Energy consumption prediction method and device of intelligent lighting management system, and storage medium

Abstract

The invention provides an energy consumption prediction method and device of an intelligent lighting management system and a storage medium, and relates to the field of data processing. The method comprises the following steps: acquiring environment data sent by an intelligent lighting management system; according to the environment data, current control input data is obtained through calculation by adopting a system state equation; calculating to obtain current energy consumption data based on the current control input data; obtaining target energy consumption data of the intelligent lighting management system; calculating an energy consumption deviation between the target energy consumption data and the current energy consumption data; generating target control input data based on a size relationship between the energy consumption deviation and a preset threshold value; and performing energy consumption management on the intelligent lighting management system by adopting the target control input data. By implementing the technical scheme provided by the invention, the accuracy of energy consumption prediction can be improved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly relates to an energy consumption prediction method, device, and storage medium for an intelligent lighting management system. Background Art

[0002] The essence of energy consumption prediction lies in estimating the current and future energy consumption states of the system, calculating the optimal control input for adjusting the operation parameters of the lighting system, so as to achieve effective management of the overall energy consumption.

[0003] Currently, intelligent lighting management systems mainly rely on manual or semi-automatic energy consumption prediction methods to calculate the control input of the system, so as to achieve the regulation of lighting brightness, switch state, and operation mode. However, due to environmental factors (such as light, temperature, humidity, etc.) and the variability of manual prediction behavior, traditional manual energy consumption prediction often relies on empirical judgment and is difficult to capture the dynamic changes of the environment in real time, resulting in low accuracy of energy consumption prediction results.

[0004] Therefore, there is an urgent need for an energy consumption prediction method, device, and storage medium for an intelligent lighting management system. Summary of the Invention

[0005] This application provides an energy consumption prediction method, device, and storage medium for an intelligent lighting management system, which is convenient for improving the accuracy of energy consumption prediction.

[0006] In the first aspect of this application, an energy consumption prediction method for an intelligent lighting management system is provided. The method includes: obtaining environmental data sent by the intelligent lighting management system; calculating the current control input data according to the environmental data by using the system state equation; calculating the current energy consumption data based on the current control input data; obtaining the target energy consumption data of the intelligent lighting management system; calculating the energy consumption deviation between the target energy consumption data and the current energy consumption data; generating target control input data based on the magnitude relationship between the energy consumption deviation and a preset threshold; and performing energy consumption management on the intelligent lighting management system by using the target control input data.

[0007] By adopting the above technical solutions, by acquiring environmental data and system status data, the system can autonomously calculate control inputs without manual intervention, improving the degree of automation. Using the system state equation to calculate control inputs makes the control strategy based on scientific calculations rather than empirical judgments, enhancing the rationality and interpretability of decision-making. By calculating the deviation between the target energy consumption and the current energy consumption, the lighting strategy can be dynamically adjusted, enabling the system energy consumption to gradually approach the optimal state, thereby improving the energy-saving effect. Error feedback can ensure that the system can adaptively adjust under different environmental conditions, improving the adaptability of intelligent lighting. By judging the relationship between the energy consumption deviation and the preset threshold, the system can flexibly decide whether to adjust the control input, avoiding over-regulation or frequent adjustment, and improving the system stability and response efficiency. This mechanism can also adapt to different energy-saving strategies, such as a more stringent energy-saving mode or a more comfortable lighting mode, meeting the requirements of different scenarios. Through the calculation of adaptive control inputs, the system can effectively reduce energy consumption while ensuring lighting quality, achieving a balance between energy saving and comfort. Therefore, it is convenient to improve the accuracy of energy consumption prediction.

[0008] Optionally, the acquiring the environmental data sent by the intelligent lighting management system specifically includes: receiving the ambient light intensity, temperature, and humidity sent by the sensors in the intelligent lighting management system; receiving the switch status and brightness level sent by the landscape lighting devices in the intelligent lighting management system; and obtaining the environmental data through data processing based on the ambient light intensity, the temperature, the humidity, the switch status, and the brightness, where the data processing includes denoising, filtering, and normalization processing.

[0009] By adopting the above technical solutions, simultaneously receiving the data sent by the environmental sensors (ambient light intensity, temperature, humidity) and the landscape lighting devices (switch status, brightness level) can comprehensively reflect the environmental and equipment status from multiple perspectives, providing a rich information basis for the system. By performing denoising and filtering on the original data, sensor noise and abnormal data can be effectively removed, improving the accuracy and reliability of the data, thus providing a solid foundation for subsequent energy consumption prediction and control. Normalization processing converts data with different dimensions and value ranges to a unified scale, facilitating subsequent data fusion and analysis, ensuring that all data is compared and calculated under the same standard, and avoiding affecting the prediction results due to excessive numerical differences. By acquiring and processing environmental data in real time, the system can quickly capture changes in the environment and equipment status, thereby achieving more accurate and real-time energy consumption prediction and control input calculation, and enhancing the overall response speed and regulation effect of the intelligent lighting management system. After being effectively processed, high-quality environmental data can significantly reduce the cumulative effect of errors in subsequent prediction and control calculations, ensuring that the system performs precise regulation according to the real environmental status, and achieving energy saving and efficient operation.

[0010] Optionally, based on the environmental data, the current control input data is calculated using the system state equation, and the specific calculation is performed using the following equation: ; where, is the system state vector at the current time t obtained by converting the environmental data, is the derivative of the system state vector at the current time t, is the current control input data, is the environmental noise is the intensity coefficient.

