Dynamic carbon reduction optimization system driven by multi-source data of campus venue

Abstract

The invention discloses a campus venue multi-source data driven dynamic carbon reduction optimization system, and particularly relates to the technical field of campus carbon reduction. A venue energy consumption acquisition module generates venue real-time energy consumption data; the geographical climate acquisition module collects campus geographical parameters and climate condition data to obtain campus geographical climate data; the illumination module and the air-conditioning module adjust lamp and air-conditioning parameters according to related data; and the multi-stadium energy consumption comparison module summarizes the unit area energy consumption data of each stadium and generates a list of stadium with abnormal energy consumption. According to the dynamic carbon reduction optimization system driven by the multi-source data of the campus venue, by collecting the real-time energy consumption data, the geographical climate data and the venue utilization rate data of the venue, accurate regulation and control of energy of the campus venue are achieved, parameters are dynamically adjusted according to the real-time environment condition and the actual use condition, the energy utilization efficiency is improved, and the energy utilization rate of the campus venue is improved. During multi-museum energy consumption comparison, energy consumption abnormal points are positioned, the campus energy waste phenomenon is effectively reduced, and the campus carbon reduction process is powerfully promoted.

Description

Technical Field

[0001] The present invention relates to the field of campus carbon reduction technology, and in particular to a dynamic carbon reduction optimization system driven by multi-source data of campus venues. Background Art

[0002] Campus carbon reduction is to reduce the overall carbon emissions of the campus through a series of specific measures, involving campus building energy management, transportation optimization, resource recycling and utilization, and other aspects; from the perspective of campus buildings, it covers energy-saving renovations of teaching buildings, libraries, gymnasiums and other buildings, such as the use of energy-saving doors and windows, and optimized lighting systems; in terms of transportation, it encourages walking and cycling, and optimizes the management of vehicle operations on campus; resource recycling and utilization includes the classification, recycling and reuse of campus garbage, etc., in order to systematically build a campus carbon reduction system.

[0003] A system for driving the dynamic optimization of dual carbon targets is built for comprehensive venues such as gymnasiums and libraries on campus in the process of achieving dual carbon targets. The system focuses on energy consumption management and carbon emission control technical issues in comprehensive campus venues. It generally collects real-time energy consumption data of venues, including electricity consumption and water consumption data, and combines external factors such as the campus's geographical location and climatic conditions, and then uses specific data processing rules to analyze these data. Based on the analysis results, the operating parameters of equipment in the venue are dynamically adjusted, such as adjusting air-conditioning temperature settings, lighting brightness, etc. to complete the dynamic optimization of dual carbon targets. Summary of the Invention

[0004] The main purpose of the present invention is to provide a dynamic carbon reduction optimization system driven by multi-source data of campus venues, which can effectively solve the problems of high energy consumption, difficult carbon emission control and lack of dynamic optimization of carbon reduction measures in campus comprehensive venues.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] A dynamic carbon reduction optimization system driven by multi-source data for campus venues, including the following modules:

[0007] Venue energy consumption collection module: collects real-time electricity consumption data and water consumption data of lighting equipment, air conditioning equipment, and sports equipment in campus venues, including but not limited to gymnasiums and libraries, and generates real-time venue energy consumption data;

[0008] Geographic climate acquisition module: obtains geographical parameters such as the latitude and longitude, altitude, and climate condition data of the campus, including but not limited to temperature, humidity, light intensity, wind direction and speed, to obtain campus geographical climate data;

[0009] Lighting analysis and control module: This module calls the lighting equipment electricity consumption data from the venue's real-time energy consumption data and the light intensity data from the campus's geographical climate data, compares the light intensity with the light intensity threshold set in advance based on the actual lighting needs of different areas of the venue, and adjusts the lighting brightness parameters based on the degree to which the light intensity exceeds the threshold and the proportion determined by statistical analysis of historical lighting energy consumption data;

[0010] Air conditioning parameter adjustment module: Based on the power consumption data of air conditioning equipment in the venue's real-time energy consumption data, the temperature and humidity data in the campus's geographical climate data, and the venue's utilization rate data in the venue's real-time energy consumption data, the air conditioning cooling power parameters and air outlet angle parameters are adjusted according to the venue's maximum capacity and the type of activity.

[0011] Multi-venue energy consumption comparison module: collects real-time energy consumption data of each venue, summarizes and compares the energy consumption data per unit area of each venue, and generates a list of venues with abnormal energy consumption based on a ratio determined by factors such as venue function, area, and equipment configuration.

