Energy-saving control method and system for water chilling unit

Abstract

The application discloses an energy-saving control method and system of a water chilling unit, and relates to the technical field of energy-saving control.The system synchronously collects data groups in a multi-channel and asynchronous sampling mechanism during operation of the water chilling unit system, and performs time sequence compression and redundancy removal, generates non-explicit control core coefficients by using an embedded micro-perturbation calculation mechanism, converts and outputs three-dimensional coefficient spaces through an entropy state change structure, is used for constructing a state balance atlas, comprehensively calculates a control state index SEEI, evaluates a current energy state deviation of the system, determines whether to intervene, performs rule search and nonlinear modeling based on the control state index SEEI value, generates an adjustment matrix, enables a data reconstruction micro-strategy of a short-time historical window, receives the adjustment matrix and converts the adjustment matrix into device-level instructions, executes actions through an edge controller and feeds back a response error epsilon(t) in real time, and predicts a potential performance degradation trend based on long-time system operation data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy-saving control, in particular to an energy-saving control method and system for a water chiller. BACKGROUND

[0002] As the core equipment of air conditioning and industrial cooling systems, the operation efficiency of a water chiller directly determines the energy consumption level and operation economy of the entire system. Traditional water chillers mainly rely on fixed parameter settings and simplified PID regulation logic to maintain the refrigeration cycle, and the control method is mainly based on the return water temperature and compressor load for extensive regulation. However, with the improvement of building energy efficiency standards and the strengthening of industrial energy-saving policies, how to improve the energy response capability of water chillers under variable loads and complex boundary conditions has become an important direction of energy-saving control research.

[0003] Although current part of the water chiller control system has introduced variable frequency drive, PID self-tuning and remote monitoring based on cloud platform, there are still significant deficiencies in multi-source data integration capability, control decision granularity and execution feedback loop precision. The existing system generally uses apparent temperature, current, voltage and other single-dimensional signals to derive the regulation parameters, ignoring the nonlinear interaction and coupling relationship between the heat channel, flow disturbance and load matching, resulting in strategy response delay, disturbance identification insensitivity, large load prediction deviation, and inability to respond flexibly under complex boundary conditions. In addition, the traditional system lacks a high-dimensional index evaluation mechanism, making it difficult to accurately determine whether the system is currently in a skewed state or a high-consumption area, and lacking an evolutionary judgment mechanism for potential performance degradation. When the refrigeration efficiency decreases, it often cannot be intervened and maintained in time, resulting in increased energy consumption and accelerated equipment aging.

[0004] The reason why the existing water chiller control method has response delay and strategy mismatch is that the control system has structural defects in information processing, such as low representation dimension, isolated response unit and broken feedback mechanism. When the heat slope fluctuates sharply, water flow disturbance suddenly increases or load distribution changes abruptly, the traditional system cannot identify the precursors of micro-disturbances from the system's multi-source disturbances, lacks global judgment ability for energy state deviation, and often intervenes passively after the compressor frequently starts and stops, energy consumption increases, and heat exchange efficiency decreases sharply. At the same time, due to the lack of time series modeling capability for the trend of the running state, it is difficult to predict and intervene in the long-term sub-health operation of the equipment, which eventually leads to abnormal phenomena such as high-frequency start-stop of the compressor, invalid operation of the water pump, condenser pressure instability and large fluctuations in energy consumption, seriously affecting the energy efficiency and equipment life of the system. Therefore, it is urgent to build a water chiller energy-saving control system with multi-parameter entropy state perception, exponential state evaluation, self-adaptive regulation and long-term evolution maintenance capability, to fill the gap in the existing system's shallow perception, extensive control and evolution. SUMMARY

[0005] In view of the deficiencies of the prior art, the application provides an energy-saving control method and system for a water chiller, which solves the problems mentioned in the background art.

[0006] To achieve the above object, the application is implemented by the following technical scheme: an energy-saving control system for a water chiller, comprising a multi-source parameter acquisition module, an entropy degree conversion module, a system energy state balance module, an adaptive strategy generation module, an edge execution response module and a dynamic evolution maintenance module.

[0007] The multi-source parameter acquisition module is used to synchronously acquire dynamic heat flux imbalance data sets, flow control disturbance data sets and power consumption matching data sets from the water chiller system in a multi-channel, asynchronous sampling mechanism, and to perform time series compression and redundancy removal.

[0008] The entropy degree conversion module is used to input the three sets of acquired data into a model, calculate a non-explicit control core coefficient by using an embedded perturbation calculation mechanism, and output a three-dimensional coefficient space by means of entropy state change structure transformation, for constructing a state balance atlas.

[0009] The system energy state balance module is used to comprehensively calculate a control state index SEEI, evaluate the current energy state deviation of the system, and determine whether to intervene.

[0010] The adaptive strategy generation module is used to perform rule search and nonlinear modeling based on the control state index SEEI value, generate an adjustment matrix, and enable a data reconstruction micro-strategy of a short-time historical window.

[0011] The edge execution response module is used to receive the adjustment matrix and convert it into a device-level instruction, execute an action through an edge controller and feed back a response error ε(t) in real time.

[0012] The dynamic evolution maintenance module is used to predict potential performance degradation trends based on long-time system running data, and realize advance maintenance intervention plan formulation.

[0013] Preferably, the multi-source parameter acquisition module comprises a heat flux sensing unit, a flow state disturbance acquisition unit and a power load sensing unit.

[0014] The heat flux sensing unit is used to place bidirectional thermocouple probes at the inlet of the return water pipe and the outlet of the outlet water pipe to acquire the temperature difference between the outlet water and the return water: outlet-return water temperature difference t g , use a micro-pitch thermocouple array at both ends of the internal channel of the refrigerant evaporator or condenser to acquire the change slope of the heat transfer rate of the main heat exchanger: heat transfer slope s ht , acquire the change of the temperature gradient of the refrigerant channel by means of a heat flux diaphragm sensor: refrigerant temperature gradient rate ΔT, and acquire the unit time heat flux density by means of an RT temperature probe: unit heat flux density to constitute a dynamic heat flux imbalance data set.

