Intelligent lighting control method and system

Abstract

The invention belongs to the technical field of intelligent control, and provides an intelligent illumination control method and system, and the method comprises the steps: obtaining the actual operation state, environment illumination intensity and personnel activity state of illumination equipment in an illumination region, and obtaining actual measurement original data; preprocessing the actually measured original data, and inputting the preprocessed actually measured original data into a pre-constructed illumination demand prediction model to obtain a demand prediction result of the illumination area in a future time period; acquiring scene information of the illumination area in a future time period, and determining a basic illumination strategy, a brightness adjustment strategy and a color temperature transition strategy of the illumination area in the future time period according to the scene information and the demand prediction result to obtain a graded illumination scheme; and performing hierarchical control on the lighting equipment in the lighting area according to the hierarchical lighting scheme. According to the scheme, accurate prediction of the illumination demand is realized through the actually measured original data containing multiple types of information, hierarchical control of the illumination equipment can be realized in cooperation with a hierarchical illumination scheme, and the accuracy of an illumination control link is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an intelligent lighting control method and system. Background Art

[0002] In the field of electricity production, power plants are core locations for energy conversion and supply, and the stable and efficient operation of their internal lighting systems is crucial. Traditional power plant lighting control mostly relies on manual on / off control and simple timing control methods. Manual on / off control relies on manual inspection and operation. Due to the large area of ​​the power plant and the scattered lighting areas, manual operation is not only extremely inefficient, but also difficult for inspectors to accurately judge the lighting needs of each area at night or in bad weather. It is very easy for some areas to be insufficiently lit, affecting operational safety, or excessive lighting to cause energy waste. Although timing control can turn lights on and off at preset times, power plant production operations are continuous and uncertain, with equipment start-up and shutdown and personnel working hours not fixed. Timing control cannot adapt to complex and changing production scenarios. Lighting equipment continues to be on during unmanned hours, resulting in large amounts of electricity consumption and increased power plant operating costs.

[0003] With the development of intelligent lighting technology, some power plants are experimenting with integrating existing intelligent lighting systems to improve lighting management. Traditional intelligent lighting systems often use a single human infrared sensor to detect human activity and control lighting equipment, which, to a certain extent, reduces energy waste in unoccupied areas. However, power plants are unique environments with numerous electromagnetic interference sources. Human infrared sensors are susceptible to interference and can misjudge the system, leading to frequent lighting equipment misoperation and disrupting normal production operations. Furthermore, this single-sensor control approach fails to comprehensively account for the varying lighting requirements across the power plant's various time periods and scenarios, making it difficult to meet the personalized lighting needs of complex power plant operations.

[0004] Therefore, traditional lighting solutions used in power plants have technical problems such as high energy consumption, poor adaptability and low intelligence. Summary of the Invention

[0005] The present invention provides an intelligent lighting control method and system to solve the defects of traditional lighting solutions used in power plants, such as high energy consumption, poor adaptability and low intelligence.

[0006] In one aspect, the present invention provides an intelligent lighting control method, comprising: Obtain the actual operating status of lighting equipment, ambient light intensity, and personnel activity status in the lighting area to obtain measured raw data; Preprocessing the measured raw data and inputting it into a pre-built lighting demand forecasting model to obtain demand forecast results for the lighting area in the future period; Obtaining scene information of the lighting area in a future time period, and determining a basic lighting strategy, a brightness adjustment strategy, and a color temperature transition strategy for the lighting area in the future time period based on the scene information and the demand forecast result, to obtain a hierarchical lighting plan; According to the hierarchical lighting scheme, the lighting equipment in the lighting area is controlled in a hierarchical manner.