[0012] Preferably, the real-time energy consumption data of the venue includes the real-time power consumption of lighting equipment, the real-time power consumption of air-conditioning equipment, the real-time power consumption of sports equipment and the real-time total consumption of water resources;

[0013] The real-time energy consumption data of the venue is calculated as follows:

[0014] Real-time power consumption of lighting equipment = real-time power of lighting equipment × power consumption time;

[0015] Real-time power consumption of air-conditioning equipment = real-time power of air-conditioning equipment × power consumption time;

[0016] Real-time power consumption of sports equipment = real-time power of sports equipment × power consumption time;

[0017] The real-time total water consumption is obtained through water meter readings.

[0018] Preferably, the campus geographical climate data specifically refers to geographical location latitude and longitude values, altitude values, real-time temperature values, real-time humidity values, real-time light intensity values, real-time wind direction and real-time wind speed values;

[0019] The data related to the geographical location of the campus are directly measured values;

[0020] The climate data is obtained in the following manner: the real-time air temperature and real-time humidity are obtained by measuring values through temperature and humidity sensors, the real-time light intensity is obtained by measuring values through light sensors, and the real-time wind direction and real-time wind speed are obtained by measuring values through wind vanes.

[0021] Preferably, the lighting analysis and control module calculates the lamp brightness parameters as follows:

[0022] The lighting equipment control parameters include the brightness value of the lamp after control and the power value of the lamp after control;

[0023] The regulated lamp brightness value L=L0×(1-A×B), and the regulated lamp power value P=P0×(1-A×B);

[0024] Among them, A is the magnitude by which the light intensity exceeds the threshold, B is the reduction ratio determined by statistical analysis of historical lighting energy consumption data, L0 is the original lamp brightness value, and P0 is the original lamp power value.

[0025] Preferably, the control parameters of the air-conditioning equipment are specifically the refrigeration power value after control and the air outlet angle value after control;

[0026] The air conditioning equipment control parameters are determined in the following manner: when the control conditions are met, they are calculated according to the formula:

[0027] The cooling power value after adjustment is: P c =P c0 ×(1+C);

[0028] The value of the air outlet angle after adjustment is: α = α0 + Δα;

[0029] Among them, P c0 is the original cooling power value, C is the power increase ratio, α0 is the original air outlet angle, and Δα is the angle adjustment value;

[0030] The power increase ratio C is determined based on the number of people in the venue, temperature and humidity, weather, and light factors;

[0031] The angle adjustment value Δα is determined according to factors such as the air conditioning refrigeration coverage, air conditioning power, and installation height.

[0032] Preferably, the energy consumption per unit area of each venue is E i (i represents different venues), the average energy consumption per unit area of other venues is The ratio determined by the venue function, area, and equipment configuration factors is D. The venue will be included in the list of venues that need to be inspected, which includes gymnasiums with excessive energy consumption per unit area, libraries with excessive energy consumption per unit area, and other campus venues with excessive energy consumption per unit area.

[0033] Preferably, the venue utilization rate data includes the real-time number of people in the venue and the actual usage area ratio of the venue;

[0034] The venue utilization rate data is calculated as follows: the real-time number of people in the venue is obtained through a people counter;

[0035] The actual usage area ratio of the venue = the actual usage area of the venue ÷ the total area of the venue.

[0036] Preferably, the ratio determined by statistical analysis of historical lighting energy consumption data specifically refers to the ratio determined based on statistical analysis of lighting energy consumption data over the past month, past quarter, and past year, and the statistical analysis adopts the formula:

[0037]

[0038] Among them, E li is the lighting energy consumption in the ith statistical period, T li is the electricity consumption time of lighting equipment in the i-th statistical period, n is the number of statistical periods, and k is the correction coefficient;

[0039] The correction coefficient k is determined according to factors such as season and venue activity type.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. This invention comprehensively integrates multi-source data, including real-time venue energy consumption data, geographic climate data, and venue utilization rate data, to achieve precise regulation of campus venue energy. In the management of lighting and air-conditioning equipment, parameters are dynamically adjusted according to real-time environmental conditions and actual usage scenarios, greatly improving energy efficiency. When comparing the energy consumption of multiple venues, the comprehensive consideration of various venue characteristics can accurately locate energy consumption anomalies, effectively reduce campus energy waste, and vigorously promote the campus's carbon reduction process, building a scientific and efficient green campus energy management system that is highly consistent with the concept of sustainable development.

