. An adaptive control system for a cooling pipeline, characterized in that, The adaptive control system for the cooling pipeline includes: The strategy initialization module is used to acquire multiple preset discrete loads, initialize the valve control strategy according to the multiple preset discrete loads, and obtain multiple preset discrete load ranges and preset valve control strategies. The demand analysis module is used to acquire users' cooling signals, perform total load analysis based on the cooling signals, and obtain the total demand load. The strategy construction module is used to perform matching processing in multiple preset discrete load intervals according to the total demand load. If a matching result is obtained, the preset valve control strategy is used as the actual valve control strategy. If no matching result is obtained, adaptive strategy construction processing is performed according to the total demand load to obtain the actual valve control strategy. The cooling control module is used to control the cooling pipeline to provide cooling to the user according to the actual valve control strategy, collect real-time cooling data, perform performance monitoring processing based on the real-time cooling data, and obtain performance monitoring results.

9. A terminal, characterized in that, The terminal includes a memory, a processor, and an adaptive control program for the cooling pipeline stored in the memory and executable on the processor. When the processor executes the adaptive control program for the cooling pipeline, it implements the steps of the adaptive control method for the cooling pipeline as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an adaptive control program for the cooling pipeline, which, when executed by a processor, implements the steps of the adaptive control method for the cooling pipeline as described in any one of claims 1-7.

Images