[0011] By adopting the above technical solution, the system state vector at the current time is obtained by converting the environmental data, which can capture the impact of environmental changes on the system state in real time, enabling the system to respond promptly to external changes. The introduction of the state vector and its derivative can comprehensively describe the dynamic evolution and change trend of the system, ensuring that the calculation of the control input is based on the actual operating conditions of the system. The intensity coefficient of the environmental noise is incorporated into the calculation, which can consider external disturbances and uncertainty factors in the model, improving the robustness and adaptability of the control input calculation. The calculation method based on the system state equation provides theoretical and data support for the intelligent lighting management system, enabling the generation of control input data not only to rely on empirical judgment but also to be based on a rigorous mathematical model, thereby improving the accuracy of energy consumption prediction and management.

[0012] Optionally, based on the current control input data, the current energy consumption data is calculated, and the specific calculation is performed using the following formula: ; where, is the current energy consumption data, represents the mapping relationship between the current energy consumption data and the current control input data.

[0013] By adopting the above technical solution, by establishing the mapping relationship between energy consumption data and control input data, the error based on empirical judgment is avoided, and the interpretability and accuracy of the calculation are improved. This method calculates energy consumption based on mathematical formulas, rather than simple historical data statistics or manual estimation, making the energy consumption prediction more accurate. Using a mathematical mapping function to calculate energy consumption can systematically consider various influencing factors and ensure the rationality of the calculation. Compared with the traditional empirical-based energy consumption estimation method, this mathematical calculation method can reduce error accumulation, improve the prediction ability and robustness of the system. Since the energy consumption calculation formula directly depends on the control input, this means that energy consumption prediction and control decision-making can be carried out synchronously, improving the response efficiency of the system. In this way, the adjustment of the control input can be directly reflected in the energy consumption calculation, thereby optimizing energy consumption management. Since it is a mapping relationship, it can be adjusted according to different lighting equipment types, power characteristics, and environmental influencing factors. Through the accurate calculation of energy consumption, the system can more accurately predict the actual energy consumption situation and avoid energy waste. Combined with closed-loop feedback control, the control input can be dynamically adjusted to keep the lighting system always in the optimal energy consumption state, reduce power consumption, and improve the overall energy efficiency of the system.

[0014] Optionally, generating target control input data based on the magnitude relationship between the energy consumption deviation and a preset threshold specifically includes: if it is determined that the energy consumption deviation is greater than or equal to the preset threshold, calculating first control input data by using a preset first method; if it is determined that the energy consumption deviation is less than the preset threshold, calculating second control input data by using a preset second method, where the target control input data includes the first control input data and the second control input data.

[0015] By adopting the above technical solution, by judging the magnitude of the energy consumption deviation through a threshold, different control strategies are distinguished, enabling the system to adopt different adjustment methods according to the actual deviation magnitude and avoiding one-size-fits-all regulation. The difference between the first control input method and the second control input method makes the regulation more refined, improving the accuracy and adaptability of the adjustment. When the energy consumption deviation is less than the threshold, the system adopts the second control input method, indicating that the system state basically meets the target energy consumption and no drastic adjustment is required, avoiding instability caused by frequent changes. Only when the energy consumption deviation is greater than the threshold, the first control input method is adopted for large-scale adjustment to ensure that the adjustment is necessary and prevent overcorrection or system oscillation. By intelligently selecting control input data, both energy consumption management can be optimized and the lighting effect can be ensured not to be affected. This method can be applied to intelligent lighting management systems of various scales, whether it is small-scale campus lighting or large-scale urban landscape lighting, and energy consumption regulation can be carried out based on the same logic. The calculation methods of the first control input method and the second control input method can be adjusted according to the characteristics of different lighting equipment to ensure the universality of the solution.

[0016] Optionally, the preset first method is specifically calculated using the following formula: ; Wherein, is the first control input data, is the energy-saving control coefficient, is the target energy consumption data, is the exponential decay factor, and t is the current time.

[0017] By adopting the above technical solution, the exponential decay factor makes the control input data gradually tend to be stable during the adjustment process, avoiding the impact on the system caused by sudden large-scale adjustments, and improving the smoothness of control. Compared with the linear adjustment method, the exponential decay method can better adapt to complex environmental changes. Especially in the landscape lighting system, it can prevent sudden changes in light from affecting the aesthetics and user experience. It is directly linked to the target energy consumption data in the formula, ensuring that the control input direction of the system is always optimized around the target energy consumption, so that the system energy consumption gradually tends to a reasonable level. This method can ensure the accuracy of energy consumption optimization, avoiding resource waste or light pollution problems caused by blind adjustments. The energy-saving control coefficient can flexibly adjust the energy consumption optimization strategy, and set different energy-saving intensities according to different scenario requirements. This method allows the system to dynamically adjust the energy-saving strategy according to factors such as scenarios, time periods, weather, and user needs, improving the self-adaptability of the system. The exponential decay model is characterized by a relatively fast adjustment speed in the initial stage and tending to be stable in the later stage, which helps to quickly respond when the energy consumption deviation is large, and reduce unnecessary adjustments after tending to be stable. This can avoid the oscillation phenomenon caused by over-adjustment, and still maintain the optimized state after long-term operation, improving the long-term stability of energy consumption management.

[0018] Optionally, the preset second method is specifically calculated using the following formula: ; Wherein, is the second control input data, k is the feedback gain coefficient, a is the dynamic response coefficient, is the target energy consumption data, is the current energy consumption data, is the energy consumption change rate.

