Campus energy consumption automatic allocation system and method based on swarm intelligence optimization algorithm

Abstract

The present invention relates to the technical field of energy consumption allocation, and specifically discloses a campus energy consumption automatic allocation system and method based on a swarm intelligence optimization algorithm. The system comprises: a data acquisition module, which is used to acquire environmental data in a public area in real time; an image acquisition module, which comprises a camera unit and a recognition unit, which is used to acquire real-time image information in the public area, and recognize and record the number of people in the image; a body temperature coefficient in the public area and a real-time indoor lighting demand coefficient are calculated by a data processing module, and the data are compared with a preset threshold value to judge the real-time comfort of the working environment; and a decision module is used to remotely adjust lighting and air conditioning according to the comfort of the overall working environment, so as to achieve precise control of energy use, thereby avoiding a part of energy waste in daily electricity use.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption allocation, and in particular to a campus energy consumption automatic allocation system and method based on a swarm intelligence optimization algorithm. Background Art

[0002] Automatic allocation of campus energy consumption refers to the use of intelligent systems and technical means to automatically manage and allocate energy use on campus to achieve efficient use and conservation of energy. This management method monitors and analyzes energy use in various areas of the campus in real time, automatically adjusts energy supply according to actual needs, avoids energy waste, and reduces energy consumption costs.

[0003] The allocation of electricity consumption in campus energy consumption is particularly important. In public areas of the campus, such as the teacher area and the office area, there are often situations where there are no people in the public areas, but the lights and air conditioners are still not turned off, or there are fewer people in the public areas and the temperature is suitable, but all the lights are still turned on and the air conditioners are turned on, which seriously wastes electricity resources. In response to this situation, it is usually necessary to rely on the smart energy consumption management system to supervise the electricity consumption process in the campus, analyze the electricity consumption data in real time, and understand the energy usage and existing problems based on the data analysis, so as to provide a basis for formulating reasonable energy-saving plans.

[0004] In the prior art, the allocation of electricity consumption on campus is usually based on the analysis of past electricity consumption data, and then a reasonable energy-saving plan is formulated based on the analysis results. Although this method can reduce the electricity consumption on campus, since this method cannot allocate electricity consumption in real time, it is impossible to achieve precise control of energy use, and some energy waste will still occur in daily electricity use. Summary of the invention

[0005] The purpose of the present invention is to provide a campus energy consumption automatic allocation system and method based on a swarm intelligence optimization algorithm to solve the following technical problems:

[0006] How to achieve precise control of energy use and avoid energy waste.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A campus energy consumption automatic allocation system and method based on a swarm intelligence optimization algorithm, the system comprising:

[0009] A data collection module, used for real-time collection of environmental data in public areas;

[0010] The image acquisition module includes a camera unit and a recognition unit, which is used to collect real-time image information in the public area and identify and record the number of people in the image;

[0011] A data processing module, used to calculate the indoor real-time perceived temperature coefficient and the indoor real-time lighting demand coefficient in the public area according to the data collected by the data collection module and the image collection module;

[0012] An analysis and comparison module is used to compare the indoor real-time perceived temperature coefficient and the indoor real-time lighting demand coefficient in the public area with the preset threshold value, judge the comfort of the working environment according to the comparison result, and issue instructions according to the judgment result;

[0013] The decision module is used to receive instructions from the analysis and comparison module and decide whether to turn on the lighting and air conditioning according to the instructions.

[0014] Furthermore, the public areas collected by the data collection module include office areas and public classrooms, and the information collected by the data collection module includes real-time temperature, humidity and light intensity in the public areas.

[0015] Furthermore, the calculation process of the data processing module includes:

[0016] By formula Calculate the first Indoor real-time lighting demand coefficient in public areas ;

[0017] in, To define a function, For any public area, For the The number of people in the public area, For the The indoor area of ​​public areas For the The number of people in the public area, For the Preset light intensity standard values ​​in public areas, For the The real-time light intensity in a public area, X1 and X2 are weight coefficients.

[0018] Furthermore, the analysis and comparison process includes:

[0019] By integrating the indoor real-time lighting demand coefficients in all public areas Respectively with the preset indoor real-time lighting demand coefficient threshold Make a comparison;

[0020] If all All less than ,The system determines that the lighting demand in the public area is low, which means that the working environment is comfortable and there is no need to turn on the lights;

[0021] If any ,The system determines that the lighting demand in the public area is high, indicating that the comfort level of the working environment is low, and issues an instruction to the decision module to turn on the lighting;

[0022] And through the formula Calculate the number of lights turned on in the public area;

[0023] in, is the total number of lighting lamps in the public area, is the error influence coefficient, It is the adjustment coefficient comparison table function.

