Multi-dimensional energy consumption statistics and intelligent dimming integrated control method and system

Abstract

The invention relates to the field of intelligent dimming, in particular to a multi-dimensional energy consumption statistics and intelligent dimming integrated control method and system, and the method comprises the steps: obtaining historical energy consumption statistics parameters; according to the historical energy consumption statistical parameters, analyzing energy consumption data of the lighting equipment of different equipment types and areas; according to the analysis result of the energy consumption data, the periodic change characteristic and the sudden characteristic of the lighting equipment are identified in combination with the time sequence; determining target energy consumption data of the lighting equipment based on the periodic change characteristics and the sudden characteristics; acquiring environment data of the lighting equipment area in real time, and analyzing illumination demand parameters of the lighting equipment according to the environment data; and generating an illumination adjustment parameter and an illumination adjustment strategy based on the illumination demand parameter and the target energy consumption data. According to the combination of the illumination demand and the target energy consumption data, the system generates an optimal adjustment scheme, energy consumption can be effectively controlled while the illumination demand is guaranteed, and dual optimization of energy saving and comfort is achieved.

Description

Technical Field

[0001] The present application relates to the field of intelligent dimming technology, and in particular to a multi-dimensional energy consumption statistics and intelligent dimming integrated control method and system. Background Art

[0002] Integrated energy consumption statistics and intelligent dimming control is a comprehensive technology used to manage and optimize intelligent lighting systems, particularly in public lighting equipment such as streetlights. It achieves energy savings and improves lighting performance by monitoring energy consumption data in real time and intelligently adjusting lighting brightness based on environmental changes.

[0003] Existing dimming technologies usually focus on how to adjust the brightness of lights according to preset standards to ensure that basic lighting needs are met. However, with the increasing demand for energy management and smart buildings, a single brightness control cannot effectively balance energy consumption optimization and actual lighting needs. Current dimming methods are difficult to find a balance between energy consumption optimization and actual lighting needs at the same time, especially under various environmental and equipment conditions. How to adjust energy consumption according to real-time changing environmental data remains a challenge. Secondly, when faced with special circumstances (such as emergencies, equipment failures, changes in personnel density, etc.), existing technologies often cause conflicts between dimming parameters and actual needs, and it is difficult to quickly provide effective solutions. Many systems fail to adjust in time when emergencies occur, resulting in energy consumption that cannot be minimized, and may even cause problems such as equipment overload or light pollution.

[0004] Therefore, the existing technology has defects and needs to be improved. Summary of the Invention

[0005] In order to solve one or several problems in the prior art, the main purpose of this application is to provide a multi-dimensional energy consumption statistics and intelligent dimming integrated control method and system.

[0006] In order to achieve the above-mentioned invention objectives, the present application proposes a multi-dimensional energy consumption statistics and intelligent dimming integrated control method, the method comprising:

[0007] Obtain historical energy consumption statistical parameters;

[0008] Analyze the energy consumption data of lighting equipment of different equipment types and regions based on the historical energy consumption statistical parameters;

[0009] Based on the results of energy consumption data analysis and combined with time series, the periodic change characteristics and sudden characteristics of lighting equipment are identified;

[0010] Determining target energy consumption data of the lighting equipment based on the periodic change characteristics and the sudden change characteristics;

[0011] Acquire environmental data of the lighting equipment area in real time, and analyze the lighting requirement parameters of the lighting equipment based on the environmental data;

[0012] Based on the lighting demand parameters and the target energy consumption data, lighting adjustment parameters and a lighting adjustment strategy are generated.

[0013] The present application also provides a multi-dimensional energy consumption statistics and intelligent dimming integrated control system, including:

[0014] The first acquisition module is used to obtain historical energy consumption statistical parameters;

[0015] An analysis module, configured to analyze energy consumption data of lighting equipment of different equipment types and regions based on the historical energy consumption statistical parameters;

[0016] The recognition module is used to identify the periodic change characteristics and sudden characteristics of lighting equipment based on the results of energy consumption data analysis and time series;

[0017] A determination module, configured to determine target energy consumption data of the lighting device based on the periodic change characteristics and the sudden change characteristics;

[0018] The second acquisition module is used to acquire the environmental data of the lighting equipment area in real time and analyze the lighting requirement parameters of the lighting equipment according to the environmental data;

[0019] A generation module is used to generate lighting adjustment parameters and lighting adjustment strategies based on the lighting demand parameters and target energy consumption data.

[0020] The present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0021] The present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0022] The multi-dimensional energy consumption statistics and intelligent dimming integrated control method and system of the embodiment of the present application realizes intelligent energy efficiency management and regulation of lighting equipment by integrating multiple factors such as historical energy consumption data, equipment type and regional analysis, and environmental data collection. By combining periodic change characteristics with sudden characteristics, accurate prediction of the working status of lighting equipment can be achieved, thereby increasing lighting during high-demand periods and reducing energy consumption during low-demand periods to avoid ineffective energy waste. Real-time acquisition and analysis of environmental data enables the system to dynamically adjust the light intensity to ensure that the light level always matches the actual demand and avoid excessive lighting. Based on the combination of lighting demand and target energy consumption data, the system generates the optimal adjustment plan, which can effectively control energy consumption while ensuring lighting needs, achieving dual optimization of energy saving and comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a multi-dimensional energy consumption statistics and intelligent dimming integrated control method according to an embodiment of the present application;

[0024] Figure 2 This is a flow chart of a multi-dimensional energy consumption statistics and intelligent dimming integrated control method according to an embodiment of the present application;

[0025] Figure 3 This is a schematic block diagram of the structure of a multi-dimensional energy consumption statistics and intelligent dimming integrated control system according to an embodiment of the present application;

[0026] Figure 4 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present application.

