Regional air conditioner cooling load dispatching system and method

Abstract

The invention relates to the technical field of air conditioner load dispatching, and discloses a regional air conditioner cooling load dispatching system and method. According to the system and the method, external meteorological data and internal environment data are acquired by applying a temperature and humidity sensor, an air quality sensor, a human flow meter and a meteorological station through a data acquisition module, and the acquired data are processed and analyzed by a data processing and analyzing module; the cooling load scheduling strategy module is used for calculating the current cooling load demand, the load prediction value, the energy cost, the environmental impact factor, the regional building thermal load, the system energy efficiency ratio, the temperature and humidity comfort index and the overall energy consumption level, and generating an optimal cooling load scheduling scheme based on a multi-objective optimization algorithm according to the calculated values and the analysis result; and the control and execution module controls the air conditioners according to the scheduling strategy, adjusts the air conditioner output cooling load of each area, controls ventilation and air return, and optimizes air circulation.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning load scheduling, and in particular to a regional air conditioning cold load scheduling system and method. Background Art

[0002] Air conditioning systems play a vital role in modern buildings and can provide a comfortable indoor environment. According to statistics, air conditioning systems usually account for a relatively high proportion of a building's energy consumption, especially in hot summer. Air conditioning not only consumes a lot of electricity, but also causes a significant increase in the load on the power grid. Many air conditioning systems need to run for a long time under high temperature conditions to maintain a comfortable indoor temperature. This long-term operation leads to high energy consumption. At the same time, if the building's insulation performance is poor or the design is defective, it will also cause heat gain and loss, so that the air conditioner needs to consume more energy to maintain the indoor temperature. In order to optimize energy consumption and improve the efficiency of the air conditioning system, regional air conditioning cooling load scheduling is an important measure. By reasonably scheduling the air conditioning system, unnecessary energy consumption can be reduced, especially when air conditioning cooling is not required in some areas. Regional air conditioning scheduling can flexibly adjust the cooling capacity according to changes in different times and usage conditions to more accurately meet user needs, so as to avoid over-cooling or insufficient cooling. At the same time, effective cooling load scheduling can reduce overall energy consumption, thereby reducing greenhouse gas emissions and having a positive impact on the environment.

[0003] Many existing air conditioning scheduling systems rely on static historical data or hypothetical models and lack real-time environmental and usage data, resulting in the inability to adjust scheduling strategies in a timely and effective manner, reducing the operating efficiency of the air conditioner. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a regional air conditioning cooling load scheduling system and method, which has the advantages of using a temperature and humidity sensor, an air quality sensor, a flow meter and a weather station to obtain external meteorological data and internal environmental data through a data acquisition module, including temperature, humidity, wind speed, precipitation, temperature, humidity, air quality, flow of people and equipment status data of each area, and a data processing and analysis module to process and analyze the collected data, mine the cooling load demand pattern, calculate the current cooling load demand Dfxq, load forecast value Fh yc, energy cost Nycb, environmental impact factor Hyyz, regional building heat load Qyrh, system energy efficiency ratio Xtnx, temperature and humidity comfort index Wssx and overall energy consumption level Xh sp, the above calculated values ​​are sent to the cooling load scheduling strategy module, which generates the optimal cooling load scheduling plan based on the multi-objective optimization algorithm according to the above calculated values ​​and analysis results, and transmits it to the control and execution module. The control and execution module controls the air conditioner according to the scheduling strategy, adjusts the cooling load output of the air conditioner in each area, controls ventilation and return air, optimizes air circulation, and collects environmental data and air conditioner usage data in real time through the application of many sensors, and accurately analyzes the data. Based on the multi-objective optimization algorithm, the optimal cooling load scheduling plan for the regional air conditioner is generated, the scheduling strategy is adjusted in a timely and effective manner, the operating efficiency of the air conditioner is improved, and the advantages of the balance rate of air conditioning energy saving and comfort are achieved, and the above problems are solved.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solutions: a regional air conditioning cooling load scheduling system, comprising a data acquisition module, a data processing and analysis module, a cooling load scheduling strategy module and a control and execution module;

[0008] The data acquisition module obtains external meteorological data and internal environmental data through temperature and humidity sensors, air quality sensors, human flow meters and weather stations, including temperature, humidity, wind speed, precipitation, temperature, humidity, air quality, human flow and equipment status data in each area, and sends the above data to the data processing and analysis module;