[0042] 2. The present invention determines the comparison ratio based on multiple factors such as venue function, area, equipment configuration, etc., and accurately screens venues with abnormal energy consumption. Based on this, targeted energy consumption optimization measures can be formulated to help campus carbon reduction in all aspects. Compared with traditional technologies, this application plan starts from multiple dimensions, deeply explores the space for energy consumption optimization, and effectively promotes the transformation of campuses to a low-carbon mode, contributing key forces to achieving the dual carbon goals.

[0043] 3. This invention establishes a scientific and efficient green campus energy management system. Through precise energy regulation and carbon reduction measures, it not only reduces campus operating costs but also creates a more environmentally friendly and comfortable learning and living environment for teachers and students. From a long-term perspective, it aligns with the concept of sustainable development, provides a solid foundation for the green and sustainable development of campuses, and sets a new example for green campus development. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The overall system architecture and data flow diagram of the present invention;

[0045] Figure 2The real-time energy consumption data acquisition and composition diagram of the venue of the present invention;

[0046] Figure 3 The campus geography and climate data collection and integration map of the present invention;

[0047] Figure 4 The lighting equipment control parameter determination process and result diagram of the present invention;

[0048] Figure 5 The flow chart and result diagram of determining the control parameters of the air-conditioning equipment of the present invention;

[0049] Figure 6 This is a diagram showing the energy consumption comparison of multiple venues and the screening of abnormal venues in the present invention;

[0050] Figure 7 The venue utilization rate data acquisition and composition diagram of the present invention;

[0051] Figure 8 This is a diagram for determining the proportion of the statistical analysis of historical lighting energy consumption data of the present invention. DETAILED DESCRIPTION

[0052] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0053] Example 1, as Figure 1 As shown in the figure, a dynamic carbon reduction optimization system driven by multi-source data for campus venues includes the following modules:

[0054] Venue energy consumption collection module: collects real-time electricity consumption data and water consumption data of lighting equipment, air conditioning equipment, and sports equipment in campus venues, including but not limited to gymnasiums and libraries, and generates real-time venue energy consumption data;

[0055] Geographic climate acquisition module: obtains geographical parameters such as the latitude and longitude, altitude, and climate condition data of the campus, including but not limited to temperature, humidity, light intensity, wind direction and speed, to obtain campus geographical climate data;

[0056] Lighting analysis and control module: This module calls the lighting equipment electricity consumption data from the venue's real-time energy consumption data and the light intensity data from the campus's geographical climate data, compares the light intensity with the light intensity threshold set in advance based on the actual lighting needs of different areas of the venue, and adjusts the lighting brightness parameters based on the degree to which the light intensity exceeds the threshold and the proportion determined by statistical analysis of historical lighting energy consumption data;

[0057] Air conditioning parameter adjustment module: Based on the power consumption data of air conditioning equipment in the venue's real-time energy consumption data, the temperature and humidity data in the campus's geographical climate data, and the venue's utilization rate data in the venue's real-time energy consumption data, the air conditioning cooling power parameters and air outlet angle parameters are adjusted according to the venue's maximum capacity and the type of activity.

[0058] Multi-venue energy consumption comparison module: collects real-time energy consumption data of each venue, summarizes and compares the energy consumption data per unit area of each venue, and generates a list of venues with abnormal energy consumption based on a ratio determined by factors such as venue function, area, and equipment configuration.

[0059] Example 2, as Figure 2 and Figure 3 As shown, this embodiment further discloses a method for collecting and calculating the real-time energy consumption data of a venue based on the first embodiment.

[0060] The venue energy consumption collection module is mainly responsible for the key task of obtaining energy consumption data of various venues on campus. Taking the gymnasium of a certain campus as an example, a power monitoring device is installed in the lighting equipment circuit of the gymnasium to collect the usage time and real-time power of the lamps in the gymnasium, thereby calculating the power consumption.

[0061] The venue's real-time energy consumption data includes real-time power consumption of lighting equipment, real-time power consumption of air-conditioning equipment, real-time power consumption of sports equipment, and real-time total water consumption;

[0062] The venue's real-time energy consumption data is calculated as follows:

[0063] Real-time power consumption of lighting equipment = real-time power of lighting equipment × power consumption time;

[0064] Real-time power consumption of air-conditioning equipment = real-time power of air-conditioning equipment × power consumption time;

[0065] Real-time power consumption of sports equipment = real-time power of sports equipment × power consumption time;

[0066] Specifically, assume that the stadium's lighting system consists of lamps in multiple areas, with a total power consumption of 50 kilowatts. At a specific moment, the lighting system has been using electricity for two hours since it was turned on. Using the formula "Real-time power consumption of lighting equipment = Real-time power consumption of lighting equipment × Power consumption time," we can calculate that the real-time power consumption of the lighting equipment at this moment is 50 kilowatts × 2 hours = 100 kWh.

