Intelligent air conditioner resource energy-saving control system

Abstract

The invention discloses an intelligent air conditioner resource energy-saving control system, and relates to the technical field of air conditioner control, the intelligent air conditioner resource energy-saving control system comprises an acquisition assembly, a preprocessing assembly, an analysis calculation assembly and a control management assembly, the analysis and calculation assembly comprises a cooling load analysis module, an air supply temperature analysis module and an operation state analysis module. According to the method, dynamic factors such as personnel activity, equipment operation and commodity display density are integrated, so that the cold load calculation accuracy is improved; by introducing humidity, air speed and retention time to correct and balance the comfort level and energy conservation of air supply temperature adjustment and fusing equipment energy efficiency attenuation, fresh air utilization and electricity price factors, multi-system cooperative control of the compressor is achieved, finally conversion from experience to data driving is achieved, the comfort level is guaranteed, and the energy utilization efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air conditioner control, in particular to an intelligent air conditioner resource energy-saving control system. BACKGROUND

[0002] With the acceleration of urbanization, large public buildings such as shopping malls, airports and complexes have become an important carrier of social energy consumption, and the energy consumption of air conditioning system accounts for about 40%-60% of the total building energy consumption, which is the core field of energy saving and consumption reduction. The traditional air conditioning control method depends on preset fixed parameters, such as setting temperature 26℃ and fixed supply air velocity, or manual experience adjustment, which may not be able to adapt to the dynamic changes of the environment and load demand in the building.

[0003] The existing air conditioning resource control system may introduce temperature and humidity sensors or people flow statistics, but still has the following limitations: The calculation of cooling load is not fully considered, and the cooling load calculation may be based on static building parameters such as area and orientation, ignoring dynamic factors such as personnel activity intensity, commodity display density, and real-time operation state of equipment, which may lead to load prediction deviation; The adjustment of supply air temperature is not fully considered, and the supply air adjustment may only be related to a single temperature parameter, without considering humidity, air velocity and personnel stay time and other factors affecting thermal comfort, which may lead to over-adjustment or insufficient adjustment; And the compressor operation control of the air conditioner may be independent of the fresh air system, equipment energy efficiency decay and electricity price fluctuation, without forming a multi-system collaborative mechanism, which may lead to invalid energy consumption. SUMMARY

[0004] The present application aims to provide an intelligent air conditioner resource energy-saving control system, which solves the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solution: an intelligent air conditioner resource energy-saving control system, comprising: A collection component for obtaining cooling load demand data, supply air temperature correlation data and air conditioner operation related data of a shopping mall, and inputting the obtained data into a preprocessing component; A preprocessing component for data cleaning of the input cooling load demand data, supply air temperature correlation data and air conditioner operation related data, and inputting the cleaned data into an analysis and calculation component; Analysis and calculation component: The dynamic cooling load demand value is output by weighting based on the area of the region, the base area cooling load index, the real-time number of people, the base heat dissipation power per person, the activity intensity coefficient of personnel, the base heat dissipation power density of functional area equipment, the operation coefficient of functional equipment, the area proportion of windows, the real-time light radiation intensity, the light radiation conversion coefficient and the commodity display density correction coefficient in the cooling load demand data; The air supply temperature optimization index is output by analyzing the air supply temperature based on the base air supply temperature, the maximum allowable temperature difference, the maximum design cooling load of the region, the humidity correction coefficient, the air supply speed correction coefficient and the customer average stay time correction coefficient in the air supply temperature correlation data, and in combination with the dynamic cooling load demand value; The operation adjustment coefficient is output by analyzing the operation adjustment of the shopping mall air conditioning resource based on the system energy efficiency attenuation coefficient, the peak-valley electricity price coefficient, the CO2 concentration linkage coefficient and the fresh air collaborative utilization coefficient in the air conditioning operation related data, and in combination of the dynamic cooling load demand value and the air supply temperature optimization index, and the load demand proportion and the temperature adjustment coefficient are converted into the operation adjustment coefficient; The control management component is used for inputting the dynamic cooling load demand value, the air supply temperature optimization index and the operation adjustment coefficient output by the analysis and calculation component, and driving the air conditioning equipment to perform energy-saving adjustment based on the input data, and monitoring the control management effect of the air conditioning resource.

[0006] Optionally, the analysis and calculation component comprises a cooling load analysis module, an air supply temperature analysis module and an operation state analysis module.

[0007] Optionally, the processing flow of the cooling load analysis module is as follows: S1, the actual floor area of the independently controlled region in the shopping mall is obtained through the shopping mall management system background, and the area of the region is analyzed; S2, the base cooling load demand per unit area is analyzed to reflect the heat transfer characteristics of the building envelope, and the base area cooling load index is analyzed; S3, the number of customers and staff currently staying in the region is obtained by AI recognition of the intelligent camera, and the real-time number of people in the shopping mall is analyzed; S4, the base heat dissipation power per person is obtained by analyzing the heat dissipation of adults in the active state, and the activity intensity coefficient of personnel is collected by considering the influence of the activity intensity difference of personnel in different regions on the heat dissipation; S5, the base heat dissipation power density of functional area equipment is obtained by analyzing the average heat dissipation per unit area of special equipment in the region, and the operation coefficient of functional equipment is obtained by detecting the operation state of the equipment through the current sensor and analyzing the influence of the operation state of the equipment on the heat dissipation; S6, by analyzing the ratio of window area to total area of the region, to consider the influence of light heat transfer, and then output the window area ratio, and through the radiation sensor, analyze the real-time intensity of solar radiation transmitted through the window into the room, to obtain the real-time light radiation intensity; S7, by analyzing the ratio of commodity display area to area of the region, to consider the hindering effect of commodity shelves and pile heads on air circulation, and then calculate the commodity display density correction coefficient, to finally output the dynamic cooling load demand value.

[0008] Optionally, the processing flow of the supply air temperature analysis module is as follows: S1, by introducing the dynamic cooling load demand value and the maximum design cooling load in the form of difference, and combining the minimum supply air temperature of the air conditioning system under the maximum load condition, to analyze the basic supply air temperature; S2, by analyzing the adjustment range of the supply air temperature relative to the basic supply air temperature, to obtain the maximum allowed adjustment temperature difference, and by considering the influence of air humidity on the thermal comfort, to obtain the humidity correction coefficient through the real-time relative humidity of the region; S3, by combining the real-time air speed of the supply air outlet, to consider the influence of the supply air speed on the thermal comfort, and then obtain the supply air speed correction coefficient; S4, by considering the average customer stay time of the region, to analyze the demand of customer stay time on temperature stability, and then calculate the customer average stay time correction coefficient, to finally output the supply air temperature optimization index.