[0024] Furthermore, the calculation process of the data processing module also includes:

[0025] By formula Calculate the first Indoor real-time temperature coefficient in public areas ;

[0026] in, For the Real-time humidity in public areas, For the The preset humidity in the indoor area of ​​the public area, For the The distribution area of ​​people in the public area, For the Real-time temperature in public areas, For the The preset temperature of the room in the public area, for The standard value of is the air velocity influence function, is the air velocity in the public area.

[0027] Furthermore, the analysis and comparison process also includes:

[0028] By setting the indoor real-time temperature coefficient of all public areas The preset standard indoor temperature coefficient threshold Make a comparison;

[0029] If all Average< ,The system determines that the indoor temperature in the public area is appropriate, indicating that the working environment is comfortable and there is no need to turn on the air conditioner;

[0030] If any , the system determines that the indoor perceived temperature in the public area is high, indicating that the comfort level of the working environment is low, and issues an instruction to the decision module to turn on the air conditioner;

[0031] And through the formula Calculate the number of air conditioners turned on;

[0032] in, The total number of air conditioners in this public area.

[0033] Furthermore, the analysis and comparison process also includes:

[0034] Through the real-time monitoring data of the data acquisition module, a real-time change curve of power consumption conditions is established , and the real-time change curve of electricity consumption in public areas ;

[0035] Collect data evenly at preset fixed time intervals and data;

[0036] And through the formula Calculate the correlation coefficient between electricity consumption and changes in electricity conditions in public areas ;

[0037] By calculating the correlation coefficient between electricity consumption in the public area and changes in electricity conditions With the preset correlation threshold Make a comparison;

[0038] like > , it is determined that the power consumption in the public area is positively correlated with the change of power conditions;

[0039] Otherwise, it is determined that the power consumption in the public area is negatively correlated with the change in power conditions;

[0040] in, is the total number of acquisitions, and , .

[0041] A campus energy consumption automatic allocation method based on a swarm intelligence optimization algorithm, the method comprising:

[0042] S1: Real-time collection of environmental data in public areas through the data collection module;

[0043] S2: Collect real-time image information in the public area through the image acquisition module, and identify and record the number of people in the image;

[0044] S3: Calculate the indoor real-time perceived temperature coefficient and the indoor real-time lighting demand coefficient in the public area through the data processing module;

[0045] S4: The indoor real-time perceived temperature coefficient and the indoor real-time lighting demand coefficient in the public area are compared with the preset threshold value through the analysis and comparison module, and the comfort level of the working environment is determined according to the comparison result;

[0046] S5: The decision module receives the command from the analysis and comparison module, and decides whether to turn on the lighting and air conditioning according to the command.

[0047] Beneficial effects of the present invention:

[0048] (1) The present invention calculates the perceived temperature coefficient and the indoor real-time lighting demand coefficient in the public area through the data processing module, and compares the data with the preset threshold value to determine the real-time comfort level of the working environment. The lighting and air conditioning are remotely controlled through the decision-making module according to the comfort level of the overall working environment, thereby achieving precise control of energy use and avoiding a portion of energy waste in daily electricity use.

[0049] (2) The present invention calculates the indoor real-time lighting demand coefficients in all public areas. Respectively with the preset indoor real-time lighting demand coefficient threshold By comparing the results, it is possible to accurately determine whether there are conditions in the public area where it is necessary to turn on the lights. The number of lights to be turned on can be calculated based on the judgment results combined with the real-time indoor lighting demand coefficient in the public area, thereby realizing intelligent lighting management and control, and further achieving precise control of energy use to avoid energy waste.

[0050] (3) The present invention calculates the indoor real-time temperature coefficient of all public areas. The preset standard indoor temperature coefficient threshold By comparing, this judgment method can accurately determine whether the indoor air conditioner in the public area needs to be turned on, and calculate the number of air conditioners turned on. While achieving energy conservation and emission reduction, it can also achieve precise control of energy use and avoid energy waste in daily electricity use.

