Equipment energy-saving control system and method based on AI large model

Abstract

The invention relates to the field of equipment control, in particular to an equipment energy-saving control system and method based on an AI large model, and the system comprises an equipment sensing module, a load prediction module, a fuzzy control module, a wind-water joint debugging module and an energy efficiency optimization module, the equipment sensing module is used for determining the thermal gain in a region, the load prediction module is used for generating a pre-control decision, and the fuzzy control module is used for generating a fuzzy control result; the fuzzy control module is used for calculating static pressure and air supply volume, the air-water joint debugging module is used for correcting the air supply volume of each area, and the energy efficiency optimization module is used for planning the refrigerating capacity supply efficiency of the air conditioner in different time periods. The stable operation of equipment such as an air-conditioning compressor, a water pump and a fan is guaranteed; the reliability of an energy-saving control system is enhanced; the energy consumption cost of building operation is reduced; and the comprehensive energy efficiency of an air-conditioning system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of device control, in particular to a device energy-saving control system and method based on an AI large model. BACKGROUND

[0002] Device energy-saving control is a technical means for monitoring and managing the running state, running time and running energy consumption of electrical equipment through technical means, so as to reduce unnecessary energy consumption of the equipment. Since the energy consumption of air conditioning equipment usually accounts for 60%-70% of the total energy consumption of the park, the energy-saving control of the central air conditioning is the key to limiting the energy use efficiency. Through accurate and effective energy-saving control, the actual temperature control demand of the building can be accurately matched, and the energy consumption of the central air conditioning equipment can be reduced by 20%-30%.

[0003] Since the central air conditioning inside the building has multiple heat exchange links and the water circulation process is relatively complex, it is difficult to achieve precise load control. The traditional PID control algorithm is easily affected by non-linear and time-delay interference factors such as equipment working state, personnel movement, air temperature change, etc., and the control accuracy of the cold source system is low, and the anti-interference ability is weak, which causes the air conditioning equipment to run at high load for a long time, affecting the energy-saving control effect.

[0004] In addition, the cooling and heating load inside the building is in dynamic and complex change, the temperature influencing factors are many, the load control method is complex, the cooperation degree of air volume and cooling water flow is not enough, causing inefficient use of energy. For server rooms, indoor construction sites and other scenes, inefficient central air conditioning equipment may cause temperature out of control, interfere with the normal operation of the equipment, and also fail to achieve the purpose of energy saving. SUMMARY

[0005] The purpose of the present application is to provide a device energy-saving control system and method based on an AI large model to solve the problems raised in the background art.

[0006] In order to solve the above technical problems, the present application provides the following technical scheme: a device energy-saving control system based on an AI large model, comprising: a device perception module, a load prediction module, a fuzzy control module, a wind-water joint debugging module and an energy efficiency optimization module;

[0007] The device perception module is used to monitor the real-time power of the device through the power monitoring socket, construct a multi-dimensional feature data set using the ratio of regional lighting power consumption to device operation power consumption, regional functions and power consumption time, input the multi-dimensional feature data set into a pre-trained machine learning model, identify the regional power change mode through a clustering algorithm, estimate the real-time personnel distribution density, obtain the personnel density distribution thermal map, deploy passive infrared sensors and contact temperature sensors inside the building, detect the surface temperature of the running equipment, and determine the heat gain in the region according to the personnel density distribution thermal map and the equipment temperature.

[0008] The load prediction module is used to establish a heat load distribution model according to an ambient temperature, a set temperature and a heat gain, to learn a heat load change mode by using a genetic algorithm to optimize a back propagation neural network, to identify a typical heat load cycle, to output a predicted heat load in a current cycle, to establish a heat load prediction model by taking a regional temperature and the predicted heat load as a state space and taking an energy reduction as a reward function, and to determine a pre-control decision for the air conditioner in the cycle by reducing actions in a temperature change space by using a DF-DQN algorithm.

[0009] The fuzzy control module is used to set a return pipe at an air conditioner terminal, to detect inlet and outlet water temperatures of chilled water, to mix the return pipe with a water supply pipe and to adjust a mixing ratio by a three-way valve, to fit a nonlinear heat transfer model of a surface heat exchanger according to historical detection data and heat exchanger parameters, to establish a relationship between heat exchanger heat transfer performance and inlet and outlet water temperatures, air supply and static pressure, to calculate the static pressure and the air supply according to the heat load prediction model so that an output heat transfer amount meets a set temperature interval of equipment and personnel in a region, to optimize a control decision based on a PMV model, and to ensure that cold usage rates of the regions are synchronized.

[0010] The air-water joint regulation module is used to adopt a conventional PID to control the three-way valve and a variable frequency fan according to a planned static pressure set value and an air supply set value, to make a regulation amount consistent with a planning amount, to input a difference between actual heat transfer amounts at ends of water tanks and estimated cold amounts into a fuzzy PID controller in real time, to output a correction amount of the air supply set value of each region, and to redistribute the cold amount among different terminals so that temperatures of all regions reach the set value at the same time.