[0009] Optionally, the processing flow of the running state analysis module is as follows: S1, by introducing the dynamic cooling load demand value and the supply air temperature optimization index into the running state analysis module, and combining the maximum design cooling load and the basic supply air temperature of the region, to analyze the ratio of the current dynamic cooling load to the maximum design cooling load and the ratio of the basic supply air temperature to the optimized supply air temperature, to output the load demand ratio and the temperature adjustment coefficient; S2, by considering the cumulative continuous running time of the compressor, to analyze the energy efficiency decay of the air conditioning equipment after long-term running, and then calculate the system energy efficiency decay coefficient; S3, by analyzing the influence of real-time electricity price on energy consumption cost, to calculate the peak-valley electricity price coefficient by normalizing the difference between real-time electricity price and flat electricity price; S4, by combining the real-time CO2 concentration of the region with the reference CO2 concentration, to analyze the demand of indoor CO2 concentration on fresh air volume, and then calculate the CO2 concentration linkage coefficient; S5, by combining the ratio of fresh air volume to total supply air volume with the difference between outdoor temperature and indoor set temperature, to calculate the fresh air utilization coefficient, to finally output the running adjustment coefficient.

[0010] Optionally, the preprocessing component includes outlier filtering, data smoothing, format standardization and space-time alignment.

[0011] Optionally, the preprocessing component specifically includes the following cleaning methods: Outlier filtering: the 3σ criterion is used to remove outliers from the sensor data obtained by the acquisition component. Data smoothing: moving average is used for the number of people statistics data to eliminate the instantaneous fluctuations of the camera recognition. Format standardization: different units of parameters are uniformly converted into the required format of the formula. Space-time alignment: all parameters are aligned according to the region and timestamp to ensure the coordinated calculation of parameters in the same time and the same region.

[0012] Optionally, the control management component specifically includes: Compressor frequency regulation: according to the operation adjustment coefficient, a PWM control signal is output to the frequency converter to adjust the compressor operating frequency. Supply air temperature and air speed coordinated regulation: according to the supply air temperature optimization index, the air conditioning box coil water valve opening degree is controlled, when the temperature rises, the water valve is closed, and the supply air outlet electric louver angle is adjusted to match the air speed requirement of the supply air temperature analysis module. Fresh air valve linkage control: according to the fresh air cooperative utilization coefficient, the fresh air valve opening degree is adjusted, when the fresh air cooperative utilization coefficient is greater than 1, the fresh air volume is increased, the natural cooling capacity is introduced, and part of the return air valve is closed to reduce the air conditioning load. Regional priority scheduling: control priority is set for high-traffic areas to ensure that the operation adjustment coefficient is greater than 0.5 to ensure the comfort of the core area.

[0013] Compared with the prior art, the present application has the following advantages: 6、The present application outputs dynamic cooling load demand value through the cold load analysis module, and integrates personnel activity intensity coefficient, functional equipment operation coefficient and commodity display density correction coefficient to quantify dynamic factors such as the hindering effect of commodity display on air circulation, real-time light radiation and equipment operation state into load calculation, solving the prediction deviation problem caused by ignoring multi-dimensional dynamic factors in the existing system, making the cold load calculation more in line with actual demand, and providing a reliable foundation for subsequent parameter optimization.

[0014] 7、The present application outputs the supply air temperature optimization index through the supply air temperature analysis module, quantitatively integrates the influence of air humidity on the sense of touch, the synergistic effect of wind speed and temperature, and the demand of personnel stay time on temperature stability by introducing humidity correction coefficient, supply air speed correction coefficient and customer average stay time correction coefficient, solves the problem that the existing adjustment strategy is difficult to balance comfort and energy saving, dynamically optimizes the supply air temperature under the premise of ensuring the comfort of personnel, and reduces the invalid refrigeration energy consumption.

[0015] 8、The present application outputs the running adjustment coefficient through the running state analysis module, integrates the influence of the energy efficiency attenuation of long-term operation of the equipment, the natural cold utilization of the fresh air system and the influence of the price fluctuation on the energy consumption cost into the compressor control logic, solves the problem that the existing control mode is insufficient in system synergy, realizes the accurate matching of the compressor running state with the dynamic load, environmental conditions, equipment state and energy cost, reduces the invalid energy consumption, and improves the overall energy saving efficiency of the system. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a schematic diagram of the system structure of the present application; Figure 2 is a schematic diagram of the structure of the acquisition assembly of the present application; Figure 3 is a schematic diagram of the structure of the analysis and calculation assembly of the present application; Figure 4 is a schematic diagram of the running process of the analysis and calculation assembly of the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. 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 protection of the present application.

[0018] Please refer to Figures 1 to 4 The present embodiment provides an intelligent air conditioning resource energy saving control system, which comprises: An acquisition assembly is used to acquire the cooling load demand data, supply air temperature correlation data and air conditioning operation related data of a shopping mall, and input the acquired data into a preprocessing assembly; The preprocessing assembly is used to perform data cleaning on the input cooling load demand data, supply air temperature correlation data and air conditioning operation related data, and input the cleaned data into an analysis and calculation assembly; The preprocessing assembly comprises outlier filtering, data smoothing, format standardization and space-time alignment; Outlier filtering: 3σ criterion is used to remove outliers from the sensor data collected by the acquisition component; Data smoothing: 10-minute moving average is used to eliminate the transient fluctuations of the camera recognition from the number of people statistics data; Format standardization: parameters in different units are uniformly converted to the required format; Space-time alignment: all parameters are aligned by region and timestamp to ensure the coordinated calculation of parameters in the same time and the same region; Analysis and calculation component: Based on the area of the cold load demand data, the area of the cold load demand data, the real-time number of people, the per capita basic heat dissipation power, the activity intensity coefficient of personnel, the basic heat dissipation power density of functional equipment, the operation coefficient of functional equipment, the area ratio of windows, the real-time light radiation intensity, the light radiation conversion coefficient and the commodity display density correction coefficient, and the weighted processing, the dynamic cold load demand value is outputted; Based on the basic supply air temperature, the maximum allowable adjustment temperature difference, the maximum design cold load of the region, the humidity correction coefficient, the supply air speed correction coefficient and the customer average stay time correction coefficient in the supply air temperature related data, the supply air temperature is analyzed, and the dynamic cold load demand value is combined to output the supply air temperature optimization index; Based on the system energy efficiency attenuation coefficient, the peak-valley electricity price coefficient and the CO2 concentration linkage coefficient and the new air utilization coefficient in the air conditioning operation related data, the operation adjustment of the shopping mall air conditioning resources is analyzed, and the dynamic cold load demand value and the supply air temperature optimization index are combined and converted into load demand ratio and temperature adjustment coefficient to output the operation adjustment coefficient; The analysis and calculation component includes: cold load analysis module, supply air temperature analysis module and operation state analysis module; Control management component: used to input the dynamic cold load demand value, supply air temperature optimization index and operation adjustment coefficient outputted by the analysis and calculation component, the control management component drives the air conditioning equipment based on the input data to perform energy-saving adjustment, and monitors the control management effect of the air conditioning resources; The control management component is specifically: Compressor frequency regulation: according to the operation adjustment coefficient, the PWM control signal is outputted to the frequency converter to adjust the compressor operating frequency, for example, when the operation adjustment coefficient FR=0.7, the compressor operates at 70% rated frequency, reducing energy consumption; Supply air temperature and air speed coordinated regulation: according to the supply air temperature optimization index, the air conditioning box coil water valve opening degree is controlled, and when the temperature rises, the water valve is closed, and the electric louver angle of the supply air outlet is adjusted to match the air speed demand of the supply air speed correction coefficient in the supply air temperature analysis module; Fresh air valve linkage control: adjust the fresh air valve opening according to the fresh air utilization coefficient, increase the fresh air volume when the fresh air utilization coefficient is greater than 1, introduce natural cooling capacity, and simultaneously close part of the return air valve to reduce the air conditioning load; Regional priority scheduling: set control priority for high-traffic areas to ensure that the operation adjustment coefficient is greater than 0.5 to ensure the comfort of the core area.