[0051] (4) The present invention calculates the correlation coefficient between the power consumption in the public area and the change of power consumption conditions. With the preset correlation threshold For comparison, due to Therefore, the electricity consumption in the public area is positively correlated with the change in electricity conditions, which means that when the electricity conditions change, the electricity consumption will also change. If the electricity conditions do not change but the electricity consumption changes in the subsequent use, it is judged that there may be abnormal electricity consumption in the public area, resulting in increased electricity consumption, and the maintenance personnel are promptly reminded to conduct electricity inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention will be further described below in conjunction with the accompanying drawings.

[0053] Figure 1 It is a schematic block diagram of the campus energy consumption automatic allocation system based on the swarm intelligence optimization algorithm in the present invention;

[0054] Figure 2 It is a flow chart of the automatic allocation method of campus energy consumption based on the swarm intelligence optimization algorithm in the present invention. DETAILED DESCRIPTION

[0055] 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.

[0056] See also Figure 1 As shown, in one embodiment, the present application provides a campus energy consumption automatic allocation system and method based on a swarm intelligence optimization algorithm, the system comprising:

[0057] A data collection module, used for real-time collection of environmental data in public areas;

[0058] The image acquisition module includes a camera unit and a recognition unit, which is used to collect real-time image information in the public area and identify and record the number of people in the image;

[0059] A data processing module, used to calculate the indoor real-time perceived temperature coefficient and the indoor real-time lighting demand coefficient in the public area according to the data collected by the data collection module and the image collection module;

[0060] An analysis and comparison module is used to compare the indoor real-time perceived temperature coefficient and the indoor real-time lighting demand coefficient in the public area with the preset threshold value, judge the comfort of the working environment according to the comparison result, and issue instructions according to the judgment result;

[0061] The decision module is used to receive the instructions from the analysis and comparison module and decide whether to turn on the lighting and air conditioning according to the instructions;

[0062] Through the above technical solution, this embodiment provides a data acquisition module and an image acquisition module. The data acquisition module can collect environmental data in the public area in real time, and the image acquisition module can collect real-time image information in the public area through the camera unit, and identify and record the number of people in the image through the recognition unit. Then, the data processing module calculates the indoor real-time body temperature coefficient and the indoor real-time lighting demand coefficient in the public area according to the data collected by the data acquisition module and the image acquisition module, and then compares the indoor real-time body temperature coefficient and the indoor real-time lighting demand coefficient in the public area with the preset threshold value through the analysis and comparison module, and judges the comfort of the working environment according to the comparison result;

[0063] Through such settings, the system first calculates the perceived temperature coefficient and the indoor real-time lighting demand coefficient in the public area through the data processing module, and compares the data with the preset threshold to determine the real-time comfort of the working environment. According to the comfort of the overall working environment, the system remotely controls the lighting and air conditioning through the decision-making module, which can achieve precise control of energy use, thereby avoiding the waste of some energy in daily electricity use.

[0064] It should be noted that the recognition unit is a trained neural network model, and the training process of the neural network model is a prior art and will not be described in detail here.

[0065] The public areas collected by the data collection module include office areas and public classrooms, and the information collected by the data collection module includes real-time temperature, humidity and light intensity in the public areas;

[0066] Through the above technical solution, this embodiment collects the real-time temperature, humidity and light intensity in the public area through the data acquisition module, which can provide the data processing module with real-time environmental data in the public area, so that the data processing module can accurately calculate the indoor body temperature coefficient and the indoor real-time lighting demand coefficient in the public area, thereby ensuring the normal development of subsequent energy allocation operations and ensuring accurate control of energy use;

[0067] It should be noted that the real-time temperature, humidity and light intensity collected by the data collection module can be collected using existing sensors, which is existing technology and will not be described in detail here.

[0068] The calculation process of the data processing module includes:

[0069] By formula Calculate the first Indoor real-time lighting demand coefficient in public areas ;

[0070] in, To define a function, if ,but ,like ,but , For any public area, For the The number of people in the public area, For the The indoor area of ​​public areas For the The number of people in the public area, For the Preset light intensity standard values ​​in public areas, For the The real-time light intensity in a public area, X1 and X2 are weight coefficients, which can be set based on empirical fitting;

[0071] Through the above technical solution, this embodiment provides the Indoor real-time lighting demand coefficient in public areas , we can use the formula Calculation shows that, obviously, when When the number of people in a public area increases and the real-time light intensity decreases, Indoor real-time lighting demand coefficient in public areas The larger the value, the lower the comfort level of the working environment in the public area, and the lighting needs to be turned on. The fewer the number of people in a public area and the stronger the real-time light intensity, the lower the comfort level of the working environment in the public area, and there is no need to turn on the lights. By setting it this way, it is possible to determine whether to turn on the lights based on the real-time indoor lighting demand coefficient in the public area, thereby achieving precise control of energy use and avoiding a portion of energy waste during daily electricity use.