[0011] The energy efficiency optimization module is used to introduce an energy consumption contribution rate index, to evaluate contribution rates of air supply regions of each central air conditioner, to evaluate contribution rates of temperature regulation of different regions in time periods, to take the contribution rate as a power distribution weight, to maximize a weighted power load efficiency as an objective function, to plan a refrigeration amount supply efficiency of the air conditioner in different time periods under a condition that a refrigeration amount meets a heat load demand, and to correct the set temperature interval of each region according to inertia, response speed and time lag of the air conditioning system.

[0012] Further, the device perception module includes a personnel distribution unit and a temperature measurement sensing unit.

[0013] The personnel distribution unit is used to determine a personnel sensible heat load according to power monitoring and infrared scanning, and to generate a personnel density thermal map of a building interior plane.

[0014] The temperature measurement sensing unit is used to accumulate heat gains of each region according to personnel sensible heat and device operation sensible heat, the heat gain being an additional heat amount generated per unit time.

[0015] Further, the load prediction module comprises a large model unit and a space reduction unit;

[0016] The large model unit is configured to establish a heat load distribution model by using AI, predict environmental temperature and heat gain fluctuation, and output overall heating load.

[0017] The space reduction unit is configured to model the heat load prediction as a sequence decision, reduce the dimension of temperature change, and generate an air conditioning refrigeration quantity execution sequence.

[0018] Further, the fuzzy control module comprises a water supply circuit unit, a setting optimization unit, and a global planning unit.

[0019] The water supply circuit unit is configured to set a return pipe in an air conditioning terminal fan coil or air handling unit, combine with the temperature of a water supply pipe, and calculate actual heat exchange capacity.

[0020] The setting optimization unit is configured to perform performance fitting on a heat exchanger, and generate a joint influence function of real-time heat exchange capacity.

[0021] The global planning unit is configured to use a model predictive control algorithm, calculate a wind pipe static pressure set value based on wind pipe resistance characteristics and fan performance curve, and optimize air conditioning static pressure air supply, with predicted cooling load and personnel distribution as input.

[0022] Further, the air-water joint debugging module comprises an air cooling distribution unit and a static pressure control unit.

[0023] The air cooling distribution unit is configured to calculate cooling capacity distribution by using an alternating direction multiplier algorithm, so that cooling capacity of each region is output synchronously.

[0024] The static pressure control unit is configured to adjust branch valve opening and variable frequency pump speed according to heat load prediction of each region, while taking pipe resistance and equipment capacity limit as adjustment constraints.

[0025] Further, the energy efficiency optimization module comprises a contribution evaluation unit, a space-time quantization unit, and an intelligent decision unit.

[0026] The contribution evaluation unit is configured to evaluate regional thermal comfort level by using a PMV-PPD model, obtain thermal influence level according to device operation parameters, determine regional weight according to regional function, and calculate regional contribution rate in time periods.

[0027] The space-time quantization unit is configured to maximize overall contribution rate and minimize total energy consumption on the premise of meeting refrigeration load demand, and determine temperature adjustment priority.

[0028] The intelligent decision unit is configured to test inertia, response speed, and hysteresis of an air conditioning system by step response, establish a transfer function model, dynamically adjust a set temperature interval of a region, and perform time lag compensation control.

[0029] An AI large model-based device energy-saving control method, comprising the following steps:

[0030] Step S1. Construct a multi-dimensional feature data set according to the regional lighting electricity consumption ratio, function setting and electricity consumption time, input a machine learning model to estimate the personnel distribution density, output a personnel density heat map, and determine the building space heat gain distribution according to the personnel density distribution heat map and the surface temperature of the running device;

[0031] Step S2. Establish a heat load distribution model according to the environment temperature, set temperature and heat gain, predict the heat gain fluctuation, output the predicted heat load in the current period, reduce the dimension of the predicted heat load temperature change, and generate an air conditioning refrigeration amount execution sequence;

[0032] Step S3. Set a return pipe at the air conditioner terminal, detect the chilled water inlet and outlet water temperature, establish the relationship between the heat exchanger heat exchange performance and the inlet and outlet water temperature, air supply quantity and static pressure, calculate the static pressure and air supply quantity according to the execution sequence, and control the output refrigeration amount to control the regional temperature in the device and personnel set temperature interval;

[0033] Step S4. Adjust the air conditioning static pressure and air supply quantity, determine the actual heat exchange amount according to the inlet and outlet water temperature and flow, input the difference between the actual heat exchange amount and the estimated refrigeration amount into the fuzzy PID controller in real time, correct the regional air supply quantity set value, and re-distribute the refrigeration amount;

[0034] Step S5. Calculate the regional contribution rate in time periods according to the personnel distribution, device running parameters and regional function, plan the refrigeration amount supply efficiency in different time periods, maximize the average contribution rate of the power load under the premise of meeting the refrigeration amount demand, and correct the set temperature interval of each region according to the inertia, response speed and time lag of the air conditioning system.

