An energy-saving management system for air-conditioning equipment based on the Internet of Things

Abstract

The present invention relates to the technical field of energy-saving management, and specifically discloses an energy-saving management system for air-conditioning equipment based on the Internet of Things. The system comprises: an area division module, which is used to divide the internal space of a target area into multiple areas, and there is at least one air-conditioning equipment in each area; a data acquisition module, which is used to collect environmental data of each area in the target area and equipment operation information; a data processing module is used to calculate the refrigeration demand value of each area in the target area according to diversified data, and an analysis and comparison module is used to compare the refrigeration demand value of each area in the target area with a preset refrigeration demand value threshold. Due to the increase in the diversity of reference data, it is possible to more accurately judge whether the air-conditioning needs to be turned on in the target area and which areas need to turn on the air-conditioning, thereby improving electricity efficiency and reducing electricity waste, so that enterprises can achieve energy-saving management of air-conditioning equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving management, and in particular to an air-conditioning equipment energy-saving management system based on the Internet of Things. Background Art

[0002] Statistics show that currently the energy consumption of medium-sized and above industrial enterprises in my country accounts for about 70% of the total energy consumption of the whole society. Promoting energy conservation and emission reduction is an important national policy of our country. Therefore, energy conservation and emission reduction are the basis for the survival of enterprises. We should establish an energy-saving awareness of "reducing costs bit by bit and increasing benefits every minute and every second" and maximize the energy-saving benefits through the best management.

[0003] In daily work, the problems of low electricity efficiency and large waste are still very prominent. In order to ensure the normal operation of the machine and the comfort of the employees' working environment, workers will turn on all air conditioners in the target area and adjust them to the lowest degree during work. This will result in low electricity efficiency and large waste. Therefore, air conditioners are now managed through centralized networking. The system will determine whether the air conditioner needs to be turned on and the area where the air conditioner needs to be turned on based on the indoor temperature, thereby achieving energy saving and consumption reduction.

[0004] In the prior art, the start and stop of the air conditioner is determined by monitoring the indoor temperature. The data it relies on is relatively single and cannot take into account the operating factors of the machine equipment and environmental factors, so the judgment result is not accurate enough, which in turn leads to low electricity efficiency and large waste. In addition, since the air conditioner degree cannot be adjusted according to different cooling demand values, if the air conditioner degree is high, the cooling effect will be poor and affect the operation of the machine. If the air conditioner degree is low, it will increase energy consumption, further increasing energy waste. Summary of the invention

[0005] The purpose of the present invention is to provide an air conditioning equipment energy-saving management system based on the Internet of Things to solve the following technical problems:

[0006] How to accurately determine whether the air conditioner needs to be turned on in the target area and adjust the air conditioner degree according to different cooling demand values.

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

[0008] An air conditioning equipment energy-saving management system based on the Internet of Things, the system comprising:

[0009] A region division module, used for dividing the internal space of the target area into multiple regions, and each region has at least one air conditioning device;

[0010] Data collection module, used to collect environmental data of each area in the target area and equipment operation information;

[0011] An image acquisition module, including a camera unit and a recording unit, is used to collect the number and distribution of people in each area of ​​the target area;

[0012] A data processing unit, used to calculate the cooling demand value of each area in the target area according to the collected data;

[0013] The analysis and comparison module compares the cooling demand values ​​of each area in the target area with the preset cooling demand value threshold, and determines which areas need to turn on the air conditioner for cooling according to the comparison results;

[0014] The temperature adjustment module is used to adjust the air conditioning degree according to the judgment results of the analysis and comparison module and the cooling demand value of each area.

[0015] The control module controls the start and stop of the air conditioning equipment according to the air conditioning degree parameters adjusted by the temperature adjustment module.

[0016] Furthermore, the data acquisition module collects environmental data in the target area including indoor temperature, humidity and solar radiation intensity, and equipment operation information including temperature and power of the machine equipment during operation.