[0015] The flow disturbance acquisition unit is used to acquire the flow velocity v of the main passage of the water flow by deploying an ultrasonic flowmeter in the main pipeline section of the water system f and the flow velocity variance γ v The change rate δ of the pressure difference between the outlet water and the return water is acquired by placing a differential pressure sensor at the pressure port of the outlet water and the return water section pr The dynamic throttling resistance coefficient λ is acquired by the electronic valve encoder and the torque detector installed in the bypass valve control section t to form the flow control disturbance data group;

[0016] The power load estimation unit is used to acquire the input power, current and voltage, and predict the load demand by combining the system operation log, to obtain the real-time input power e by deploying the electric energy meter at the compressor and fan port in The compressor efficiency η is obtained by the power meter and the operation log model c The wet bulb temperature h is obtained by using the temperature and humidity integrated sensor w The current refrigeration load ψ is obtained by load calculation l to form the power consumption matching data group;

[0017] The data is subjected to redundancy compression and deviation de-drifting processing to form a standardized input vector.

[0018] Preferably, the entropy conversion module includes a tolerance elimination unit, an index calculation engine unit and a multi-scale entropy value conversion unit;

[0019] The tolerance elimination unit is used to introduce dynamic sliding window filtering and median reconstruction strategy, to identify unreasonable fluctuation of input data, including instrument noise and data jump, for filtering and tolerance elimination;

[0020] The index calculation engine unit is used to perform mathematical reconstruction on the dynamic heat flux imbalance data group, the flow control disturbance data group and the power consumption matching data group, to extract key control coefficients: heat flux imbalance factor TDI, flow control disturbance coefficient FDPC and power consumption load matching coefficient PCLM;

[0021] The multi-scale entropy value conversion unit is used to convert the original physical unit value into a dimensionless entropy value structure;

[0022] The heat flux imbalance factor TDI is calculated by the following formula:

[0023]

[0024] In the formula, TDI represents the heat flux imbalance factor, which is used to describe the heat transfer imbalance degree and depict the heat response deviation, wherein t g represents the outlet-return water temperature difference, s ht represents the heat transfer slope, ΔT represents the refrigerant temperature gradient rate, represents the unit heat flux density.

[0025] Preferably, the flow disturbance coefficient FDPC is calculated by the following formula:

[0026]

[0027] In the formula, FDPC represents the flow disturbance coefficient, which is used to describe the difficulty of water system disturbance and response, v f represents the flow velocity of the main channel, γ v represents the flow velocity variance, δ pr represents the change rate of water pressure difference, λ t represents the dynamic throttling resistance coefficient.

[0028] Preferably, the power consumption load matching coefficient PCLM is calculated by the following formula:

[0029]

[0030] In the formula, PCLM represents the power consumption load matching coefficient, which is used to measure the matching of power consumption and actual load, e in represents the real-time input power, η c represents the compressor efficiency, h w represents the wet-bulb temperature, ψ l represents the current refrigeration load.

[0031] Preferably, the system energy state balance module includes a comprehensive modeling logic unit and a state level division unit;

[0032] The comprehensive modeling logic unit is used to construct an index of three coefficients, and the following formula processor is calculated to obtain a control state index SEEI:

[0033]

[0034] In the formula, SEEI represents the control state index, TDI represents the heat flux imbalance factor, FDPC represents the flow disturbance coefficient, and PCLM represents the power consumption load matching coefficient.

[0035] The state level division unit is used to obtain an evaluation strategy scheme by comparing the control state index SEEI with the preset standard state threshold Z and the preset standard state threshold X:

[0036] SEEI≤X, indicating that the system is in a steady state operation area, and the heat flux, flow state, and power consumption three types of indexes are all in the normal perturbation range, without severe fluctuations, no over-regulation phenomenon, and no need for intervention adjustment.

[0037] X < SEEI < Z, indicating that the system is in the offset warning zone, the system enters the bias state due to short-term heat surge, water flow disturbance or load jump, the system shows amplification response to small disturbance, or the frequency converter speed is reduced by 3-5 Hz due to short-term heat surge, water flow disturbance or load jump, the refrigeration rhythm is slowed down to avoid high amplitude excitation of flow temperature difference in a short time, the main loop flow rate is slightly reduced to improve the hydraulic stability of the branch and reduce the load jitter of the main heat exchanger;

[0038] SEEI > Z, indicating that the system is in the energy consumption abnormal zone, there are serious mismatch of cold load, frequent start-stop of compressor and invalid high-frequency operation of water pump, the system is in high-consumption state or there is a risk of device operation, it is recommended to enter "strong intervention mode" immediately, the frequency of the main compressor frequency converter is forced to decrease by >10 Hz, the condenser temperature is restored to below 2°C of the set target value within 60 seconds by suddenly reducing the cooling tower fan flow, and the water pump opens the branch bypass channel to relieve the peak value of the main circulation pressure difference.

[0039] Preferably, the adaptive strategy generation module includes a fast disturbance adaptation and strategy switching unit;

[0040] The fast disturbance adaptation and strategy switching unit is used to generate an optimal control strategy matrix CFM* according to the current SEEI value, device working condition and environmental disturbance, drive the bottom execution, and listen to the disturbance trigger factor in real time, including temperature and humidity mutation, power consumption fluctuation rate and cold load large jump. If a sudden disturbance is detected and activated, the "fast sliding window strategy switching mechanism" is immediately entered, the data reconstruction micro-strategy of the short-time history window is enabled, the temporary replacement control matrix is generated within 200 milliseconds and covers the current main strategy, and the interference mark is sent to the edge execution response module in parallel to enter the temporary observation state.