[0027] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0029] Reference Figure 1 In an embodiment of the present application, a multi-dimensional energy consumption statistics and intelligent dimming integrated control method is provided, the method comprising:

[0030] S1. Obtain historical energy consumption statistical parameters;

[0031] S2. Analyze the energy consumption data of lighting equipment of different equipment types and regions based on the historical energy consumption statistical parameters;

[0032] S3. Based on the results of energy consumption data analysis and combined with time series, identify the periodic change characteristics and sudden characteristics of lighting equipment;

[0033] S4. Determining target energy consumption data of the lighting device based on the periodic change characteristics and the sudden change characteristics;

[0034] S5. Acquire environmental data of the lighting equipment area in real time, and analyze lighting requirement parameters of the lighting equipment based on the environmental data;

[0035] S6. Generate lighting adjustment parameters and lighting adjustment strategies based on the lighting demand parameters and target energy consumption data.

[0036] As described in steps S1-S3 above, historical data is collected and recorded from existing energy consumption data. This data may include energy consumption records of lighting equipment at different times and in different areas. This process typically requires collecting data from multiple sensors or devices and storing it in a database or cloud platform for subsequent analysis. Obtaining historical energy consumption data is the foundation for analyzing and optimizing future energy use. Understanding the historical usage of equipment provides reliable data support for subsequent analysis, avoiding the need to start energy efficiency optimization from scratch. By classifying and comparing energy consumption data for different equipment types and regions, it is possible to identify which equipment has high or low energy consumption in certain areas. This helps identify equipment or areas with energy efficiency issues and implement targeted optimization. This analysis process can provide decision support for optimizing equipment or area energy efficiency. For example, it can be found that certain areas or equipment have higher energy consumption during off-peak hours, providing a basis for adjusting and optimizing lighting control strategies, thereby reducing unnecessary energy consumption. By comparing and analyzing energy consumption data with time series, it is possible to identify periodic changes in equipment (for example, higher energy consumption during certain times of the day or on specific days of the week) and sudden changes in equipment (for example, a sudden increase in energy consumption during specific events or situations). This analysis helps accurately identify the operating patterns of lighting equipment. Identifying periodic and sudden changes can effectively help the system predict the operating status of equipment and provide forward-looking guidance for adjustments. For example, during peak hours, the system can proactively make energy-saving adjustments, and in sudden situations (such as emergency lighting needs), the system can increase light output to ensure safety.

[0037] As described in steps S4-S6 above, based on the periodic and sudden characteristics analyzed previously, the system sets target energy consumption data for lighting devices. This means that the system will set an ideal energy consumption level based on the device's operating mode during specific time periods or scenarios. Setting target energy consumption data provides a clear basis for subsequent dimming strategies. During high-demand periods, the system can provide sufficient lighting according to the target energy consumption, while reducing energy consumption during low-demand periods, achieving energy savings. Sensors capture real-time environmental data (such as light intensity, temperature, and humidity) in the area where the lighting devices are located. This data reflects the lighting needs under current environmental conditions. Real-time collection of environmental data ensures that lighting adjustments match actual needs. Real-time acquisition of environmental data enables the system to dynamically adjust the operating status of lighting devices. Different environmental conditions (such as daytime and nighttime, sunny and cloudy) affect lighting needs. Real-time data analysis allows for more precise lighting adjustments to avoid energy waste. Based on real-time environmental data, the system analyzes the lighting needs of the current area. For example, if the current natural light intensity is insufficient, the system may need to increase artificial lighting. By analyzing lighting needs, the system can precisely control the activation or dimming of lighting devices. Adjusting according to lighting needs ensures that lighting levels always meet usage requirements while avoiding energy waste caused by excessive lighting. If the lighting demand is low, the system can automatically reduce the lighting intensity to improve energy efficiency. The system combines the lighting demand parameters with the target energy consumption data and generates specific lighting adjustment parameters and strategies through calculation. For example, the system may decide to reduce the brightness, change the working mode, or increase the lighting in certain areas to ensure that the target energy consumption is achieved and the lighting needs are met. Combining lighting needs and energy consumption targets generates an optimal adjustment plan to ensure energy saving without affecting the lighting effect. The system can intelligently adjust lighting equipment under different environmental conditions, saving energy while ensuring user comfort.

[0038] As described above, by integrating multiple factors such as historical energy consumption data, equipment type and regional analysis, and environmental data collection, the present invention achieves intelligent energy efficiency management and regulation of lighting equipment. By combining periodic change characteristics with sudden changes, accurate predictions of the operating status of lighting equipment can be achieved, thereby increasing lighting during high-demand periods and reducing energy consumption during low-demand periods to avoid ineffective energy waste. Real-time acquisition and analysis of environmental data enables the system to dynamically adjust light intensity, ensuring that light levels always match actual needs and avoiding excessive lighting. Based on the combination of lighting needs and target energy consumption data, the system generates an optimal adjustment plan that can effectively control energy consumption while ensuring lighting needs, achieving dual optimization of energy saving and comfort.

[0039] Reference Figure 2In one embodiment, the step of determining the target energy consumption data of the lighting device based on the periodic change characteristics and the sudden change characteristics includes:

[0040] S41. Divide the energy consumption temporal pattern according to the periodic variation characteristics, and generate a basic energy consumption curve including a weekday benchmark, a holiday benchmark, and a seasonal benchmark;

[0041] S42. Mark the abnormal event period corresponding to the sudden feature on the basic energy consumption curve, and record the energy consumption deviation amplitude during the event;

[0042] S43, extracting patterns from recurring abnormal events to form a predictable energy consumption correction factor library;

[0043] S44, superimposing the basic energy consumption curve matched in the current period and the adjustment amount of the relevant events in the correction factor library to generate a dynamic target energy consumption baseline;

[0044] S45. Evaluate the health status of the lighting equipment.