[0072] The analysis and comparison process includes:

[0073] By integrating the indoor real-time lighting demand coefficients in all public areas Respectively with the preset indoor real-time lighting demand coefficient threshold Make a comparison;

[0074] If all All less than ,The system determines that the lighting demand in the public area is low, which means that the working environment is comfortable and there is no need to turn on the lights;

[0075] If any ,The system determines that the lighting demand in the public area is high, indicating that the comfort level of the working environment is low, and issues an instruction to the decision module to turn on the lighting;

[0076] And through the formula Calculate the number of lights turned on in the public area;

[0077] in, is the total number of lighting lamps in the public area, is the error influence coefficient. The error function can be set based on empirical fitting. It is the adjustment coefficient comparison table function, because according to the empirical data The influence of the range of values ​​on the number of lights turned on is based on the test data. The above indoor real-time lighting demand coefficient threshold Can be set based on experience fitting;

[0078] Through the above technical solution, by integrating the indoor real-time lighting demand coefficients in all public areas Respectively with the preset indoor real-time lighting demand coefficient threshold Compare, if all All less than The system determines that the lighting demand in the public area is low, indicating that the working environment is comfortable and there is no need to turn on the lights. , the system determines that the lighting demand in the public area is high, indicating that the comfort level of the working environment is low, and issues an instruction to the decision module to turn on the lighting. Then, the system can use the formula The number of lights turned on in the public area is calculated. Through this judgment method, it is possible to accurately determine whether there are conditions in the public area that require turning on the lights, and calculate the number of lights turned on based on the judgment result combined with the real-time indoor lighting demand coefficient in the public area, thereby realizing intelligent lighting management and control, and then achieving precise control of energy use to avoid energy waste.

[0079] The calculation process of the data processing module also includes:

[0080] By formula Calculate the first Indoor real-time temperature coefficient in public areas ;

[0081] in, For the Real-time humidity in public areas, For the The preset humidity in the indoor area of ​​the public area, For the The distribution area of ​​people in the public area, For the Real-time temperature in public areas, For the The preset temperature of the room in the public area, for The standard value of is the air velocity influence function, which can be obtained by testing the influence of air velocity on the indoor real-time perceived temperature. is the air velocity in the public area;

[0082] Through the above technical solution, this embodiment provides the Indoor real-time temperature coefficient in public areas , we can use the formula Calculation shows that, obviously, when The higher the real-time humidity and real-time temperature in the public area, the higher the indoor real-time temperature coefficient The higher the level, the lower the comfort level of the working environment in the public area, and the air conditioner needs to be turned on. The lower the real-time humidity and real-time temperature in a public area, the higher the indoor real-time perceived temperature coefficient. The lower it is, the more comfortable the working environment in the public area is, and there is no need to turn on the air conditioner. Through such a setting, it is possible to determine whether to turn on the air conditioner based on indoor environmental factors and personnel distribution, thereby achieving real-time adjustment of electricity consumption and precise control of energy use to avoid energy waste.

[0083] The analysis and comparison process also includes:

[0084] By setting the indoor real-time temperature coefficient of all public areas The preset standard indoor temperature coefficient threshold Make a comparison;

[0085] If all all ,The system determines that the indoor temperature in the public area is appropriate, indicating that the working environment is comfortable and there is no need to turn on the air conditioner;

[0086] If any , the system determines that the indoor perceived temperature in the public area is high, indicating that the comfort level of the working environment is low, and issues an instruction to the decision module to turn on the air conditioner;

[0087] And through the formula Calculate the number of air conditioners turned on;

[0088] in, is the total number of air conditioners in the public area, and the threshold value of the standard indoor temperature coefficient is Can be set based on experience fitting;

[0089] Through the above technical solution, by setting the indoor real-time body temperature coefficient in all public areas The preset standard indoor temperature coefficient threshold Compare, if all Average< , the system determines that the indoor temperature in the public area is appropriate, indicating that the working environment is comfortable and there is no need to turn on the air conditioner. , the system determines that the indoor temperature in the public area is high, indicating that the comfort level of the working environment is low, and issues an air-conditioning start instruction to the decision module. At this time, the data processing module will use the formula The number of air conditioners turned on is calculated, and the air conditioners are turned on remotely through the decision-making module. Through this judgment method, it is possible to accurately determine whether the air conditioners need to be turned on indoors in public areas, and calculate the number of air conditioners turned on. While achieving energy conservation and emission reduction, it can also achieve precise control of energy use and avoid energy waste in daily electricity use.