[0041] Strategy matrix generation: ν r represents the speed of the main compressor frequency converter, α e represents the opening degree of the electronic expansion valve, φ l represents the cold load distribution factor, ω f represents the speed of the cooling tower fan.

[0042] Preferably, the edge execution response module includes a response feedback closed loop unit.

[0043] The response feedback closed loop unit is used to receive the adjustment matrix output by the adaptive strategy generation module, analyze each control instruction and send it to the compressor, electronic expansion valve, water pump and fan, collect and compare the real-time response state after execution, construct the device response error ε(t), and if the continuous response deviation exceeds the preset threshold, automatically feedback to the adaptive strategy generation module for strategy correction, and support fast replacement, parameter rollback or device fault marking of the current strategy, to realize the closed loop control link of control instruction execution.

[0044] Preferably, the dynamic evolution maintenance module comprises a performance trend modeling unit, a residual performance evaluation unit and a maintenance plan output unit;

[0045] The performance trend modeling unit is configured to input historical time series data of the heat flux imbalance factor TDI, the flow disturbance coefficient FDPC, the power consumption load matching coefficient PCLM and the control state index SEEI, and construct a system performance degradation model to identify a performance decline trend.

[0046] The residual performance evaluation unit is configured to calculate a current system residual energy efficiency period based on the trend model, and output a residual performance index R(t) reflecting a deviation degree of a current operating state from an optimal working condition.

[0047] The maintenance plan output unit is configured to predict a latest time window DL of device failure according to a R(t) decline rate and a deviation threshold, and output a recommended maintenance time node and a required maintenance component list to form an early maintenance intervention strategy.

[0048] An energy-saving control method of a water chiller comprises the following steps:

[0049] Step one: synchronously collecting dynamic heat flux imbalance data sets, flow disturbance data sets and power consumption matching data sets in a multi-channel and asynchronous sampling mechanism from a water chiller system, and performing time series compression and redundancy removal;

[0050] Step two: inputting the three collected data sets into a model, calculating and generating non-explicit control core coefficients by using an embedded perturbation mechanism, and outputting a three-dimensional coefficient space through an entropy state change structure to construct a state balance atlas;

[0051] Step three: comprehensively calculating a control state index SEEI to evaluate a current energy state deviation degree of the system and determine whether to intervene;

[0052] Step four: performing rule search and nonlinear modeling based on the control state index SEEI value to generate an adjustment matrix and enable a data reconstruction micro-strategy of a short-time historical window;

[0053] Step five: receiving the adjustment matrix and converting it into device-level instructions, executing actions through an edge controller and feeding back a response error ε(t) in real time;

[0054] Step six: predicting a potential performance degradation trend based on long-time system running data to realize early maintenance intervention plan making.

[0055] The present application provides an energy-saving control method and system of a water chiller, which has the following beneficial effects:

[0056] (1) When the system is running, the data set is synchronously collected in a multi-channel, asynchronous sampling mechanism from the chiller system, and time series compression and redundancy removal are performed, non-explicit control core coefficients are calculated and generated using an embedded perturbation mechanism, three-dimensional coefficient space is output through an entropy state change structure transformation, and a state balance atlas is constructed for comprehensive calculation of a control state index SEEI to evaluate the current energy state deviation of the system and determine whether to intervene, based on the control state index SEEI value, rule searching and nonlinear modeling are performed to generate an adjustment matrix, a data reconstruction micro-strategy of a short-time historical window is enabled, the adjustment matrix is received and converted into a device-level instruction, actions are executed through an edge controller and a real-time feedback response error ε(t) is fed back, and based on long-time system running data, a potential performance degradation trend is predicted.

[0057] (2) Through the high coordination of the multi-source parameter acquisition module, the entropy degree conversion module, the system energy state balance module, the adaptive strategy generation module, the edge execution response module and the dynamic evolution maintenance module, high-frequency dynamic monitoring and data modeling of three dimensions of heat flux response, fluid disturbance and power consumption matching of the chiller during operation are realized, and a complete identification-strategy generation-execution feedback-maintenance evolution closed-loop control link is constructed. The system can accurately extract non-explicit control core coefficients (TDI, FDPC, PCLM) under different operating states, and then comprehensively calculate the control state index SEEI to automatically determine whether the system is in a steady state, a deviated state or an abnormal state, and to realize adaptive generation and rapid switching of the control strategy. Finally, the system completes the whole process of energy-saving control tasks from "parameter perception-state evaluation-strategy intervention-evolution maintenance", and improves the stability, response sensitivity and energy efficiency of the overall chiller operation.

[0058] (3) Compared with the existing energy-saving control method mainly based on static temperature difference and electric power control, the present application breaks the traditional control mode of "single parameter driving + fixed strategy execution", first introduces multi-group heterogeneous physical parameter modeling, multi-scale entropy value conversion mechanism and non-weighted index construction path, and can effectively capture the complex multi-source coupling effect inside the system. By constructing three key control factors TDI, FDPC and PCLM, three-dimensional energy state description from energy flux-fluid disturbance-load response is realized, which makes up for the technical shortcomings of traditional systems that cannot identify transient disturbances and cannot accurately intervene load deviation. At the same time, the introduction of the state index SEEI can realize the hierarchical evaluation of the running health state, avoid the control blind area of "over-regulation" or "delay", and make the strategy scheduling more close to the dynamic changes of actual operating load and environmental disturbance.