[0019] By adopting the above technical solutions, the formula uses a feedback gain coefficient, enabling the system to automatically adjust according to the deviation between the current energy consumption data and the target energy consumption data. This closed-loop feedback control method can ensure that the system is always optimized around the target energy consumption, avoiding out-of-control energy consumption or light pollution caused by long-term deviation accumulation. The feedback control can also prevent oscillations or mutations during the energy consumption management process, ensuring the stable operation of the lighting system. Through the dynamic response coefficient, the system can adaptively adjust the correction amplitude of the control input according to the magnitude of the current energy consumption change rate. If the energy consumption change rate is large, the system will respond faster and adjust the control input in a timely manner to prevent the energy consumption from deviating too much from the target value. If the energy consumption change rate is small, the system will make smooth fine-tuning to avoid unnecessary frequent adjustments and improve the control stability. This method ensures that the energy consumption management can respond quickly to sudden changes and maintain precise control when stable. When calculating the control input, this method does not simply force the reduction of energy consumption, but makes reasonable adjustments through the feedback gain coefficient and the dynamic response coefficient to ensure that the lighting effect is not affected.

[0020] In the second aspect of the present application, an energy consumption prediction device for an intelligent lighting management system is provided. The device includes an acquisition module and a processing module. Among them, the acquisition module is used to acquire the environmental data sent by the intelligent lighting management system; the processing module is used to calculate the current control input data according to the environmental data by using the system state equation; the processing module is also used to calculate the current energy consumption data based on the current control input data; the acquisition module is also used to acquire the target energy consumption data of the intelligent lighting management system; the processing module is also used to calculate the energy consumption deviation between the target energy consumption data and the current energy consumption data; the processing module is also used to generate target control input data based on the magnitude relationship between the energy consumption deviation and a preset threshold; the processing module is also used to perform energy consumption management on the intelligent lighting management system by using the target control input data.

[0021] In the third aspect of the present application, an electronic device is provided. The electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. The user interface and the network interface are both used to communicate with other devices. The processor is used to execute the instructions stored in the memory so that the electronic device executes the method described above.

[0022] In the fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed, the method described above is executed.

[0023] In summary, one or more technical solutions provided in the present application have at least the following technical effects or advantages: By obtaining environmental data (such as light, temperature, humidity) and system status data (such as brightness level, switch status), the system can autonomously calculate control inputs without manual intervention, improving the degree of automation. Using the system state equation to calculate control inputs makes the control strategy based on scientific calculations rather than empirical judgments, enhancing the rationality and interpretability of decision-making. By calculating the deviation between the target energy consumption and the current energy consumption, the lighting strategy can be dynamically adjusted, enabling the system's energy consumption to gradually approach the optimal state, thereby improving the energy-saving effect. Error feedback can ensure that the system can adaptively adjust under different environmental conditions, improving the adaptability of intelligent lighting. By judging the relationship between the energy consumption deviation and the preset threshold, the system can flexibly decide whether to adjust the control input, avoiding over-regulation or frequent adjustment, and improving the system stability and response efficiency. This mechanism can also adapt to different energy-saving strategies, such as a more stringent energy-saving mode or a more comfortable lighting mode, to meet the needs of different scenarios. Through the calculation of adaptive control inputs, the system can effectively reduce energy consumption while ensuring lighting quality, achieving a balance between energy saving and comfort. Therefore, it is convenient to improve the accuracy of energy consumption prediction. Description of the Drawings

[0024] Figure 1 It is a schematic flowchart of an energy consumption prediction method for an intelligent lighting management system provided by an embodiment of the present application; Figure 2 It is another schematic flowchart of an energy consumption prediction method for an intelligent lighting management system provided by an embodiment of the present application; Figure 3 It is a schematic block diagram of an energy consumption prediction device for an intelligent lighting management system provided by an embodiment of the present application; Figure 4 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0025] Description of the reference numerals: 31, acquisition module; 32, processing module; 41, processor; 42, communication bus; 43, user interface; 44, network interface; 45, memory. Detailed Embodiments

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0027] In the description of the embodiments of the present application, words such as "for example" or "for instance" are used to give examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "for example" or "for instance" is intended to present relevant concepts in a specific manner.

[0028] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0029] The core objective of energy consumption prediction is to calculate the optimal control input by accurately estimating the current and future energy consumption states of the system, so as to dynamically adjust the operating parameters of the lighting system, thereby achieving efficient energy consumption management.

[0030] The current intelligent lighting management system mainly relies on manual or semi-automatic energy consumption prediction methods to determine the control input for adjusting the lighting brightness, switch state and operating mode. However, affected by changes in environmental factors (such as light, temperature, humidity, etc.) and the dependence of manual prediction methods on empirical judgment, these traditional methods are difficult to respond to complex environmental dynamics in real time. Due to the lack of accurate modeling and real-time adjustment capabilities for energy consumption changes, manual prediction often has large errors, resulting in insufficient accuracy of energy consumption regulation and affecting the overall energy-saving effect.

[0031] To solve the above technical problems, the present application provides an energy consumption prediction method for an intelligent lighting management system, referring to Figure 1 , Figure 1 is a schematic flow chart of an energy consumption prediction method for an intelligent lighting management system provided by an embodiment of the present application. This method is applied to a server and includes steps S110 to S170. The above steps are as follows: S110. Obtain the environmental data sent by the intelligent lighting management system.