[0017] Furthermore, the calculation process of the data processing unit includes:

[0018] By formula Calculate the environmental impact coefficient A at the ath time point in the i-th region ai ;

[0019] Among them, i is any divided area, a is any data collection at a fixed time interval, WD ai is the temperature at the ath time point in the ith region, WD 0 is the preset temperature threshold, θ ai is the solar radiation influence coefficient at the ath time point in the ith region, f a To define the function, SD ai is the humidity at the ath time point in the ith region, SD 0 is the preset humidity threshold, SD 1 For SD ai X1 and X2 are weight coefficients.

[0020] Furthermore, the analysis process of the analysis and comparison module includes:

[0021] By adding the environmental impact coefficient A at the a-th time point in all regions ai The environmental impact coefficient threshold A L Make a comparison;

[0022] If any A ai >A L , the system determines that the environmental impact coefficient in this area is high, and controls the air conditioning equipment to turn on through the control module;

[0023] If all A ai all L ,The system determines that the environmental impact coefficient in all areas is low and there is no need to turn on the air conditioner, and calculates the cooling demand value in all areas.

[0024] Furthermore, the process of calculating the refrigeration demand value includes:

[0025] By formula Calculate the cooling demand value B at the ath time point in the i-th area ai ;

[0026] in, is the equipment status influence coefficient at the ath time point in the ith region, f L is the adjustment coefficient comparison table function, f c To define the function, MD ai is the population density at the ath time point in the ith region, MD 0 is the preset population density threshold, MD 1 For MD ai The reference value of is the error adjustment factor.

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

[0028] By taking the cooling demand value B of all regions at the ath time point ai Respectively with the preset cooling demand value B L Make a comparison;

[0029] If any B ai >B L , the system determines that the cooling demand of the area at the current time point is high, adjusts the air conditioning degree according to the cooling demand value of the area through the temperature adjustment module, and controls the air conditioning equipment to turn on according to the air conditioning degree parameter adjusted by the temperature adjustment module through the control module;

[0030] If all B ai all L , the system determines that the cooling demand in all areas is low and there is no need to turn on the air conditioning equipment.

[0031] Furthermore, the process of calculating the refrigeration demand value also includes:

[0032] By formula Calculate the equipment status influence coefficient at the ath time point in the i-th region​​

[0033] Among them, c is any machine device, F is the total number of enabled devices, SW C is the device temperature of the cth device in the ith region at the ath time point, SG C is the device power of the cth machine in the ith region at the ath time point, SW 0 is the preset device temperature threshold, SG 0 The preset device power threshold.

[0034] Furthermore, the process of adjusting the air conditioning degree includes:

[0035] By formula Calculate and obtain the adjusted air conditioning degree r;

[0036] Among them, r 0 The preset air conditioning degree.

[0037] Beneficial effects of the present invention:

[0038] (1) The present invention calculates the refrigeration demand value of each area in the target area according to the diversified data through the data processing module, and compares the refrigeration demand value of each area in the target area with the preset refrigeration demand value threshold through the analysis and comparison module. Since the diversification of the reference data is increased, it is possible to more accurately judge whether the air conditioner needs to be turned on in the target area and which areas need to turn on the air conditioner, thereby improving the power efficiency and reducing the power waste, so that the enterprise can achieve energy-saving management of air-conditioning equipment.

[0039] (2) The present invention calculates the cooling demand value B at the ath time point in all regions. ai Respectively with the preset cooling demand value B L By making a comparison, it can be determined more accurately whether the air conditioner needs to be turned on in the target area. By dividing the target area into regions through the regional division module, the area where the air conditioner is turned on can be determined based on the cooling demand values ​​of different areas, further improving electricity efficiency and reducing electricity waste, enabling enterprises to achieve energy conservation and emission reduction.

[0040] (3) The present invention obtains the adjusted air-conditioning degree r by calculation, and can adjust the air-conditioning degree according to different cooling demand values, thereby avoiding the situation where the air-conditioning has insufficient cooling capacity, resulting in a longer operation time to reach the required temperature, thereby increasing power consumption. It can also avoid the situation where the air-conditioning has excessive cooling capacity, which will result in increased energy consumption and energy waste, thereby further achieving energy conservation and emission reduction and improving electricity efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] Figure 1 It is a schematic block diagram of an air conditioning equipment energy-saving management system based on the Internet of Things in the present invention; DETAILED DESCRIPTION

[0043] 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.