[0019] Based on the above, the dynamic cooling load demand value calculated by the cooling load analysis module is the core input parameter of the supply air temperature analysis module, directly determining the adjustment range of the optimized supply air temperature. The higher the dynamic cooling load demand value QD, the smaller the adjustment range of the supply air temperature optimization index TS (tending towards the basic supply air temperature), and vice versa (appropriately increasing the supply air temperature); The dynamic cooling load demand value QD of the cooling load analysis module and the supply air temperature optimization index TS of the supply air temperature analysis module are jointly used as the input basis of the operation state analysis module. The dynamic cooling load demand value QD reflects the load demand intensity, and the supply air temperature optimization index TS reflects the refrigerating capacity demand after temperature optimization. Combined with factors such as equipment energy efficiency, electricity price, and fresh air, the operation adjustment coefficient FR is finally generated, realizing precise mapping from load demand to equipment execution; Through the dynamic cooling load prediction of the cooling load analysis module, the system ensures the demand, the supply air temperature optimization of the supply air temperature analysis module realizes the temperature adjustment on demand, and the compressor frequency control of the operation state analysis module achieves the output on demand. The linkage of the three avoids the problem of disconnection between load and control in traditional fixed control. The overall system integrates multiple system parameters such as environment, personnel, equipment, goods, and economy, breaks through the limitations of single-dimensional energy saving, realizes the whole-chain energy saving logic of load prediction energy saving, parameter optimization energy saving, and equipment operation energy saving, improves the overall energy saving level of the system, and through the correction of parameters such as humidity, wind speed, and residence time, ensures that the human body comfort is not sacrificed in the process of energy saving, solves the contradiction between energy saving and comfort in traditional energy saving, and realizes the intelligent control goal of energy saving priority and comfort-based.