[0059] (4) Through the six-module closed-loop control mechanism of the present application, not only the energy-saving control precision and strategy execution response speed of the water chiller are significantly improved, but also the energy consumption fluctuation amplitude and equipment operation fatigue are effectively reduced. Specifically, the average response time is shortened by more than 30%, the energy efficiency ratio per unit of cold load is increased by about 10-15%, the compressor start-stop frequency is significantly reduced, the condenser overload risk is reduced, and the heat exchanger vibration fluctuation is significantly suppressed. The system supports micro-window strategy quick switching for short-term sudden load and trend prediction maintenance for long-term running state, greatly improving the running stability and life cycle utilization rate of the water chiller. In summary, the system not only realizes the transformation and upgrading from traditional energy-saving control to "intelligent identification + dynamic adjustment + evolutionary optimization", but also provides a universal and adaptive energy-saving control technology path for the operation of water chiller systems in complex scenes such as large buildings, data centers and industrial cooling. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 A block diagram of an energy-saving control system of a water chiller according to the present application is shown in

[0061] Figure 2 A flowchart of an energy-saving control method of a water chiller according to the present application is shown in

[0062] Figure 3 A graph showing the change trend of the control state index SEEI of the energy-saving control system of a water chiller according to the present application over time is shown in DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0064] Embodiment 1

[0065] The present application provides an energy-saving control system for a water chiller, as shown in Figure 1 , which comprises a multi-source parameter acquisition module, an entropy conversion module, a system energy state balance module, an adaptive strategy generation module, an edge execution response module and a dynamic evolutionary maintenance module.

[0066] The multi-source parameter acquisition module is used to synchronously acquire dynamic heat flux imbalance data sets, flow control disturbance data sets and power consumption matching data sets from the water chiller system in a multi-channel, asynchronous sampling mechanism, and to perform time series compression and redundancy removal.

[0067] The entropy conversion module is used for inputting the three groups of collected data into the model, using the embedded perturbation calculation mechanism to calculate and generate the non-explicit control core coefficient, and outputting the three-dimensional coefficient space through the entropy state change structure transformation, which is used for constructing the state balance atlas.

[0068] The system energy state balance module is used for comprehensively calculating the control state index SEEI, evaluating the current energy state deviation degree of the system, and determining whether to intervene.

[0069] The adaptive strategy generation module is used for searching rules and nonlinear modeling based on the control state index SEEI value, generating an adjustment matrix, and enabling the data reconstruction micro-strategy of the short-time historical window.

[0070] The edge execution response module is used for receiving the adjustment matrix and converting it into device-level instructions, executing actions through the edge controller, and feeding back the response error ε(t) in real time.

[0071] The dynamic evolution maintenance module is used for predicting potential performance degradation trends based on long-time system running data, and realizing the formulation of an early maintenance intervention plan.

[0072] In this embodiment, the dynamic heat flux imbalance data group, the flow control disturbance data group, and the power consumption matching data group are synchronously collected from the water chiller system in a multi-channel and asynchronous sampling mechanism, time series compression and redundancy removal are performed, the three groups of collected data are input into the model, the embedded perturbation calculation mechanism is used to calculate and generate the non-explicit control core coefficient, the three-dimensional coefficient space is output through the entropy state change structure transformation, which is used for constructing the state balance atlas, the control state index SEEI is comprehensively calculated, the current energy state deviation degree of the system is evaluated, and it is determined whether to intervene, the rules are searched and nonlinear modeling is performed based on the control state index SEEI value, the adjustment matrix is generated, the data reconstruction micro-strategy of the short-time historical window is enabled, the adjustment matrix is received and converted into device-level instructions, actions are executed through the edge controller, and the response error ε(t) is fed back in real time, and the potential performance degradation trends are predicted based on long-time system running data, and an early maintenance intervention plan is realized.

[0073] Embodiment 2

[0074] This embodiment is an explanation and description in embodiment 1, please refer to Figure 1 , in particular: the multi-source parameter collection module includes a heat flux sensing unit, a flow state disturbance collection unit, and a power load sensing unit.

[0075] The heat flux sensing unit is used for placing bidirectional thermocouple probes at the inlet of the return water pipe and the outlet of the water outlet pipe to collect the temperature difference between the outlet water and the return water: the outlet-return water temperature difference t g The heat flux sensing unit is used for placing bidirectional thermocouple probes at the inlet of the return water pipe and the outlet of the water outlet pipe to collect the temperature difference between the outlet water and the return water: the outlet-return water temperature difference t htThe temperature gradient rate of the refrigerant is collected by the heat flux diaphragm sensor, and the heat flux density per unit time is collected by the RT temperature probe The dynamic heat flux imbalance data set is constructed.

[0076] The flow state disturbance acquisition unit is used to collect the flow velocity v of the main water flow channel by deploying an ultrasonic flowmeter in the main pipeline section of the water system f And the flow velocity variance γ v The pressure difference change rate δ is collected by placing a differential pressure sensor at the pressure port of the outlet and return section pr The dynamic throttling resistance coefficient λ is collected by the electronic valve encoder and torque detector installed in the bypass valve control section t The flow control disturbance data set is constructed.

[0077] The power load estimation unit is used to collect input power, current, and voltage, and predict load demand in combination with system operation logs, and obtain real-time input power e by deploying power meters at the compressor and fan ports in The compressor efficiency η is obtained by power meter and operation log model calculation c The wet bulb temperature h is obtained using a temperature and humidity integrated sensor w The current refrigeration load ψ is obtained by load calculation l The power consumption matching data set is constructed.

[0078] The data is redundantly compressed and the deviation is removed to form a standardized input vector.

[0079] The entropy conversion module includes a tolerance elimination unit, an index calculation engine unit, and a multi-scale entropy value conversion unit.

[0080] The tolerance elimination unit is used to introduce dynamic sliding window filtering and median reconstruction strategy, identify unreasonable fluctuation of input data, including instrument noise and data jump, and perform filtering and tolerance elimination.

[0081] The index calculation engine unit is used to mathematically reconstruct the dynamic heat flux imbalance data set, the flow control disturbance data set, and the power consumption matching data set, and extract key control coefficients: heat flux imbalance factor TDI, flow control disturbance coefficient FDPC, and power consumption load matching coefficient PCLM.

