Exhibition hall low-carbon self-adaptive illumination and energy cooperative control system
By collecting and cleaning the operating data of the lighting fixtures in real time, and combining this with monitoring and prediction modules, the lighting brightness is dynamically adjusted, solving the problem of inaccurate heat load analysis of the exhibition hall lighting fixtures, and achieving efficient energy utilization and reduced energy loss.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JIANGSU SCI DREAM EXHIBITION TECH CO LTD
- Filing Date
- 2026-01-26
- Publication Date
- 2026-04-21
AI Technical Summary
In existing technologies, the heat load analysis of exhibition hall lighting has low sensitivity, resulting in the inability to effectively reduce energy consumption.
The system collects real-time operating power and status data of the lighting fixtures through the data acquisition module, performs data cleaning and real-time monitoring through the risk monitoring module, predicts future heat load risks through the risk prediction unit, and finally adjusts the lighting brightness through the energy coordination module to achieve coordinated control of lighting and energy.
It improved the accuracy of heat load analysis results, reduced energy consumption, enhanced emergency response capabilities, and ensured the synergistic control of lighting and energy.
Smart Images
Figure CN121908433A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of lighting control technology, specifically to a low-carbon adaptive lighting and energy synergy control system for exhibition halls. Background Technology
[0002] The exhibition hall lighting and energy collaborative control system is an intelligent solution that integrates the Internet of Things, artificial intelligence, big data analysis and sensor technology. Its core is to achieve the dual goals of optimizing lighting effects and efficiently utilizing energy through environmental perception, equipment linkage and data analysis.
[0003] In the hot summer, to ensure the comfort of visitors in the exhibition hall, the temperature inside the hall is dynamically adjusted through the air conditioning system. During this process, since the lighting generates heat load during use, in order to avoid the heat load affecting the energy consumption of the air conditioning system, the real-time operating temperature of the lighting is generally analyzed based on sensor technology. Based on the analysis results, a decision is made on whether it is necessary to reduce the brightness of the lighting to reduce the heat load, thereby achieving coordinated control of lighting and energy, reducing unnecessary lighting heat generation, and thus reducing the load on the air conditioning system.
[0004] In existing technologies, heat load analysis for lighting is generally based on real-time operating temperature data. However, analyzing heat load based on a single data point has low sensitivity, leading to errors in the heat load analysis results. Consequently, it is impossible to achieve coordinated control of lighting and energy, resulting in energy waste. Summary of the Invention
[0005] The purpose of this invention is to provide a low-carbon adaptive lighting and energy-coordinated control system for exhibition halls, solving the following technical problems: How to improve the accuracy of heat load analysis results in order to reduce energy loss.
[0006] The objective of this invention can be achieved through the following technical solutions: A low-carbon adaptive lighting and energy coordination control system for exhibition halls, the system comprising: The data acquisition module is used to collect real-time data on the operating power and operating status of different lights in the exhibition hall during operation. The risk monitoring module includes a data cleaning unit, a real-time monitoring unit, and a risk prediction unit. The data cleaning unit is used to clean the real-time operating power data of different lights in the exhibition hall during operation. The real-time monitoring unit combines the real-time operating power data and operating status data of different lighting lamps after cleaning during operation to monitor the heat load risk of different lamps in real time. The risk prediction unit combines the operating power data and operating status data of different lighting fixtures after cleaning during historical operation to predict the future heat load risk of different lighting fixtures; The energy coordination module is used to combine the real-time monitoring results of the heat load risk of different lamps with the prediction results of future heat load risk to adjust the lighting brightness of different lamps in real time.
[0007] Furthermore, the data collected by the data acquisition module includes: The operating status data of different lights in the exhibition hall during operation includes the lamp temperature, energy consumption, power factor and operating voltage of different lights.
[0008] Furthermore, the cleaning process of the data cleaning unit includes: Through formula Calculate the corrected operating power of the a-th lamp during the i-th data acquisition. ; Where i represents a single data acquisition by the data acquisition module at fixed time intervals, and a represents any single light source in the exhibition hall. Let be the operating power of the a-th lamp during the i-th data acquisition. This represents the total number of data acquisitions performed by the data acquisition module from the moment the light is turned on until the i-th data acquisition session. Let be the operating voltage of the a-th lamp during the i-th data acquisition. For all The average value, To adjust the coefficient lookup table function, based on empirical data... The impact of the range of numerical values on operating power is based on test results.