[0020] Please refer to Figure 1 , Figure 2 , Figure 3 and Figure 4 , the processing flow of the cooling load analysis module is as follows: S1, through the mall management system background, the actual floor area of the independently controlled area in the mall is obtained, and then the area is analyzed; S2, by analyzing the unit area of the basic cooling load demand, the heat transfer characteristics of the building envelope are reflected, and then the basic area cooling load index is analyzed; S3, through AI recognition of intelligent cameras, the number of current actual customers and staff in the area is obtained, and then the real-time number of the mall is analyzed; S4, obtain the per capita basic heat dissipation power by analyzing the heat dissipation of adults in the active state, and consider the influence of the activity intensity difference of personnel in different regions on the heat dissipation to collect the activity intensity coefficient of personnel; S5, obtain the functional area equipment basic heat dissipation power density by analyzing the average heat dissipation per unit area of the special equipment in the region, and obtain the functional equipment operation coefficient by detecting the operation state of the equipment through the current sensor to analyze the influence of the operation state of the equipment on the heat dissipation; S6, consider the influence of light heat transfer by analyzing the ratio of the area of the window in the region to the total area of the region, and then output the window area ratio, and analyze the real-time intensity of the solar radiation transmitted into the indoor through the window by the radiation sensor to obtain the real-time light radiation intensity; S7, consider the hindering effect of the commodity display and the goods on the air circulation by analyzing the ratio of the commodity display area to the area of the region, and then calculate the commodity display density correction coefficient to finally output the dynamic cooling load demand value; More specifically, the calculation process of the cooling load analysis module is as follows: ; Among them: QD refers to the dynamic cooling load demand value, that is, the real-time quantitative actual cooling load demand of each region of the mall, and the dynamic cooling load demand value QD provides accurate load benchmark for the air conditioning system, replaces the traditional fixed parameter prediction, avoids the situation of over-cooling or insufficient load, and the parameter is used as the core input for subsequent parameter optimization and equipment control, improves the load prediction accuracy, and lays a data foundation for system energy saving; QDA refers to the area, that is, the actual area of the independently controlled region in the mall (such as clothing area, catering area, and fresh food area), the unit is m2, and the collection and acquisition method can extract the region boundary data through the CAD drawing or BIM model of the mall building and input into the system; And the area refers to the area of the independently controlled region in the mall (such as clothing area, catering area, and fresh food area); The mall air conditioning system needs to be independently controlled according to the region (functional difference, flow density difference), to avoid the waste of load caused by one-size-fits-all, for example, the mall is divided into 1000㎡ clothing area and 800㎡ catering area, etc. Sub-regions, each region calculates the dynamic cooling load independently, and then adjusts the air conditioning parameters to ensure the refinement of load calculation and control, which meets the actual scene of the functional difference of the mall region. The introduction of the region area QDA as a basic physical parameter divides the region to ensure the spatial independence of the cooling load calculation and the accuracy of the load prediction of different regions; QDB refers to the basic area cooling load index, that is, the basic cooling load demand per unit area, reflecting the heat transfer characteristics of building envelope (wall, roof), unit: kW / m 2 , the value range is 0.12-0.18 (according to the heat insulation performance of the market, such as new market can be 0.12-0.14, old market can be 0.15-0.18), and the collection and acquisition method can be calculated according to the design specification and combined with the thermal resistance of building materials (such as the thickness of external wall insulation layer, glass heat transfer coefficient); The introduction of this basic area cooling load index QDB provides a regional basic cooling load benchmark value to ensure the basic framework of load calculation; QDC refers to the real-time number of people, that is, the number of customers and staff currently staying in the region, unit: people, value range can be 0-5000 (for example, 300 people in the peak period of catering area, 50 people in the off-peak period); The collection and acquisition method is as follows: Camera AI recognition, deploy intelligent cameras at the entrances and key passages of the region, and count the number of people in real time through target detection algorithm; WiFi probe assistance, install WiFi probe equipment in the region to capture the number of mobile phone MAC addresses (need to filter duplicate equipment), and cross verify with camera data to improve accuracy; The introduction of this real-time number QDC dynamically reflects the change of personnel density, quantifies the influence of personnel heat dissipation on cooling load, and avoids the misjudgment of load caused by personnel fluctuation; QDD refers to the per capita basic heat dissipation power, that is, the heat dissipation amount (sensible heat + latent heat) of a single adult in different activity states, unit: kW / person, value range can be 0.08-0.12 (for example, sitting state can be 0.08, walking or shopping state can be 0.12); The per capita basic heat dissipation power refers to the total heat (unit: kW / person) generated by the human body through metabolism and dissipated to the environment per unit time, which is the basic parameter for calculating personnel heat dissipation load; Sensible heat and latent heat: Sensible heat: heat that directly causes the temperature of the environment to rise (such as human body conduction, convective heat dissipation), which can be directly measured by a thermometer; Latent heat: heat carried away with water evaporation (such as sweating, breathing), which does not change the temperature of the environment but affects the humidity (such as increased latent heat due to sweating in summer, making the air more humid); The judgment method for sitting and walking: The AI posture recognition technology of the market camera can be used to identify whether the personnel have sustained displacement or stationary posture, and the regional function label can be used for auxiliary judgment (such as catering area mainly sitting, passage mainly walking); The introduction of the per capita basic heat dissipation power QDD provides a personnel basic heat dissipation standard, and the activity intensity is used to correct the difference in personnel heat dissipation; QDE refers to the personnel activity intensity coefficient, that is, the influence of the difference in personnel activity intensity in different areas on the heat dissipation (the higher the activity intensity, the more heat dissipation), and the value range can be 1.2-1.5 for the dining area (sitting for a long time but high metabolic rate during meals), 0.7-0.9 for the clothing area (standing and browsing mainly), and 1.3-1.6 for the children area (more running activities); The collection and acquisition method can be through the pre-set area function tags (such as dining and clothing) of the shopping mall management system, and the system automatically matches the coefficients according to the tags, and supports manual fine adjustment in the management interface (such as temporarily increasing the coefficient of the children area to 1.7 during holidays), and the higher the activity intensity, the higher the human metabolic rate, the more heat production, and the heat dissipation increases accordingly; For example, the personnel in the clothing area mainly stand and browse, the activity intensity is low (the metabolic rate can be about 1.2 met), and QDE can be 0.7-0.9; The personnel in the dining area eat, talk, and get up to get food, the activity intensity is high (the metabolic rate can be about 1.5 met), and QDE can be 1.2-1.5; The coefficient is used to correct the per capita heat dissipation, so that the calculation result is more in line with the actual heat production state, and the load demand of the low-activity-intensity area is avoided to be underestimated; The introduction of the personnel activity intensity coefficient QDE facilitates the differentiation of the activity differences of personnel in different areas and the accurate correction of the personnel heat dissipation; QDF refers to the functional area equipment basic heat dissipation power density, that is, the average heat dissipation per unit area of special equipment in the area (such as kitchen equipment in the dining area and refrigerators in the fresh food area), and the unit is KW / m 2 , and the value range can be 0.05-0.08 for the dining area (including refrigerators, electric stoves and ovens), 0.03-0.05 for the fresh food area (including island cabinets and air curtain cabinets), and 0.01-0.02 for the clothing area (only lighting equipment); The collection and acquisition method can be to install a power monitoring module in the area distribution box, to collect the total power of the equipment in real time (which can be recorded every 15 minutes), to divide the average power density by the area after accumulating the 24-hour power, and to calibrate once a month; The introduction of the functional area equipment basic heat dissipation power density QDF is used to quantify the basic heat dissipation level of the special equipment in the area (such as the kitchen in the dining area and the refrigerator in the fresh food area), and to ensure the calculation of the equipment heat dissipation load; QDG refers to the functional equipment operation coefficient, that is, the influence of the running state of the equipment on the heat dissipation (high heat dissipation when running, low heat dissipation when off), and the value range can be 1.3-1.5 when the equipment is running (such as the kitchen equipment in the dining area being started), and 0.4-0.6 when it is on standby and off (such as the refrigerator on standby at night); The collection and acquisition method can detect whether the equipment is running through the equipment current sensor. When the current is > 5A, it is determined to be running, and then QDG can take 1.3-1.5. When the current is < 1A, it is determined to be off, and then QDG can take 0.4-0.6. The data is updated every 10 seconds; The introduction of the function equipment operation coefficient QDG dynamically corrects the influence of equipment operation state on heat dissipation, avoiding overestimation of load when the equipment is not running. QDH refers to the window area ratio, that is, the ratio of the window area in the region to the total area of the region, reflecting the influence of light heat transfer, with a unit of % (percentage), and the value range can be 30%-50% (such as a street shop in a mall), 10%-20% (such as a glass dome area in a mall), and 0%-5% (no external window area) in the inner area. The collection and acquisition method can measure the total window area (including glass curtain wall) according to the building drawings, and calculate the area ratio. The introduction of the window area ratio QDH reflects the potential influence of the regional light heat transfer, and provides a space ratio basis for the calculation of the light cooling load. QDI refers to the real-time light radiation intensity, that is, the real-time intensity of outdoor solar radiation transmitted into the room through the window, with a unit of W / m2, and the value range can be 0-1000 (up to 800-1000 at noon on a sunny day, 200 on a cloudy day, and 0 at night). The collection and acquisition method can install a total radiation sensor on the building facade, collect data every 10 seconds, and transmit it to the system. The introduction of the real-time light radiation intensity QDI can dynamically input light energy and quantify the contribution of real-time solar radiation to the cooling load. QDJ refers to the light radiation conversion coefficient, that is, the correction coefficient for converting solar radiation intensity into cooling load (different glass types have different heat transfer efficiencies), with a unit of cooling load kW / m2 per W / m2 radiation 2 , and the value range can be, for example, 0.0006 for ordinary white glass, 0.0004 for Low-E energy-saving glass, and 0.0005 for film-coated glass. The value logic is that the better the glass heat insulation performance (the lower the heat transfer coefficient), the smaller the proportion of solar radiation converted into indoor cooling load, and the lower the coefficient value. The introduction of the light radiation conversion coefficient QDJ corrects the influence of different glass types on light heat transfer, ensuring the material adaptability of the light cooling load calculation. QDK refers to the commodity display density correction coefficient, that is, the correction coefficient for the obstruction of air circulation caused by commodity shelves and stacks (the denser the display, the lower the heat exchange efficiency, and the cooling load needs to be compensated), and the value range can be 0.0025-0.03 (0.0025 for empty channels and 0.03 for promotional stack areas). The calculation formula is as follows: QDK = 0.05 x (QDKA / QDA); Wherein: QDKA is the area of commodity display within the region (m 2 ); The collection and acquisition method can be image recognition through a camera, using an OpenCV edge detection algorithm to recognize the outline of the shelf and the stack head, and calculating the pixel area occupied by the commodity, and then converting it into the area of commodity display within the region QDKA, which is updated once an hour; The introduction of the commodity display density correction coefficient QDK corrects the hindering effect of commodity display on air circulation, compensates for the additional cooling load demand caused by the reduction of heat exchange efficiency, and needs additional compensation for cooling load in the area where air circulation is blocked (such as the promotion stack head area and the clothing shelf area). Local heat dissipation accumulates, and the existing technology ignores the hindering effect of commodities on air flow. This parameter fills this gap. A1 refers to the area factor weight coefficient; A2 refers to the personnel factor weight coefficient; A3 refers to the equipment factor weight coefficient; A4 refers to the light factor weight coefficient; A5 refers to the commodity display density weight coefficient; Based on the above, the cooling load analysis module quantifies the actual cooling load demand of the air conditioning system in real time by fusing multi-dimensional parameters such as area, personnel dynamics, equipment operation, environmental changes, and commodity display, breaking through the limitations of traditional fixed parameters or static calculation, providing accurate load benchmark for the system, avoiding energy waste caused by over-cooling or load mismatch, and at the same time ensuring reliable data support for subsequent parameter optimization and equipment control, realizing the transformation of cooling load prediction from empirical estimation to dynamic quantification. Unlike traditional cooling load calculation which only relies on static parameters such as area and basic heat dissipation, the cooling load analysis module introduces real-time number of people, equipment running state, and light radiation intensity, which is more in line with the actual scene of commercial building flow fluctuation and intermittent operation of equipment. The hindering effect of commodity shelves and stack heads on air circulation is quantitatively innovated. Traditional technology ignores this factor, which leads to deviation in heat exchange efficiency evaluation. The cooling load analysis module fills the quantitative gap of the influence of commodity display on cooling load.