[0044] See also Figure 1 As shown, in one embodiment, the present application provides an air conditioning equipment energy-saving management system based on the Internet of Things, the system comprising:

[0045] A region division module, used for dividing the internal space of the target area into multiple regions, and each region has at least one air conditioning device;

[0046] Data collection module, used to collect environmental data of each area in the target area and equipment operation information;

[0047] An image acquisition module, including a camera unit and a recording unit, is used to collect the number and distribution of people in each area of ​​the target area;

[0048] A data processing unit, used to calculate the cooling demand value of each area in the target area according to the collected data;

[0049] The analysis and comparison module compares the cooling demand values ​​of each area in the target area with the preset cooling demand value threshold, and determines which areas need to turn on the air conditioner for cooling according to the comparison results;

[0050] The temperature adjustment module is used to adjust the air conditioning degree according to the judgment results of the analysis and comparison module and the cooling demand value of each area.

[0051] The control module controls the start and stop of the air conditioning equipment according to the air conditioning degree parameters adjusted by the temperature adjustment module;

[0052] Through the above technical solution, the implementation system divides the internal space of the target area into multiple areas through the area division module, and there is at least one air-conditioning device in each area. Then, the environmental data of each area in the target area and the equipment operation information are collected through the data acquisition module, and the number of people and distribution in each area in the target area are collected through the image acquisition module, so that diversified data can be obtained. Then, the cooling demand value of each area in the target area can be calculated according to the collected data through the data processing unit, and the cooling demand value of each area in the target area is compared with the preset cooling demand value threshold through the analysis and comparison module, and it is determined which areas need to turn on the air conditioner for cooling according to the comparison results;

[0053] Through such settings, the system calculates the cooling demand value of each area in the target area according to the diversified data through the data processing module, and compares the cooling demand value of each area in the target area with the preset cooling demand value threshold through the analysis and comparison module. Due to the increase in the diversity of reference data, it can more accurately determine whether the air conditioner needs to be turned on in the target area, and which areas need to turn on the air conditioner, thereby improving electricity efficiency and reducing electricity waste, so that enterprises can achieve energy-saving management of air-conditioning equipment;

[0054] And the temperature adjustment module can adjust the air-conditioning degree according to the judgment result of the analysis and comparison module and the cooling demand value of each area, and control the start and stop of the air-conditioning equipment according to the air-conditioning degree parameters adjusted by the temperature adjustment module through the control module. Through such settings, the degree of the air-conditioning can be adjusted according to different cooling demand values, so as to avoid the situation that if the cooling capacity of the air-conditioning is insufficient, it will take a longer time to run to reach the required temperature, thereby increasing power consumption, and it can also avoid the situation that the cooling capacity of the air-conditioning is too large, which will cause increased energy consumption and energy waste, and further achieve energy conservation and emission reduction;

[0055] It should be noted that the recognition unit is a trained neural network model. The training process of the neural network model is a prior art and will not be described in detail here. The target area is an office space of an enterprise such as an office building or a factory building.

[0056] The data acquisition module collects environmental data in the target area including indoor temperature, humidity and solar radiation intensity, and equipment operation information including temperature and power of the machine equipment during operation;

[0057] Through the above technical solution, the environmental data in the target area including indoor temperature, humidity and solar radiation intensity, as well as the temperature and power of the machine equipment during operation are collected by the collection module, which can provide accurate and diversified data for the subsequent calculation of the cooling demand value of each area in the target area, thereby improving the accuracy of the calculation results and subsequent judgment results, and the diversified data can increase the sensitivity of the judgment results, further improving the accuracy of the judgment results;

[0058] It should be noted that the above data acquisition methods can all be obtained through existing technologies and will not be described in detail here.