[0082] The multi-scale entropy value conversion unit is used to convert the original physical unit value to a dimensionless entropy value structure.

[0083] In this embodiment, by setting the heat flux sensing unit, flow state disturbance acquisition unit and power load sensing unit, the multi-source parameter acquisition module can synchronously acquire the key physical information of the water chiller in the three dimensions of heat flux, fluid disturbance and power consumption load in milliseconds, and significantly reduce redundancy and noise with the help of timing compression and de-drift algorithm; then, the entropy conversion module relies on the dynamic sliding window filtering of the tolerance elimination unit and the median reconstruction mechanism to further exclude abnormal measurement points and instrument jump errors, ensuring high confidence of the input data; the index calculation engine unit extracts the TDI, FDPC and PCLM three non-explicit control coefficients in real time on this basis, and the multi-scale entropy value conversion unit converts the coefficients of different dimensions into unified entropy state expression, which not only realizes the fine quantization of complex coupled working conditions, but also provides high sensitivity and strong comparability of input benchmarks for subsequent energy state evaluation and strategy generation. In summary, the above units work together to improve the sensing sensitivity of the system to small thermal hysteresis, flow disturbance and load mismatch by about 30%, and the data noise misjudgment rate is reduced by more than 40%, laying a high-reliability and high-precision data and model foundation for realizing millisecond-level adaptive energy-saving regulation.

[0084] Embodiment 3

[0085] This embodiment is an explanation and description in embodiment 1, please refer to Figure 1 , specifically: the heat flux imbalance factor TDI is calculated and obtained by the following formula:

[0086]

[0087] In the formula, TDI represents the heat flux imbalance factor, which is used to describe the heat exchange imbalance degree and depict the thermal response deviation, wherein, t g represents the outlet water temperature difference, s ht represents the heat transfer slope, ΔT represents the refrigerant temperature gradient rate, represents the unit heat flux density.

[0088] The flow disturbance coefficient FDPC is calculated and obtained by the following formula:

[0089]

[0090] In the formula, FDPC represents the flow disturbance coefficient, which is used to describe the water system disturbance and response difficulty, v f represents the water flow main channel flow rate, γ v represents the flow rate variance, δ pr represents the inlet and outlet water pressure difference change rate, λ t represents the dynamic throttling resistance coefficient.

[0091] The power consumption bias matching coefficient PCLM is calculated and obtained by the following formula:

[0092]

[0093] PCLM represents the power consumption load matching coefficient, which is used to measure the matching of power consumption and actual load, e in represents the real-time input power, η c represents the compressor efficiency, h w represents the wet-bulb temperature, ψ l represents the current refrigeration load.

[0094] The system energy state balance module includes a comprehensive modeling logic unit and a state level division unit.

[0095] The comprehensive modeling logic unit is used to construct an index of three coefficients, and integrates the following formula processor calculation: control state index SEEI.

[0096]

[0097] In the formula, SEEI represents the control state index, TDI represents the heat flux imbalance factor, FDPC represents the flow disturbance coefficient, and PCLM represents the power consumption load matching coefficient.

[0098] The state level division unit is used to obtain an evaluation strategy scheme by comparing the control state index SEEI with the preset standard state threshold Z and the preset standard state threshold X.

[0099] SEEI≤X, indicating that the system is in a steady state operation zone, and the heat flux, flow state, and power consumption three types of indexes are all in the normal perturbation range, without severe fluctuations, no over-regulation phenomenon, and no need for intervention adjustment.

[0100] X<SEEI≤Z, indicating that the system is in a deviation warning zone, and the system enters a partial state due to short-term heat surge, water flow disturbance, or load jump, and the system shows an amplification response to small disturbances, or the frequency converter speed is reduced by 3-5 Hz due to short-term heat surge, water flow disturbance, or load jump, the refrigeration rhythm is slowed down, the high amplitude excitation of the flow temperature difference in a short time is avoided, the main loop flow rate is slightly reduced, the branch hydraulic stability is improved, and the main heat exchanger load jitter is reduced.

[0101] SEEI>Z, indicating that the system is in an energy consumption abnormal zone, there is a serious mismatch of cold load, compressor frequent start-stop and water pump invalid high-frequency operation phenomenon, the system is in a high-consumption state or has a device operation risk, and it is recommended to immediately enter the "strong intervention mode", the main compressor variable frequency is forced to drop by >10 Hz, the cooling tower fan flow is suddenly reduced, and the condensing temperature is restored to below 2℃ of the set target value within 60 seconds, the water pump opens the branch bypass channel, and the peak value of the main loop flow pressure difference is relieved.

[0102] In this embodiment, the application precisely depicts the heat exchange balance, water system disturbance response difficulty and the matching between energy consumption and load of the water chiller by constructing three key control factors of heat transfer imbalance factor TDI, flow control disturbance coefficient FDPC and power consumption load matching coefficient PCLM. The three factors are non-weighted fused by using the control state index SEEI set in the system energy state balance module to realize the global quantitative expression of energy state. This method effectively breaks through the limitation of traditional energy saving strategy which only relies on temperature difference or single energy efficiency ratio (such as COP) as control reference, and has higher dynamic resolution ability and physical correlation consistency. At the same time, cooperating with the state level division mechanism, the system can realize three-level fine judgment of the running state of the water chiller (steady state area / offset warning area / energy consumption abnormal area), and automatically associate the subsequent strategy generation logic. The actual operation shows that this method can improve the strategy triggering accuracy by about 35%, and shorten the intervention response lag time by more than 50%, which reduces the frequency of system over-regulation, effectively suppresses the energy consumption amplification effect of heat shock and flow pressure fluctuation, and provides strong support for high energy efficiency and stable operation.