[0067] For air-conditioning equipment and sports equipment, similar power monitoring devices are installed on their power supply lines to obtain their power values in real time;

[0068] For example, if the real-time power of a large air-conditioning device in a gymnasium is 15 kilowatts and it runs for 3 hours, the real-time power consumption of the air-conditioning device is 15 kilowatts x 3 hours = 45 kWh.

[0069] The acquisition of water consumption data is achieved by installing smart water meters on water pipes. Smart water meters can accurately measure the total real-time consumption of water flowing through the pipes, whether it is daily cleaning water in the gymnasium or water used by athletes for washing, etc., and can accurately record it.

[0070] The same approach can be applied to other campus venues, such as libraries, cafeterias, large auditoriums, tiered conference rooms, and classrooms. For example, the power consumption of lighting equipment, air conditioning equipment (to provide a comfortable environment for readers), and electronic equipment (such as computers and printers) in the library can be monitored in real time, and the power consumption can be calculated based on the power consumption time. Water consumption can also be measured through water meters.

[0071] Finally, all kinds of energy consumption data obtained from the gymnasium, library and other comprehensive venues on campus through the above methods are integrated and summarized to generate real-time energy consumption data of the venues. This data set covers the energy consumption of electricity, water resources and other energy sources of various venues on campus at different time points, providing basic data support for subsequent system analysis and decision-making.

[0072] Furthermore, the campus geography and climate are also one of the indicators that affect the energy consumption of campus venues. In extreme weather conditions such as heavy rain, cloudy days, typhoons, or in seasons with different temperatures such as autumn and winter, the consumption of lighting, air conditioning, and water resources are all different. Therefore, it is necessary to collect geographical and climate data as a reference for regulation.

[0073] Specifically, the campus geographical climate data specifically refers to the geographical location latitude and longitude values, altitude values, real-time temperature values, real-time humidity values, real-time light intensity values, real-time wind direction and real-time wind speed values;

[0074] Data related to the campus's geographical location are directly measured values;

[0075] The climate data is obtained as follows: the real-time temperature and real-time humidity are obtained by measuring the values of the temperature and humidity sensors, the real-time light intensity is obtained by measuring the values of the light sensor, and the real-time wind direction and real-time wind speed are obtained by measuring the values of the wind vane.

[0076] Taking a certain campus as an example, its geographical location and longitude are determined by the Global Positioning System (GPS) equipment to be 30 degrees north latitude and 120 degrees east longitude, and its altitude is measured to be 50 meters using a high-precision altimeter.

[0077] Methods for obtaining climate condition data include: real-time air temperature and real-time humidity are measured through temperature and humidity sensors installed at multiple points on campus (such as the gymnasium, library roof, open areas on campus, etc.). These sensors can continuously and accurately sense changes in temperature and humidity in the surrounding environment and transmit data to the data collection center.

[0078] Real-time light intensity is collected by light sensors distributed on the tops of different buildings, open lawns and other locations on campus. These sensors are sensitive to changes in light intensity and can convert the received light information into electrical signals and transmit them.

[0079] The real-time wind direction is measured by a wind vane, which is usually installed on the top of a taller building on campus to obtain more accurate wind direction information, and the real-time wind speed is measured by an anemometer.

[0080] Finally, by integrating these geographical parameters and climate condition data, we can obtain the campus geographical climate data; this data reflects the natural conditions of the campus environment and has important reference value for subsequent venue equipment regulation.

[0081] Example 3, as Figure 4 and Figure 5 As shown, this embodiment further discloses the specific control process of the lighting analysis and control module and the air conditioning parameter adjustment module on the basis of the second embodiment.

[0082] For example, some campuses only control the switching of lighting equipment according to a pre-set time program, such as turning on at 7 am and turning off at 10 pm, without considering the changes in natural light intensity in different time periods.