[0035] Further, step S1 comprises:

[0036] Step S11. Monitor the real-time power of the device through the power monitoring socket, construct a multi-dimensional feature data set by using the regional lighting electricity consumption and the proportion of device running electricity consumption, regional function setting and electricity consumption time, and input the multi-dimensional feature data set into the pre-trained machine learning model;

[0037] Select K time series in each dimension of the multi-dimensional feature data set as the initial cluster center, and assign the features to the cluster centers to minimize the objective function: ;

[0038] Wherein, J is the intra-cluster squared error, K is the number of clusters, is determined by the data silhouette coefficient, is the center of the kth cluster, x i is the ith sample point, is the Euclidean distance from the sample point to the cluster center, C k indicates the kth cluster, the cluster with the minimum J is determined, and the cluster center of each cluster is output as a power change mode, a real-time personnel distribution density is estimated according to the regional power change mode, and a personnel density distribution heat map is obtained;

[0039] Step S12. Deploying passive infrared sensors and contact temperature sensors inside the building to detect the surface temperature of the running equipment, and accumulating the heat gain of each region according to the sensible heat of personnel and the sensible heat of equipment operation. The heat gain is the additional heat generated by the region per unit time.

[0040] Further, step S2 includes:

[0041] Step S21. According to the ambient temperature, the set temperature and the heat gain, a heat load distribution model is established by using AI, a back propagation neural network is optimized by using genetic algorithm to learn the heat load change mode, a typical heat load cycle is identified, and a predicted heat load in the current cycle is output.

[0042] Step S22. A heat load prediction model is established by taking the regional temperature and the predicted heat load as the state space and the energy reduction as the reward function, and the model function table is: ;

[0043] Wherein, C is the indoor heat capacity, R is the building thermal resistance, T out (t) and T in (t) are the outdoor temperature and indoor temperature functions respectively, Q load (t) is the internal heat load, Q cool (t) is the air conditioning refrigerating capacity, is the differential of indoor temperature with respect to time, and t is time.

[0044] The DF-DQN algorithm is used to reduce the action in the temperature change space, to determine the pre-control decision of the air conditioner in the cycle, and to generate a refrigerating capacity execution sequence, which contains the refrigerating capacity and the refrigerating time.

[0045] Further, step S3 includes:

[0046] Step S31. A return pipe is arranged in the air conditioner terminal fan coil or air handling unit to detect the inlet and outlet water temperature of chilled water. The return pipe is mixed with the water supply pipe, and the mixing ratio is adjusted by a three-way valve. According to the historical detection data and the heat exchanger parameters, a nonlinear heat transfer model of the surface heat exchanger is fitted, and a relationship between the heat exchanger performance and the inlet and outlet water temperature, air supply and static pressure is established.

[0047] Step S32. Using a model predictive control algorithm, based on the wind pipe resistance characteristics and the fan performance curve, the wind pipe static pressure set value is planned to make the area temperature within the set temperature interval of the equipment and personnel, and the PMV model is used to optimize the control decision, and the cold quantity usage rate of each area is synchronized.

[0048] Further, step S4 includes:

[0049] Step S41. According to the planned static pressure set value and the supply air volume set value, the cold quantity distribution is calculated by using the conventional PID control three-way valve and the variable frequency fan, and the alternating direction multiplier algorithm is used to make the cold quantity of each area output synchronously.

[0050] Step S42. The difference between the actual heat exchange capacity at the end of each water tank and the estimated cold quantity is input into the fuzzy PID controller in real time, and the correction amount of the supply air volume set value of each area is output, and the branch valve opening and the variable frequency pump speed are adjusted according to the heat load prediction of each area, and the pipeline resistance and equipment capacity limit are used as adjustment constraints.

[0051] Further, step S5 includes:

[0052] Step S51. The PMV-PPD model is used to evaluate the thermal comfort level of the area, the thermal influence level is obtained according to the equipment operation parameters, the area weight is determined according to the area function, and the thermal comfort level and the thermal influence level of each period are weighted and added according to the area weight to obtain the time-sharing area contribution rate.

[0053] Step S52. The contribution rate is used as the power distribution weight, the maximum weighted power load efficiency is used as the objective function, the refrigeration capacity meets the heat load demand as the restriction condition, the refrigeration capacity supply efficiency of the air conditioner in different time periods is planned, the inertia, response speed and time lag of the air conditioning system are tested through step response, the transfer function model is established, the set temperature interval of the area is dynamically adjusted, and the time lag compensation control is performed.

[0054] Compared with the prior art, the present application has the following beneficial effects:

[0055] 1. The present application identifies personnel distribution based on the proportion of area lighting electricity and equipment operation electricity, detects mobile equipment temperature using a PCR sensor, learns equipment temperature change mode, establishes a heat load prediction model, realizes optimization and control of the central air conditioning system, matches the actual demand of the building internal air conditioner, realizes more stable temperature and humidity control, improves indoor environment comfort and equipment operation stability, reduces the operation time of the water chiller unit, reduces energy consumption cost, and improves the energy saving effect of the park.