[0009] The data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, calculates the current cooling load demand Dfxq, load forecast value Fh yc, energy cost Nycb, environmental impact factor Hyyz, regional building heat load Qyrh, system energy efficiency ratio Xtnx, temperature and humidity comfort index Wssx and overall energy consumption level Xh sp, and the above calculated values ​​are sent to the cooling load scheduling strategy module;

[0010] The cooling load scheduling strategy module generates an optimal cooling load scheduling scheme based on the above-mentioned calculated values ​​and analysis results based on a multi-objective optimization algorithm and transmits it to the control and execution module;

[0011] The control and execution module controls the air conditioner according to the scheduling strategy, adjusts the air conditioner output cooling load of each area, and controls the air conditioning ventilation and return air.

[0012] Preferably, the data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the current cooling load demand Dfxq. The calculation formula is as follows:

[0013] Dfxq=Snr h+Cdf h+Nbry

[0014] In the formula, Dfxq represents the current cooling load demand, Snr h represents the indoor heat load, Cdf h represents the conductive heat load caused by the exterior walls and windows, etc., and Nbry represents the load caused by the internal heat source, which is obtained through actual measurement.

[0015] Preferably, the data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the current load forecast value Fh yc. The calculation formula is as follows:

[0016] Fh yc=β0+β1*T t +β2*H t +∈

[0017] In the formula, Fh yc represents the load forecast value, β0, β1, β2 represent regression coefficients, which are set by the system, and T t represents the temperature at time t, H t Represents the humidity at time t.

[0018] Preferably, the data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the energy cost Nycb. The calculation formula is as follows:

[0019]

[0020] In the formula, Nycb represents the energy cost, E t represents the electrical energy consumed at time t, P t represents the electricity price at time t, and n represents the total time interval.

[0021] Preferably, the data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the environmental impact factor Hyyz. The calculation formula is as follows:

[0022] Hyyz=Ea*F

[0023] In the formula, Hyyz represents the environmental impact factor, Ea represents the total energy consumption, and F represents the carbon emission factor.

[0024] Preferably, the data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the regional building heat load Qyrh. The calculation formula is as follows:

[0025] Qyrh=U*A*ΔT+Fsrh+Nrfh+V*Cp*ΔT

[0026] In the formula, Qyrh represents the regional building heat load, U represents the overall heat transfer coefficient, A represents the area of ​​the heat transfer component, ΔT represents the temperature difference between the internal space and the external environment, Fsrh represents the radiation heat load, Nrfh represents the internal heat source load, V represents the air flow rate, and Cp represents the specific heat capacity.

[0027] Preferably, the data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the system energy efficiency ratio Xtnx. The calculation formula is as follows:

[0028]

[0029] In the formula, Xtnx represents the system energy efficiency ratio, Zlnl represents the cooling capacity, and Srgl represents the system input power.

[0030] Preferably, the data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the temperature and humidity comfort index Wssx. The calculation formula is as follows:

[0031] Wssx=(6.5+0.004*(MW)*(Tb-Ta))

[0032] In the formula, Wssx represents the temperature and humidity comfort index, M represents the metabolic rate, W represents the thermal resistance of clothing, Tb represents the air temperature, and Ta represents the absolute temperature.

[0033] Preferably, the data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the overall energy consumption level Xh sp. The calculation formula is as follows:

[0034] Xh sp=Ktnh+Zmld+Sbnh

[0035] In the formula, Xh sp represents the overall energy consumption level, Ktnh represents the air conditioning energy consumption, Zmld represents the lighting brightness, and Sbnh represents the energy consumption of other equipment.

[0036] A method for dispatching cooling load of regional air conditioner comprises the following steps:

[0037] S1. Use temperature and humidity sensors, air quality sensors, human flow meters and weather stations to obtain external meteorological data and internal environmental data through the data acquisition module, including temperature, humidity, wind speed, precipitation, temperature, humidity, air quality, human flow and equipment status data of each area;

[0038] S2, the data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, calculates the current cooling load demand Dfxq, load forecast value Fh yc, energy cost Nycb, environmental impact factor Hyyz, regional building heat load Qyrh, system energy efficiency ratio Xtnx, temperature and humidity comfort index Wssx and overall energy consumption level Xh sp, and the above calculated values ​​are sent to the cooling load scheduling strategy module;

[0039] S3, the cooling load scheduling strategy module generates the optimal cooling load scheduling scheme based on the multi-objective optimization algorithm according to the above calculation values ​​and analysis results and transmits it to the control and execution module;

[0040] S4, the control and execution module controls the air conditioner according to the scheduling strategy, adjusts the air conditioning output cooling load of each area, and controls the air conditioning ventilation and return air.