[0083] In the existing campus energy consumption control method, lighting equipment is still turned on when there is sufficient sunlight during the day, resulting in a large amount of electricity waste. Some campuses only control air-conditioning equipment based on fixed temperature settings. For example, in summer, the air-conditioning temperature is uniformly set to 26 degrees Celsius. This does not consider the impact of factors such as the actual number of people in the venue and the type of activity on the heat load, resulting in excessive or insufficient cooling or heating of the air-conditioning, which not only affects comfort but also wastes energy.

[0084] Based on the data detected in Example 2, a summary process is performed. Assume that the temperature and humidity sensor located on the roof of the gymnasium measures a real-time temperature of 32 degrees Celsius and a real-time humidity of 60%, the light sensor measures a value of 1000 lux, the wind direction is south, and the wind speed is 3 meters per second;

[0085] Furthermore, the main work of the lighting analysis and control module is to analyze and control the lighting equipment power consumption data in the venue's real-time energy consumption data and the light intensity data in the campus's geographical climate data;

[0086] The calculation method of the lighting analysis and control module's lamp brightness parameters is:

[0087] The lighting equipment control parameters include the brightness value of the lamp after control and the power value of the lamp after control;

[0088] The brightness value of the lamp after adjustment is L = L0 × (1-A × B), and the power value of the lamp after adjustment is P = P0 × (1-A × B);

[0089] Among them, A is the magnitude by which the light intensity exceeds the threshold, B is the reduction ratio determined by statistical analysis of historical lighting energy consumption data, L0 is the original lamp brightness value, and P0 is the original lamp power value.

[0090] Specifically, taking a gymnasium as an example, in the early stages, professionals will set the light intensity thresholds for each area in advance based on the actual lighting needs of different areas of the gymnasium (such as competition venues, auditoriums, aisles, etc.) through professional lighting design specifications and actual tests.

[0091] Assume that the light intensity threshold set in advance in the competition venue area is 800 lux.

[0092] When the system is running, if the real-time light intensity obtained by the light sensor is 1000 lux, it is higher than the pre-set threshold of 800 lux.

[0093] First, calculate the magnitude A by which the light intensity exceeds the threshold, A = (1000 - 800) ÷ 800 = 0.25. Through a detailed statistical analysis of the gymnasium's lighting energy consumption data over the past year, it is concluded that when the light intensity exceeds the threshold, the appropriate ratio to reduce the brightness and power of the lamps is B = 0.2.

[0094] It is known that the original lamp brightness value L0 = 1000 lumens and the original lamp power value P0 = 50 kilowatts.

[0095] According to the formula, the brightness value of the lamp after adjustment can be calculated as L = 1000 × (1-0.25 × 0.2) = 950 lumens;

[0096] According to the formula, the regulated lamp power value P = 50 × (1-0.25 × 0.2) = 47.5 kilowatts.

[0097] Subsequently, the system connects to a lighting control device (such as an intelligent dimmer) and sends the calculated control instructions to the control device. The control device adjusts the driving current or voltage and other parameters of the lamp according to the instructions, thereby adjusting the brightness and power of the lamp to the calculated values of 950 lumens and 47.5 kilowatts. Finally, the parameters of the lighting equipment after control are obtained, thereby reducing the total energy consumption of the venue and achieving a carbon reduction effect.

[0098] Furthermore, the control parameters of the air conditioning equipment are specifically the cooling power value after control and the air outlet angle value after control;

[0099] The method for determining the control parameters of air-conditioning equipment is as follows: when the control conditions are met, the original cooling power value is set to P c0, the power increase ratio is C, the original air outlet angle is α0, the angle adjustment value is Δα, then the cooling power value after adjustment is P c =P c0 ×(1+C), the adjusted air outlet angle value α=α0+Δα;

[0100] The power increase ratio C is determined based on the number of people in the venue, temperature and humidity, weather, and light factors;

[0101] The angle adjustment value Δα is determined based on factors such as the air conditioning coverage, air conditioning power, and installation height.

[0102] Specifically, taking the gymnasium as an example, assuming that the original cooling power value P in the gymnasium is c0 =30 kW, the original air outlet angle is α0=45 degrees.

[0103] When the system detects that the real-time temperature is 32 degrees Celsius, which is higher than the pre-set reference temperature of 30 degrees Celsius, and the real-time humidity is in the range of 50%-70% (currently 60%).

[0104] At the same time, the real-time number of people in the stadium was measured by the people counters installed at each entrance of the stadium, which was 500 people. The maximum capacity of the stadium is 800 people. According to the type of activities currently being held (such as large-scale sports events with a relatively dense crowd), it was determined that the current number of people exceeded the value set based on experience and the venue comfort model.