[0007] According to the intelligent lighting control method provided by the present invention, the lighting demand prediction model includes: The input layer is used to receive the pre-processed measured raw data; A convolutional neural network layer is used to extract local spatial features from the preprocessed measured raw data, perform dimensionality reduction on the local spatial features to obtain a two-dimensional feature map, and expand the two-dimensional feature map into a one-dimensional feature vector; A long short-term memory network layer is used to convert the one-dimensional feature vector into a data sequence in the form of a time series, perform time series feature extraction and memory processing on the data sequence, and obtain a spatiotemporal fusion feature vector; The output layer is used to output the demand forecast result based on the spatiotemporal fusion feature vector.

[0008] According to the intelligent lighting control method provided by the present invention, based on the scene information and the demand forecast result, a basic lighting strategy, a brightness adjustment strategy, and a color temperature transition strategy for the lighting area in a future period are determined, including: Set basic lighting templates, brightness adjustment templates, and color temperature transition templates for different time periods, different lighting scenes, and different lighting requirements, and build a template database; According to the scene information and the demand prediction result, matching the target basic lighting template, target brightness adjustment template, and target color temperature transition template corresponding to the lighting area in the future time period from the template database; The target basic template is used as the basic lighting strategy of the lighting area in the future period, the target brightness adjustment template is used as the brightness adjustment strategy of the lighting area in the future period, and the target color temperature transition template is used as the color temperature transition strategy of the lighting area in the future period.

[0009] According to the intelligent lighting control method provided by the present invention, the lighting equipment in the lighting area is controlled according to the hierarchical lighting scheme, including: Performing text decomposition on the hierarchical lighting scheme to obtain key text information; Mapping the key text information into corresponding code instructions according to a preset communication protocol; The code instructions are subjected to standard verification, and the code instructions that pass the standard verification are sent to the lighting devices in the lighting area to control the lighting devices in the lighting area.

[0010] According to the intelligent lighting control method provided by the present invention, the code instruction is subjected to standard verification, including: Performing syntax verification on the code instruction using regular expressions and protocol syntax rules to obtain a syntax verification result; Performing conflict detection on the code instructions to obtain a conflict detection result; A specification verification result is obtained based on the syntax check result and the conflict detection result.

[0011] According to the intelligent lighting control method provided by the present invention, the method further includes: Receive a mode selection instruction input by a user; In response to the mode selection instruction, determining a target lighting mode and target lighting parameters corresponding to the target lighting mode; The lighting equipment in the lighting area is controlled to operate according to the target lighting parameters.

[0012] According to the intelligent lighting control method provided by the present invention, controlling the lighting equipment in the lighting area to operate according to the target lighting parameters includes: Acquire actual lighting parameters of lighting equipment in the lighting area; Subtracting the actual lighting parameter from the target lighting parameter to obtain a parameter deviation value; According to the parameter deviation value, the actual lighting parameter of the lighting device is adjusted by the PID controller until the parameter deviation value is within the set range.

[0013] According to the intelligent lighting control method provided by the present invention, after performing hierarchical control on the lighting devices within the lighting area, the method further includes: Obtaining multiple lighting index values ​​corresponding to the lighting area; Determining the indicator weight corresponding to each lighting indicator value based on the scene information of the lighting area; Normalizing the multiple lighting index values, and performing a weighted operation on the normalized multiple lighting index values ​​according to the index weights to obtain a lighting quality evaluation value; A quality assessment is performed on the graded lighting scheme according to the lighting quality assessment value to obtain a quality assessment result.

[0014] According to the intelligent lighting control method provided by the present invention, the multiple lighting index values ​​include: brightness adjustment response time of the lighting equipment, energy consumption fluctuation data and regional illumination uniformity.

[0015] In another aspect, the present invention further provides an intelligent lighting control system, comprising: The acquisition module is used to obtain the actual operating status of the lighting equipment, the ambient light intensity and the activity status of people in the lighting area to obtain the measured raw data; A prediction module is used to pre-process the measured raw data and input it into a pre-built lighting demand prediction model to obtain a demand prediction result for the lighting area in the future period; a determination module configured to obtain scene information of the lighting area in a future time period, and determine, based on the scene information and the demand forecast result, a basic lighting strategy, a brightness adjustment strategy, and a color temperature transition strategy for the lighting area in the future time period, to obtain a hierarchical lighting solution; A control module is used to perform hierarchical control on the lighting equipment in the lighting area according to the hierarchical lighting scheme.