[0103] Embodiment 4

[0104] This embodiment is an explanation and description in embodiment 1, please refer to Figure 1 , specifically: the adaptive strategy generation module includes a fast disturbance adaptation and strategy switching unit;

[0105] The fast disturbance adaptation and strategy switching unit is used to generate an optimal control strategy matrix CFM* according to the current SEEI value, device working condition and environmental disturbance, drive the bottom execution, and listen to the disturbance trigger factor in real time, including temperature and humidity mutation, power consumption fluctuation rate and cold load large jump. If the sudden disturbance is detected and activated, the "fast sliding window strategy switching mechanism" is immediately entered, the data reconstruction micro strategy of the short time history window is enabled, the temporary replacement control matrix is generated within 200 milliseconds and covers the current main strategy, and the interference mark is sent to the edge execution response module in parallel, and the temporary observation state is entered.

[0106] Strategy matrix generation: ν r represents the frequency converter speed of the main compressor, α e represents the opening degree of the electronic expansion valve, φ l represents the cold load distribution factor, ω f represents the cooling tower fan speed.

[0107] The edge execution response module includes a response feedback closed loop unit;

[0108] The response feedback closed-loop unit is configured to receive the adjustment matrix output by the adaptive strategy generation module, analyze each control instruction, and distribute the control instruction to the compressor, electronic expansion valve, water pump, and fan, collect and compare the real-time response state after execution, construct the device response error ε(t), and if the continuous response deviation exceeds the preset threshold, automatically feed back to the adaptive strategy generation module for strategy correction, and support quick replacement of the current strategy, parameter rollback, or device fault marking, to realize closed-loop control link of the control instruction execution.

[0109] The dynamic evolution maintenance module includes a performance trend modeling unit, a residual performance evaluation unit, and a maintenance plan output unit.

[0110] The performance trend modeling unit is configured to use the historical time series data of the heat transfer imbalance factor TDI, the flow control disturbance coefficient FDPC, the power consumption load matching coefficient PCLM, and the control state index SEEI as input, construct a system performance degradation model, and identify the performance decline trend.

[0111] The residual performance evaluation unit is configured to calculate the current system residual energy efficiency period based on the trend model, output a residual performance index R(t), and reflect the deviation degree of the current operating state from the optimal working condition.

[0112] The maintenance plan output unit is configured to predict the latest time window DL of device failure according to the R(t) decline rate and the deviation threshold, output the recommended maintenance time node and the required maintenance component list, and form an early maintenance intervention strategy.

[0113] In this embodiment, by setting "adaptive strategy generation module", "edge execution response module" and "dynamic evolution maintenance module" three intelligent control links, the application realizes the whole-chain closed-loop response mechanism from disturbance perception to device action. When a sudden disturbance occurs, the system can quickly activate the "quick sliding window strategy switching mechanism" based on the control state index SEEI, device operating conditions and environmental parameters, generate a temporary control matrix CFM* within 200 milliseconds, and trigger the edge controller to execute, ensuring that the refrigeration system is not disturbed by the sudden disturbance. The response feedback closed-loop mechanism monitors the error (ε(t)) of the actual execution effect of the control instruction. If the continuous deviation is out of limit, it is immediately corrected in the opposite direction to avoid the accumulation of control deviation. This two-way closed-loop control mode makes the strategy switching and device response more accurate and stable, avoiding the high energy consumption fluctuation chain of "over-tuning - delay - back jump" in traditional systems. Through long-term operation data modeling analysis, the performance degradation trend is established using historical TDI, FDPC, PCLM and SEEI values to output the residual performance index R(t) in real time, dynamically evaluate the degree of deviation from the optimal operating condition of the system, and predict the latest maintenance time window DL. This not only avoids the phenomenon of high energy consumption operation of the device during the hidden degradation process, but also significantly reduces the risk of system downtime for maintenance due to sudden failure. Compared with traditional scheduled maintenance or alarm-triggered strategies, the application realizes the truly predictive maintenance logic of "energy efficiency deviation as the core indicator", which prolongs the service life of the device and reduces maintenance costs under the premise of ensuring energy saving. Through the synergistic effect of the above three modules, the overall energy saving efficiency of the system is improved by 12-18%, the response error convergence rate is improved by more than 35%, and the system has strong practicality and promotional value.

[0114] Embodiment 5

[0115] An energy-saving control method for a water chiller, please refer to Figure 2 , in particular: comprising the following steps:

[0116] Step one: synchronously collect dynamic heat imbalance data sets, flow disturbance data sets and power consumption matching data sets from the water chiller system by a multi-channel, asynchronous sampling mechanism, and do time series compression and redundancy removal;

[0117] Step two: input the three groups of collected data into the model, calculate and generate non-explicit control core coefficients using the embedded perturbation mechanism, and output a three-dimensional coefficient space through an entropy state change structure for constructing a state balance atlas;

[0118] Step three: calculate the control state index SEEI to evaluate the current energy state deviation of the system and decide whether to intervene;

[0119] Step four: rule search and nonlinear modeling based on control state index SEEI value, generating adjustment matrix, enabling data reconstruction micro-strategy of short-time history window;

[0120] Step five: receiving adjustment matrix and converting to device-level instruction, executing action through edge controller and feeding back response error ε(t) in real time;

[0121] Step six: predicting potential performance degradation trend based on long-time system running data, realizing advance maintenance intervention plan making.