[0056] 2.The application sets a return pipe at the end of the central air conditioner, fits the influence of chilled water flow, surface heat exchanger parameters and air supply on the heat exchange performance of the surface heat exchanger and the chilled water temperature, realizes air-water joint regulation decoupling, balances the cooling heat exchange system, reduces the peak power demand of the air conditioner, guarantees the smooth operation of the air conditioner compressor, water pump, fan and other equipment, and improves the comprehensive energy efficiency of the air conditioning system.

[0057] 3.The application introduces an energy consumption contribution rate index to evaluate the contribution rate of each central air conditioning air supply area, adjusts and controls the energy supply efficiency of the air conditioner in different periods according to the inertia, response speed and hysteresis of the air conditioning system, realizes the rapid disturbance response of the sudden load, reduces the waste of electric power resources, enhances the reliability of the energy saving control system, reduces the building operation energy consumption cost, and improves the value of the park building. BRIEF DESCRIPTION OF DRAWINGS

[0058] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, which together with the embodiments of the application are used to explain the application, and do not constitute a limitation on the application. In the drawings:

[0059] Figure 1 is a structural schematic diagram of an equipment energy saving control system based on an AI large model of the application;

[0060] Figure 2 is a step schematic diagram of an equipment energy saving control method based on an AI large model of the application. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the application will be described below in conjunction with the drawings in the embodiments of the application, obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0062] Please refer to Figure 1 The application provides a technical solution: an equipment energy saving control system based on an AI large model, comprising: a device perception module, a load prediction module, a fuzzy control module, an air-water joint regulation module and an energy efficiency optimization module.

[0063] The device perception module is used to monitor the real-time power of the device through the power monitoring socket, construct a multi-dimensional feature data set using the proportion of regional lighting power consumption and device operation power consumption, regional functions and power consumption time, input the multi-dimensional feature data set into a pre-trained machine learning model, identify regional power change patterns through a clustering algorithm, estimate real-time personnel distribution density, obtain a personnel density distribution heat map, deploy passive infrared sensors and contact temperature sensors inside the building to detect the surface temperature of the running device, and determine the heat gain in the region according to the personnel density distribution heat map and the device temperature.

[0064] The device perception module comprises a personnel distribution unit and a temperature measurement sensing unit.

[0065] The personnel distribution unit is used to determine the personnel sensible heat load according to the power monitoring and infrared scanning, and generate a personnel density heat map of the building interior plane.

[0066] The temperature measurement sensing unit is used to accumulate the heat gain of each region according to the personnel sensible heat and the device operation sensible heat, and the heat gain is the additional heat generated per unit time.

[0067] The load prediction module is used to establish a heat load distribution model according to the environmental temperature, set temperature and heat gain, optimize a back propagation neural network to learn heat load change patterns using a genetic algorithm, identify typical heat load cycles, output the predicted heat load in the current cycle, establish a heat load prediction model using the regional temperature and the predicted heat load as the state space and the energy reduction as the reward function, and determine the pre-control decision of the air conditioner in the cycle by reducing the actions in the temperature change space using a DF-DQN algorithm.

[0068] The load prediction module comprises a large model unit and a space reduction unit.

[0069] The large model unit is used to establish a heat load distribution model using AI, predict environmental temperature and heat gain fluctuations, and output the overall heating load.

[0070] The space reduction unit is used to model the heat load prediction as a sequence decision, reduce the dimension of temperature change, and generate an air conditioner cooling capacity execution sequence.

[0071] The fuzzy control module is used to set a return pipe at the air conditioner terminal, detect the chilled water inlet and outlet temperature, mix the return pipe and the water supply pipe, and adjust the mixing ratio through a three-way valve, fit a nonlinear heat transfer model of the surface heat exchanger according to historical detection data and heat exchanger parameters, establish a relationship between the heat exchanger heat transfer performance and the inlet and outlet water temperature, air supply and static pressure, calculate the static pressure and air supply according to the heat load prediction model, so that the output heat transfer amount meets the set temperature interval requirements of the device and personnel, optimize the control decision based on the PMV model, and ensure that the cold quantity usage rate of each region is synchronized.

[0072] The fuzzy control module comprises a water supply loop unit, a setting optimization unit and a global planning unit;

[0073] The water supply loop unit is used for setting a return pipe in an air conditioner terminal fan coil or an air handling unit, combining with a water supply pipe temperature to calculate an actual heat exchange amount;

[0074] The setting optimization unit is used for performance fitting of a heat exchanger to generate a joint influence function of real-time heat exchange amount;

[0075] The global planning unit is used for taking predicted cooling load and personnel distribution as input, using a model predictive control algorithm, calculating a wind pipe static pressure setting value based on wind pipe resistance characteristics and fan performance curve, and optimizing air conditioner static pressure air supply.