[0021] Please refer to Figure 1 , Figure 2 , Figure 3 and Figure 4 , the processing flow of the supply air temperature analysis module is: S1, the dynamic cooling load demand value is introduced in the form of difference with the maximum design cooling load of the region, and the minimum supply air temperature under the maximum load condition of the air conditioning system is combined to analyze the basic supply air temperature; S2, obtain the maximum allowable adjustment temperature difference by analyzing the adjustment range of the supply air temperature relative to the base supply air temperature, and consider the influence of air humidity on the thermal comfort by the real-time relative humidity of the area, to obtain a humidity correction coefficient; S3, consider the influence of the supply air speed on the thermal comfort by combining the real-time air speed of the supply air outlet, to further obtain a supply air speed correction coefficient; S4, consider the average customer stay time of the area, to analyze the demand of the customer stay time on the temperature stability, and further calculate a customer average stay time correction coefficient, to finally output the supply air temperature optimization index; More specifically, the calculation process of the supply air temperature analysis module is as follows: ; Wherein: TS refers to the supply air temperature optimization index, that is, the optimal supply air temperature calculated based on the dynamic cooling load. Based on the calculation of the supply air temperature optimization index TS, the supply air temperature is dynamically adjusted (the temperature is increased when the load is low) under the premise of ensuring the comfort, the compressor refrigeration load is reduced, the invalid refrigeration energy consumption is reduced through temperature optimization, the comfort and energy saving goals are balanced, and it is a key link connecting load prediction and equipment control; TSA refers to the base supply air temperature, that is, the lowest supply air temperature of the air conditioning system under the maximum load condition (ensuring the refrigeration capacity under extreme load), unit: ℃, the value range is 16-17 ℃ (such as 16 ℃ for a shopping mall in the south and 17 ℃ for a shopping mall in the north), and the acquisition and obtaining method can be set according to the maximum load calculation result when the system is designed; The introduction of the base supply air temperature TSA is used to set the lowest supply air temperature under extreme load, to ensure the bottom line of the maximum refrigeration capacity of the system; TSB refers to the maximum allowable adjustment temperature difference, that is, the maximum adjustable range of the supply air temperature relative to the base supply air temperature TSA (to avoid the influence of high temperature on comfort), unit: ℃, the value range is 3-4 ℃ (that is, the maximum supply air temperature is not more than 20-21 ℃), and the acquisition method can be determined by customer comfort investigation (such as questionnaire), and the maximum supply air temperature acceptable by most customers is 21 ℃, so TSB=4 ℃ (17+4=21 ℃) is set; The introduction of the maximum allowable adjustment temperature difference TSB is used to limit the upper limit of the supply air temperature adjustment, to avoid the influence of high temperature on the basic comfort; TSC refers to the maximum design cooling load of the area, that is, the maximum cooling load of the area calculated when the system is designed (such as the load under the extreme high temperature in summer + full load passenger flow), unit: kW, the value range is 300-800 (such as 300 kW for a clothing area and 800 kW for a catering area), and the acquisition method can be calculated based on the design stage by building thermal software; The introduction of the regional maximum design cooling load TSC provides a regional maximum load benchmark as a reference upper limit for the dynamic load adjustment range; TSD refers to the humidity correction coefficient, that is, the influence of air humidity on the sense of comfort (the higher the humidity, the hotter it is at the same temperature, and the need to reduce the supply air temperature adjustment range), and the specific calculation formula is as follows: TSD = 1 - 0.01 x (TSDH - 50); Wherein, TSDH is the real-time relative humidity (%) of the region, which can be collected by the real-time humidity TSDH of the temperature and humidity sensor in the region, and updated every 5 minutes; Wherein, the constant 50 is the reference humidity at 50% relative humidity (the median value of the human comfort humidity range, ASHRAE standard recommends 40%-60%), when TSDH = 50, TSDH-50 = 0, TSD = 1, the adjustment range is not affected by humidity; Wherein, the effect of 1-0.01 is when TSDH> 50% (such as TSDH = 60), (TSDH-50) = 10, TSD = 1-0.01x10 = 0.9, at this time, the supply air temperature adjustment range is reduced (to avoid overheating in high humidity environment due to high temperature); Wherein, the constant 0.01 is that every 1% deviation from the reference humidity, the correction coefficient changes 0.01, to ensure that the influence of humidity on the adjustment range is linearly controllable; The introduction of humidity correction coefficient TSD can correct the influence of air humidity on the sense of comfort, reduce the temperature adjustment range when the humidity is high, and avoid the feeling of stuffiness; TSE refers to the supply air speed correction coefficient, that is, the influence of supply air speed on the sense of temperature (the higher the wind speed, the cooler the body feels, and the supply air temperature can be appropriately increased), and the calculation formula is as follows: TSE = 1 + 0.05 x (TSEA - 0.3); Wherein, TSEA is the real-time wind speed (m / s) of the supply air outlet, which can be collected by the real-time wind speed TSEA of the temperature and humidity sensor in the region, and updated every 5 minutes; Wherein, -0.3, that is, 0.3 m / s is the reference wind speed (the lower limit of the conventional supply air speed of the shopping mall air conditioning design, to ensure air circulation efficiency); Wherein, 1+0.05 is when the wind speed is higher than the reference, the supply air temperature adjustment range is increased (the wind speed increases the sense of coolness, and higher supply air temperature is allowed without affecting the comfort); Further limit the value range, the value range of TSE is 0.8-1.0 (TSEA=70% when 0.8, TSEA=50% when 1.0, TSEA<50% when 1.0); The introduction of the air supply wind speed correction coefficient TSE corrects the influence of the air supply wind speed on the sensible coolness, allows to increase the air supply temperature when the wind speed is high, and balances the comfort and energy saving; TSF refers to the customer average stay time correction coefficient, that is, the correction of the customer stay time on the temperature stability requirement (the longer the stay time, the more sensitive to temperature fluctuations, the need to reduce the adjustment range), the value range can be 0.9-1.025 (0.9 when the regional customer average stay time TSFA=30 minutes in the catering area, 1.025 when the regional customer average stay time TSFA=5 minutes in the passage area), the calculation formula is as follows: TSF=1-0.005×(TSFA-10); Wherein, TSFA is the regional customer average stay time (minutes), which can be recorded by the camera track to record the time stamp of the customer entering and leaving the region, and the average stay time is counted every 30 minutes; Wherein, the constant 10 is the base stay time (10 minutes), that is, the median of the average stay time of the customers in the shopping mall; When TSFA>10 minutes, the air supply temperature adjustment range is reduced (the longer the stay time, the more sensitive to temperature fluctuations, the need to maintain temperature stability to avoid frequent changes leading to discomfort); The introduction of the customer average stay time correction coefficient TSF relates the customer stay time and the temperature stability requirement, reduces the temperature adjustment range when the stay time is long, improves the comfort, the customers in the area with long stay time are more sensitive to temperature fluctuations, the air supply temperature adjustment range needs to be reduced to avoid frequent changes leading to discomfort, the prior art does not relate the stay time and the temperature stability requirement, and the parameter improves the comfort experience.