[0032] Specifically, the server mainly obtains environmental data from various sensors and devices in the intelligent lighting system. These sensors can be installed on the lighting device body, lamp posts, the surrounding environment, or independent data acquisition terminals. The main data types include: Ambient light intensity: Used to determine the current external lighting level to decide whether to turn on or adjust the lighting brightness; Temperature: High temperature may affect the heat dissipation efficiency of the lamp, and at the same time, the power consumption of some intelligent lamps is affected by temperature; Humidity: A humid environment may affect the performance of the lamp, even causing light decay or short circuit; Lighting device status: Switch status: Whether the current light is on; Brightness level: The brightness output level of the current light, used to analyze the power consumption level. These data are transmitted to the server through wireless communication (Wi-Fi, LoRa, NB-IoT, Zigbee, 5G) or wired network (RS485, Ethernet). Among them, the server can obtain data in a timed polling or event-triggered manner: Timed polling: The server actively queries the sensor status at regular intervals, for example, requests the latest environmental data every 10 seconds. Event trigger: When the ambient light intensity changes drastically, the sensor actively reports new data to the server. For example, when it changes from day to night, the illuminance drops rapidly, triggering data upload.

[0033] In a possible implementation manner, obtaining the environmental data sent by the intelligent lighting management system specifically includes: receiving the ambient light intensity, temperature, and humidity sent by the sensors in the intelligent lighting management system; receiving the switch status and brightness level sent by the landscape lighting devices in the intelligent lighting management system; and obtaining the environmental data through data processing based on the ambient light intensity, temperature, humidity, switch status, and brightness. The data processing includes denoising, filtering, and normalization processing.

[0034] Specifically, the intelligent lighting management system mainly obtains data from two types of devices: Sensors (environmental perception devices) Ambient light sensor: Used to measure the current external light intensity (unit: Lux). Temperature sensor: Detects the current ambient temperature (unit: °C). Humidity sensor: Measures the air humidity (unit: %RH). Landscape lighting devices (controlled lamps) Switch status: Records whether the lamp is on (ON) or off (OFF). Brightness level: Indicates the current light brightness output (0%-100%). After these data are collected by the sensors and lamps, they are transmitted to the server of the intelligent lighting management system through wireless communication (such as NB-IoT, LoRa, Zigbee) or wired communication (such as RS485, Ethernet) for further energy consumption calculation and optimization control.

[0035] Among them, the original environmental data usually contains noise or outliers and must be preprocessed to improve the accuracy of calculations. Here are three key data processing methods: Denoising: Abnormal data is removed. For example, a drastic fluctuation in light intensity within a short period may be caused by sensor failure or external interference (such as headlight illumination) and needs to be smoothed. Filtering: Mean filtering or Kalman filtering is used to reduce measurement errors and make the data more stable. For example, the instantaneous jitter of a temperature and humidity sensor can be processed by filtering to obtain more reliable environmental data. Normalization: Data with different units (such as illuminance Lux, temperature °C, humidity %RH) is standardized so that subsequent calculation models can uniformly process it. For example, normalizing the light intensity of 0 - 50000 Lux to between 0 and 1 makes the numerical scale of the model inputs consistent and avoids problems of overly large or small data weights.

[0036] S120. Calculate the current control input data according to the environmental data using the system state equation.

[0037] Specifically, the environmental light intensity, temperature, humidity, etc. data collected by the sensor represents the current external environmental state. The information such as the switch state and brightness level feedback by the lighting fixture itself represents the current working state of the lighting system. The system state equation is a dynamic equation used to describe the relationship between the state of the lighting system changing over time and its control input.

[0038] In a possible implementation, calculate the current control input data according to the environmental data using the system state equation, and specifically calculate using the following equation: ; Where, is the system state vector at the current moment t converted from the environmental data, is the derivative of the system state vector at the current moment t, is the current control input data, is the environmental noise is the intensity coefficient.

[0039] Specifically, the system state vector is converted from the environmental data and represents the state of the current lighting system. The system state change rate indicates how the system state changes over time. The current control input data is the lighting adjustment parameter calculated by the server (such as adjusting the brightness or switch state). The environmental noise term represents the uncertain factors of the environment (such as sudden light changes or sensor errors). The system state equation describes how the lighting system adjusts according to the current state and control input.

[0040] For example, automatic adjustment in the evening. At 18:30 in the evening, the ambient light drops to 30 Lux. The server hopes to ensure that the ground illumination intensity is not less than 80 Lux while saving energy. The server obtains environmental data: illumination 30 Lux, temperature 22 °C, humidity 55%RH, current brightness 70%. The calculated control input is 25.07. Server instruction: Increase the brightness to 90% and maintain the ground illuminance.

[0041] S130. Calculate the current energy consumption data based on the current control input data.

[0042] Specifically, the lighting control instructions calculated by the server based on the environmental state, such as light brightness adjustment, switch status, etc. According to the power characteristics of the lighting equipment, calculate its power consumption under the current input. Current energy consumption data: The power consumption situation of the lighting system at the current moment finally obtained, and the unit is usually watt (W) or kilowatt-hour (kWh). Calculate the real-time power consumption at different brightness levels through a mathematical model to avoid waste. Traditional calculations only focus on instantaneous energy consumption, while this method additionally calculates the additional energy consumption brought by brightness changes to improve prediction accuracy. Applicable to different types of lighting equipment such as LED, sodium lamp, intelligent dimming lamp, etc. The server can predict the future energy consumption trend based on the calculation results, adjust the brightness in advance, and further optimize the energy-saving strategy.