[0009] Furthermore, the monitoring process of the real-time monitoring unit includes: Through formula Calculate the heat load risk index of the a-th lamp during the i-th data acquisition. ; in, The preset operating power of the lighting fixtures, for The standard value, To define a function, if ,make Otherwise, let , Let be the energy consumption of the a-th lamp during the i-th data acquisition. The preset energy consumption of the lighting fixtures, Let be the power factor of the a-th lamp during the i-th data acquisition. The preset power factor, The temperature of the a-th light fixture in the exhibition hall during the i-th data collection. , The preset lamp temperature, and These are weighting coefficients, set based on empirical fitting.
[0010] Furthermore, the monitoring process of the real-time monitoring unit also includes: By using the heat load risk index of the a-th lamp during the i-th data acquisition... With the preset heat load risk index Perform a comparison; like The system determines that during the i-th data collection, the heat load of the a-th lamp in the exhibition hall is high, and adjusts the lighting brightness of the a-th lamp through the energy coordination module. like The system determines that the heat load of the a-th lamp in the exhibition hall is low during the i-th data collection and predicts the future heat load of the a-th lamp.
[0011] Furthermore, the prediction process of the risk prediction unit includes: The heat load risk index of the a-th lighting lamp at the i-th data acquisition time is calculated by the real-time monitoring unit. Establish the heat load risk index change curve of the a-th lighting lamp. ; And through the formula The change in the heat load risk index of the a-th light bulb from the time it was turned on to the time of the i-th data acquisition was calculated. ; in, Let be the time point of the first data collection after the a-th light is turned on. From the time the a-th light is turned on until the i-th data acquisition time... This is a proportionality coefficient, set based on empirical fitting. For all The maximum value in, For all The minimum value in.
[0012] Furthermore, the prediction process of the risk prediction unit also includes: Through formula The predicted future heat load risk value of the a-th lighting lamp at the i-th data acquisition time was calculated. ; in, For the IF function, based on empirical data... The impact of the range of numerical values on the predicted value of future heat load risk is based on test results. For all The average value, This represents the change in the preset heat load risk index.
[0013] Furthermore, the prediction process of the risk prediction unit also includes: By predicting the future heat load risk value of the a-th lamp during the i-th data acquisition Compared with the preset future heat load risk prediction threshold Perform a comparison; like The system determines that the a-th lamp will have a high risk of heat load during future operation, and the lighting brightness of the lamp needs to be adjusted through the energy coordination module; like The system determines that the a-th lamp is unlikely to experience a high risk of heat load during future operation, and therefore there is no need to adjust the lamp's brightness for the time being.
[0014] Furthermore, the coordination process of the energy coordination module includes: When it is determined that the real-time heat load of the a-th lamp in the exhibition hall is high during the i-th data collection, it is necessary to reduce the brightness of the a-th lamp to reduce the load on the air conditioning system. If it is determined that the a-th light bulb will have a high risk of heat load during future operation, the brightness of the a-th light bulb needs to be reduced in advance to avoid heat load generation.
[0015] Furthermore, the monitoring process of the risk monitoring module includes: S1: The data cleaning unit is used to clean and correct the real-time operating power data of different lights in the exhibition hall during operation. S2: By combining the real-time operating power data and operating status data of different lighting fixtures after cleaning during operation with the real-time monitoring unit, the heat load risk of different lighting fixtures can be monitored in real time. S3: By combining the operating power data and operating status data of different lighting fixtures after cleaning during historical operation, the risk prediction unit predicts the future heat load risk of different lighting fixtures.
[0016] The beneficial effects of this invention are: (1) By collecting the operating power and operating status data of different lighting fixtures in the exhibition hall during operation, the present invention can obtain diversified data support. After cleaning the real-time operating power data of different lighting fixtures in the exhibition hall during operation, it can avoid interference factors from affecting the accuracy of the operating power data, thereby improving the reliability of the operating power data. Based on the high accuracy and diversified data, the heat load risk of different lighting fixtures can be monitored and predicted in real time, which can not only improve the accuracy of heat load analysis results, but also play an early warning role, thereby enhancing the emergency response capability.