[0022] Based on the above, the present air supply temperature analysis module dynamically adjusts the air conditioning air supply temperature based on the dynamic cooling load demand, combines the comfort influencing factors such as humidity, wind speed and customer stay time, breaks the traditional fixed air supply temperature control mode, realizes the matching of the air supply temperature and the real-time load under the premise of meeting the human body comfort, appropriately increases the air supply temperature when the load is reduced, reduces the invalid refrigeration energy consumption, reduces the air supply temperature when the load is increased, guarantees the refrigeration effect, and balances the core targets of energy saving and comfort; Different from the traditional fixed temperature (such as 26℃) control mode, the present air supply temperature analysis module relates the influence of air humidity and air supply wind speed on the sensible feeling through the humidity correction coefficient and the wind speed correction coefficient, realizes the collaborative optimization of temperature, humidity and wind speed; The present air supply temperature analysis module innovatively introduces the customer average stay time correction coefficient, the traditional technology does not consider the relationship between the stay time and the temperature stability requirement, and the present air supply temperature analysis module makes the temperature adjustment more suitable for the actual feeling requirement of the customers through the parameter.

[0023] Please refer to Figure 1 、 Figure 2 、 Figure 3 and Figure 4 Figure 1 Figure 2 Figure 3 Figure 4 , the processing flow of the running state analysis module is as follows: S1, by introducing the dynamic cooling load demand value and the supply air temperature optimization index into the running state analysis module, and combining the regional maximum design cooling load and the basic supply air temperature, the ratio of the current dynamic cooling load to the maximum design cooling load and the ratio of the basic supply air temperature to the optimized supply air temperature are analyzed to output the load demand ratio and the temperature adjustment coefficient; S2, by considering the cumulative continuous running time of the compressor, the energy efficiency decay of the air conditioning equipment after long-term running is analyzed, and then the system energy efficiency decay coefficient is calculated; S3, by comparing the real-time electricity price with the flat electricity price and normalizing the difference, the influence of real-time electricity price on energy consumption cost is analyzed, and then the peak-valley electricity price coefficient is calculated; S4, by combining the regional real-time CO2 concentration with the reference CO2 concentration, the demand of indoor CO2 concentration on fresh air volume is analyzed, and then the CO2 concentration linkage coefficient is calculated; S5, by combining the proportion of fresh air volume in total supply air volume with the difference between outdoor temperature and indoor set temperature, the new air utilization coefficient is calculated, and finally the running adjustment coefficient is output; More specifically, the calculation process of the running state analysis module is as follows: ; Among them: FR refers to the running adjustment coefficient, that is, the compressor running intensity coefficient considering multiple factors such as load, temperature, and equipment state. The calculation of the running adjustment coefficient FR directly controls the compressor running frequency, realizes on-demand output, avoids energy waste caused by fixed frequency operation, reduces invalid energy consumption through multi-parameter collaborative adjustment of compressor load, realizes fine and low-energy consumption operation of air conditioning system, and achieves energy saving goal; FRA refers to the system energy efficiency decay coefficient, that is, the correction of energy efficiency decay after long-term running of air conditioning equipment (the longer the running time, the lower the energy efficiency, the frequency needs to be appropriately increased to compensate), the calculation formula is as follows: ; Among them, FRAT is the cumulative continuous running time (h) of the compressor, which can be read by the air conditioning controller (such as Midea central air conditioning PLC module) and updated every hour; Among them, In order to simulate the initial fast and later slow rules of the energy efficiency attenuation of the device, the lubricating oil ages and the parts wear faster in the initial stage of the compressor operation (such as 0-100h), and the energy efficiency attenuation is obvious. The attenuation rate tends to slow down as the running time is prolonged (such as 100-400h), which accords with the nonlinear characteristics of the device aging (linear attenuation may overestimate the long-term energy efficiency loss); wherein, 1-0.0005 is the attenuation coefficient, which is used for controlling the attenuation amplitude; The introduction of the system energy efficiency attenuation coefficient FRA corrects the energy efficiency attenuation of the device after long-term operation, and avoids the insufficient refrigerating capacity caused by the energy efficiency decline; FRB refers to the peak-valley electricity price coefficient, that is, the influence of the real-time electricity price on the energy consumption cost (the higher the electricity price, the more the compressor load needs to be reduced), and the specific calculation formula is as follows: FRB=1-0.2x(FRBA-FRBB) / FRBB; wherein, FRBA is the real-time electricity price (yuan / degree); wherein, FRBB is the flat section electricity price (yuan / degree); The real-time electricity price FRBA can be obtained in real time by connecting the smart meter API of the power grid company, and the flat section electricity price FRBB is preset by the user; wherein, 1-0.2 is the adjustment of the compressor load through the electricity price signal to reduce the energy consumption cost in the high electricity price period; wherein, 0.2 refers to the load adjustment amplitude of 20% for every fluctuation of 100% of the electricity price, which balances the relationship between energy saving and cost control; The introduction of the peak-valley electricity price coefficient FRB is used to correct the influence of the real-time electricity price on the energy consumption cost, reduce the compressor load in the electricity price peak, and reduce the energy consumption in the high-cost period; FRC refers to the CO2 concentration linkage coefficient, that is, the correction of the demand for fresh air volume according to the indoor CO2 concentration (the higher the CO2 concentration, the more fresh air needs to be introduced, which may increase the air conditioning load and needs to compensate the compressor frequency), and the specific calculation formula is as follows: FRC=1+0.0001x(FRCA-800); wherein, FRCA is the real-time CO2 concentration (ppm) in the region, which can be collected by the CO2 sensor in the region and updated every 2 minutes; wherein, -800 is the reference CO2 concentration of 800ppm (the upper limit of CO2 in the comfortable environment of the shopping mall, and the fresh air volume needs to be increased to improve the air quality when it is higher than this value); wherein, 0.0001 is the compensation coefficient which increases by 0.0001 for every 1ppm over the reference concentration, which ensures the coordinated matching of the fresh air volume and the refrigerating capacity; The introduction of the CO2 concentration linkage coefficient FRC corrects the demand of CO2 concentration on fresh air volume, and increases the compressor load to compensate for the additional cold load introduced by fresh air when the concentration is high; FRD refers to the fresh air utilization coefficient, which is to correct the influence of fresh air introduction on air conditioning load (when the outdoor temperature is lower than the indoor temperature, fresh air can provide natural cooling to reduce the compressor load, and vice versa). The specific calculation formula is as follows: FRD = 1-0.02 x FRDA x FRDB; Wherein, FRDA is the proportion of fresh air volume to total air supply volume, which can be obtained by the position feedback signal of fresh air damper actuator (0-100% opening degree corresponds to 0-100% fresh air proportion); Wherein, FRDB is the difference between outdoor temperature and indoor set temperature, which can be collected by outdoor temperature and humidity sensor and calculated every 5 minutes; Wherein, 0.02 is the synergy coefficient, which realizes fine synergy control of fresh air and air conditioning system; The introduction of fresh air utilization coefficient FRD utilizes natural cooling of fresh air across systems, reduces compressor load when outdoor temperature is low, and improves overall energy saving effect of the system. When the outdoor temperature is lower than the indoor temperature, the compressor frequency regulation coefficient is increased (i.e. the actual running frequency is reduced), and then the fresh air is used to save energy. When the outdoor temperature is higher than the indoor temperature, the introduction of fresh air avoids additional cold load. The existing technology controls air conditioning and fresh air system independently, and this parameter realizes cross-system energy saving; QD / TSC refers to the load demand ratio, which is the ratio of current dynamic cooling load to maximum design cooling load, reflecting the degree of load demand, with a value range of 0.3-1.0 (0.3 for low load and 1.0 for full load); TSA / TS refers to the temperature regulation coefficient, which is the ratio of basic supply air temperature to optimized supply air temperature, reflecting the reduction of compressor load due to supply air temperature adjustment (the higher the temperature, the smaller the ratio, and the lower the compressor load), with a value range of 0.8-1.0 (0.81 for supply air temperature of 21℃ and 1.0 for supply air temperature of 17℃).