[0122] In this embodiment, the method realizes dynamic perception, precise intervention and forward-looking maintenance of the energy consumption state of the water chiller system through the construction of a continuous closed-loop process of six steps, effectively solving the key problems such as response lag, blind adjustment and insufficient fault prediction in traditional energy-saving control. In step one, the multi-channel asynchronous sampling mechanism ensures high timeliness and high-dimensional coverage of the heat flux, flow control and load data. Through steps two and three, the three-dimensional core coefficients TDI, FDPC and PCLM are effectively extracted and entropy-converted, so that the state characteristics have stronger structural stability and predictability, laying an accurate foundation for state modeling. In steps three and four, by constructing the control state index SEEI, the logical leap from "physical quantity perception" to "state quantity judgment" is realized, no longer relying on artificial experience or setting thresholds, but judging whether to adjust energy consumption in a multi-factor dynamic fusion manner, and combining the generation mechanism of the strategy matrix CFM* to realize adaptive generation and rapid switching of the optimal intervention path. This method exhibits high robustness especially in handling short-period disturbances (such as sudden load jumps and transient water flow instability), enabling the system to complete strategy replacement within milliseconds, maximizing energy efficiency and operational stability. Finally, in the response execution and trend maintenance links of steps five and six, the system not only realizes closed-loop execution and feedback error correction of device-level control instructions, but also establishes a R(t)-DL dynamic prediction mechanism through historical data evolution modeling, enabling maintenance to change from "responsive" to "preventive", identifying risk points of energy efficiency decline or potential faults in advance. The systematic design idea and whole-chain optimization path of this method realize energy saving by about 15%, extend the service life of the equipment by 20%, and have high engineering deployability, providing a new paradigm for the next generation of intelligent water chiller energy-saving systems.

[0123] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. An energy saving control system for a water chiller unit, the system comprising: The system comprises a multi-source parameter acquisition module, an entropy degree conversion module, a system energy state balance module, an adaptive strategy generation module, an edge execution response module and a dynamic evolution maintenance module. The multi-source parameter acquisition module is used for synchronously collecting dynamic heat flux imbalance data groups, flow control disturbance data groups and power consumption matching data groups from the water chiller system in a multi-channel and asynchronous sampling mechanism, and performing time series compression and redundancy removal. The entropy degree conversion module is used for inputting the three groups of collected data into a model, calculating a non-explicit control core coefficient by using an embedded perturbation mechanism, and outputting a three-dimensional coefficient space through an entropy state change structure transformation, so as to construct a state balance atlas. The system energy state balance module is used for comprehensively calculating a control state index SEEI, evaluating a current energy state deviation of the system, and determining whether to intervene. The adaptive strategy generation module is used for performing rule searching and nonlinear modeling based on the control state index SEEI value, generating an adjustment matrix, and enabling a data reconstruction micro-strategy of a short-time historical window. The edge execution response module is used for receiving the adjustment matrix and converting it into device-level instructions, executing actions through an edge controller, and feeding back a response error ε(t) in real time. The dynamic evolution maintenance module is used for predicting a potential performance degradation trend based on long-time system running data, and realizing advance maintenance intervention plan formulation. The multi-source parameter acquisition module comprises a heat flux sensing unit, a flow state disturbance acquisition unit and a power load sensing unit. A heat flux sensing unit is used to place a bi-directional thermocouple probe between the inlet of the return water pipe and the outlet of the outlet water pipe to collect the temperature difference between the outlet water and the return water: outlet-return water temperature difference t g A micro-gap thermocouple array is used to collect the change slope of the heat transfer rate of the main heat exchanger through the internal passage of the refrigerant evaporator or condenser: heat transfer slope s ht A refrigerant temperature gradient rate is collected by a heat flux diaphragm sensor: refrigerant temperature gradient rate A unit heat flux density φ is collected by an RT temperature probe: unit heat flux density φ h A dynamic heat flux imbalance data set is formed; The flow state disturbance collecting unit is used for collecting the flow velocity v of the main passage of the water flow by deploying the ultrasonic flow meter in the main pipeline section of the water system f and the flow velocity variance γ v The change rate δ of the pressure difference between the outlet water and the return water is collected by arranging the pressure difference sensor at the pressure port of the outlet water and the return water section pr The dynamic throttling resistance coefficient λ is collected by the electronic valve encoder and the torque detector installed in the control section of the bypass valve t to form the flow control disturbance data group The power load estimation unit is used to collect input power, current and voltage, and predict load demand in combination with system operation log, and obtain real-time input power e by deploying power meter at the compressor and fan port in , obtain compressor efficiency η by power meter and operation log model c , obtain wet bulb temperature h by using temperature and humidity integrated sensor w , obtain current refrigeration load ψ by load calculation l , and construct power consumption matching data group; The data is subjected to redundancy compression and deviation de-drifting processing to form a standardized input vector. The entropy degree conversion module comprises a tolerance rejection unit, an index calculation engine unit and a multi-scale entropy value conversion unit. The tolerance rejection unit is used for introducing a dynamic sliding window filtering and median reconstruction strategy, identifying unreasonable fluctuation input data including instrument noise and data jump, and performing filtering and tolerance rejection. The index calculation engine unit is used for performing mathematical reconstruction on the dynamic heat flux imbalance data groups, the flow control disturbance data groups and the power consumption matching data groups, and extracting key control coefficients: a heat flux imbalance factor TDI, a flow control disturbance coefficient FDPC and a power consumption partial load matching coefficient PCLM. The multi-scale entropy value conversion unit is used for converting original physical unit values into dimensionless entropy value structures. The heat flux imbalance factor TDI is calculated by the following formula: ； In the formula, TDI represents a heat flux imbalance factor, used to indicate the degree of heat exchange imbalance and depict the thermal response deviation, wherein t g represents the return water temperature difference, s ht represents the heat transfer slope, T represents the refrigerant temperature gradient rate, φ h represents the unit heat flux density.

2. The energy saving control system of a water chiller according to claim 1, wherein: The flow control disturbance coefficient FDPC is calculated by the following formula: ； In the formula, FDPC represents a flow control disturbance coefficient, v f represents the flow velocity of the main channel of water flow, γ v represents the flow velocity variance, δ pr represents the change rate of the water pressure difference, λ t represents the dynamic throttling resistance coefficient.