[0017] (2) The present invention uses the heat load risk index of the a-th lamp during the i-th data acquisition to... With the preset heat load risk index By comparing the data, an accurate analysis of the real-time heat load of the a-th lighting fixture in the exhibition hall during the i-th data collection can be made. Furthermore, since the data is based on cleaned and corrected diversified data, the sensitivity of the analysis data can be improved, thereby increasing the reliability of the heat load risk analysis results. On this basis, the accuracy of subsequent coordinated control of lighting and energy can be further improved, and energy consumption can be reduced.
[0018] (3) The present invention uses the predicted value of the future heat load risk of the a-th lighting lamp at the i-th data acquisition time. Compared with the preset future heat load risk prediction threshold By comparing the data, an accurate judgment can be made as to whether the a-th lamp will face a high risk of heat load during future operation. Furthermore, since this data is calculated based on diversified data after data cleaning, the reliability of the data is high. On this basis, the accuracy of the heat load analysis results can be further improved to reduce energy consumption.
[0019] (4) By combining the real-time heat load analysis results of different lighting lamps with the future heat load prediction results, the present invention dynamically adjusts the lighting brightness of the lighting lamps, thereby achieving coordinated control of lighting and energy, reducing unnecessary lighting heat generation, and thus reducing the load of the air conditioning system.
[0020] (5) The present invention uses a data cleaning unit to clean and correct the real-time operating power data of different lighting lamps in the exhibition hall during operation, which can improve the accuracy of the operating power data. Based on the highly accurate operating power data and diversified data such as operating status, the sensitivity of monitoring data can be improved, thereby improving the reliability of subsequent real-time heat load risk monitoring and future heat load risk prediction results of different lighting lamps, thus providing data support for the coordinated regulation of lighting and energy, ensuring the rationality of regulation results, and reducing energy loss. Attached Figure Description
[0021] The invention will now be further described with reference to the accompanying drawings.
[0022] Figure 1 This is a schematic block diagram of a low-carbon adaptive lighting and energy-coordinated control system for exhibition halls according to the present invention; Figure 2 This is a flowchart of the risk monitoring module in this invention. Detailed Implementation
[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0024] Please see Figure 1 As shown, in one embodiment, this application provides a low-carbon adaptive lighting and energy-coordinated control system for exhibition halls, the system comprising: The data acquisition module is used to collect real-time data on the operating power and operating status of different lights in the exhibition hall during operation. The risk monitoring module includes a data cleaning unit, a real-time monitoring unit, and a risk prediction unit. The data cleaning unit is used to clean the real-time operating power data of different lights in the exhibition hall during operation. The real-time monitoring unit combines the real-time operating power data and operating status data of different lighting lamps after cleaning during operation to monitor the heat load risk of different lamps in real time. The risk prediction unit combines the operating power data and operating status data of different lighting fixtures after cleaning during historical operation to predict the future heat load risk of different lighting fixtures; The energy coordination module is used to combine the real-time monitoring results of the heat load risk of different lamps with the future heat load risk prediction results to adjust the lighting brightness of different lamps in real time. Through the above technical solution, this embodiment provides a data acquisition module for real-time acquisition of the operating power and operating status data of different lighting fixtures in the exhibition hall. During the operation of the lighting fixtures, the real-time operating power data of different lighting fixtures in the exhibition hall can be cleaned by the data cleaning unit in the risk monitoring module. Then, the real-time monitoring unit combines the cleaned real-time operating power data and operating status data of different lighting fixtures to monitor the heat load risk of different lighting fixtures in real time. The risk prediction unit combines the cleaned operating power data and operating status data of different lighting fixtures in historical operation to predict the future heat load risk of different lighting fixtures. Finally, the energy coordination module combines the real-time monitoring results of the heat load risk of different lighting fixtures with the future heat load risk prediction results to adjust the lighting brightness of different lighting fixtures in real time. By setting it up in this way, and collecting data on the operating power and status of different lights in the exhibition hall during operation, diverse data support can be obtained. Then, the real-time operating power data of different lights in the exhibition hall can be cleaned to avoid interference factors affecting the accuracy of the operating power data, thereby improving the reliability of the operating power data. Based on highly accurate and diverse data, real-time monitoring and prediction of the heat load risk of different lights can not only improve the accuracy of heat load analysis results, but also play an early warning role, thereby enhancing emergency response capabilities. Finally, the energy coordination module can combine the real-time monitoring results of the heat load risk of different lamps with the prediction results of future heat load risk to adjust the lighting brightness of different lamps in real time. On this basis, by reducing the brightness of the lamps, the heat load can be effectively reduced, unnecessary lighting heat generation can be reduced, thereby reducing the load on the air conditioning system and reducing energy consumption.