[0043] In a possible implementation manner, calculate the current energy consumption data based on the current control input data, and specifically use the following formula for calculation: ; Where is the current energy consumption data, represents the mapping relationship between the current energy consumption data and the current control input data.

[0044] Specifically, this paragraph describes how the intelligent lighting management system calculates the current energy consumption data based on the current control input data and gives an energy consumption calculation formula. The lighting adjustment parameters calculated by the server, such as light brightness, switch status, etc. Energy consumption calculation relationship: The energy consumption data depends on the current control input and is mapped through a certain function. Final energy consumption data: The server calculates the energy consumption value at the current moment, and the unit is usually watt (W) or kilowatt-hour (kWh). The energy consumption data at the current moment t (unit: W or kWh). Energy consumption calculation function, representing the relationship between energy consumption and control input data. This function can be a linear function or a non-linear function. In most LED lighting systems, the relationship between energy consumption and brightness is usually not completely linear, but follows a power function or an exponential relationship. Current control input data, such as the brightness level of the light (0 - 100%).

[0045] For example, Example 1: The energy consumption calculation scenario of a square lighting system. Lighting equipment: LED street lights, with a maximum power of 100W, and the current control input: the server calculates a brightness of 80%. Assuming that the energy consumption function is a linear model, the current energy consumption data calculated by the server is 80W. Therefore, the actual power consumption at 80% brightness is 80W. Example 2: The scenario of park landscape lights (non-linear energy consumption). Lighting equipment: LED landscape lights, with a maximum power of 200W. Current control input: the server calculates a brightness of 50%. Assuming that the energy consumption function of the LED lighting system is a non-linear model with an exponent of 1.2, the current energy consumption data calculated by the server is approximately 87W. Therefore, at 50% brightness, the actual energy consumption is less than 100W but still higher than 50W, which conforms to the non-linear power consumption characteristics of LED lighting.

[0046] S140. Obtain the target energy consumption data of the intelligent lighting management system.

[0047] Specifically, the server obtains the target energy consumption data of the intelligent lighting management system, that is, the server reads the preset target energy consumption value from the system as the benchmark for optimizing the control strategy. As the energy consumption value in the ideal state, the server needs to adjust the operating parameters of the lighting equipment to make the actual energy consumption as close as possible to this target value. The target energy consumption data can be manually set by the system administrator or generated by methods such as historical data analysis and AI prediction. The server uses the target energy consumption data to compare with the current energy consumption data and calculate the energy consumption deviation for adjusting the lighting control strategy. The target energy consumption data can be obtained from the following methods: The preset target value is manually set. For example, if the administrator hopes that the park landscape lighting remains at 200 kWh from 20:00 to 23:00 every night, the server directly reads this preset value. Historical data statistics (setting the target based on past energy consumption situations). For example, the average nighttime lighting energy consumption of a certain square in the past 30 days is 150 kWh, and the server can automatically set 150 kWh as the target value. Or the server dynamically calculates the optimal energy consumption target for the day based on data such as weather, holidays, and tourist flow. For example, when it is raining, the brightness is appropriately reduced, and the target value is set to 120 kWh.

[0048] S150. Calculate the energy consumption deviation between the target energy consumption data and the current energy consumption data.

[0049] Specifically, the server calculates the energy consumption deviation between the target energy consumption data and the current energy consumption data, that is, the server compares the target energy consumption of the system (the energy consumption value in the ideal state) with the current actual energy consumption and calculates the difference between the two as the basis for subsequent adjustment of the lighting control strategy. Therefore, by calculating the deviation, the server can accurately control the lighting equipment and avoid unnecessary energy waste. The server can dynamically adjust the lighting brightness and switching time to adapt to different environmental requirements. If the energy consumption exceeds the standard, the server can reduce the brightness or adjust the lighting mode to achieve energy conservation and consumption reduction.

[0050] S160. Generate target control input data based on the magnitude relationship between the energy consumption deviation and a preset threshold.

[0051] Specifically, the server generates target control input data based on the magnitude relationship between the energy consumption deviation and a preset threshold. That is, the server first calculates the deviation between the current energy consumption and the target energy consumption, and then determines how to adjust the control parameters of the lighting system (such as brightness, switch time, etc.) according to whether this deviation value exceeds the preset threshold, so as to ensure that the energy consumption is within a reasonable range. If the deviation is large, it indicates that the system deviates from the target and requires a large adjustment. If the deviation is small, it indicates that the system is close to the target and only requires fine adjustment or no adjustment. The preset threshold is used to determine whether the deviation is acceptable. If it exceeds the threshold, the control input data needs to be adjusted; otherwise, the system maintains its current state. According to different situations, the server calculates new control inputs for adjusting the lighting system, such as changing the light brightness, adjusting the switch time, optimizing the lighting mode, etc.

[0052] In a possible implementation manner, refer to Figure 2 , Figure 2 FIG. is another flowchart of an energy consumption prediction method for an intelligent lighting management system provided by an embodiment of this application. Generate target control input data based on the magnitude relationship between the energy consumption deviation and a preset threshold, which specifically includes steps S210 to S220. The above steps are as follows: S210. If it is determined that the energy consumption deviation is greater than or equal to the preset threshold, calculate the first control input data using a preset first method; S220. If it is determined that the energy consumption deviation is less than the preset threshold, calculate the second control input data using a preset second method. The target control input data includes the first control input data and the second control input data.