[0090] The analysis and comparison process also includes:

[0091] Through the real-time monitoring data of the data acquisition module, a real-time change curve of power consumption conditions is established , and the real-time change curve of electricity consumption in public areas ;

[0092] Collect data evenly at preset fixed time intervals and data;

[0093] And through the formula Calculate the correlation coefficient between electricity consumption and changes in electricity conditions in public areas ;

[0094] By calculating the correlation coefficient between electricity consumption in the public area and changes in electricity conditions With the preset correlation threshold Make a comparison;

[0095] like , it is determined that the power consumption in the public area is positively correlated with the change of power conditions;

[0096] Otherwise, it is determined that the power consumption in the public area is negatively correlated with the change in power conditions;

[0097] in, is the total number of acquisitions, and , ;

[0098] Through the above technical solution, the formula Calculate the correlation coefficient between electricity consumption and changes in electricity conditions in public areas , and by combining the correlation coefficient between electricity consumption in the public area and changes in electricity conditions With the preset correlation threshold For comparison, if , it is judged that the power consumption in the public area is positively correlated with the change of power conditions, otherwise, it is judged that the power consumption in the public area is negatively correlated with the change of power conditions; Therefore, the power consumption in the public area is positively correlated with the change in power conditions, which means that when the power conditions change, the power consumption will also change. If the power conditions do not change but the power consumption changes in the subsequent use, it is judged that there may be abnormal power consumption in the public area, resulting in increased power consumption, and the maintenance personnel are reminded to conduct power inspection in time.

[0099] If the power conditions change but the power consumption does not, it is judged that the power equipment in the public area is faulty and cannot be started, and the maintenance personnel need to be reminded in time to inspect the power equipment;

[0100] It should be noted that the real-time change curve of power conditions You can use the formula Calculate and obtain;

[0101] in, It is the real-time temperature change curve of the room. It is the real-time indoor lighting demand change curve. and They are and The standard value of can be set based on empirical fitting. and is the weight coefficient, which can be set according to empirical fitting.

[0102] See also Figure 2 As shown, a campus energy consumption automatic allocation method based on a swarm intelligence optimization algorithm comprises:

[0103] S1: Real-time collection of environmental data in public areas through the data collection module;

[0104] S2: Collect real-time image information in the public area through the image acquisition module, and identify and record the number of people in the image;

[0105] S3: Calculate the indoor real-time perceived temperature coefficient and the indoor real-time lighting demand coefficient in the public area through the data processing module;

[0106] S4: The indoor real-time perceived temperature coefficient and the indoor real-time lighting demand coefficient in the public area are compared with the preset threshold value through the analysis and comparison module, and the comfort level of the working environment is determined according to the comparison result;

[0107] S5: The decision module receives the command from the analysis and comparison module, and decides whether to turn on the lighting and air conditioning according to the command;

[0108] Through the above technical solution, when the campus energy consumption is allocated, the environmental data in the public area is firstly collected in real time through the data acquisition module, and the real-time image information in the public area is collected through the image acquisition module, and the number of people in the image is identified and recorded. After that, the data processing module will calculate the indoor real-time body temperature coefficient and the indoor real-time lighting demand coefficient in the public area according to the collected information and data, and then the indoor real-time body temperature coefficient and the indoor real-time lighting demand coefficient in the public area are compared with the preset threshold value through the analysis and comparison module, and the comfort of the working environment is judged according to the comparison result; if the comfort of the working environment is low, the analysis and comparison module will issue an instruction to the decision module to turn on the lighting or air conditioning, and finally the decision module receives the instruction of the analysis and comparison module, and decides whether to turn on the lighting and air conditioning according to the command, so as to realize real-time allocation of electricity consumption and realize precise control of energy use to avoid energy waste.