[0025] The data collected by the data acquisition module includes: Operating status data of different lights in the exhibition hall during operation, including the lamp temperature, energy consumption, power factor and operating voltage of different lights; Through the above technical solution, this example provides data collected by the data acquisition module, including the operating status data of different lighting fixtures in the exhibition hall. Among them, the data such as the temperature, energy consumption and power factor of different lighting fixtures can directly reflect the heat load of different lighting fixtures, while the operating voltage data can be used as auxiliary data to clean the operating power parameters, so as to improve the reliability of the operating power data. In this way, it is possible to realize real-time monitoring and prediction of the heat load risk of different lighting fixtures based on highly accurate and diversified data, thereby improving the accuracy of heat load analysis results.
[0026] The cleaning process of the data cleaning unit includes: Through formula Calculate the corrected operating power of the a-th lamp during the i-th data acquisition. ; Where i represents a single data acquisition by the data acquisition module at fixed time intervals, and a represents any single light source in the exhibition hall. Let be the operating power of the a-th lamp during the i-th data acquisition. This represents the total number of data acquisitions performed by the data acquisition module from the moment the light is turned on until the i-th data acquisition session. Let be the operating voltage of the a-th lamp during the i-th data acquisition. For all The average value, To adjust the coefficient lookup table function, based on empirical data... The impact of the range of numerical values on operating power is based on test results; Using the above technical solution, this example provides the corrected operating power of the a-th lamp during the i-th data acquisition. It can be done through the formula The calculation yields the result, where the formula is... The operating voltage fluctuation value of the a-th lamp from its activation to the i-th data acquisition can be calculated. Since the lamp may consume more power when the operating voltage is higher than its rated voltage, leading to inflated operating power data, and higher voltage fluctuation values indicate lower power quality, this also results in inflated operating power data. Therefore, by incorporating the voltage data of the lamps during operation, the operating power data of different lamps at each data acquisition can be corrected to obtain operating power data closer to the true value. This provides reliable data support for subsequent analysis of the heat load of different lamps, ensuring the reliability of the analysis results.
[0027] The monitoring process of the real-time monitoring unit includes: Through formula Calculate the heat load risk index of the a-th lamp during the i-th data acquisition. ; in, The preset operating power of the lighting fixtures, for The standard value mentioned above can be selected and set based on the allowable error in empirical data. To define a function, if ,make Otherwise, let , Let be the energy consumption of the a-th lamp during the i-th data acquisition. The preset energy consumption of the lighting fixtures, Let be the power factor of the a-th lamp during the i-th data acquisition. The preset power factor, The temperature of the a-th light fixture in the exhibition hall during the i-th data collection. , The preset lamp temperature, and These are weighting coefficients, set based on empirical fitting. Using the above technical solution, this example provides the heat load risk index of the a-th lighting lamp during the i-th data acquisition. It can be done through the formula The calculation yields the result, where the formula is... The average energy consumption of the a-th lamp from its activation to the i-th data acquisition can be calculated. Clearly, the higher the corrected operating power and lamp temperature of the a-th lamp at the i-th data acquisition, the higher the average energy consumption of the a-th lamp from its activation to the i-th data acquisition, and the lower the power factor of the a-th lamp at the i-th data acquisition, the higher the heat load risk index of the a-th lamp at the i-th data acquisition. The higher the value, the higher the heat load risk of the a-th lamp during this data collection, and its brightness needs to be adjusted to reduce unnecessary lighting heat generation; Specifically, the higher the power of the lamp, the more electrical energy it consumes and the more heat it generates, resulting in a corresponding increase in heat load. Electrical energy consumption data directly reflects the energy consumption of the lighting system. High electrical energy consumption usually means a high heat load because most electrical energy is ultimately converted into heat. A low power factor means low energy utilization efficiency, with some electrical energy being wasted as heat, thus increasing the heat load. Therefore, this calculation method allows for the analysis of real-time heat load risks generated by different lighting fixtures based on diverse data. Furthermore, analyzing diverse data improves data sensitivity and, consequently, the reliability of the heat load risk analysis results.