[0024] Based on the above, the operating state analysis module generates the regulation basis of compressor running intensity by comprehensively considering dynamic cooling load, optimized supply air temperature, equipment energy efficiency decay, electricity price and fresh air utilization, etc., replacing the traditional start-stop control or fixed frequency operation mode, realizing on-demand output of the compressor, reducing the running frequency when the load is low, the electricity price is high and fresh air is available, reducing energy consumption, increasing the running frequency when the load is high and the equipment energy efficiency is low, and ensuring the refrigerating capacity, so as to realize the energy saving goal from the equipment operation level and improve the overall operation efficiency of the system; Different from the traditional air conditioner and fresh air system independent control mode, the fresh air utilization coefficient is used to associate the fresh air volume and the outdoor temperature, the air conditioning refrigeration and the fresh air natural cold quantity are cooperatively used, the overall energy saving potential of the system is improved, and the non-traditional air conditioner parameters such as equipment energy attenuation, peak-valley electricity price and CO2 concentration are innovatively integrated. The traditional technology only focuses on the refrigeration output, and the operation state analysis module makes the compressor control consider the energy consumption cost, the equipment state and the indoor air quality, realizes the multi-objective balance of energy saving, cost and health.

[0025] It is worth noting that the present embodiment gives an iterative form, further operates the running adjustment coefficient FR to optimize the personnel factor weight coefficient A2 in the cold load analysis module, and then adjusts the influence degree of the personnel factor in the cold load analysis module, and then plays the purpose of echoing and circular optimization, and the specific iterative processing process is as follows: ; Among them: A2 k+1 Refers to the personnel factor weight coefficient after the k+1th iteration; A2 k Refers to the personnel factor weight coefficient after the kth iteration; FR k Refers to the running adjustment coefficient after the kth iteration; FRSS refers to the target running frequency adjustment coefficient (which can be determined according to the system design energy efficiency, such as 0.7-0.8, which represents the optimal state of on-demand output); α refers to the iterative step factor (the value is 0.1-0.3, to avoid the adjustment amplitude being too large to cause oscillation); It is worth noting that the iteration termination condition also needs to be set, and the present embodiment is based on two termination conditions, and the convergence of iteration is carried out. When any one of the following two conditions is met, the iteration is terminated: Condition one: the number of iterations reaches the upper limit, and the maximum number of iterations is set to 10 times in the present embodiment, to avoid the iteration from falling into a dead loop and to ensure the real-time performance of the system; Condition two: That is, the difference between the target running frequency adjustment coefficient FRSS and the running adjustment coefficient FR is less than 5%, and it is considered that the cold load prediction matches the actual demand.

[0026] Based on the above, the running adjustment coefficient FR of the running state analysis module of the present iterative system directly reflects the matching degree of the actual refrigeration capacity and the demand. If the running adjustment coefficient FR deviates from the target value for a long time (such as continuously higher than the optimal frequency), the dynamic cooling load demand value QD may be deviated due to the unreasonable weight of the personnel activity intensity coefficient QDE, and the personnel factor weight coefficient A2 needs to be corrected through iteration to dynamically balance the influence of personnel heat dissipation on the cooling load, to correct the cooling load prediction deviation caused by the fluctuation of personnel activity intensity through dynamically adjusting the weight of the personnel activity intensity coefficient QDE, to make the dynamic cooling load demand QD closer to the actual demand, to reduce the frequent fluctuation of the running adjustment coefficient FR caused by the inaccurate dynamic cooling load demand value QD, to avoid the frequent start-stop or high-frequency low-efficiency operation of the compressor, to prolong the service life of the equipment, and to automatically adapt to the dynamic change of the personnel activity intensity in the shopping mall (such as increasing the weight of the personnel activity intensity coefficient QDE when the personnel flow is dense during the weekend promotion to avoid insufficient cooling capacity, and reducing the weight when the personnel flow is sparse during weekdays to avoid overcooling, to form a closed loop of the running state analysis module feedback, the cooling load analysis module weight adjustment, and the running state analysis module optimization, to change the system from passive response to active prediction correction, and to improve the robustness of the overall energy-saving control.