[0076] The air-water joint regulation module is used for adopting a conventional PID control three-way valve and a variable frequency fan according to the planned static pressure setting value and air supply amount setting value, making the regulation amount consistent with the planning amount, inputting the difference between actual heat exchange amount of each water tank terminal and estimated cooling capacity into a fuzzy PID controller in real time, outputting a correction amount of each regional air supply amount setting value, and redistributing the cooling capacity among different terminals to make the temperature of all regions reach the setting value at the same time.

[0077] The air-water joint regulation module comprises an air cooling distribution unit and a static pressure control unit;

[0078] The air cooling distribution unit is used for calculating cooling capacity distribution by using an alternating direction multiplier algorithm to make the cooling capacity of each region output synchronously.

[0079] The static pressure control unit is used for adjusting branch valve opening and variable frequency pump speed according to the heat load prediction of each region, and simultaneously taking pipe resistance and equipment capacity limit as adjustment constraints.

[0080] The energy efficiency optimization module is used for introducing an energy consumption contribution rate index to evaluate the contribution rate of each central air conditioner air supply region, evaluating the contribution rate of temperature regulation of different regions in different time periods, taking the contribution rate as the power distribution weight, taking the maximum weighted power load efficiency as the objective function, taking the cooling capacity meeting the heat load demand as the restriction condition, planning the cooling capacity supply efficiency of the air conditioner in different time periods, and correcting the setting temperature interval of each region according to the inertia, response speed and time lag of the air conditioning system.

[0081] The energy efficiency optimization module comprises a contribution evaluation unit, a space-time quantization unit and an intelligent decision unit;

[0082] The contribution evaluation unit is used for evaluating the regional thermal comfort level by using a PMV-PPD model, obtaining the thermal influence level according to the equipment operation parameters, determining the regional weight according to the regional function, and calculating the regional contribution rate in different time periods;

[0083] The space-time quantization unit is used to maximize the overall contribution rate and minimize the total energy consumption on the premise of meeting the refrigeration load demand, and to determine the temperature adjustment priority.

[0084] The intelligent decision unit is used to test the inertia, response speed and time lag of the air conditioning system through step response, establish a transfer function model, dynamically adjust the set temperature interval of the region, and perform time lag compensation control.

[0085] As shown in Figure 2 An AI large model-based equipment energy-saving control method, comprising the following steps:

[0086] Step S1. Construct a multi-dimensional feature data set according to the regional lighting electricity consumption ratio, function setting and electricity consumption time, input the machine learning model to estimate the personnel distribution density, output the personnel density heat map, and determine the building space heat gain distribution according to the personnel density distribution heat map and the surface temperature of the running equipment.

[0087] Step S1 includes:

[0088] Step S11. Monitor the real-time power of the equipment through the power monitoring socket, construct a multi-dimensional feature data set using the regional lighting electricity consumption and equipment operation electricity consumption ratio, regional function setting and electricity consumption time, input the multi-dimensional feature data set into the pre-trained machine learning model, identify the regional power change mode through the clustering algorithm, estimate the real-time personnel distribution density, and obtain the personnel density distribution heat map.

[0089] Step S12. Deploy passive infrared sensors and contact temperature sensors inside the building to detect the surface temperature of the running equipment, accumulate the heat gain of each region according to the personnel sensible heat and equipment operation sensible heat, and the heat gain is the additional heat generated by the region per unit time.

[0090] Step S2. Establish a heat load distribution model according to the environment temperature, set temperature and heat gain, predict the heat gain fluctuation, output the predicted heat load in the current period, reduce the dimension of the predicted heat load temperature change, and generate an air conditioning refrigeration amount execution sequence.

[0091] Step S2 includes:

[0092] Step S21. According to the environment temperature, set temperature and heat gain, an AI is used to establish a heat load distribution model, a genetic algorithm is used to optimize a back propagation neural network to learn the heat load change mode, a typical heat load cycle is identified, and a predicted heat load in the current period is output.

[0093] Step S22. Establish a heat load prediction model with the zone temperature and predicted heat load as the state space and the energy reduction as the reward function, reduce the actions in the temperature change space using the DF-DQN algorithm, determine the pre-control decision of the air conditioner in the period, and generate a refrigeration capacity execution sequence, which includes the refrigeration capacity and the refrigeration time.

[0094] Step S3. Set a return pipe at the air conditioner terminal to detect the chilled water inlet and outlet water temperature, establish the relationship between the heat exchanger performance and the inlet and outlet water temperature, air supply quantity and static pressure, calculate the static pressure and air supply quantity according to the execution sequence, and control the output refrigeration capacity in the set temperature interval of the equipment and personnel;

[0095] Step S3 includes:

[0096] Step S31. Set a return pipe at the air conditioner terminal fan coil or air handling unit to detect the chilled water inlet and outlet water temperature, mix the return pipe with the water supply pipe through a three-way valve to adjust the mixing ratio, fit a nonlinear heat transfer model of the surface heat exchanger according to the historical detection data and heat exchanger parameters, and establish the relationship between the heat exchanger performance and the inlet and outlet water temperature, air supply quantity and static pressure;

[0097] Step S32. Use the model predictive control algorithm with the predicted cooling load and personnel distribution as the input, plan the static pressure set value of the air duct based on the air duct resistance characteristics and the fan performance curve to make the zone temperature in the set temperature interval of the equipment and personnel, and optimize the control decision based on the PMV model to ensure the synchronous use of cooling capacity in each region.