[0041] Compared with the prior art, the present invention provides a regional air conditioning cooling load scheduling system and method, which has the following beneficial effects:

[0042] The present invention uses a temperature and humidity sensor, an air quality sensor, a flow meter and a weather station to obtain external meteorological data and internal environmental data, including temperature, humidity, wind speed, precipitation, temperature, humidity, air quality, flow of people and equipment status data of each area through a data acquisition module. The data processing and analysis module processes and analyzes the collected data, mines the cooling load demand mode, and calculates the current cooling load demand Dfxq, load forecast value Fh yc, energy cost Nycb, environmental impact factor Hyyz, regional building heat load Qyrh, system energy efficiency ratio Xtnx, temperature and humidity comfort index Wssx and overall energy consumption level Xh sp, the above calculated values ​​are sent to the cooling load scheduling strategy module, which generates the optimal cooling load scheduling plan based on the multi-objective optimization algorithm according to the above calculated values ​​and analysis results, and transmits it to the control and execution module. The control and execution module controls the air conditioner according to the scheduling strategy, adjusts the cooling load output of the air conditioner in each area, controls ventilation and return air, optimizes air circulation, collects environmental data and air conditioner usage data in real time through the application of many sensors, and accurately analyzes the data. Based on the multi-objective optimization algorithm, the optimal cooling load scheduling plan for the regional air conditioner is generated, the scheduling strategy is adjusted in a timely and effective manner, the operating efficiency of the air conditioner is improved, and the balance rate of air conditioning energy saving and comfort is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the system flow of the present invention.

[0044] Figure 2 It is a schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Many existing air conditioning scheduling systems rely on static historical data or hypothetical models, lack real-time environment and usage data, resulting in the inability to adjust the scheduling strategy in a timely and effective manner, which reduces the operating efficiency of the air conditioner. Therefore, a regional air conditioning cooling load scheduling system is proposed. Figure 1 ,The system includes a data acquisition module, a data processing and analysis module, a cooling load scheduling ,strategy module, and a control and execution module;

[0047] The data acquisition module uses a variety of sensors and monitoring equipment to comprehensively obtain environmental and meteorological data to achieve comprehensive monitoring and analysis of indoor and outdoor environments. The temperature and humidity sensors monitor the ambient temperature and relative humidity in real time. The DHT series of high-precision sensors are used to ensure the accuracy of the data. The cooling or heating settings of the air-conditioning system can be adjusted through sensor data analysis to achieve optimal comfort and energy efficiency. The data can also be used to analyze the impact of humidity on air quality, equipment operation and building materials. The air quality sensor monitors the concentration of harmful substances in indoor and outdoor air, providing users with real-time air quality assessment. After data analysis, it can be used to improve air conditioning ventilation. The flow meter uses thermal Technology like real-time measurement of personnel flow in each area to understand the density and activity patterns of personnel flow. By analyzing the change data of personnel flow, it is used to optimize the scheduling of regional air-conditioning cooling load, ensure the strengthening of air-conditioning supply in areas with concentrated personnel, and reduce energy consumption or appropriately shut down the air-conditioning system in areas with sparse personnel. The key meteorological data such as temperature, humidity, wind speed, precipitation, air pressure, etc. obtained by meteorological monitoring sensors can provide support for the energy-saving design of buildings, control the configuration of natural ventilation, sunlight and shading devices, ensure a comfortable indoor environment, and effectively respond to the impact of external climate change. The above-mentioned collected data are uniformly sent to the data processing and analysis module through the network transmission protocol;

[0048] The data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the current cooling load demand Dfxq, load forecast value Fh yc, energy cost Nycb, environmental impact factor Hyyz, regional building heat load Qyrh, system energy efficiency ratio Xtnx, temperature and humidity comfort index Wssx, and overall energy consumption level Xh sp, where:

[0049] The current cooling load demand calculation formula is as follows:

[0050] Dfxq=Snrh+Cdfh+Nbry

[0051] Real-time calculation of cooling load demand helps the system dynamically adjust cooling load supply according to actual conditions to ensure comfort. In the formula, Dfxq represents the current cooling load demand, Snrh represents the indoor heat load, Cdfh represents the conductive heat load caused by the exterior wall and windows, and Nbry represents the load caused by the internal heat source. It is obtained through actual measurement. By understanding the current cooling load demand, it is possible to optimize equipment operation, reduce unnecessary energy loss, and improve energy efficiency.