[0045] S46. Based on the latest health status assessment results of the lighting equipment, perform attenuation compensation on the dynamic target energy consumption baseline and output target energy consumption data with an allowable deviation range.

[0046] As described in the previous steps, energy consumption baselines are established for lighting equipment by analyzing energy consumption characteristics over different time periods (such as weekdays, holidays, and seasonal variations). These baseline curves help predict the equipment's typical energy consumption patterns under different scenarios. The weekday baseline reflects typical weekday energy consumption, the holiday baseline accounts for special holiday energy demands (such as increased lighting demand), and the seasonal baseline adjusts energy consumption patterns based on seasonal variations in daylight duration. This categorization accurately reflects equipment energy consumption variations over different time periods, avoiding the rough estimation of uniform energy consumption standards across all time periods and improving the accuracy of energy efficiency predictions. Monitoring the baseline energy consumption curves identifies and flags unexpected events in energy consumption patterns (such as equipment failures and lighting for large events). Unexpected patterns refer to non-periodic or unexpected factors that affect energy consumption and can cause significant deviations in energy consumption during a specific period. Recording the magnitude of these deviations facilitates subsequent analysis and adjustments. Monitoring and flagging unexpected events helps the system identify and record anomalies, providing more comprehensive data support for energy efficiency optimization. This step provides a foundation for subsequent analysis and correction of anomalies. Identify and extract recurring abnormal events to create a correction factor library. These factors are analyzed based on the characteristics of past emergency events and the magnitude of energy consumption deviations. By extracting regularities, the system can predict the occurrence of similar events and make proactive energy consumption adjustments. Preemptively extracting and predicting abnormal event patterns enables the system to make more precise adjustments, avoiding the need to re-invent the wheel each time an abnormal event occurs. The correction factor library improves the system's adaptability to recurring events and reduces uncertainty in energy management. The current period's base energy consumption curve is superimposed with the relevant adjustments extracted from the correction factor library to generate a dynamic target energy consumption baseline. This baseline considers the dual impact of base energy consumption and correction factors, ensuring that the target energy consumption is more aligned with actual needs. The dynamic target energy consumption baseline can flexibly adjust based on both regular energy consumption patterns and unexpected events, adapting to changes in energy demand in real time and ensuring more accurate energy efficiency management. The health of lighting equipment is assessed, including equipment operating conditions, including malfunctions and performance degradation. Optimal equipment performance is assessed by analyzing equipment operating data, such as power fluctuations and operating hours. Health status assessments help the system identify potential device issues and avoid inappropriate energy efficiency management when devices are in poor health. Accurately assessing device health ensures that subsequent adjustments are more precise and effective. Based on the latest device health status assessment results, the dynamic target energy consumption baseline is attenuated. For example, if a device experiences performance degradation, the system adjusts the target energy consumption baseline based on the assessment results, lowering the expected energy consumption and providing a certain tolerance range for the device's health status. Attenuation compensation ensures that the system maintains proper energy efficiency management even when device status changes.By setting the allowable deviation range, the system can better adapt to changes in the health of the equipment and avoid the risk of excessive or low energy consumption due to changes in equipment performance.

[0047] In one embodiment, the steps of acquiring environmental data of the lighting equipment area in real time and analyzing the lighting requirement parameters of the lighting equipment according to the environmental data include:

[0048] Determine ambient light intensity distribution data, personnel dynamic distribution data, and real-time monitoring values of natural light sources based on the environmental data;

[0049] Divide functional areas according to the dynamic distribution data of personnel, and match the lighting requirements of each functional area according to preset standards;

[0050] Based on the real-time monitoring value of the natural light source, obtaining historical time series data corresponding to the real-time monitoring value of the natural light source;

[0051] Combining the historical time series data with the real-time monitoring value of natural light sources, predicting the natural light attenuation within a future preset time period;

[0052] Calculate the artificial lighting requirements for each functional area by combining the current ambient light intensity distribution with the predicted natural light attenuation;

[0053] Integrate the lighting requirement level and artificial lighting requirement of the functional area to generate a dynamic lighting requirement parameter set;

[0054] Obtain the operating status parameters of lighting equipment in real time, including current brightness output value, power consumption value and dimmer working mode;

[0055] The operating state parameters are compared with the dynamic lighting demand parameter set to output the lighting gap parameters of each area, including brightness gap value and energy consumption gap value.