[0098] Step S4. Adjust the air conditioner static pressure and air supply quantity, determine the actual heat transfer according to the inlet and outlet water temperature and flow, input the difference between the actual heat transfer and the estimated refrigeration capacity into the fuzzy PID controller in real time, correct the air supply quantity set value of each region, and redistribute the refrigeration capacity;

[0099] Step S4 includes:

[0100] Step S41. According to the planned static pressure set value and air supply quantity set value, use the conventional PID control three-way valve and variable frequency fan, and use the alternating direction multiplier algorithm to calculate the cooling capacity distribution to make the synchronous output of cooling capacity in each region.

[0101] Step S42. Input the difference between the actual heat transfer of each water tank terminal and the estimated cooling capacity into the fuzzy PID controller in real time to output the correction amount of the air supply quantity set value of each region, adjust the branch valve opening and variable frequency pump speed according to the heat load prediction of each region while taking the pipe resistance and equipment capacity limit as the adjustment constraint.

[0102] Step S5. Calculate the area contribution rate by time period according to the personnel distribution, equipment operation parameters and area functions, plan the refrigeration capacity supply efficiency of different time periods, maximize the average contribution rate of power load under the premise of meeting the refrigeration capacity demand, and correct the set temperature interval of each area according to the inertia, response speed and time lag of the air conditioning system.

[0103] Step S5 includes:

[0104] Step S51. Evaluate the area thermal comfort level using the PMV-PPD model, obtain the thermal influence level according to the equipment operation parameters, determine the area weight according to the area function, and add the thermal comfort level and the thermal influence level of each time period according to the area weight to obtain the area contribution rate by time period;

[0105] Step S52. Take the contribution rate as the power distribution weight, take the maximization of the weighted power load efficiency as the objective function, take the refrigeration capacity meeting the thermal load demand as the restriction condition, plan the refrigeration capacity supply efficiency of the air conditioner in different time periods, test the inertia, response speed and time lag of the air conditioning system through step response, establish a transfer function model, dynamically adjust the set temperature interval of the area, and perform time lag compensation control.

[0106] Embodiment: A single-story building has three areas of office, machine room and control room, the air conditioning system predicts the thermal gain distribution in the area through the AI model, determines the refrigeration capacity distribution as 20000 KJ, 50000 KJ and 10000 KJ under the current environmental temperature, and then performs heat transfer in the next time period according to the refrigeration capacity distribution, detects the return water temperature during the refrigeration process, corrects the air supply volume, so that the temperature in each area is located in the temperature interval determined according to the contribution rate index, and adjusts the static pressure distribution in the area to make the refrigeration capacity in the area be consumed at the same time.

[0107] It should be noted that in this text, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0108] Finally, it should be noted that the above only describes the preferred embodiments of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art will appreciate that the technical solutions described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalent ones. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A device energy-saving control method based on an AI large-scale model, characterized in that, The method includes the following steps: Step S1. Construct a multidimensional feature dataset based on the proportion of regional lighting electricity consumption, functional settings, and electricity consumption time. Input the dataset into a machine learning model to estimate the population distribution density and output a population density heat map. Based on the population density distribution heat map and the surface temperature of the operating equipment, determine the thermal gain distribution of the building space. Step S2. Establish a heat load distribution model based on ambient temperature, set temperature and heat gain, predict heat gain fluctuations, output the predicted heat load in the current cycle, reduce the dimension of predicted heat load temperature change, and generate an air conditioning cooling capacity execution sequence. Step S3. Install a return pipe at the air conditioning terminal, detect the inlet and outlet temperatures of chilled water, establish the relationship between the heat exchanger's heat exchange performance and the inlet and outlet water temperatures, air volume, and static pressure, calculate the static pressure and air volume according to the execution sequence, so that the output cooling capacity controls the area temperature within the set temperature range for equipment and personnel. Step S4. Adjust the static pressure and air volume of the air conditioner, determine the actual heat exchange based on the inlet and outlet water temperature and flow rate, input the difference between the actual heat exchange and the estimated cooling capacity into the fuzzy PID controller in real time, correct the air volume set value of each area, and redistribute the cooling capacity. Step S5. Based on personnel distribution, equipment operating parameters, and regional functions, calculate the regional contribution rate in different time periods, plan the cooling capacity supply efficiency for different time periods, maximize the average contribution rate of power load while meeting the cooling capacity demand, and adjust the set temperature range of each region according to the inertia, response speed, and time delay of the air conditioning system.