[0059] The calculation process of the data processing unit includes:

[0060] By formula Calculate the environmental impact coefficient A at the ath time point in the i-th region ai ;

[0061] Among them, i is any divided area, a is any data collection at a fixed time interval, WD ai is the temperature at the ath time point in the ith region, WD 0 is the preset temperature threshold, which can be set based on empirical fitting. ai is the solar radiation influence coefficient at the ath time point in the ith region, which can be set according to empirical data fitting, f a To define a function, if SD ai -SD 0 >0, then f a =SD ai -SD 0 , if SD ai -SD 0 <0, then f a =0, then SD ai is the humidity at the ath time point in the ith region, SD 0 is the preset humidity threshold, which can be set based on empirical data fitting. 1 For SD ai The standard value of , which can be set according to the allowable error fitting in the empirical data, X1 and X2 are weight coefficients, which can be set according to empirical fitting;

[0062] Through the above technical solution, this embodiment provides the environmental impact coefficient A at the ath time point in the i-th region: ai , can be obtained by formula It is obvious from the calculation that when the temperature and solar radiation at the a-th time point in the i-th region are greater, and the humidity is greater than the preset humidity threshold, the environmental impact coefficient at the a-th time point in the i-th region is greater, indicating that the air conditioner needs to be turned on in this region at this time; when the temperature and solar radiation at the a-th time point in the i-th region are smaller, and the humidity is less than the preset humidity threshold, the environmental impact coefficient at the a-th time point in the i-th region is smaller, indicating that the air conditioner does not need to be turned on in this region at this time;

[0063] Through such settings, it is possible to preliminarily judge whether the air conditioner needs to be turned on indoors based on the environmental impact coefficient at the a-th time point in the ith area, and the accuracy of calculating the cooling demand value at the a-th time point in the ith area can be improved, thereby improving the accuracy of judging whether the air conditioner needs to be turned on in the target area, improving electricity efficiency and reducing electricity waste.

[0064] The analysis process of the analysis and comparison module includes:

[0065] By adding the environmental impact coefficient A at the a-th time point in all regions ai The environmental impact coefficient threshold A L Make a comparison;

[0066] If any A ai >A L , the system determines that the environmental impact coefficient in this area is high, and controls the air conditioning equipment to turn on through the control module;

[0067] If all A ai all L , the system determines that the environmental impact coefficient in all areas is low and there is no need to turn on the air conditioner, and calculates the cooling demand value in all areas;

[0068] Through the above technical solution, this embodiment calculates the environmental impact coefficient A at the ath time point in all regions. ai The environmental impact coefficient threshold A L Compare, if any A ai >A L The system determines that the environmental impact coefficient in this area is high, indicating that the air conditioner needs to be turned on in this area, and controls the air conditioner to turn on through the control module. If all A ai all L , the system determines that the environmental impact coefficient in all areas is low and there is no need to turn on the air conditioner;

[0069] Through this judgment method, based on the environmental impact coefficient A at the ath time point in all regions ai The environmental impact coefficient threshold A L ​​When the comparison is performed, it is possible to preliminarily determine whether the target area needs to turn on the air conditioner and which areas need to turn on the air conditioner, and turn on the corresponding air conditioner based on the judgment result, thereby improving electricity efficiency and avoiding electricity waste, and achieving energy conservation and emission reduction for the enterprise;

[0070] It should be noted that the above environmental impact coefficient threshold A L It can be set based on experience fitting.

[0071] The process of calculating the cooling demand value includes:

[0072] By formula Calculate the cooling demand value B at the ath time point in the i-th area ai ;

[0073] in, is the equipment status influence coefficient at the ath time point in the ith region, f L The adjustment coefficient comparison table function can be based on the empirical data A ai The influence of the range of values ​​on the cooling demand value is obtained based on the test data, f c To define a function, if MD ai -MD 0 >1, then f c =MD ai -MD 0 , if MD ai -MD 0 <1, then f c =1,MD ai is the population density at the ath time point in the ith region, MD 0 is the preset population density threshold, which can be set based on empirical fitting. 1 For MD ai The reference value can be set based on the allowable error in the empirical data. is the error adjustment coefficient, which can be set based on empirical fitting;