[0053] Specifically, the server needs to determine how to adjust the operating parameters of the intelligent lighting system according to the deviation between the current energy consumption data and the target energy consumption data. If the energy consumption deviation is greater than or equal to the preset threshold, it indicates that the current energy consumption exceeds the reasonable range and a large adjustment (such as significantly reducing the light brightness, shortening the lighting time, etc.) needs to be made using a preset first method. If the energy consumption deviation is less than the preset threshold, it indicates that the current energy consumption is already close to the target value and only a fine adjustment (such as slightly reducing the brightness, delaying the light-off time, etc.) needs to be made using a preset second method. The server finally determines the target control input data based on the calculated first control input data and second control input data, and sends it to the intelligent lighting management system to achieve energy consumption optimization.

[0054] For example, Example 1: Park landscape lights. Scenario: The target energy consumption of the night lighting system in a certain park is 200 kWh, and the current actual energy consumption is 260 kWh, and the threshold is set at 20 kWh.

[0055] Calculate the energy consumption deviation of -60 kWh. The deviation exceeds the threshold, and significant adjustment is required. Adjustment plan (preset first method): Reduce the brightness of park landscape lights by 40%, turn off the lights in non-primary areas 1 hour in advance, enable the induction mode, and only turn on the lights when there are pedestrians. After execution, the energy consumption is reduced to 200 kWh, meeting the target requirements.

[0056] Example 2: LED advertising screen in a commercial street. Scenario: The target energy consumption of the LED advertising screen in a certain commercial street is 500 kWh, the current actual energy consumption is 510 kWh, and the threshold is set at 20 kWh. Calculate the energy consumption deviation of -10 kWh. The deviation is within the threshold range, and only fine-tuning is required. Adjustment plan (preset second method): Reduce the brightness of the LED screen by 5% and shorten the advertising playback time by 10 minutes. After execution, the energy consumption is reduced to 500 kWh, meeting the target requirements.

[0057] In a possible implementation, the preset first method is specifically calculated using the following formula: ; where is the first control input data, is the energy-saving control coefficient, is the target energy consumption data, is the exponential decay factor, and t is the current time.

[0058] Specifically, this description explains the method of calculating the first control input data by the preset first method. The core idea is: When the energy consumption deviation exceeds the set threshold, a significant adjustment strategy needs to be adopted to optimize the energy consumption. The first control input data (i.e., the control signal for the intelligent lighting system, such as adjusting the brightness, switch time, etc.). The energy-saving control coefficient determines the impact of the control input on the energy consumption, and the larger the value, the greater the adjustment amplitude. The target energy consumption data is the energy consumption level that the intelligent lighting system expects to achieve. The exponential decay factor controls the time decay rate of the adjustment amplitude, and the larger the value, the faster the energy consumption adjustment speed. The current time, as time progresses, the adjustment strength gradually decays to ensure the stability of the control. This formula adopts an exponential decay model, which means that the adjustment amplitude decreases with time, preventing the system from experiencing severe oscillations after the initial adjustment and ensuring that the energy consumption gradually approaches the target value. The server detects that the energy consumption deviation exceeds the threshold. Calculate the first control input data and determine the adjustment amplitude based on the target energy consumption, time decay effect, and energy-saving coefficient. The server sends an adjustment instruction to the intelligent lighting system, such as: lowering the light brightness, shortening the lighting time, and turning off some lighting areas. As time goes by, the exponential decay factor makes the adjustment strength gradually decrease to ensure smooth control.

[0059] In a possible implementation, the preset second method is specifically calculated using the following formula: ; Among them, is the second control input data, k is the feedback gain coefficient, a is the dynamic response coefficient, is the target energy consumption data, is the current energy consumption data, is the energy consumption change rate.

[0060] Specifically, this description introduces the calculation method of the preset second method, that is, when the energy consumption deviation is less than the set threshold, a feedback control method is used to calculate the second control input data to fine-tune the energy consumption control strategy of the intelligent lighting system. The feedback gain coefficient determines the response intensity of the system to the energy consumption deviation. aaa: The dynamic response coefficient determines the adjustment strength of the system to the energy consumption change rate. The target energy consumption is the energy consumption value that the intelligent lighting system expects to achieve. The current energy consumption is the actual energy consumption value currently measured by the system. The energy consumption change rate is the change speed of the current energy consumption data over time, reflecting the dynamic trend of energy consumption. This method uses feedback control + dynamic adjustment to ensure that during the energy consumption fine-tuning process, it can respond in a timely manner without generating excessive fluctuations. When the energy consumption deviation is small, the system does not need to make drastic adjustments, but gradually makes the energy consumption approach the target value through small adjustments (such as adjusting the brightness by 5% and optimizing the lighting duration by 10 minutes, etc.). The server determines that the energy consumption deviation is less than the threshold and enters the fine-tuning mode. Calculate the second control input data and adjust the lighting parameters to make the system slowly converge to the target energy consumption. The server sends a fine-tuning instruction to the intelligent lighting system, such as: slightly adjusting the light brightness, optimizing the switch time, and adjusting the induction lamp trigger threshold. The server continuously monitors the energy consumption change. If there is still a deviation, continue to make fine adjustments to ensure the stability of the system.

[0061] S170. Use the target control input data to perform energy consumption management on the intelligent lighting management system.