[0109] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. The campus energy consumption automatic allocation system based on swarm intelligence optimization algorithm is characterized by: The system comprises: A data collection module, used for real-time collection of environmental data in public areas; The image acquisition module includes a camera unit and a recognition unit, which is used to collect real-time image information in the public area and identify and record the number of people in the image; A data processing module, used to calculate the indoor real-time perceived temperature coefficient and the indoor real-time lighting demand coefficient in the public area according to the data collected by the data collection module and the image collection module; An analysis and comparison module is used to compare the indoor real-time perceived temperature coefficient and the indoor real-time lighting demand coefficient in the public area with the preset threshold value, judge the comfort of the working environment according to the comparison result, and issue instructions according to the judgment result; The decision module is used to receive the instructions from the analysis and comparison module and decide whether to turn on the lighting and air conditioning according to the instructions; The calculation process of the data processing module includes: By formula Calculate the first Indoor real-time lighting demand coefficient in public areas ; in, To define a function, For any public area, For the The number of people in the public area, For the The indoor area of ​​public areas For the The number of people in the public area, For the Preset light intensity standard values ​​in public areas, For the The real-time light intensity in the public area, X1 and X2 are weight coefficients; The analysis and comparison process includes: By integrating the indoor real-time lighting demand coefficients in all public areas Respectively with the preset indoor real-time lighting demand coefficient threshold Make a comparison; If all All less than ,The system determines that the lighting demand in the public area is low, which means that the working environment is comfortable and there is no need to turn on the lights; If any > ,The system determines that the lighting demand in the public area is high, indicating that the comfort level of the working environment is low, and issues an instruction to the decision module to turn on the lighting; And through the formula Calculate the number of lights turned on in the public area; in, is the total number of lighting lamps in the public area, is the error influence coefficient, It is the adjustment coefficient comparison table function; The calculation process of the data processing module also includes: By formula Calculate the first Indoor real-time temperature coefficient in public areas ; in, For the Real-time humidity in public areas, For the The preset humidity in the indoor area of ​​the public area, For the The distribution area of ​​people in the public area, For the Real-time temperature in public areas, For the The preset indoor temperature in the public areas, for The standard value of is the air velocity influence function, is the air velocity in the public area; The analysis and comparison process also includes: By setting the indoor real-time temperature coefficient of all public areas The preset standard indoor temperature coefficient threshold Make a comparison; If all Average< ,The system determines that the indoor temperature in the public area is appropriate, indicating that the working environment is comfortable and there is no need to turn on the air conditioner; If any > , the system determines that the indoor perceived temperature in the public area is high, indicating that the comfort level of the working environment is low, and issues an instruction to the decision module to turn on the air conditioner; And through the formula Calculate the number of air conditioners turned on; in, is the total number of air conditioners in the public area; The analysis and judgment process also includes: Through the real-time monitoring data of the data acquisition module, a real-time change curve of power consumption conditions is established , and the real-time change curve of electricity consumption in public areas ; Collect data evenly at preset fixed time intervals and data; And through the formula Calculate the correlation coefficient between electricity consumption and changes in electricity conditions in public areas ; By calculating the correlation coefficient between electricity consumption in the public area and changes in electricity conditions With the preset correlation threshold Make a comparison; like > ,It is judged that the power consumption in the public area is positively correlated with the change of power conditions; Otherwise, it is judged that the power consumption in the public area is negatively correlated with the change in power conditions; in, is the total number of acquisitions, and , r .

2. The campus energy consumption automatic allocation system based on swarm intelligence optimization algorithm according to claim 1 is characterized in that: The public areas collected by the data collection module include office areas and public classrooms, and the information collected by the data collection module includes real-time temperature, humidity and light intensity in the public areas.

3. The automatic allocation method of campus energy consumption based on swarm intelligence optimization algorithm is characterized by: The method adopts the campus energy consumption automatic allocation system based on the swarm intelligence optimization algorithm as claimed in claim 1, and the method comprises: S1: Real-time collection of environmental data in public areas through the data collection module; S2: Collect real-time image information in the public area through the image acquisition module, and identify and record the number of people in the image; S3: Calculate the indoor real-time perceived temperature coefficient and the indoor real-time lighting demand coefficient in the public area through the data processing module; S4: The real-time indoor temperature coefficient and the real-time indoor lighting demand coefficient in the public area are compared with the preset threshold value through the analysis and comparison module, and the comfort level of the working environment is determined according to the comparison result; S5: The decision module receives the command from the analysis and comparison module, and decides whether to turn on the lighting and air conditioning according to the command.

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