2. The energy-saving control method for equipment based on an AI large model according to claim 1, characterized in that: Step S1 includes: Step S11. Monitor the real-time power of the equipment through the power monitoring socket, construct a multi-dimensional feature dataset using the ratio of regional lighting power consumption to equipment operation power consumption, regional function settings and power consumption time, and input the multi-dimensional feature dataset into the pre-trained machine learning model; In a multidimensional feature dataset, select K time series as initial cluster centers within each feature dimension, assign features to these cluster centers, and minimize the objective function: Where J is the intra-cluster squared error and K is the number of clusters, determined by the data silhouette coefficient. x is the center of the k-th cluster. i It is the i-th sample point. C is the Euclidean distance from the sample point to the cluster center. k Let J represent the k-th cluster. Determine the cluster where J is the smallest. Output the cluster center of each cluster as a power change pattern. Estimate the real-time population distribution density based on the regional power change pattern to obtain a population density distribution heat map. Step S12. Deploy passive infrared sensors and contact temperature sensors inside the building to detect the surface temperature of the operating equipment. Based on the sensible heat of personnel and the sensible heat of equipment operation, accumulate the thermal gain of each area, where the thermal gain is the additional heat generated by the area per unit time.

3. The energy-saving control method for equipment based on an AI large model according to claim 2, characterized in that: Step S2 includes: Step S21. Based on the ambient temperature, set temperature and heat gain, use AI to establish a heat load distribution model, use a genetic algorithm to optimize the backpropagation neural network to learn the heat load change pattern, identify typical heat load cycles, and output the predicted heat load in the current cycle. Step S22. Establish a heat load prediction model with regional temperature and predicted heat load as the state space and energy reduction as the reward function. The model function table is as follows: Where C is the indoor heat capacity, R is the building thermal resistance, and T is the building thermal resistance. out (t) and T in (t) represents the outdoor temperature and indoor temperature, respectively, and Q is a function of the indoor temperature. load (t) represents the internal heat load, Q cool (t) represents the air conditioning cooling capacity. Let t be the differential of indoor temperature with respect to time; The DF-DQN algorithm is used to reduce the actions within the temperature change space, determine the pre-control decisions for the air conditioner within the period, and generate a cooling capacity execution sequence, which includes cooling capacity and cooling time.

4. The energy-saving control method for equipment based on an AI large model according to claim 3, characterized in that: Step S3 includes: Step S31. Install a return pipe in the fan coil unit or air handling unit of the air conditioner terminal, detect the inlet and outlet water temperatures of chilled water, mix the return pipe with the supply water pipe, and adjust the mixing ratio through a three-way valve. Based on historical detection data and heat exchanger parameters, fit a nonlinear heat transfer model of the surface heat exchanger, and establish the relationship between the heat exchanger heat transfer performance and the inlet and outlet water temperatures, air volume and static pressure. Step S32. Using the predicted cooling load and personnel distribution as input, the model predictive control algorithm is used to plan the duct static pressure setpoint based on the duct resistance characteristics and fan performance curve, so that the area temperature is within the set temperature range of equipment and personnel, and the control decision is optimized based on the PMV model to ensure that the cooling capacity utilization rate of each area is synchronized.

5. The energy-saving control method for equipment based on an AI large model according to claim 4, characterized in that: Step S4 includes: Step S41. Based on the planned static pressure setpoint and air volume setpoint, use conventional PID control of the three-way valve and variable frequency fan, and use the alternating direction multiplier algorithm to calculate the cooling capacity distribution so that the cooling capacity of each area is output synchronously. Step S42. Input the difference between the actual heat exchange at the end of each water tank and the estimated cooling capacity into the fuzzy PID controller in real time, and output the correction amount of the air supply set value of each area. Based on the heat load prediction of each area, and with pipeline resistance and equipment capacity limit as adjustment constraints, adjust the opening of branch valves and variable frequency pump speed. Step S5 includes: Step S51. Use the PMV-PPD model to evaluate the regional thermal comfort level, obtain the thermal impact level based on the equipment operating parameters, determine the regional weight based on the regional function, and add the thermal comfort level and thermal impact level of each time period according to the regional weight to obtain the regional contribution rate of each time period. Step S52. Using the contribution rate as the power allocation weight, maximizing the weighted power load efficiency as the objective function, and ensuring that the cooling capacity meets the heat load demand as the constraint, plan the cooling capacity supply efficiency of the air conditioner at different times. Test the inertia, response speed, and time delay of the air conditioning system through step response testing, establish a transfer function model, dynamically adjust the set temperature range of the area, and perform time delay compensation control.