[0074] Through the above technical solution, this embodiment provides the cooling demand value B at the ath time point in the i-th area ai , can be obtained through the formula It is obvious from the calculation that when the equipment status influence coefficient and the environmental influence coefficient at the a-th time point in the i-th region are higher, the cooling demand value at the a-th time point in the i-th region is higher, which means that the air conditioner needs to be turned on for cooling in this region to ensure the normal operation of the machine and the working comfort of the workers. On the contrary, when the equipment status influence coefficient and the environmental influence coefficient at the a-th time point in the i-th region are lower, the cooling demand value at the a-th time point in the i-th region is lower, which means that the air conditioner does not need to be turned on for cooling in this region.

[0075] Through such settings, it is possible to determine whether the area needs to turn on the air conditioner for cooling based on the cooling demand value at the ath time point in the ith area, and due to the diversity of data, the accuracy of determining whether the target area needs to turn on the air conditioner can be improved. By dividing the target area into regions through the regional division module, the region to turn on the air conditioner can be determined based on the cooling demand values ​​of different regions, thereby further improving electricity efficiency and reducing electricity waste.

[0076] The analysis process of the analysis and comparison module also includes:

[0077] By taking the cooling demand value B of all regions at the ath time point ai Respectively with the preset cooling demand value B L Make a comparison;

[0078] If any B ai >B L , the system determines that the cooling demand of the area at the current time point is high, adjusts the air conditioning degree according to the cooling demand value of the area through the temperature adjustment module, and controls the air conditioning equipment to turn on according to the air conditioning degree parameter adjusted by the temperature adjustment module through the control module;

[0079] If all B ai all L , the system determines that the cooling demand in all areas is low and there is no need to turn on the air conditioning equipment;

[0080] Through the above technical solution, this embodiment calculates the cooling demand value B at the ath time point in all regions. ai Respectively with the preset cooling demand value B L Compare, if any B ai >B L , the system determines that the cooling demand of the area at the current time point is high, and the air conditioner needs to be turned on in this area, and the temperature adjustment module adjusts the air conditioning degree according to the cooling demand value of the area, and then controls the air conditioning equipment to be turned on according to the air conditioning degree parameters adjusted by the temperature adjustment module through the control module. If all B ai all L , the system determines that the cooling demand in all areas is low and there is no need to turn on the air conditioning equipment;​​

[0081] Through this judgment method, it is possible to more accurately judge whether the air conditioner needs to be turned on in the target area, and the target area can be divided into regions through the regional division module, and the area where the air conditioner is turned on can be determined according to the cooling demand values ​​of different areas, further improving the power efficiency and reducing power waste, so that the enterprise can achieve energy conservation and emission reduction;

[0082] It should be noted that the above preset cooling demand value B L It can be set based on experience fitting.

[0083] The process of calculating the cooling demand value further includes:

[0084] By formula Calculate the equipment status influence coefficient at the ath time point in the i-th region

[0085] Among them, c is any machine device, F is the total number of enabled devices, SW C is the device temperature of the cth device in the ith region at the ath time point, SG C is the device power of the cth machine in the ith region at the ath time point, SW 0 is the preset device temperature threshold, SG 0 is a preset device power threshold, and the above preset device temperature threshold and the preset device power threshold can be set based on empirical data fitting;

[0086] Through the above technical solution, the formula The influence coefficient of the equipment status at the ath time point in the i-th region By performing calculations in this way, the equipment status influence coefficient at the a-th time point in the ith area can be used as one of the diversified data sets to improve the accuracy of the calculation of the cooling demand value by the data processing module, so that the system can make a more accurate judgment on whether the air conditioner needs to be turned on in the target area, thereby realizing energy-saving management of air-conditioning equipment, improving electricity efficiency and reducing electricity waste.