[0062] Specifically, the target control input data calculated by the server (i.e., adjusting the operating parameters of the lighting system, such as brightness, switching time, etc.) will be applied to the actual lighting system, so as to optimize and manage the system energy consumption. In the intelligent lighting management system, the goal of energy consumption management is to accurately control the working state of the lighting system according to factors such as environmental data, energy consumption deviation, and target energy consumption, so as to achieve the purposes of reducing energy consumption, improving efficiency, and extending the equipment life. The server calculates the target control input data based on environmental data, current energy consumption, target energy consumption, etc. through the previously discussed formulas (such as feedback control, etc.). This usually includes adjusting control parameters such as lighting brightness, light switching time, and lamp switching state. The server adjusts the operating behavior of the intelligent lighting system by transmitting these calculated control instructions. The goal of these adjustments is to make the actual energy consumption as close as possible to or equal to the preset target energy consumption. By intelligently adjusting the parameters of the lighting system, the system can accurately control the energy consumption level, avoid unnecessary energy waste, and ensure efficient energy utilization.

[0063] Therefore, by obtaining environmental data and system status data, the system can calculate the control input independently without manual intervention, improving the degree of automation. Using the system state equation to calculate the control input makes the control strategy based on scientific calculation rather than empirical judgment, enhancing the rationality and interpretability of decision-making. By calculating the deviation between the target energy consumption and the current energy consumption, the lighting strategy can be dynamically adjusted to make the system energy consumption gradually approach the optimal state, thereby improving the energy-saving effect. Error feedback can ensure that the system can adaptively adjust under different environmental conditions, improving the adaptability of intelligent lighting. By judging the relationship between the energy consumption deviation and the preset threshold, the system can flexibly decide whether to adjust the control input, avoiding over-regulation or frequent adjustment, and improving the system stability and response efficiency. This mechanism can also adapt to different energy-saving strategies, such as a more stringent energy-saving mode or a more comfortable lighting mode, to meet the needs of different scenarios. Through the calculation of the adaptive control input, the system can effectively reduce energy consumption while ensuring the lighting quality, achieving the balance between energy conservation and comfort. Therefore, it is convenient to improve the accuracy of energy consumption prediction.

[0064] This application also provides an energy consumption prediction device for an intelligent lighting management system. Refer to Figure 3 , Figure 3It is a schematic diagram of the modules of an energy consumption prediction device for an intelligent lighting management system provided by an embodiment of the present application. The energy consumption prediction device is a server, and the server includes an acquisition module 31 and a processing module 32. Among them, the acquisition module 31 acquires the environmental data sent by the intelligent lighting management system; the processing module 32 calculates the current control input data according to the environmental data by using the system state equation; the processing module 32 calculates the current energy consumption data based on the current control input data; the acquisition module 31 acquires the target energy consumption data of the intelligent lighting management system; the processing module 32 calculates the energy consumption deviation between the target energy consumption data and the current energy consumption data; the processing module 32 generates the target control input data based on the magnitude relationship between the energy consumption deviation and the preset threshold; the processing module 32 performs energy consumption management on the intelligent lighting management system by using the target control input data.

[0065] In a possible implementation manner, the acquisition module 31 acquires the environmental data sent by the intelligent lighting management system, specifically including: the acquisition module 31 receives the ambient light intensity, temperature, and humidity sent by the sensors in the intelligent lighting management system; the acquisition module 31 receives the switch state and brightness level sent by the landscape lighting devices in the intelligent lighting management system; the processing module 32 obtains the environmental data through data processing based on the ambient light intensity, temperature, humidity, switch state, and brightness, and the data processing includes denoising, filtering, and normalization processing.

[0066] In a possible implementation manner, the processing module 32 calculates the current control input data according to the environmental data by using the system state equation, and specifically calculates by using the following equation: ; Among them, is the system state vector at the current moment t converted from the environmental data, is the derivative of the system state vector at the current moment t, is the current control input data, is the environmental noise is the intensity coefficient.

[0067] In a possible implementation manner, the processing module 32 calculates the current energy consumption data based on the current control input data, and specifically calculates by using the following formula: ; Among them, is the current energy consumption data, represents the mapping relationship between the current energy consumption data and the current control input data.

[0068] In a possible implementation, the processing module 32 generates target control input data based on the magnitude relationship between the energy consumption deviation and a preset threshold, specifically including: if the processing module 32 determines that the energy consumption deviation is greater than or equal to the preset threshold, it calculates the first control input data using a preset first method; if the processing module 32 determines that the energy consumption deviation is less than the preset threshold, it calculates the second control input data using a preset second method. The target control input data includes the first control input data and the second control input data.

[0069] In a possible implementation, the preset first method of the processing module 32 is specifically calculated using the following formula: ; where is the first control input data, is the energy-saving control coefficient, is the target energy consumption data, is the exponential decay factor, and t is the current time.

[0070] In a possible implementation, the preset second method of the processing module 32 is specifically calculated using the following formula: ; where is the second control input data, k is the feedback gain coefficient, a is the dynamic response coefficient, is the target energy consumption data, is the current energy consumption data, is the energy consumption change rate.

[0071] It should be noted that when the device provided in the above embodiments realizes its functions, only the above-mentioned division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be seen in the method embodiments, which will not be elaborated here.

[0072] This application also provides an electronic device. Referring to Figure 4 , Figure 4 is a schematic structural diagram of an electronic device provided in an embodiment of this application. The electronic device may include: at least one processor 41, at least one network interface 44, a user interface 43, a memory 45, and at least one communication bus 42.

[0073] Among them, the communication bus 42 is used to realize the connection and communication between these components.

[0074] Among them, the user interface 43 may include a display screen and a camera. Optionally, the user interface 43 may further include a standard wired interface and a wireless interface.

[0075] Among them, the network interface 44 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface).