[0052] The load forecast value calculation formula is as follows:

[0053] Fh yc=β0+β1*T t +β2*H t +∈

[0054] Effective load forecasting helps to prepare in advance during high demand periods, ensure cooling capacity, and enhance system stability. In the formula, Fh yc represents the load forecast value, β0, β1, and β2 represent regression coefficients set by the system, and T t represents the temperature at time t, H t Represents the humidity at time t. The forecast data can be coordinated with the grid load, which helps to supply more cooling load during off-peak hours, thereby reducing electricity costs.

[0055] The energy cost calculation formula is as follows:

[0056]

[0057] The calculation of energy costs can provide accurate cost analysis, help control operating costs, and provide an economic basis for energy efficiency transformation. In the formula, Nycb represents energy cost, E t represents the electrical energy consumed at time t, P t represents the electricity price at time t, and n represents the total time interval. Through the analysis of energy costs, it provides guidance for management and establishment of reasonable energy budgets, and helps determine long-term energy strategies and investment plans;

[0058] The calculation formula of environmental impact factor is as follows:

[0059] Hyyz=Ea*F

[0060] Evaluate environmental factors such as greenhouse gas emissions and impact on air quality, indicating the impact of the system on the environment, which helps adjust the operation strategy to reduce negative impacts. In the formula, Hyyz represents the environmental impact factor, Ea represents the total energy consumption, and F represents the carbon emission factor. By analyzing the environmental impact factor, the abnormal air conditioning emissions can be adjusted in time to avoid polluting the ecological environment.

[0061] The calculation formula for regional building heat load is as follows:

[0062] Qyrh=U*A*ΔT+Fsrh+Nrfh+V*Cp*ΔT

[0063] By calculating the heat load of each area, we can reasonably divide the functional areas, optimize the regional air conditioning layout, and improve user comfort. In the formula, Qyrh represents the regional building heat load, U represents the overall heat transfer coefficient, A represents the area of ​​the heat transfer component, ΔT represents the temperature difference between the internal space and the external environment, Fsrh represents the radiation heat load, Nrfh represents the internal heat source load, V represents the air flow rate, and Cp represents the specific heat capacity. It provides clear heat load data to support the decision-making of energy-saving transformation and equipment upgrade, and optimize energy use strategies;

[0064] The system energy efficiency ratio calculation formula is as follows:

[0065]

[0066] Real-time monitoring of the system's energy efficiency ratio can timely evaluate the operating performance of the air conditioning system and ensure that it operates within the optimal range. In the formula, Xtnx represents the system energy efficiency ratio, Zlnl represents the cooling capacity, and Srgl represents the system input power. Analysis based on the energy efficiency ratio data can help identify potential areas for efficiency improvement and make necessary operational adjustments and equipment replacements.

[0067] The calculation formula of temperature and humidity comfort index is as follows:

[0068] Wssx=(6.5+0.004*(MW)*(Tb-Ta))

[0069] Ensuring appropriate temperature and humidity can reduce health problems caused by the environment, such as allergies and respiratory diseases, and improve the quality of life and work. In the formula, Wssx represents the temperature and humidity comfort index, M represents the metabolic rate, W represents the thermal resistance of clothing, Tb represents the air temperature, and Ta represents the absolute temperature. A good comfort index can enhance customer satisfaction and improve the user experience of a building or place.