[0028] The monitoring process of the real-time monitoring unit also includes: By using the heat load risk index of the a-th lamp during the i-th data acquisition... With the preset heat load risk index Perform a comparison; like The system determines that during the i-th data collection, the heat load of the a-th lamp in the exhibition hall is high, and adjusts the lighting brightness of the a-th lamp through the energy coordination module. like The system determines that the heat load of the a-th lamp in the exhibition hall is low during the i-th data collection, and predicts the future heat load of the a-th lamp. Using the above technical solution, this example demonstrates the heat load risk index of the a-th lighting lamp during the i-th data acquisition. With the preset heat load risk index By comparing the data, an accurate analysis of the real-time heat load of the a-th lighting fixture in the exhibition hall during the i-th data collection can be made. Furthermore, since the data is based on cleaned and corrected diversified data, the sensitivity of the analysis data can be improved, thereby increasing the reliability of the heat load risk analysis results. On this basis, the accuracy of subsequent coordinated control of lighting and energy can be further improved, and energy consumption can be reduced.
[0029] The prediction process of the risk prediction unit includes: The heat load risk index of the a-th lighting lamp at the i-th data acquisition time is calculated by the real-time monitoring unit. Establish the heat load risk index change curve of the a-th lighting lamp. ; And through the formula The change in the heat load risk index of the a-th light bulb from the time it was turned on to the time of the i-th data acquisition was calculated. ; in, Let be the time point of the first data collection after the a-th light is turned on. From the time the a-th light is turned on until the i-th data acquisition time... This is a proportionality coefficient, set based on empirical fitting. For all The maximum value in, For all The minimum value in; Using the above technical solution, this example provides the change in the heat load risk index of the a-th light bulb from the time it is turned on to the time of the i-th data acquisition. It can be done through the formula The calculation shows that the data reflects the changing trend of the heat load risk index of the a-th lamp from the time it is turned on to the i-th data collection. Since the heat load risk index can directly reflect the heat load risk brought by the lamp, potential risks can be identified in advance based on this data, thus providing data support for the subsequent analysis of the future heat load risk of the a-th lamp and ensuring the reliability of the analysis results.
[0030] The prediction process of the risk prediction unit also includes: Through formula The predicted future heat load risk value of the a-th lighting lamp at the i-th data acquisition time was calculated. ; in, For the IF function, based on empirical data... The impact of the range of numerical values on the predicted value of future heat load risk is based on test results. For all The average value, This represents the change in the preset heat load risk index; Using the above technical solution, this example provides a predicted future heat load risk value for the a-th lighting lamp during the i-th data acquisition. It can be done through the formula The calculation yields the result, where the formula is... The fluctuation value of the heat load risk index change from the time the a-th light lamp is turned on to the time of the i-th data acquisition can be calculated. Using this calculation method, the change in the heat load risk index from the time the a-th light lamp is turned on to the time of the i-th data acquisition can be used as the basis for calculation. Based on this, the future heat load risk of the a-th light bulb at the i-th data acquisition time is analyzed. This analysis considers the change in the heat load risk index of the a-th light bulb from its activation to the i-th data acquisition time. The results are obtained from diversified data after data cleaning, so they can accurately reflect the changing trend of the heat load risk index of the a-th lamp. Then, the fluctuation data of the heat load risk index change during the i-th data collection process after the a-th lamp is turned on is introduced for correction, which can improve the reliability of the calculation results and provide reliable data support for the subsequent analysis of the future heat load risk of the a-th lamp.
[0031] The prediction process of the risk prediction unit also includes: By predicting the future heat load risk value of the a-th lamp during the i-th data acquisition Compared with the preset future heat load risk prediction threshold Perform a comparison; like The system determines that the a-th lamp will have a high risk of heat load during future operation, and the lighting brightness of the lamp needs to be adjusted through the energy coordination module; like The system determines that the a-th lamp is unlikely to experience a high risk of heat load during future operation, and therefore there is no need to adjust the lamp's brightness for the time being. Using the above technical solution, this example uses the predicted future heat load risk value of the a-th lighting lamp at the i-th data acquisition time. Compared with the preset future heat load risk prediction threshold By comparing the data, an accurate judgment can be made as to whether the a-th lamp will face a high risk of heat load during future operation. Furthermore, since this data is calculated based on diversified data after data cleaning, the reliability of the data is high. On this basis, the accuracy of the heat load analysis results can be further improved to reduce energy consumption.