[0105] Taking all of the above factors into consideration, the system's built-in decision-making algorithm analyzed the relationship between factors such as temperature, humidity, number of people, and activity type and air conditioning energy consumption and cooling effect. The power increase ratio C = 0.1 was determined, and the angle adjustment value Δα = 10 degrees was determined based on the air conditioning cooling coverage model and the distribution of people in the gymnasium.

[0106] The refrigeration power value P after adjustment is calculated according to the formula c =30×(1+0.1)=33 kW;

[0107] According to the formula, the adjusted air outlet angle value α=45+10=55 degrees is obtained.

[0108] Afterwards, the system is connected to the air-conditioning equipment through a remote control device (such as an air-conditioning controller based on the Internet of Things), and the calculated regulated cooling power and air outlet angle parameters are sent to the air-conditioning equipment. After receiving the instructions, the air-conditioning equipment adjusts internal parameters such as the compressor operating frequency and fan speed to change the cooling power, and at the same time adjusts the air outlet blade angle through the motor drive, and finally obtains the regulated parameters of the air-conditioning equipment. Such adjustments enable the air-conditioning to reasonably control energy consumption while meeting the comfort needs of people in the venue.

[0109] Example 4, as Figure 6 、 Figure 7 and Figure 8 As shown, this embodiment further discloses, on the basis of the third embodiment, a method for calculating the energy consumption of the venue area and a method for troubleshooting and analyzing abnormal venues with excessively high energy consumption per unit area.

[0110] Furthermore, let the energy consumption per unit area of each venue be E i (i represents different venues), the average energy consumption per unit area of other venues is The ratio determined by the comprehensive factors of venue function, area, equipment configuration, etc. is D. The venues on the list of venues that need to be investigated include gymnasiums with excessive energy consumption per unit area, libraries with excessive energy consumption per unit area, and other campus venues with excessive energy consumption per unit area.

[0111] Specifically, assuming that there are a gymnasium and a library on campus, the gymnasium area is measured to be 5,000 square meters, and the total energy consumption in a certain period of time is obtained to be 200 kWh through the venue energy consumption collection module. Then its energy consumption per unit area is E1 = 200 ÷ 5,000 = 0.04 kWh / square meter.

[0112] The library area is 3,000 square meters, and the total energy consumption in the same time period is 120 kWh. Then its energy consumption per unit area is E2 = 120 ÷ 3,000 = 0.04 kWh / square meter.

[0113] Calculate the average energy consumption per unit area of other venues When calculating the energy consumption per unit area of other venues on campus except the gymnasium and library, first calculate them separately and then take the arithmetic average.

[0114] Assume that the average energy consumption per unit area of each venue on campus is It is 0.035 kWh / m2, and based on factors such as the function of the venue (such as a gymnasium with many sports equipment and high electricity consumption, and a library with many electronic equipment, etc.), area size, equipment configuration (energy consumption varies among equipment of different ages and brands), through a large amount of historical data statistical analysis and a professional energy consumption assessment model, a ratio D=0.1 for comparison is comprehensively determined.

[0115] When comparing the energy consumption per unit area of the gymnasium E1 with When E1=0.04 kWh / m2, because Therefore, the gymnasium was included in the list of venues that need to be inspected.

[0116] Through this comparative analysis of the energy consumption of various venues, venues with relatively abnormal energy consumption can be screened out, which facilitates the subsequent formulation and implementation of targeted energy consumption optimization measures for these venues and improves the overall energy utilization efficiency of the campus.

[0117] Furthermore, venue utilization data includes the real-time number of people in the venue and the actual percentage of the venue’s area in use;

[0118] The venue utilization rate data is calculated as follows: the real-time number of people in the venue is obtained through the people counter;

[0119] The actual usage area ratio of the venue = the actual usage area of the venue ÷ the total area of the venue.

[0120] Specifically, in the actual operation scenarios of campus venues, it is crucial to obtain venue utilization data.

[0121] Taking the campus gymnasium as an example, in order to accurately count the real-time number of people in the venue, people counters are installed at each main entrance of the gymnasium. The people counters use infrared sensing technology. When someone passes through the entrance, the infrared sensing device detects that the human body blocks the infrared light, and automatically counts and transmits the data in real time to the data aggregation center.

[0122] Assume that during a campus basketball game, data from the crowd counters at the four entrances of the gymnasium is aggregated and shows that a total of 500 people entered the gymnasium within one hour after the game started.