[0070] The overall energy consumption level is calculated as follows:

[0071] Xh sp=Ktnh+Zmld+Sbnh

[0072] Comprehensive energy consumption analysis helps to obtain a clear view of the overall energy consumption of the system, so as to develop targeted energy-saving plans. In the formula, Xh sp represents the overall energy consumption level, Ktnh represents the air conditioning energy consumption, Zmld represents the lighting brightness, and Sbnh represents the energy consumption of other equipment. By comparing the energy consumption level with historical data and industry benchmarks, potential energy-saving opportunities can be discovered, so as to adjust measures;

[0073] The cooling load scheduling strategy module sets the decision variables in the optimization based on the above calculated values ​​and analysis results based on the particle swarm optimization algorithm, including the start and stop status of the cooling equipment in each area, the priority of the cooling source, the cooling load distribution, etc., and builds the system efficiency maximization objective function according to the set goals and constraints. Ecool(t) represents the cooling output at time t, and E(t) represents the energy consumption at time t. This function is used to evaluate the advantages of various possible scheduling schemes. Through multiple iterative calculations, the optimal scheme is gradually approached. In this process, the memory strategy is used to record the optimal solution to avoid repeated calculations. The optimization results are evaluated and compared with the current system operation status to ensure that the generated scheme is feasible in practical applications. Finally, a detailed cooling load scheduling scheme is generated based on the output of the optimization algorithm, including specific equipment operation schedules, cooling capacity allocation, and adjustment suggestions. Considering the dynamic nature of the system, the generated scheduling scheme is updated in real time to respond to environmental changes and user demand changes.

[0074] The control and execution module controls the air conditioner according to the scheduling strategy, adjusts the air conditioning output cooling load of each area, controls the air conditioning ventilation and return air, and provides a friendly user interface, allowing users to view the temperature, humidity and air quality status of each area in real time, facilitating manual adjustment by users. At the same time, users can set personalized cooling load requirements and preferences as needed to achieve a higher level of comfort. After the implementation of the scheduling strategy, the actual operating status such as indoor temperature changes, load changes, etc. is continuously monitored, and the data is recorded. Once it is found that the indoor environment does not reach the preset comfort level, the system automatically adjusts or prompts the user to intervene.

[0075] See also Figure 2 , a regional air conditioning cooling load scheduling method, comprising the following steps:

[0076] S1. Use temperature and humidity sensors, air quality sensors, human flow meters and weather stations to obtain external meteorological data and internal environmental data through the data acquisition module, including temperature, humidity, wind speed, precipitation, temperature, humidity, air quality, human flow and equipment status data of each area;

[0077] S2, the data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, calculates the current cooling load demand Dfxq, load forecast value Fh yc, energy cost Nycb, environmental impact factor Hyyz, regional building heat load Qyrh, system energy efficiency ratio Xtnx, temperature and humidity comfort index Wssx and overall energy consumption level Xh sp, and the above calculated values ​​are sent to the cooling load scheduling strategy module;

[0078] S3, the cooling load scheduling strategy module generates the optimal cooling load scheduling scheme based on the multi-objective optimization algorithm according to the above calculation values ​​and analysis results and transmits it to the control and execution module;

[0079] S4, the control and execution module controls the air conditioner according to the scheduling strategy, adjusts the air conditioning output cooling load of each area, and controls the air conditioning ventilation and return air.

[0080] By combining the above-mentioned systems and methods, many sensors are used to collect environmental data and air-conditioning usage data in real time, and the data is accurately analyzed. Based on the multi-objective optimization algorithm, the optimal cooling load scheduling plan for regional air-conditioning is generated, and the scheduling strategy is adjusted in a timely and effective manner, thereby improving the operating efficiency of the air-conditioning and achieving a balance between air-conditioning energy saving and comfort.

[0081] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A regional air conditioning cooling load scheduling system, characterized in that: It includes data acquisition module, data processing and analysis module, cooling load scheduling strategy module and control and execution module; The data acquisition module obtains external meteorological data and internal environmental data through temperature and humidity sensors, air quality sensors, human flow meters and weather stations, including temperature, humidity, wind speed, precipitation, temperature, humidity, air quality, human flow and equipment status data in each area, and sends the above data to the data processing and analysis module; The data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, calculates the current cooling load demand Dfxq, load forecast value Fhyc, energy cost Nycb, environmental impact factor Hyyz, regional building heat load Qyrh, system energy efficiency ratio Xtnx, temperature and humidity comfort index Wssx and overall energy consumption level Xhsp, and the above calculated values ​​are sent to the cooling load scheduling strategy module; The cooling load scheduling strategy module generates an optimal cooling load scheduling scheme based on the above-mentioned calculated values ​​and analysis results based on a multi-objective optimization algorithm and transmits it to the control and execution module; The control and execution module controls the air conditioner according to the scheduling strategy, adjusts the air conditioner output cooling load of each area, and controls the air conditioning ventilation and return air.