[0032] The coordination process of the energy coordination module includes: When it is determined that the real-time heat load of the a-th lamp in the exhibition hall is high during the i-th data collection, it is necessary to reduce the brightness of the a-th lamp to reduce the load on the air conditioning system. When it is determined that the a-th lamp will have a high risk of heat load in the future, the brightness of the a-th lamp needs to be reduced in advance to avoid heat load generation. Through the above technical solution, this example provides the coordination process of the energy coordination module. Specifically, when it is determined during the i-th data acquisition that the real-time heat load of the a-th light in the exhibition hall is high, the brightness of the a-th light needs to be reduced to reduce the load on the air conditioning system. When it is determined that the a-th light will have a high risk of heat load in the future, the brightness of the a-th light needs to be reduced in advance to avoid heat load generation. By setting it in this way, the brightness of the lights can be dynamically adjusted by combining the real-time heat load analysis results of different lights with the future heat load prediction results, thereby achieving coordinated control of lighting and energy, reducing unnecessary lighting heat generation, and thus reducing the load on the air conditioning system.
[0033] Please see Figure 2 As shown, the monitoring process of the risk monitoring module includes: S1: The data cleaning unit is used to clean and correct the real-time operating power data of different lights in the exhibition hall during operation. S2: By combining the real-time operating power data and operating status data of different lighting fixtures after cleaning during operation with the real-time monitoring unit, the heat load risk of different lighting fixtures can be monitored in real time. S3: By combining the operating power data and operating status data of different lighting fixtures after cleaning during historical operation, the risk prediction unit predicts the future heat load risk of different lighting fixtures; Through the above technical solution, this example provides the monitoring process of the risk monitoring module. First, the data cleaning unit is used to clean and correct the real-time operating power data of different lights in the exhibition hall during operation. Then, the real-time monitoring unit combines the cleaned real-time operating power data and operating status data of different lights during operation to monitor the heat load risk of different lights in real time. Finally, the risk prediction unit combines the cleaned operating power data and operating status data of different lights during historical operation to predict the future heat load risk of different lights. With this setup, when monitoring the heat load risk of different lighting fixtures, the data cleaning unit cleans and corrects the real-time operating power data of different lighting fixtures in the exhibition hall during operation. This improves the accuracy of the operating power data. Based on highly accurate operating power data and diverse data such as operating status, the sensitivity of the monitoring data can be increased, thereby improving the reliability of subsequent real-time heat load risk monitoring and future heat load risk prediction results for different lighting fixtures. This provides data support for the coordinated regulation of lighting and energy, ensures the rationality of the regulation results, and reduces energy consumption.
[0034] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.
Claims
1. A low-carbon adaptive lighting and energy-coordinated control system for exhibition halls, characterized in that, The system includes: The data acquisition module is used to collect real-time data on the operating power and operating status of different lights in the exhibition hall during operation. The risk monitoring module includes a data cleaning unit, a real-time monitoring unit, and a risk prediction unit. The data cleaning unit is used to clean the real-time operating power data of different lights in the exhibition hall during operation. The real-time monitoring unit combines the real-time operating power data and operating status data of different lighting lamps after cleaning during operation to monitor the heat load risk of different lamps in real time. The risk prediction unit combines the operating power data and operating status data of different lighting fixtures after cleaning during historical operation to predict the future heat load risk of different lighting fixtures; The energy coordination module is used to combine the real-time monitoring results of the heat load risk of different lamps with the prediction results of future heat load risk to adjust the lighting brightness of different lamps in real time.
2. The exhibition hall low-carbon adaptive lighting and energy coordination control system according to claim 1, characterized in that, The data collected by the data acquisition module includes: The operating status data of different lights in the exhibition hall during operation includes the lamp temperature, energy consumption, power factor and operating voltage of different lights.
3. The exhibition hall low-carbon adaptive lighting and energy coordination control system according to claim 1, characterized in that, The cleaning process of the data cleaning unit includes: Through formula Calculate the corrected operating power of the a-th lamp during the i-th data acquisition. ; Where i represents a single data acquisition by the data acquisition module at fixed time intervals, and a represents any single light source in the exhibition hall. Let be the operating power of the a-th lamp during the i-th data acquisition. This represents the total number of data acquisitions performed by the data acquisition module from the moment the light is turned on until the i-th data acquisition session. Let be the operating voltage of the a-th lamp during the i-th data acquisition. For all The average value, To adjust the coefficient lookup table function, based on empirical data... The impact of the range of numerical values on operating power is based on test results.