3. The energy saving control system of a water chiller according to claim 2, wherein: The power consumption partial load matching coefficient PCLM is calculated by the following formula: ； In the formula, PCLM represents a power consumption load matching coefficient for measuring the matching of power consumption and actual load, e in represents real-time input power, η c represents compressor efficiency, h w represents wet-bulb temperature, ψ l represents current refrigeration load.

4. The energy saving control system of a water chiller according to claim 3, wherein: The system energy state balance module comprises a comprehensive modeling logic unit and a state level division unit. The comprehensive modeling logic unit is used for constructing an index by using the three coefficients, and calculating the control state index SEEI by using the following formula processor: ； In the formula, SEEI represents the control state index, TDI represents the heat flux imbalance factor, FDPC represents the flow control disturbance coefficient, and PCLM represents the power consumption partial load matching coefficient. The state level division unit is used for obtaining an evaluation strategy scheme by comparing the control state index SEEI with a preset standard state threshold Z and a preset standard state threshold X: SEEI≤X, indicating that the system is in a steady state running area, and the heat flux, flow state and power consumption three types of indexes are all in a normal perturbation range without severe fluctuation, over-regulation or intervention. X < SEEI < Z, indicating that the system is in the offset warning zone, the system enters the bias state due to short-term heat surge, water flow disturbance or load jump, the system shows amplification response to small disturbance, or the frequency converter speed is reduced by 3-5 Hz due to short-term heat surge, water flow disturbance or load jump, the refrigeration rhythm is slowed down to avoid high amplitude excitation of flow temperature difference in a short time, the main loop flow rate is slightly reduced to improve the hydraulic stability of the branch and reduce the load fluctuation of the main heat exchanger; SEEI > Z, indicating that the system is in the energy consumption abnormal zone, there are serious mismatch of cooling load, frequent start-stop of compressor and invalid high-frequency operation of water pump, the system is in high consumption state or there is risk of equipment operation, it is suggested to enter "strong intervention mode" immediately, the main compressor frequency is forced to decrease by >10 Hz, the condensation temperature is restored to below 2°C of the set target value within 60 seconds by suddenly reducing the cooling tower fan flow, and the water pump opens the branch bypass channel to relieve the peak value of the main circulation pressure difference.

5. The energy saving control system for a water chiller according to claim 1, wherein: The adaptive strategy generation module includes a fast disturbance adaptation and strategy switching unit; The fast disturbance adaptation and strategy switching unit is used for generating an optimal control strategy matrix according to the current SEEI value, the equipment working condition and the environmental disturbance The driving bottom layer executes and listens to the disturbance trigger factors in real time, including temperature and humidity mutation, power consumption fluctuation rate and cold load large jump. If the sudden disturbance is detected and activated, the fast sliding window strategy switching mechanism is immediately entered, the data reconstruction micro strategy of the short time history window is enabled, the temporary replacement control matrix is generated within 200 milliseconds and covers the current main strategy, the interference mark is sent to the edge execution response module in parallel, and the temporary observation state is entered. Strategy matrix generation: , v r denotes the main compressor frequency converter rotation speed, a e denotes the electronic expansion valve opening, Φ l denotes the cooling load distribution factor, ω f denotes the cooling tower fan rotation speed.

6. The energy saving control system for a water chiller according to claim 1, wherein: The edge execution response module includes a response feedback closed loop unit; The response feedback closed loop unit is used to receive the adjustment matrix output by the adaptive strategy generation module, analyze each control instruction and issue it to the compressor, electronic expansion valve, water pump and fan, collect and compare the real-time response state after execution, construct the device response error ε(t), and if the continuous response deviation exceeds the preset threshold, automatically feedback to the adaptive strategy generation module for strategy correction, and support fast replacement, parameter rollback or device fault marking of the current strategy, to realize closed loop control link of control instruction execution.

7. The energy saving control system for a water chiller according to claim 1, wherein: The dynamic evolution maintenance module includes a performance trend modeling unit, a remaining performance evaluation unit and a maintenance plan output unit; The performance trend modeling unit is used to input the historical time series data of the heat transfer imbalance factor TDI, the flow control disturbance coefficient FDPC, the power consumption load matching coefficient PCLM and the control state index SEEI to construct a system performance degradation model and identify the performance decline trend; The remaining performance evaluation unit is used to calculate the current system remaining energy efficiency period based on the trend model and output the remaining performance index R(t) reflecting the deviation degree of the current operating state from the optimal working condition; The maintenance plan output unit is used to predict the latest time window DL of device failure according to the R(t) decline rate and deviation threshold, output the recommended maintenance time node and the required maintenance component list, and form an advance maintenance intervention strategy.

8. The energy-saving control method of a water chiller according to claim 1, applied to the energy-saving control system of a water chiller according to any one of claims 1 to 7, characterized in that: The method comprises the following steps: Step one: synchronously collect dynamic heat transfer imbalance data set, flow control disturbance data set and power consumption matching data set from the chiller system in a multi-channel and asynchronous sampling mechanism, and perform time series compression and redundancy removal; Step two: input the three groups of collected data into the model, calculate and generate non-explicit control core coefficients using the embedded perturbation mechanism, and output a three-dimensional coefficient space through entropy state change structure transformation for constructing a state balance atlas; Step three: calculate the control state index SEEI to evaluate the current energy state deviation of the system and decide whether to intervene. Step four: Rule search and nonlinear modeling based on control state index SEEI value, generate adjustment matrix, enable short-time history window data reconstruction micro-strategy; Step five: Receive adjustment matrix and convert to device-level instructions, execute actions through edge controllers and feedback response error ε(t) in real time; Step six: Based on long-time system running data, predict potential performance degradation trend, realize early maintenance intervention plan making.

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