2. A regional air conditioning cooling load scheduling system according to claim 1, characterized in that: The data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the current cooling load demand Dfxq. The calculation formula is as follows: Dfxq=Snrh+Cdfh+Nbry In the formula, Dfxq represents the current cooling load demand, Snrh represents the indoor heat load, Cdfh represents the conductive heat load caused by the exterior walls and windows, etc., and Nbry represents the load caused by the internal heat source, which is obtained through actual measurement.

3. A regional air conditioning cooling load scheduling system according to claim 2, characterized in that: The data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the current load forecast value Fhyc. The calculation formula is as follows: Fhyc=β0+β1*T t +β2*H t +∈ In the formula, Fhyc represents the load forecast value, β0, β1, β2 represent regression coefficients, which are set by the system, and T t represents the temperature at time t, H t Represents the humidity at time t.

4. A regional air conditioning cooling load scheduling system according to claim 3, characterized in that: The data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the energy cost Nycb. The calculation formula is as follows: In the formula, Nycb represents the energy cost, E t represents the electrical energy consumed at time t, P t represents the electricity price at time t, and n represents the total time interval.

5. A regional air conditioning cooling load scheduling system according to claim 4, characterized in that: The data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the environmental impact factor Hyyz. The calculation formula is as follows: Hyyz=Ea*F In the formula, Hyyz represents the environmental impact factor, Ea represents the total energy consumption, and F represents the carbon emission factor.

6. A regional air conditioning cooling load scheduling system according to claim 5, characterized in that: The data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the regional building heat load Qyrh. The calculation formula is as follows: Qyrh=U*A*ΔT+Fsrh+Nrfh+V*Cp*ΔT In the formula, Qyrh represents the regional building heat load, U represents the overall heat transfer coefficient, A represents the area of ​​the heat transfer component, ΔT represents the temperature difference between the internal space and the external environment, Fsrh represents the radiation heat load, Nrfh represents the internal heat source load, V represents the air flow rate, and Cp represents the specific heat capacity.

7. A regional air conditioning cooling load scheduling system according to claim 6, characterized in that: The data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the system energy efficiency ratio Xtnx. The calculation formula is as follows: In the formula, Xtnx represents the system energy efficiency ratio, Zlnl represents the cooling capacity, and Srgl represents the system input power.

8. A regional air conditioning cooling load scheduling system according to claim 7, characterized in that: The data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the temperature and humidity comfort index Wssx. The calculation formula is as follows: Wssx=(6.5+0.004*(MW)*(Tb-Ta)) In the formula, Wssx represents the temperature and humidity comfort index, M represents the metabolic rate, W represents the thermal resistance of clothing, Tb represents the air temperature, and Ta represents the absolute temperature.

9. A regional air conditioning cooling load scheduling system according to claim 8, characterized in that: The data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, and calculates the overall energy consumption level Xhsp. The calculation formula is as follows: Xhsp=Ktnh+Zmld+Sbnh In the formula, Xhsp represents the overall energy consumption level, Ktnh represents the air conditioning energy consumption, Zmld represents the lighting brightness, and Sbnh represents the energy consumption of other equipment.

10. A method for dispatching cooling load of regional air conditioner, characterized in that: The following steps are involved: S1. Use temperature and humidity sensors, air quality sensors, human flow meters and weather stations to obtain external meteorological data and internal environmental data through the data acquisition module, including temperature, humidity, wind speed, precipitation, temperature, humidity, air quality, human flow and equipment status data of each area; S2, the data processing and analysis module processes and analyzes the collected data, mines the cooling load demand pattern, calculates the current cooling load demand Dfxq, load forecast value Fhyc, energy cost Nycb, environmental impact factor Hyyz, regional building heat load Qyrh, system energy efficiency ratio Xtnx, temperature and humidity comfort index Wssx and overall energy consumption level Xhsp, and the above calculated values ​​are sent to the cooling load scheduling strategy module; S3, the cooling load scheduling strategy module generates the optimal cooling load scheduling scheme based on the multi-objective optimization algorithm according to the above calculation values ​​and analysis results and transmits it to the control and execution module; S4, the control and execution module controls the air conditioner according to the scheduling strategy, adjusts the air conditioning output cooling load of each area, and controls the air conditioning ventilation and return air.

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