[0056] As mentioned above, environmental information about the areas where lighting equipment is located is collected to analyze and calculate lighting requirements for each area. Environmental data includes real-time data on light intensity, occupancy distribution, and natural light sources. This information helps the system understand the current lighting requirements and predict the need for supplemental lighting. By acquiring this real-time environmental data, the system can dynamically adjust lighting to ensure appropriate brightness. Real-time monitoring of environmental data ensures that lighting adjustments are tailored to actual needs, avoiding over- or under-lighting. This improves energy efficiency and enhances user comfort. By analyzing the light intensity distribution, occupancy distribution, and real-time monitoring values of natural light sources in environmental data, we can understand the current lighting conditions and occupant activity. Light intensity distribution reflects the light intensity in each area, while dynamic occupancy data helps identify areas with higher lighting requirements. Real-time monitoring values of natural light sources help understand changes in the intensity of external light sources (such as sunlight) and assist in calculating the need for supplemental artificial light sources. This helps the system more accurately understand environmental conditions and provides data support for subsequent lighting requirement calculations. Artificial light sources can be dynamically adjusted based on changes in light intensity, occupant activity, and natural light sources, avoiding energy waste. Based on the dynamic distribution of personnel, the system divides the area into functional zones (such as office areas, meeting areas, corridors, etc.). Each functional zone has a different lighting requirement level, and the lighting intensity requirements for each zone are determined based on pre-set standards. For example, a meeting room may require higher lighting intensity, while a corridor requires lower lighting. This step optimizes the allocation of lighting resources based on the lighting requirement levels of the functional zones. By precisely dividing the functional zones and adjusting lighting according to the required levels, over- or under-lighting can be avoided, improving lighting efficiency and contributing to energy conservation. To analyze the changing patterns of natural light, real-time natural light source data is combined with historical data. By collecting historical time series data, the system can better predict the attenuation or enhancement of natural light sources, thereby more accurately calculating the demand for artificial light sources. Changes in natural light sources directly affect the demand for artificial lighting, so continuous monitoring is necessary. By combining historical and real-time data, future natural light changes can be more accurately predicted, helping to dynamically adjust artificial light sources, avoiding excessive artificial light even when natural light is sufficient, and thus saving energy. Combining historical time series data with real-time monitoring values can also analyze the attenuation trends of natural light sources. By predicting future natural light attenuation, the system can proactively adjust the brightness of artificial light sources. Predicting the period of natural light attenuation helps the system prepare for environmental changes and avoid situations of insufficient or excessive light. Combining the current ambient light intensity with the predicted natural light attenuation, the system calculates the amount of artificial light required for each functional area. By comparing the current ambient light intensity with the expected natural light attenuation, the system determines the amount of artificial light required during a specific period to ensure that the lighting needs of each functional area are met.By accurately calculating fill light requirements, each functional area maintains appropriate lighting levels under varying environmental conditions, thereby improving the energy efficiency of the lighting system. The functional area's lighting requirement level is combined with the calculated artificial fill light requirement to generate a dynamic set of lighting requirement parameters. This allows the system to dynamically adjust lighting parameters for each area to meet varying environmental conditions. This set provides a continuously updated lighting requirement model, ensuring efficient lighting management in a changing environment. Real-time monitoring of lighting device operating status, including brightness output, power consumption, and dimmer operating mode, allows the system to understand the actual operating status of the device and compare it with the set lighting requirements to determine whether adjustments are needed. By comparing the actual operating status of the device with the dynamic lighting requirements, the system calculates the lighting and energy gaps for each functional area. These gap parameters reflect the gap between the current system and the target lighting requirements, helping to further adjust the lighting device operating parameters. By comparing and outputting the gap parameters in real time, the system can precisely adjust the operating status of the lighting devices to ensure that the lighting and energy requirements of each area are met. This not only improves lighting quality but also optimizes energy use and enhances overall system efficiency.

[0057] In one embodiment, after the step of generating the lighting adjustment parameters and the lighting adjustment strategy based on the lighting demand parameters and the target energy consumption data, the method further includes:

[0058] Receive trigger signals of sudden environmental events in real time;

[0059] When an emergency environmental event trigger signal is received, the emergency environmental event trigger signal is parsed to extract an event type identifier and an impact parameter set, wherein the event type includes sudden weather change, temporary activities, and security alerts;

[0060] Retrieve the preset emergency lighting template according to the event type identifier to cover the original lighting requirement level of the corresponding functional area;

[0061] Analyzing the spatial range and duration parameters within the impact parameter set;

[0062] Correcting the artificial fill light requirement based on the spatial range and duration parameters;

[0063] The corrected artificial fill light requirement and emergency lighting template are reinjected into the dynamic lighting requirement parameter set to generate an emergency adjustment parameter package;

[0064] The emergency channel transmission mechanism is started, and the emergency adjustment parameter package is executed first.

[0065] As mentioned above, the system monitors and receives signals from external environmental emergencies in real time, indicating that these events may impact existing lighting requirements. The system needs to connect to external sensors, weather forecast systems, security systems, and other systems to ensure that signals are rapidly captured and processed should an emergency occur. By receiving trigger signals in real time, the system can promptly respond to sudden environmental changes, ensuring that lighting requirements are adjusted immediately to avoid environmental adaptation issues. Upon receiving the trigger signal for an emergency, the system parses the signal to extract the specific event type identifier (e.g., weather change, temporary event, or security alert) and influencing parameters (e.g., spatial scope and duration of the event). This step requires the system to decode and classify the signal so that different response measures can be implemented for different types of events. By accurately classifying and extracting key information about the emergency, the system can quickly formulate appropriate lighting adjustment strategies based on the event type, thereby enhancing the system's flexibility and emergency response capabilities. Based on the event type identifier parsed in the previous step, the system retrieves the corresponding template from a pre-set emergency lighting template library. These templates pre-set the lighting requirements required for specific environmental changes, such as increasing indoor lighting intensity during sudden weather changes or focusing on specific areas for temporary events. These templates override the original lighting requirements. This ensures the system can adjust lighting requirements on an emergency basis, ensuring that lighting in relevant areas can meet requirements promptly under special environmental changes, avoiding issues such as insufficient or excessive lighting. The analysis focuses on the specific spatial and temporal impacts of sudden events. The impact parameter set includes the area potentially affected by the event (spatial scope) and the duration of the event (duration parameter). By analyzing this information, the system determines which areas require lighting adjustments and the duration of these adjustments. Combined with the spatial scope and duration parameters analyzed previously, the system adjusts the original artificial fill lighting requirements based on these factors. If the event's impact area is large or its duration is long, the system may increase the fill lighting level; otherwise, it may reduce it. By adjusting the artificial fill lighting requirements, the system can precisely adjust the energy consumption and intensity of lighting equipment based on actual environmental changes, optimizing energy use and ensuring that the lighting effect meets actual requirements. After correcting the fill light requirements, the system integrates these new parameters with the emergency lighting template to generate an emergency adjustment parameter package. This package contains all adjusted lighting requirements and strategies and will be used for actual lighting adjustments. The purpose of generating an emergency adjustment parameter package is to ensure that all adjustments can be implemented using a unified set of parameters, avoiding confusion and errors during the lighting adjustment process and improving system coordination and execution. In emergency situations, the system quickly transmits the generated emergency adjustment parameter package to the lighting control system through the emergency channel transmission mechanism, ensuring its priority execution.Emergency channels typically use higher-priority communication protocols to ensure that information reaches the target device and is executed in a short period of time. This ensures that lighting adjustments can be responded to as quickly as possible after an emergency occurs.