[0016] The intelligent lighting control method and system provided by the present invention obtains measured raw data by acquiring the actual operating status of lighting equipment in the lighting area, the ambient light intensity, and the activity status of personnel; pre-processes the measured raw data and inputs it into a pre-built lighting demand prediction model to obtain demand prediction results for the lighting area in the future time period; obtains scene information of the lighting area in the future time period, and based on the scene information and demand prediction results, determines the basic lighting strategy, brightness adjustment strategy, and color temperature transition strategy for the lighting area in the future time period to obtain a graded lighting plan; and performs graded control of the lighting equipment in the lighting area according to the graded lighting plan. Accurate prediction of lighting demand is achieved through measured raw data containing multiple types of information, and combined with the graded lighting plan, graded control of lighting equipment can be achieved, improving the accuracy of the lighting control link, thereby better meeting the lighting needs of different scenes and different time periods. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 is a flow chart of an intelligent lighting control method provided by an embodiment of the present invention; Figure 2 It is a structural diagram of the intelligent lighting control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0020] The following combination Figure 1 and Figure 2 Describe the details of the intelligent lighting control method and system provided by the embodiments of the present invention.

[0021] like Figure 1 As shown, the intelligent lighting control method provided in the embodiment of the present invention can be executed by a computer or server with data transmission, reception and processing capabilities. The method mainly includes the following steps: Step 110: Acquire the actual operating status of the lighting equipment, the ambient light intensity, and the activity status of personnel in the lighting area to obtain measured raw data.

[0022] In this embodiment, the actual operating status of the lighting device may specifically include parameters such as current, voltage, and electric energy.

[0023] In some implementations, the smart circuit breaker can be upgraded with new interfaces and expanded sensors based on the existing current, voltage, and energy data collection. A new industrial-grade ambient light sensor interface has been added. The ambient light sensor uses spectral compensation technology to accurately sense ambient light intensity in different scenarios. It also integrates a dual-element passive infrared sensor for real-time monitoring of human activity.

[0024] Step 120: Pre-process the measured raw data and input it into a pre-built lighting demand forecasting model to obtain demand forecast results for the lighting area in the future period.

[0025] In this embodiment, the measured raw data is preprocessed, specifically including operations such as data cleaning, data normalization, and data feature extraction.

[0026] In practical applications, data cleaning algorithms can be used to identify and remove duplicate data, missing values, and outliers from measured raw data. Missing values ​​can be filled using mean filling, median filling, or time series interpolation, depending on the data characteristics. Outliers can be detected and corrected through statistical analysis to ensure data quality.

[0027] Afterwards, data of different dimensions can be uniformly mapped to the numerical range [0, 1], eliminating the impact of data dimension and improving the convergence speed and accuracy of model training. For example, for numerical data such as current and voltage, the minimum-maximum normalization method can be used for normalization.

[0028] Finally, based on domain knowledge and control requirements, the normalized data can be subjected to feature extraction and transformation. For example, time features such as hour, day, week, and month can be generated based on timestamps. Lighting demand intensity features can be calculated by combining occupant activity status and ambient light intensity, enhancing data representation capabilities.

[0029] Step 130: Obtain scene information of the lighting area in the future time period, and determine the basic lighting strategy, brightness adjustment strategy, and color temperature transition strategy of the lighting area in the future time period based on the scene information and demand prediction results to obtain a hierarchical lighting plan.

[0030] It is understandable that the hierarchical lighting solution can achieve refined control of lighting equipment through three links: basic lighting, brightness adjustment, and color temperature transition, thereby improving the accuracy of lighting control.

[0031] Step 140: Perform hierarchical control on lighting equipment within the lighting area according to the hierarchical lighting plan.