[0076] Among them, the processor 41 may include one or more processing cores. The processor 41 connects various parts within the entire server using various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 45, and by calling data stored in the memory 45, it performs various functions of the server and processes data. Optionally, the processor 41 may be implemented in at least one of the following hardware forms: digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 41 may integrate one or a combination of several of the following: a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 41 and may be implemented separately by a single chip.

[0077] Among them, the memory 45 may include a random access memory (RAM) and may also include a read-only memory. Optionally, the memory 45 includes a non-transitory computer-readable storage medium. The memory 45 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 45 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store the data involved in the above-mentioned method embodiments. Optionally, the memory 45 may further be at least one storage device located far from the aforementioned processor 41. Such as Figure 4As shown in the figure, the memory 45, which is a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program of an energy consumption prediction method for an intelligent lighting management system.

[0078] In Figure 4 In the electronic device shown in the figure, the user interface 43 is mainly used to provide an interface for the user to input data and obtain the data input by the user; while the processor 41 can be used to call the application program of the energy consumption prediction method for the intelligent lighting management system stored in the memory 45. When executed by one or more processors, the electronic device is caused to execute the method of one or more of the above embodiments.

[0079] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0080] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device is caused to execute the method of one or more of the above embodiments.

[0081] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0082] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0083] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0084] In addition, the functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0085] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the embodiments of the present application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, mobile hard disks, magnetic disks, or optical discs.

[0086] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, all equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and the practice of the present disclosure, those skilled in the art will easily think of other implementation manners of the present disclosure. The present application aims to cover any variations, uses, or adaptation changes of the present disclosure, and these variations, uses, or adaptation changes follow the general principles of the present disclosure and include the common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for predicting the energy consumption of an intelligent lighting management system, characterized in that, The method includes: Obtaining environmental data sent by an intelligent lighting management system; Calculating current control input data according to the environmental data by using a system state equation; Calculating current energy consumption data based on the current control input data; Obtaining the target energy consumption data of the intelligent lighting management system; Calculating the energy consumption deviation between the target energy consumption data and the current energy consumption data; Generating target control input data based on the magnitude relationship between the energy consumption deviation and a preset threshold; Performing energy consumption management on the intelligent lighting management system by using the target control input data.

2. The energy consumption prediction method of the intelligent lighting management system according to claim 1, wherein The obtaining of the environmental data sent by the intelligent lighting management system specifically includes: Receiving the ambient light intensity, temperature, and humidity sent by sensors in the intelligent lighting management system; Receiving the switch state and brightness level sent by landscape lighting devices in the intelligent lighting management system; Based on the ambient light intensity, the temperature, the humidity, the switch state, and the brightness, obtaining the environmental data through data processing, where the data processing includes denoising, filtering, and normalization processing.

3. The energy consumption prediction method of the intelligent lighting management system according to claim 1, wherein The calculating of the current control input data according to the environmental data by using a system state equation specifically uses the following equation for calculation: ； Among them, is the system state vector at the current moment t obtained by converting environmental data, is the derivative of the system state vector at the current moment t, is the current control input data, is the environmental noise and is the intensity coefficient.

4. The energy consumption prediction method of the intelligent lighting management system according to claim 1, characterized in that The calculating of the current energy consumption data based on the current control input data specifically uses the following formula for calculation: ； Among them, is the current energy consumption data, represents the mapping relationship between the current energy consumption data and the current control input data.

5. The energy consumption prediction method of the intelligent lighting management system according to claim 1, characterized in that, The generating of the target control input data based on the magnitude relationship between the energy consumption deviation and a preset threshold specifically includes: If it is determined that the energy consumption deviation is greater than or equal to the preset threshold, calculating first control input data by using a preset first method; If it is determined that the energy consumption deviation is less than the preset threshold, calculating second control input data by using a preset second method, where the target control input data includes the first control input data and the second control input data.

6. The energy consumption prediction method of the intelligent lighting management system according to claim 5, wherein The preset first method specifically uses the following formula for calculation: ； Among them, is the first control input data, is the energy-saving control coefficient, is the target energy consumption data, is the exponential decay factor, and t is the current time.

7. The energy consumption prediction method of the intelligent lighting management system according to claim 5, characterized in that The preset second method specifically uses the following formula for calculation: ； Among them, is the second control input data, k is the feedback gain coefficient, a is the dynamic response coefficient, is the target energy consumption data, is the current energy consumption data, is the energy consumption change rate.

8. An energy consumption prediction device for an intelligent lighting management system, characterized in that, The device includes an obtaining module (31) and a processing module (32), where The obtaining module (31) is used to obtain environmental data sent by an intelligent lighting management system; The processing module (32) is used to calculate current control input data according to the environmental data by using a system state equation; The processing module (32) is further used to calculate current energy consumption data based on the current control input data; The obtaining module (31) is further used to obtain the target energy consumption data of the intelligent lighting management system; The processing module (32) is further used to calculate the energy consumption deviation between the target energy consumption data and the current energy consumption data; The processing module (32) is further used to generate target control input data based on the magnitude relationship between the energy consumption deviation and a preset threshold; The processing module (32) is further used to perform energy consumption management on the intelligent lighting management system by using the target control input data.

9. An electronic device, characterized in that, The electronic device includes a processor (41), a memory (45), a user interface (43), and a network interface (44). The memory (45) is used to store instructions. Both the user interface (43) and the network interface (44) are used to communicate with other devices. The processor (41) is used to execute the instructions stored in the memory (45) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions which, when executed, perform the method according to any one of claims 1 to 7.

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