[0066] In one embodiment, the steps of generating the lighting adjustment parameters and the lighting adjustment strategy based on the lighting demand parameters and the target energy consumption data include:

[0067] Construct a multi-dimensional constraint set, including brightness gap parameters, target energy consumption tolerance range, and device dimming step size limit;

[0068] Based on the multi-dimensional constraint condition set, generating a set of candidate dimming solutions within a rolling optimization window, each solution including a brightness adjustment value and an expected energy consumption change;

[0069] Performing compliance rate prediction on the candidate dimming solution set to screen out a subset of feasible solutions that both meet the brightness gap compensation requirements and meet the preset brightness gap compensation requirements, and the energy consumption change amount meets the target energy consumption deviation limit;

[0070] Extracting light spot distribution characteristics from the subset of feasible solutions, and eliminating solutions with illumination uniformity lower than a preset uniformity threshold based on the light spot distribution characteristics to obtain an optimized dimming solution;

[0071] Based on the optimized dimming solution, light adjustment parameters are determined.

[0072] As described above, a multi-dimensional constraint set is constructed, primarily consisting of three key factors: "Brightness gap parameter: represents the difference between the current ambient lighting and the target lighting, determining the brightness difference that needs to be adjusted. Target energy consumption tolerance: defines the permissible range of energy consumption variation, ensuring that lighting adjustments do not exceed predetermined energy consumption limits. Device dimming step limit: limits the minimum device dimming step size to ensure that each adjustment is within the device's capabilities and does not cause excessive or unstable changes." This setting ensures that lighting adjustments are performed within certain physical and energy constraints, avoiding over-adjustments and ensuring system stability and efficiency. A rolling optimization window (i.e., a progressively updated time period or data window) is used to generate a set of candidate dimming solutions. Each solution, based on the aforementioned constraints, proposes a brightness adjustment value and a corresponding energy consumption change. The brightness adjustment value: adjusts the light source brightness based on the size of the brightness gap. The expected energy consumption change: predicts the energy consumption impact of each brightness adjustment. This rolling optimization window allows the system to continuously adjust the candidate solution set based on the latest data (such as environmental changes or current energy consumption), ensuring that the lighting adjustment solution always meets real-time requirements. This ensures that the dimming scheme can be flexibly adjusted and updated in real time over time and as the environment changes, providing the best balance between brightness and energy efficiency. The system predicts the compliance rate of each candidate dimming scheme and evaluates whether each scheme meets two core conditions:

[0073] Brightness gap compensation requirements: The solution must ensure that the adjusted brightness meets the preset standard and eliminates the brightness gap.

[0074] Energy consumption deviation limit: The energy consumption change of the plan must comply with the target deviation range to avoid energy consumption fluctuations beyond the allowable range.

[0075] The compliance rate prediction mechanism helps the system automatically evaluate the effectiveness of each candidate solution, thereby selecting a subset of feasible solutions that meet the requirements. By screening these feasible solutions, the final dimming solution is ensured to meet lighting needs while effectively controlling energy consumption and avoiding excessive energy waste. The solutions selected from the subset of feasible solutions are analyzed for their light spot distribution characteristics. Light spot distribution characteristics refer to the distribution of brightness within the area illuminated by the adjusted light source, primarily evaluating illumination uniformity.

[0076] Light spot distribution characteristics: Analyze the distribution of light to ensure uniform lighting and avoid areas that are too bright or too dark.

[0077] Illumination uniformity: If a solution's illumination uniformity falls below a set uniformity threshold, it will be eliminated to ensure comfortable and functional lighting. The adjusted lighting must not only meet brightness and energy efficiency requirements, but also ensure comfort and uniformity, avoiding areas of excessive brightness or darkness. By eliminating solutions with poor illumination uniformity, the final selected dimming solution will deliver better visual quality, avoiding discomfort or excessive energy consumption. Ultimately, based on the optimized dimming solution, the system determines specific lighting adjustment parameters. These parameters include: Brightness adjustment value: This determines the specific brightness level of each light source. Dimming step size: This determines the dimming step size during the adjustment process to ensure smoothness and stability. Energy consumption limit: This sets an energy consumption range to ensure the final adjusted solution does not exceed the predetermined energy efficiency target. The lighting adjustment solution can then be implemented, resulting in an optimized lighting effect that meets the needs of the application while maximizing energy efficiency.