[0032] In this embodiment, through the hierarchical control method, the lighting equipment can be controlled and adjusted step by step according to the scene and lighting requirements, so as to better meet the actual lighting scene requirements and make the lighting control process more intelligent and precise.

[0033] In one embodiment, the lighting demand prediction model specifically includes: The input layer is used to receive the preprocessed measured raw data.

[0034] The convolutional neural network layer is used to extract local spatial features from the preprocessed measured raw data, perform dimensionality reduction on the local spatial features to obtain a two-dimensional feature map, and expand the two-dimensional feature map into a one-dimensional feature vector.

[0035] In this embodiment, the convolutional neural network layer specifically includes a convolution layer, a pooling layer, and a fully connected layer, wherein the convolution layer uses a plurality of convolution kernels of different sizes to perform convolution operations on the pre-processed measured raw data. Through the convolution operation, the local spatial features in the data are extracted, such as the distribution characteristics of the ambient light intensity, the regional characteristics of the personnel activities, etc., to capture the regularity of the data in the spatial dimension. The pooling layer can use the maximum pooling or average pooling method to reduce the dimensionality of the local spatial features output by the convolution layer, reduce the amount of data and the computational complexity, while retaining important feature information and avoiding overfitting problems. The fully connected layer can expand the two-dimensional feature map output by the pooling layer into a one-dimensional feature vector, and connect it to the subsequent long short-term memory network layer in a fully connected manner, thereby integrating the spatial feature information extracted previously.

[0036] The long short-term memory network layer is used to convert the one-dimensional feature vector into a data sequence in the form of a time series, extract the time series features and perform memory processing on the data sequence to obtain a spatiotemporal fusion feature vector.

[0037] In this embodiment, the long short-term memory network layer can receive the one-dimensional feature vector output by the fully connected layer, convert the one-dimensional feature vector into a data sequence in the form of a time series, and utilize the unique gating mechanism of the LSTM unit to effectively process the time series data, capture the long-term dependency of lighting demand over time, and learn the association information between different time points.

[0038] The output layer is used to output the demand forecast results based on the spatiotemporal fusion feature vector.

[0039] In practical applications, the output layer can use a suitable activation function, such as a linear activation function, to output the final demand forecast result. The demand forecast result specifically includes key information such as the human activity density and natural light intensity predicted value in the lighting area in the future period, which can provide data support for intelligent decision-making.

[0040] It's not hard to see that the demand forecasting model utilizes a hybrid deep learning architecture combining a long-short-term memory network (LSTM) and a convolutional neural network (CNN). LSTM effectively processes time series data, capturing the long-term dependencies of lighting demand over time. Convolutional neural networks excel at extracting local features from data, effectively analyzing spatial characteristics like ambient light intensity and human activity. The combination of these two approaches enables a more comprehensive understanding of the underlying patterns of lighting demand.

[0041] In one embodiment, based on the scene information and demand prediction results, a basic lighting strategy, a brightness adjustment strategy, and a color temperature transition strategy for the lighting area in the future period are determined, specifically including: The first step is to set basic lighting templates, brightness adjustment templates, and color temperature transition templates for different time periods, different lighting scenes, and different lighting requirements, and build a template database.

[0042] In practical applications, basic lighting templates, brightness adjustment templates, and color temperature transition templates covering different time periods, different lighting scenes, and different lighting requirements can be pre-formulated to obtain a template database.

[0043] Taking normal production time as an example, for the lighting scenes of core areas such as the main control room and distribution room, and when setting the range of personnel activity density and natural light intensity, the basic lighting template can be set to: use 500lux light for lighting, the brightness adjustment template can be set to: increase the brightness to 750lux in steps of 50lux, and the color temperature transition template can be set to: adjust the color temperature uniformly according to the time period, for example, gradually increase from 4000K to 5000K natural light color temperature from 8:00-12:00 in the morning, and then slowly decrease from 5000K to 4500K from 13:00 to 18:00 in the afternoon.