4. The exhibition hall low-carbon adaptive lighting and energy coordination control system according to claim 3, characterized in that, The monitoring process of the real-time monitoring unit includes: Through formula Calculate the heat load risk index of the a-th lamp during the i-th data acquisition. ; in, The preset operating power of the lighting fixtures, for The standard value, To define a function, if ,make Otherwise, let , Let be the energy consumption of the a-th lamp during the i-th data acquisition. The preset energy consumption of the lighting fixtures, Let be the power factor of the a-th lamp during the i-th data acquisition. The preset power factor, The temperature of the a-th light fixture in the exhibition hall during the i-th data collection. , The preset lamp temperature, and These are weighting coefficients, set based on empirical fitting.
5. The exhibition hall low-carbon adaptive lighting and energy coordination control system according to claim 4, characterized in that, The monitoring process of the real-time monitoring unit also includes: By using the heat load risk index of the a-th lamp during the i-th data acquisition... With the preset heat load risk index Perform a comparison; like The system determines that during the i-th data collection, the heat load of the a-th lamp in the exhibition hall is high, and adjusts the lighting brightness of the a-th lamp through the energy coordination module. like The system determines that the heat load of the a-th lamp in the exhibition hall is low during the i-th data collection and predicts the future heat load of the a-th lamp.
6. The exhibition hall low-carbon adaptive lighting and energy coordination control system according to claim 5, characterized in that, The prediction process of the risk prediction unit includes: The heat load risk index of the a-th lighting lamp at the i-th data acquisition time is calculated by the real-time monitoring unit. Establish the heat load risk index change curve of the a-th lighting lamp. ; And through the formula The change in the heat load risk index of the a-th light bulb from the time it was turned on to the time of the i-th data acquisition was calculated. ; in, Let be the time point of the first data collection after the a-th light is turned on. From the time the a-th light is turned on until the i-th data acquisition time... This is a proportionality coefficient, set based on empirical fitting. For all The maximum value in, For all The minimum value in.
7. A low-carbon adaptive lighting and energy coordination control system for exhibition halls according to claim 6, characterized in that, The prediction process of the risk prediction unit also includes: Through formula The predicted future heat load risk value of the a-th lighting lamp at the i-th data acquisition time was calculated. ; in, For the IF function, based on empirical data... The impact of the range of numerical values on the predicted value of future heat load risk is based on test results. For all The average value, This represents the change in the preset heat load risk index.
8. The exhibition hall low-carbon adaptive lighting and energy coordination control system according to claim 7, characterized in that, The prediction process of the risk prediction unit also includes: By predicting the future heat load risk value of the a-th lamp during the i-th data acquisition Compared with the preset future heat load risk prediction threshold Perform a comparison; like The system determines that the a-th lamp will have a high risk of heat load during future operation, and the lighting brightness of the lamp needs to be adjusted through the energy coordination module; like The system determines that the a-th lamp is unlikely to experience a high risk of heat load during future operation, and therefore there is no need to adjust the lamp's brightness for the time being.
9. A low-carbon adaptive lighting and energy coordination control system for exhibition halls according to claim 8, characterized in that, The coordination process of the energy coordination module includes: When it is determined that the real-time heat load of the a-th lamp in the exhibition hall is high during the i-th data collection, it is necessary to reduce the brightness of the a-th lamp to reduce the load on the air conditioning system. If it is determined that the a-th light bulb will have a high risk of heat load during future operation, the brightness of the a-th light bulb needs to be reduced in advance to avoid heat load generation.
10. A low-carbon adaptive lighting and energy-coordinated control system for exhibition halls according to claim 1, characterized in that, The monitoring process of the risk monitoring module includes: S1: The data cleaning unit is used to clean and correct the real-time operating power data of different lights in the exhibition hall during operation. S2: By combining the real-time operating power data and operating status data of different lighting fixtures after cleaning during operation with the real-time monitoring unit, the heat load risk of different lighting fixtures can be monitored in real time. S3: By combining the operating power data and operating status data of different lighting fixtures after cleaning during historical operation, the risk prediction unit predicts the future heat load risk of different lighting fixtures.