[0123] To calculate the actual usage area percentage of the venue, we first need to clarify the total area of the gymnasium. Assuming that the total area of the gymnasium is accurately measured to be 5,000 square meters, when hosting basketball games, the actual venues used include the basketball court, the audience area, and the necessary surrounding service areas. After field measurement and calculation, the actual usage area is 4,000 square meters.

[0124] According to the formula, the actual usage area of the gymnasium at this time is 4000÷5000=0.8. By integrating the real-time number of people in the venue, 500, with the actual usage area ratio of 0.8, we can obtain the gymnasium usage rate data for this period.

[0125] In the library scenario, personnel counters are also installed at each entrance and exit to count the real-time number of people. The actual area of open borrowing areas, study areas, etc. is obtained through the library management system. Compared with the total area of the library, the actual proportion of the used area is obtained, and finally the library venue utilization rate data is integrated to generate.

[0126] Other venues on campus also obtain venue usage data in this way. This data provides key information for the air conditioning parameter adjustment module, etc., so that the system can perform more accurate equipment control based on the actual usage of the venue.

[0127] Furthermore, the proportion determined by the statistical analysis of historical lighting energy consumption data specifically refers to the proportion determined based on the statistical analysis of lighting energy consumption data for the past month, the past quarter, and the past year. The statistical analysis uses the formula: Among them E li is the lighting energy consumption in the ith statistical period, T li is the electricity consumption time of lighting equipment in the i-th statistical period, n is the number of statistical periods, and k is the correction coefficient;

[0128] The correction factor k is determined based on the season and the type of venue activities.

[0129] Taking the campus library’s lighting system as an example, detailed records of the library’s lighting energy consumption data were kept every month over the past year.

[0130] Assume that the library lighting equipment uses electricity for 300 hours in January, and the lighting energy consumption for that month is 3,000 kWh; in February, the electricity consumption is shortened to 250 hours due to holidays and other factors, and the lighting energy consumption is 2,500 kWh, and so on, recording data for 12 months of the year.

[0131] Since the library's lighting needs vary in different seasons and when hosting different activities, the correction coefficient k = 1.1 was determined after a comprehensive analysis of factors such as lighting usage over the years, seasonal changes, and activity types.

[0132] Calculate the total lighting energy consumption data for the past year (Add up the energy consumption for 12 months), assuming the total is 30,000 kWh;

[0133] Total electricity consumption hours of lighting equipment (Add up the electricity usage hours for 12 months), assuming the total is 3,000 hours.

[0134] Substituting the data into the formula, we can get

[0135] This ratio B is used in the lighting analysis and control module to determine the extent to which the brightness parameters of the lamps are reduced when the light intensity is higher than the threshold. Combined with information such as the extent to which the real-time light intensity exceeds the threshold, the lighting equipment is precisely controlled to achieve a balance between energy saving and meeting lighting needs.

[0136] Similarly, for other venues on campus, such as gymnasiums, the lighting energy consumption data for the past month, quarter, and year are also collected in this way. The correction coefficient k is determined based on their actual conditions, and the corresponding ratio is calculated to provide data support for the intelligent energy-saving control of the entire campus lighting system.

[0137] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A dynamic carbon reduction optimization system driven by multi-source data for campus venues, characterized by: The system includes the following modules: Venue energy consumption collection module: collects real-time electricity consumption data and water consumption data of lighting equipment, air conditioning equipment, and sports equipment in campus venues, including but not limited to gymnasiums and libraries, and generates real-time venue energy consumption data; Geographic climate acquisition module: obtains geographical parameters such as the latitude and longitude, altitude, and climate condition data of the campus, including but not limited to temperature, humidity, light intensity, wind direction and speed, to obtain campus geographical climate data; Lighting analysis and control module: This module calls the lighting equipment electricity consumption data from the venue's real-time energy consumption data and the light intensity data from the campus's geographical climate data, compares the light intensity with the light intensity threshold set in advance based on the actual lighting needs of different areas of the venue, and adjusts the lighting brightness parameters based on the degree to which the light intensity exceeds the threshold and the proportion determined by statistical analysis of historical lighting energy consumption data; Air conditioning parameter adjustment module: Based on the power consumption data of air conditioning equipment in the venue's real-time energy consumption data, the temperature and humidity data in the campus's geographical climate data, and the venue's utilization rate data in the venue's real-time energy consumption data, the air conditioning cooling power parameters and air outlet angle parameters are adjusted according to the venue's maximum capacity and the type of activity. Multi-venue energy consumption comparison module: collects real-time energy consumption data of each venue, summarizes and compares the energy consumption data per unit area of each venue, and generates a list of venues with abnormal energy consumption based on a ratio determined by factors such as venue function, area, and equipment configuration.