[0087] The process of adjusting the air conditioning degree includes:

[0088] By formula Calculate and obtain the adjusted air conditioning degree r;

[0089] Among them, r 0 The preset air conditioning degree;

[0090] Through the above technical solution, this embodiment provides an adjusted air conditioning degree r, which can be calculated using the formula The air conditioner degree r after adjustment can be obtained by calculation, and the air conditioner degree can be adjusted according to different cooling demand values ​​to avoid the situation that if the cooling capacity of the air conditioner is insufficient, it will take longer to run to reach the required temperature, thereby increasing power consumption. It can also avoid the situation that the cooling capacity of the air conditioner is too large, which will increase energy consumption and waste energy, further realizing energy conservation and emission reduction, and improving electricity efficiency.

[0091] It should be noted that the preset air conditioning degree can be set based on experience.

[0092] 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. An air conditioning equipment energy-saving management system based on the Internet of Things, characterized in that: The system comprises: The area division module is used to divide the internal space of the factory into multiple areas, and there is at least one air conditioning device in each area; Data collection module, used to collect environmental data and equipment operation information from various areas in the plant; The image acquisition module includes a camera unit and a recording unit, which is used to collect the number and distribution of personnel in each area of ​​the factory; A data processing unit is used to calculate the cooling demand value of each area in the plant based on the collected data; The analysis and comparison module compares the cooling demand values ​​of each area in the factory with the preset cooling demand value threshold, and determines which areas need to turn on the air conditioner for cooling based on the comparison results; The temperature adjustment module is used to adjust the air conditioning degree according to the judgment results of the analysis and comparison module and the cooling demand value of each area. The control module controls the start and stop of the air conditioning equipment according to the air conditioning degree parameters adjusted by the temperature adjustment module; The data acquisition module collects environmental data in the factory, including indoor temperature, humidity and solar radiation intensity, and equipment operation information, including temperature and power of machine equipment during operation; The calculation process of the data processing unit includes: By formula Calculate the environmental impact coefficient at the ath time point in the i-th region ; in, For any divided area, For any data collection at a fixed time interval, is the temperature at the ath time point in the ith region, is the preset temperature threshold, is the solar radiation influence coefficient at the ath time point in the ith region, To define a function, is the humidity at the ath time point in the ith region, is the preset humidity threshold, for The standard value of, X1 and X2 are weight coefficients; The analysis process of the analysis and comparison module includes: By combining the environmental impact coefficients at the ath time point in all regions The environmental impact coefficient threshold Make a comparison; If any > , the system determines that the environmental impact coefficient in this area is high, and controls the air conditioning equipment to turn on through the control module; If all Average< , the system determines that the environmental impact coefficient in all areas is low and there is no need to turn on the air conditioner, and calculates the cooling demand value in all areas; The process of calculating the cooling demand value includes: By formula Calculate the cooling demand value at the ath time point in the i-th area ; in, is the equipment status influence coefficient at the ath time point in the ith region, To adjust the coefficient comparison table function, To define a function, is the population density at the ath time point in the ith region, is the preset population density threshold, for The reference value of is the error adjustment coefficient; The process of calculating the refrigeration demand value further includes: By formula Calculate the equipment status influence coefficient at the ath time point in the i-th region ; Where c is any machine device, F is the total number of enabled devices, is the device temperature of the cth device in the ith region at the ath time point, is the equipment power of the cth machine in the ith area at the ath time point, is the preset device temperature threshold, The preset device power threshold.

2. According to the Internet of Things-based air conditioning equipment energy-saving management system of claim 1, it is characterized in that: The analysis process of the analysis and comparison module also includes: By taking the cooling demand value of all regions at the ath time point Respectively with the preset cooling demand value Make a comparison; If any , the system determines that the cooling demand of the area at the current time point is high, adjusts the air conditioning degree according to the cooling demand value of the area through the temperature adjustment module, and controls the air conditioning equipment to turn on according to the air conditioning degree parameter adjusted by the temperature adjustment module through the control module; If all , the system determines that the cooling demand in all areas is low and there is no need to turn on the air conditioning equipment.

3. According to the Internet of Things-based air conditioning equipment energy-saving management system of claim 2, it is characterized in that: The process of adjusting the air conditioning degree includes: By formula Calculate the adjusted air conditioning degree ; in, The preset air conditioning degree.

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