6. An energy-saving control system for equipment based on an AI large-scale model, characterized in that, The system includes the following modules: equipment sensing module, load prediction module, fuzzy control module, wind and water joint regulation module, and energy efficiency optimization module; The device sensing module is used to monitor the real-time power of the device through the power monitoring socket. It constructs a multi-dimensional feature dataset using the ratio of regional lighting power consumption to equipment operation power consumption, regional functions, and power consumption time. The multi-dimensional feature dataset is input into a pre-trained machine learning model. The clustering algorithm is used to identify regional power change patterns, estimate real-time personnel distribution density, and obtain a personnel density distribution heat map. Passive infrared sensors and contact temperature sensors are deployed in the building to detect the surface temperature of the operating equipment. Based on the personnel density distribution heat map and equipment temperature, the thermal gain in the area is determined. The load prediction module is used to establish a heat load distribution model based on ambient temperature, set temperature and heat gain, use a genetic algorithm to optimize the backpropagation neural network to learn the heat load change pattern, identify typical heat load cycles, and output the predicted heat load in the current cycle. The heat load prediction model is established with the regional temperature and predicted heat load as the state space and the energy reduction amount as the reward function. The DF-DQN algorithm is used to reduce the actions in the temperature change space and determine the pre-control decision of the air conditioner in the cycle. The fuzzy control module is used to install a return pipe at the air conditioning terminal, detect the inlet and outlet temperatures of chilled water, mix the return pipe with the supply water pipe, and adjust the mixing ratio through a three-way valve. Based on historical detection data and heat exchanger parameters, it fits a nonlinear heat transfer model of the surface heat exchanger, establishes the relationship between the heat exchanger's heat exchange performance and the inlet and outlet water temperatures, air volume, and static pressure, and calculates the static pressure and air volume based on the heat load prediction model to ensure that the output heat exchange meets the set temperature range requirements of equipment and personnel in the area. Based on the PMV model, it optimizes control decisions to ensure that the cooling capacity utilization rate of each area is synchronized. The air-water joint adjustment module is used to adjust the three-way valve and variable frequency fan according to the planned static pressure setpoint and air supply setpoint, so that the adjustment amount is consistent with the planned amount. The difference between the actual heat exchange at the end of each water tank and the estimated cooling amount is input into the fuzzy PID controller in real time, and the correction amount of the air supply setpoint of each area is output to redistribute the cooling amount among different ends so that the temperature of all areas reaches the set value at the same time. The energy efficiency optimization module is used to introduce an energy consumption contribution rate index to evaluate the contribution rate of each central air conditioning air supply area, evaluate the contribution rate of temperature regulation in different areas at different times, use the contribution rate as the power allocation weight, take maximizing the weighted power load efficiency as the objective function, and take meeting the heat load demand as the constraint condition to plan the cooling capacity supply efficiency of the air conditioner at different times, and correct the set temperature range of each area according to the inertia, response speed and time delay of the air conditioning system.

7. The energy-saving control system for equipment based on an AI large model according to claim 6, characterized in that: The device sensing module includes: a personnel distribution unit and a temperature sensing unit; The personnel distribution unit is used to determine the sensible heat load of personnel based on power monitoring and infrared scanning, and generate a thermal map of personnel density on the interior plane of the building. The temperature sensing unit is used to accumulate the thermal gain of each area based on the sensible heat of personnel and the sensible heat of equipment operation. The thermal gain is the additional heat generated per unit time. The load forecasting module includes: a large model unit and a space reduction unit; The large model unit is used to establish a heat load distribution model using AI, predict fluctuations in ambient temperature and heat gain, and output the overall heating load. The space reduction unit is used to model heat load prediction as a sequence decision, reduce the dimensionality of temperature changes, and generate an air conditioning cooling capacity execution sequence.

8. The energy-saving control system for equipment based on an AI large model according to claim 7, characterized in that: The fuzzy control module includes: a water supply loop unit, a setting optimization unit, and a global planning unit; The water supply circuit unit is used to install a return pipe in the air conditioning terminal fan coil unit or air handling unit, and calculate the actual heat exchange by combining the water supply pipe temperature; The setting optimization unit is used to fit the performance of the heat exchanger and generate a joint influence function for real-time heat exchange. The global planning unit is used to calculate the duct static pressure setpoint based on the predicted cooling load and personnel distribution, using a model predictive control algorithm, and optimizing the air conditioning static pressure supply by taking these as inputs.

9. The energy-saving control system for equipment based on an AI large model according to claim 8, characterized in that: The air-cooled distribution module includes: an air-cooled distribution unit and a static pressure control unit; The air-cooled distribution unit is used to calculate the cooling capacity distribution using an alternating direction multiplier algorithm, so that the cooling capacity of each area is output synchronously. The static pressure control unit is used to adjust the opening degree of branch valves and the speed of variable frequency pumps according to the heat load forecast of each area, while using pipeline resistance and equipment capacity limits as adjustment constraints.

10. The equipment energy-saving control system based on an AI large model according to claim 9, characterized in that: The energy efficiency optimization module includes: a contribution evaluation unit, a spatiotemporal quantification unit, and an intelligent decision-making unit; The contribution evaluation unit is used to assess the regional thermal comfort level using the PMV-PPD model, obtain the thermal impact level based on equipment operating parameters, determine the regional weight based on regional functions, and calculate the regional contribution rate by time period. The spatiotemporal quantization unit is used to maximize the overall contribution rate and minimize the total energy consumption while meeting the cooling load requirements, and to determine the temperature control priority. The intelligent decision-making unit is used to test the inertia, response speed, and time delay of the air conditioning system through step response testing, establish a transfer function model, dynamically adjust the set temperature range of the region, and perform time delay compensation control.

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