[0078] In one embodiment, generating the lighting adjustment strategy further includes a conflict resolution mechanism, and the steps include:

[0079] When a conflict signal between the brightness gap compensation requirement and the target energy consumption deviation tolerance is detected, the brightness gap value and energy consumption overflow of the conflicting area are extracted;

[0080] Selecting a dominant optimization objective according to a preset priority rule base, wherein the priority rule base includes a safety priority mode, a comfort priority mode, and an energy saving priority mode;

[0081] Acquire power grid load status and personnel distribution density data in real time, and dynamically adjust the weight coefficient of the dominant optimization objective according to the power grid load status and personnel distribution density data;

[0082] Set a flexible compromise interval for non-dominant optimization objectives and generate a conflict resolution solution that includes brightness compromise values and energy consumption compromise values;

[0083] A conflict resolution mechanism is determined based on the conflict resolution scheme.

[0084] As described above, a conflict between two key parameters is detected: the brightness gap and the energy consumption tolerance. When these two conditions cannot be met simultaneously, the system detects a conflict signal and extracts the brightness gap value and energy consumption overflow in the conflicting area. The brightness gap value represents the required brightness increase in the current environment, while the energy consumption overflow represents the excess energy consumption caused by the brightness adjustment. Since there is often a trade-off between brightness and energy efficiency during lighting adjustment, it is necessary to accurately identify and extract the conflicting area when a conflict occurs to facilitate subsequent optimization. This ensures that a reasonable balance between brightness and energy efficiency is achieved when resolving the conflict. After detecting a conflict signal, the system selects the dominant optimization objective based on a pre-set priority rule base. This rule base includes three modes: Safety Priority Mode: Prioritizes environmental safety and ensures that lighting does not affect safety perceptions. Comfort Priority Mode: Prioritizes user comfort and ensures appropriate lighting effects. Energy Saving Priority Mode: Prioritizes reducing energy consumption and ensures optimal energy efficiency while meeting the minimum brightness. By selecting these priority modes, the system can determine which objective to prioritize in conflicting situations. Each application scenario may have different requirements for different objectives. Flexible priority selection allows for better adaptation to diverse environments and application needs. Real-time grid load and occupancy density data provides additional dynamic information for conflict resolution. Grid load status: This provides information on the current grid load. If the grid is overloaded, energy conservation should be prioritized. Occupancy density: This provides information on the distribution of occupants within the lighting area. Highly populated areas can be brightened appropriately, while sparsely populated areas can be reduced to conserve energy. Based on this real-time data, the system dynamically adjusts the weightings of optimization objectives. For example, if the grid load is high, energy conservation may be prioritized; if the occupancy density is high, comfort may be prioritized. Real-time grid load and occupancy density data enable the system to flexibly adjust to different times and circumstances, achieving a better balance between brightness and energy efficiency. Dynamic adjustment of weightings allows the system to adapt to actual conditions, improving adjustment accuracy and real-time responsiveness. A flexible compromise range is set for non-dominant optimization objectives (i.e., optimization directions not selected as the dominant objective). Specifically, if the system selects a certain optimization goal (such as energy saving or comfort), the remaining goals (such as brightness or energy efficiency) will be compromised according to the preset flexible range, that is, reasonable concessions will be made within a certain range. Brightness compromise value: Under the premise of ensuring that the brightness meets the basic requirements, the brightness is allowed to be adjusted within a certain range. Energy consumption compromise value: Under the premise of ensuring that the energy efficiency does not exceed the standard, energy consumption is allowed to fluctuate within a certain range. By setting a flexible compromise interval, the system can maintain flexibility in the event of conflict and avoid being overly rigid about a single goal. The flexible compromise interval can prevent the goal from being too rigid and allow the system to weigh different goals within a certain range to obtain the optimal dimming solution.Through flexible compromise, the system can better balance the contradictions between various optimization goals, provide a more humane lighting adjustment solution, and avoid discomfort or energy waste caused by strict constraints. Based on the conflict resolution solution generated above, a specific conflict resolution mechanism is determined. The conflict resolution mechanism refers to how to achieve the final dimming goal through reasonable adjustments and compromises in the case of conflict, usually including dynamic adjustment of multiple weights and goals. Ensure that when multiple goals such as brightness and energy efficiency conflict, the weights or parameters of each goal can be dynamically adjusted to balance the needs of all parties and ultimately reach an optimal solution. The determination of the conflict resolution mechanism ensures that the system can still make reasonable decisions in the case of conflict, and ensures that the final dimming solution meets actual needs and priorities, ensuring that the lighting meets environmental requirements without exceeding energy efficiency limits.

[0085] In one embodiment, after the step of generating a conflict resolution solution including a brightness compromise value and an energy consumption compromise value, the method further includes:

[0086] The conflict resolution solution is injected into the digital twin platform for virtual scene verification. After confirming that there is no risk of equipment overload and light pollution, it is marked as an executable strategy;

[0087] The executable strategy is issued through an independent communication channel, and the brightness balance and energy consumption convergence status after the strategy is executed are monitored in real time.