2. The campus venue multi-source data-driven dynamic carbon reduction optimization system according to claim 1 is characterized by: The real-time energy consumption data of the venue includes the real-time power consumption of lighting equipment, real-time power consumption of air-conditioning equipment, real-time power consumption of sports equipment and real-time total water consumption; The real-time energy consumption data of the venue is calculated as follows: Real-time power consumption of lighting equipment = real-time power of lighting equipment × power consumption time; Real-time power consumption of air-conditioning equipment = real-time power of air-conditioning equipment × power consumption time; Real-time power consumption of sports equipment = real-time power of sports equipment × power consumption time; The real-time total water consumption is obtained through water meter readings.

3. The campus venue multi-source data-driven dynamic carbon reduction optimization system according to claim 1 is characterized by: The campus geographical climate data specifically refers to the geographical location latitude and longitude values, altitude values, real-time temperature values, real-time humidity values, real-time light intensity values, real-time wind direction and real-time wind speed values; The data related to the geographical location of the campus are directly measured values; The climate data is obtained in the following manner: the real-time air temperature and real-time humidity are obtained by measuring values through temperature and humidity sensors, the real-time light intensity is obtained by measuring values through light sensors, and the real-time wind direction and real-time wind speed are obtained by measuring values through wind vanes.

4. The campus venue multi-source data-driven dynamic carbon reduction optimization system according to claim 1 is characterized by: The calculation method of the lamp brightness parameters of the illumination analysis and control module is: The lighting equipment control parameters include the brightness value of the lamp after control and the power value of the lamp after control; The regulated lamp brightness value L=L0×(1-A×B), and the regulated lamp power value P=P0×(1-A×B); Among them, A is the magnitude by which the light intensity exceeds the threshold, B is the reduction ratio determined by statistical analysis of historical lighting energy consumption data, L0 is the original lamp brightness value, and P0 is the original lamp power value.

5. The campus venue multi-source data-driven dynamic carbon reduction optimization system according to claim 1 is characterized by: The air conditioning equipment control parameters are specifically the refrigeration power value after control and the air outlet angle value after control; The air conditioning equipment control parameters are determined in the following manner: when the control conditions are met, they are calculated according to the formula: The cooling power value after adjustment is: P c =P c0 ×(1+C); The value of the air outlet angle after adjustment is: α = α0 + Δα; Among them, P c0 is the original cooling power value, C is the power increase ratio, α0 is the original air outlet angle, and Δα is the angle adjustment value; The power increase ratio C is determined based on the number of people in the venue, temperature and humidity, weather, and light factors; The angle adjustment value Δα is determined according to factors such as the air conditioning refrigeration coverage, air conditioning power, and installation height.

6. The campus venue multi-source data-driven dynamic carbon reduction optimization system according to claim 1 is characterized by: Assume that the energy consumption per unit area of each venue is E i (i represents different venues), the average energy consumption per unit area of other venues is The ratio determined by the venue function, area, and equipment configuration factors is D. The venue will be included in the list of venues that need to be inspected, which includes gymnasiums with excessive energy consumption per unit area, libraries with excessive energy consumption per unit area, and other campus venues with excessive energy consumption per unit area.

7. The campus venue multi-source data-driven dynamic carbon reduction optimization system according to claim 1 is characterized by: The venue utilization rate data includes the real-time number of people in the venue and the actual usage area ratio of the venue; The venue utilization rate data is calculated as follows: the real-time number of people in the venue is obtained through a people counter; The actual usage area ratio of the venue = the actual usage area of the venue ÷ the total area of the venue.

8. The campus venue multi-source data-driven dynamic carbon reduction optimization system according to claim 1 is characterized by: The ratio determined by the statistical analysis of historical lighting energy consumption data specifically refers to the ratio determined based on the statistical analysis of lighting energy consumption data for the past month, the past quarter, and the past year. The statistical analysis uses the formula: Among them, E li is the lighting energy consumption in the ith statistical period, T li is the electricity consumption time of lighting equipment in the i-th statistical period, n is the number of statistical periods, and k is the correction coefficient; The correction coefficient k is determined according to factors such as season and venue activity type.

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