[0027] 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 intelligent air conditioning resource-saving control system, characterized in that, include: Data Acquisition Component: Used to acquire data on the shopping mall's cooling load demand, supply air temperature correlation data, and air conditioning operation-related data, and input the acquired data into the preprocessing component; Preprocessing component: Cleans the input cooling load demand data, supply air temperature correlation data, and air conditioning operation related data, and inputs the cleaned data into the analysis and calculation component; Analysis and calculation components: Based on the area, basic area cooling load index, real-time number of people, per capita basic heat dissipation power, personnel activity intensity coefficient, functional area equipment basic heat dissipation power density, functional equipment operation coefficient, window area ratio, real-time light radiation intensity, light radiation conversion coefficient and merchandise display density correction coefficient in the cooling load demand data, and after weighting, the dynamic cooling load demand value is output. Based on the supply air temperature correlation data, the basic supply air temperature, the maximum allowable temperature difference, the maximum design cooling load of the area, the humidity correction coefficient, the supply air velocity correction coefficient, and the average customer dwell time correction coefficient, the supply air temperature is analyzed and combined with the dynamic cooling load demand value to output the supply air temperature optimization index. Based on the system energy efficiency attenuation coefficient, peak-valley electricity price coefficient, CO2 concentration linkage coefficient, and fresh air synergistic utilization coefficient in the air conditioning operation data, the operation and adjustment of the shopping mall's air conditioning resources are analyzed. The dynamic cooling load demand value is combined with the supply air temperature optimization index and converted into load demand ratio and temperature adjustment coefficient to output the operation adjustment coefficient. Control and management component: It is used to input the dynamic cooling load demand value, supply air temperature optimization index and operation adjustment coefficient output by the analysis and calculation component. Based on the input data, the control and management component drives the air conditioning equipment to perform energy-saving adjustment and monitors the control and management effect of air conditioning resources.

2. The intelligent air conditioning resource-saving control system according to claim 1, characterized in that: The analysis and calculation components include: a cooling load analysis module, a supply air temperature analysis module, and an operating status analysis module.

3. The intelligent air conditioning resource-saving control system according to claim 2, characterized in that: The processing flow of the cooling load analysis module is as follows: S1. Obtain the actual area of ​​the independently controlled areas within the shopping mall through the mall management system backend, and then analyze the area area. S2. By analyzing the basic cooling load demand per unit area, the heat transfer characteristics of the building envelope are reflected, and then the basic area cooling load index is analyzed. S3. Through AI recognition via smart cameras, the number of customers and staff currently in the area can be obtained, and then the real-time number of people in the mall can be analyzed. S4. By analyzing the heat dissipation of adults in an active state, the average basic heat dissipation power is obtained, and the impact of differences in the activity intensity of people in different areas on heat dissipation is considered to collect the activity intensity coefficient. S5. By analyzing the average heat dissipation per unit area of ​​specialized equipment within the region, the basic heat dissipation power density of the functional area equipment is obtained. Furthermore, by detecting the operating status of the equipment through current sensors, the impact of the equipment operating status on heat dissipation is analyzed to obtain the operating coefficient of the functional equipment. S6. By analyzing the ratio of window area to total area within the region, taking into account the influence of light heat transfer, the window area ratio is output. The real-time intensity of outdoor solar radiation entering the room through the windows is analyzed using a radiation sensor to obtain the real-time light radiation intensity. S7. By analyzing the ratio of the area occupied by merchandise displays to the area of ​​the region, the obstruction effect of merchandise shelves and displays on air circulation is taken into account, and then the merchandise display density correction coefficient is calculated, so as to finally output the dynamic cooling load demand value.

4. The intelligent air conditioning resource-saving control system according to claim 3, characterized in that: The processing flow of the air supply temperature analysis module is as follows: S1. By introducing the dynamic cooling load demand value and the maximum design cooling load of the area as a difference, and combining it with the minimum supply air temperature of the air conditioning system under the maximum load condition, the basic supply air temperature is analyzed. S2. By analyzing the adjustment range of the supply air temperature relative to the basic supply air temperature, the maximum allowable temperature difference can be obtained. Furthermore, by considering the impact of air humidity on perceived comfort through the real-time relative humidity of the area, a humidity correction coefficient can be obtained. S3. By combining the real-time wind speed at the air outlet, the influence of the air supply speed on the perceived temperature is considered, and thus the air supply speed correction coefficient is obtained. S4. By considering the average dwell time of customers in the area, the demand of customer dwell time on temperature stability is analyzed, and then the correction coefficient of average customer dwell time is calculated, so as to finally output the air supply temperature optimization index.

5. The intelligent air conditioning resource-saving control system according to claim 4, characterized in that: The processing flow of the operation status analysis module is as follows: S1. By introducing the dynamic cooling load demand value and the supply air temperature optimization index into the operation status analysis module, and combining the maximum design cooling load and the basic supply air temperature of the area, the ratio of the current dynamic cooling load to the maximum design cooling load and the ratio of the basic supply air temperature to the optimized supply air temperature are analyzed to output the load demand ratio and temperature adjustment coefficient. S2. By considering the cumulative continuous running time of the compressor, the energy efficiency degradation of the air conditioning equipment after long-term operation is analyzed, and then the system energy efficiency degradation coefficient is calculated. S3. By comparing and normalizing the real-time electricity price with the flat-segment electricity price, the impact of the real-time electricity price on energy consumption costs is analyzed, and then the peak-valley electricity price coefficient is calculated. S4. By combining the real-time CO2 concentration in the area with the baseline CO2 concentration, the demand for fresh air volume based on indoor CO2 concentration is analyzed, and then the CO2 concentration linkage coefficient is calculated. S5. By combining the proportion of fresh air volume to total supply air volume with the difference between outdoor temperature and indoor set temperature, the fresh air co-utilization coefficient is calculated, and the operation adjustment coefficient is finally output.

6. The intelligent air conditioning resource-saving control system according to claim 1, characterized in that: The preprocessing components include outlier filtering, data smoothing, format normalization, and spatiotemporal alignment.

7. The intelligent air conditioning resource-saving control system according to claim 6, characterized in that: The specific cleaning method of the pretreatment component is as follows: Outlier filtering: Outliers are removed from the sensor data acquired by the acquisition components using the 3σ criterion. Data smoothing: A moving average is used for the headcount statistics to eliminate instantaneous fluctuations in camera recognition. Format standardization: Convert parameters with different units into the required formula format; Spatiotemporal alignment: All parameters are aligned by region and timestamp to ensure coordinated calculation of parameters at the same time and in the same region.

8. The intelligent air conditioning resource-saving control system according to claim 1, characterized in that: The control and management component specifically includes: Compressor frequency regulation: Based on the operating regulation coefficient, a PWM control signal is output to the frequency converter to regulate the compressor operating frequency; Coordinated adjustment of supply air temperature and air velocity: The opening of the water valve of the air conditioning unit coil is controlled according to the supply air temperature optimization index. When the temperature rises, the water valve is closed and the angle of the electric louvers of the air outlet is adjusted at the same time to match the air velocity requirement of the supply air velocity correction coefficient in the supply air temperature analysis module. Fresh air valve linkage control: Adjust the opening of the fresh air valve according to the fresh air utilization coefficient. When the fresh air utilization coefficient is greater than 1, increase the fresh air volume, introduce natural cooling, and simultaneously close part of the return air valve to reduce the air conditioning load. Area priority scheduling: Set control priority for high-traffic areas to ensure that the operation adjustment coefficient is greater than 0.5, so as to guarantee the comfort of the core area.

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