[0088] As described above, the conflict resolution solution (including brightness and energy compromise values) generated above is input into the digital twin platform for virtual verification. The digital twin platform is a virtual model that simulates a real-world environment to test and analyze the actual impact of different strategies on the system. Equipment overload: This ensures that the lighting equipment will not malfunction due to excessive load when the solution is implemented. Light pollution risk: This test tests whether the solution will cause unnecessary light pollution that could impact the environment and surrounding areas. This virtual verification can identify potential issues in advance and ensure the solution will be successfully implemented in practice. Digital twin technology makes the solution verification process more efficient and safer, avoiding unnecessary losses caused by incorrect solutions in real-world environments. Virtual scene verification ensures the solution's feasibility and reliability. After virtual scene verification, the system conducts a final check of the lighting equipment's load and environmental impact to ensure there are no risks of equipment overload or light pollution. If verification passes, the system marks the conflict resolution solution as "executable." Equipment overload: During verification, the system checks whether the lighting equipment's power exceeds its load capacity to prevent overheating or damage. Light pollution risk: The system also checks whether the new dimming strategy poses any risk of light spillover or impact on the surrounding environment. This step ensures the feasibility of the solution by ensuring it has been fully validated. Confirming that the solution is free of these risks can avoid failures that could occur during the implementation phase, enhancing the security of the implementation process. The strategy is distributed to the actual devices for execution via an independent communication channel (such as a dedicated network or signal channel). During execution, the system monitors two key parameters in real time: Brightness balance: This monitors the uniformity of brightness distribution across the illuminated area to ensure that there are no areas that are too bright or too dark. Energy consumption convergence: This monitors energy consumption trends to ensure that energy consumption meets preset energy-saving targets and does not exceed the set maximum value. Independent communication channels ensure efficient and secure execution of conflict resolution strategies, free from interference from other communications. Independent communication channels protect the system from external network interference during strategy execution, ensuring stable execution. Furthermore, real-time monitoring ensures that the dimming strategy can be adjusted and optimized immediately to avoid deviation from the target.

[0089] In another embodiment,

[0090] Reference Figure 3 In an embodiment of the present application, a multi-dimensional energy consumption statistics and intelligent dimming integrated control system is provided, including:

[0091] The first acquisition module 1 is used to obtain historical energy consumption statistical parameters;

[0092] Analysis module 2, for analyzing energy consumption data of lighting equipment of different equipment types and regions based on the historical energy consumption statistical parameters;

[0093] Identification module 3 is used to identify the periodic change characteristics and sudden characteristics of lighting equipment based on the results of energy consumption data analysis and combined with time series;

[0094] Determination module 4, for determining target energy consumption data of the lighting device based on the periodic change characteristics and the sudden change characteristics;

[0095] The second acquisition module 5 is used to acquire the environmental data of the lighting equipment area in real time and analyze the lighting requirement parameters of the lighting equipment according to the environmental data;

[0096] The generating module 6 is configured to generate lighting adjustment parameters and a lighting adjustment strategy based on the lighting demand parameters and the target energy consumption data.

[0097] As described above, it can be understood that the various components of the multi-dimensional energy consumption statistics and intelligent dimming integrated control system proposed in this application can realize the functions of any of the multi-dimensional energy consumption statistics and intelligent dimming integrated control methods described above, and the specific structure will not be repeated.

[0098] Reference Figure 4 In the embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as monitoring data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a multi-dimensional energy consumption statistics and intelligent dimming integrated control method is implemented.

[0099] The above-mentioned processor executes the above-mentioned multi-dimensional energy consumption statistics and intelligent dimming integrated control method, including: obtaining historical energy consumption statistical parameters; analyzing the energy consumption data of lighting equipment of different equipment types and areas based on the historical energy consumption statistical parameters; identifying the periodic change characteristics and sudden characteristics of the lighting equipment based on the results of the energy consumption data analysis and combined with the time series; determining the target energy consumption data of the lighting equipment based on the periodic change characteristics and sudden characteristics; obtaining environmental data of the lighting equipment area in real time, and analyzing the lighting demand parameters of the lighting equipment based on the environmental data; generating lighting adjustment parameters and lighting adjustment strategies based on the lighting demand parameters and target energy consumption data.

[0100] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a multi-dimensional energy consumption statistics and intelligent dimming integrated control method is implemented, including the following steps: obtaining historical energy consumption statistical parameters; analyzing energy consumption data of lighting equipment of different equipment types and areas based on the historical energy consumption statistical parameters; identifying the periodic change characteristics and sudden characteristics of the lighting equipment based on the results of the energy consumption data analysis and in combination with a time series; determining the target energy consumption data of the lighting equipment based on the periodic change characteristics and sudden characteristics; obtaining environmental data of the lighting equipment area in real time, and analyzing the lighting demand parameters of the lighting equipment based on the environmental data; and generating lighting adjustment parameters and a lighting adjustment strategy based on the lighting demand parameters and the target energy consumption data.

[0101] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM), etc.

[0102] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0103] The above description is only a preferred embodiment of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A multi-dimensional energy consumption statistics and intelligent dimming integrated control method, characterized in that: The method comprises: Obtain historical energy consumption statistical parameters; Analyze the energy consumption data of lighting equipment of different equipment types and regions based on the historical energy consumption statistical parameters; Based on the results of energy consumption data analysis and combined with time series, the periodic change characteristics and sudden characteristics of lighting equipment are identified; Determining target energy consumption data of the lighting equipment based on the periodic change characteristics and the sudden change characteristics; Acquire environmental data of the lighting equipment area in real time, and analyze the lighting requirement parameters of the lighting equipment based on the environmental data; Based on the lighting demand parameters and the target energy consumption data, lighting adjustment parameters and a lighting adjustment strategy are generated.

2. The multi-dimensional energy consumption statistics and intelligent dimming integrated control method according to claim 1 is characterized in that: The step of determining target energy consumption data of the lighting device based on the periodic change characteristics and the sudden change characteristics includes: According to the periodic change characteristics, the energy consumption temporal pattern is divided to generate a basic energy consumption curve including a weekday benchmark, a holiday benchmark, and a seasonal benchmark; Marking the abnormal event period corresponding to the sudden feature on the basic energy consumption curve, and recording the energy consumption deviation amplitude during the event; Extract patterns from recurring abnormal events to form a predictable energy consumption correction factor library; The basic energy consumption curve matching the current period and the adjustment amount of related events in the correction factor library are superimposed to generate a dynamic target energy consumption baseline; Performing a health status assessment on the lighting equipment; Based on the latest health status assessment results of the lighting equipment, the dynamic target energy consumption baseline is attenuated and compensated, and the target energy consumption data with an allowable deviation range is output.

3. The multi-dimensional energy consumption statistics and intelligent dimming integrated control method according to claim 2 is characterized in that: The steps of acquiring environmental data of the lighting equipment area in real time and analyzing the lighting requirement parameters of the lighting equipment according to the environmental data include: Determine ambient light intensity distribution data, personnel dynamic distribution data, and real-time monitoring values of natural light sources based on the environmental data; Divide functional areas according to the dynamic distribution data of personnel, and match the lighting requirements of each functional area according to preset standards; Based on the real-time monitoring value of the natural light source, obtaining historical time series data corresponding to the real-time monitoring value of the natural light source; Combining the historical time series data with the real-time monitoring value of natural light sources, predicting the natural light attenuation within a future preset time period; Calculate the artificial lighting requirements for each functional area by combining the current ambient light intensity distribution with the predicted natural light attenuation; Integrate the lighting requirement level and artificial lighting requirement of the functional area to generate a dynamic lighting requirement parameter set; Obtain the operating status parameters of lighting equipment in real time, including current brightness output value, power consumption value and dimmer working mode; The operating state parameters are compared with the dynamic lighting demand parameter set to output the lighting gap parameters of each area, including brightness gap value and energy consumption gap value.

4. The multi-dimensional energy consumption statistics and intelligent dimming integrated control method according to claim 3 is characterized in that: After the step of generating the lighting adjustment parameters and the lighting adjustment strategy based on the lighting demand parameters and the target energy consumption data, the method further includes: Receive trigger signals of sudden environmental events in real time; When an emergency environmental event trigger signal is received, the emergency environmental event trigger signal is parsed to extract an event type identifier and an impact parameter set, wherein the event type includes sudden weather change, temporary activities, and security alerts; Retrieve the preset emergency lighting template according to the event type identifier to cover the original lighting requirement level of the corresponding functional area; Analyzing the spatial range and duration parameters within the impact parameter set; Correcting the artificial fill light requirement based on the spatial range and duration parameters; The corrected artificial fill light requirement and emergency lighting template are reinjected into the dynamic lighting requirement parameter set to generate an emergency adjustment parameter package; The emergency channel transmission mechanism is started, and the emergency adjustment parameter package is executed first.

5. The multi-dimensional energy consumption statistics and intelligent dimming integrated control method according to claim 3 is characterized in that: The step of generating lighting adjustment parameters and lighting adjustment strategies based on the lighting demand parameters and target energy consumption data includes: Construct a multi-dimensional constraint set, including brightness gap parameters, target energy consumption tolerance range, and device dimming step size limit; Based on the multi-dimensional constraint condition set, generating a set of candidate dimming schemes within a rolling optimization window, each scheme including a brightness adjustment value and an expected energy consumption change; Performing compliance rate prediction on the candidate dimming solution set to screen out a subset of feasible solutions that both meet the brightness gap compensation requirements and meet the preset brightness gap compensation requirements, and the energy consumption change amount meets the target energy consumption deviation limit; Extracting light spot distribution characteristics from the subset of feasible solutions, and eliminating solutions with illumination uniformity lower than a preset uniformity threshold based on the light spot distribution characteristics to obtain an optimized dimming solution; Based on the optimized dimming solution, light adjustment parameters are determined.

6. The multi-dimensional energy consumption statistics and intelligent dimming integrated control method according to claim 5, characterized in that: The generation of the illumination adjustment strategy also includes a conflict resolution mechanism, and the steps include: When a conflict signal between the brightness gap compensation requirement and the target energy consumption deviation tolerance is detected, the brightness gap value and energy consumption overflow of the conflicting area are extracted; Selecting a dominant optimization objective according to a preset priority rule base, wherein the priority rule base includes a safety priority mode, a comfort priority mode, and an energy saving priority mode; Acquire power grid load status and personnel distribution density data in real time, and dynamically adjust the weight coefficient of the dominant optimization objective according to the power grid load status and personnel distribution density data; Set a flexible compromise interval for non-dominant optimization objectives and generate a conflict resolution solution that includes brightness compromise values and energy consumption compromise values; A conflict resolution mechanism is determined based on the conflict resolution scheme.

7. The multi-dimensional energy consumption statistics and intelligent dimming integrated control method according to claim 6, characterized in that: After the step of generating a conflict resolution solution including a brightness compromise value and an energy consumption compromise value, the method further includes: The conflict resolution solution is injected into the digital twin platform for virtual scene verification. After confirming that there is no risk of equipment overload and light pollution, it is marked as an executable strategy; The executable strategy is issued through an independent communication channel, and the brightness balance and energy consumption convergence status after the strategy is executed are monitored in real time.

8. A multi-dimensional energy consumption statistics and intelligent dimming integrated control system, characterized in that: include: The first acquisition module is used to obtain historical energy consumption statistical parameters; An analysis module, configured to analyze energy consumption data of lighting equipment of different equipment types and regions based on the historical energy consumption statistical parameters; The recognition module is used to identify the periodic change characteristics and sudden characteristics of lighting equipment based on the results of energy consumption data analysis and time series; A determination module, configured to determine target energy consumption data of the lighting device based on the periodic change characteristics and the sudden change characteristics; The second acquisition module is used to acquire the environmental data of the lighting equipment area in real time and analyze the lighting requirement parameters of the lighting equipment according to the environmental data; A generation module is used to generate lighting adjustment parameters and lighting adjustment strategies based on the lighting demand parameters and target energy consumption data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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