[0044] For the lighting scenario of a work shop, during normal production hours, due to the frequent operation of equipment and workers, the basic lighting template can be set to: use 600 lux of light for illumination, ensuring that workers can clearly observe the details of equipment operation and operating procedures, and avoid safety accidents caused by insufficient light. When the density of personnel activities increases, such as when multiple people are working together, or the intensity of natural light is significantly reduced due to weather, the brightness adjustment template can be set to increase the brightness in steps of 75 lux, up to a maximum of 900 lux, to meet the lighting needs of complex working environments. The color temperature transition template can be set to: uniformly adjust the color temperature according to the time of day. For example, from 8:00 am to 12:00 pm, the color temperature gradually increases from 3800K to 4800K to improve workers' concentration; from 13:00 to 18:00 pm, it slowly decreases from 4800K to 4300K ​​to relieve visual fatigue caused by long-term work.

[0045] For warehouse lighting scenarios, warehouses are primarily used for material storage, with relatively little human activity. Taking normal production hours as an example, the basic lighting template can be set to 250 lux for illumination, which can meet occasional material searches and inventory checks while reducing energy consumption. When personnel enter the warehouse for activities such as handling and sorting, the density of human activity increases, and the brightness adjustment template can be set to increase the brightness in steps of 50 lux, up to a maximum of 400 lux, to ensure sufficient light in the work area. After the personnel leave, the brightness gradually returns to the base value within 5 minutes. The color temperature transition template can be set to maintain a neutral color temperature of 4000K-4200K throughout the day, facilitating accurate identification of material colors and label information, while reducing energy consumption and control complexity caused by frequent color temperature adjustments.

[0046] For office lighting scenarios, where normal production hours are dominated by activities such as office work and meetings, the basic lighting template can be set to 400 lux, creating a comfortable office lighting environment and reducing visual fatigue. When the density of human activity increases, such as when holding group meetings, or when natural light intensity is insufficient, the brightness adjustment template can be set to increase the brightness in steps of 50 lux, up to a maximum of 600 lux. The color temperature transition template can be set to gradually increase the color temperature from 4000K to 4500K from 8:00 AM to 12:00 PM, boosting employee productivity; and then gradually decreasing from 4500K to 4200K from 1:00 PM to 6:00 PM, creating a relaxed and focused office atmosphere and improving work efficiency.

[0047] In the second step, based on the scene information and demand prediction results, the target basic lighting template, target brightness adjustment template, and target color temperature transition template corresponding to the lighting area in the future period are matched from the template database.

[0048] It is understandable that, given known scene information, demand forecast results, and future time periods, a target basic lighting template, target brightness adjustment template, and target color temperature transition template that meet the current lighting situation can be matched from the template database.

[0049] In the third step, the target basic template is used as the basic lighting strategy for the lighting area in the future period, the target brightness adjustment template is used as the brightness adjustment strategy for the lighting area in the future period, and the target color temperature transition template is used as the color temperature transition strategy for the lighting area in the future period.

[0050] In one embodiment, controlling lighting devices within a lighting area according to a hierarchical lighting scheme specifically includes: The first step is to deconstruct the text of the hierarchical lighting plan to obtain key text information.

[0051] In this embodiment, the text information in the hierarchical lighting scheme can be disassembled through natural language processing technology, and key text information such as control area, time conditions, brightness threshold, etc. can be extracted.

[0052] The second step is to map the key text information into corresponding code instructions based on the preset communication protocol.

[0053] In this embodiment, the preset communication protocol can be Modbus or DALI protocol, and the key text information is mapped into corresponding code instructions. For example, "adjust the brightness of the lighting equipment in the main control room A to 500 lux from 8:00 to 12:00" is converted into "0x01 0x03 0x00 0x05 0x00 0x01 0x64", where the first two digits are the device address, the middle one is the function code, and the last four digits are the brightness parameter.

[0054] The third step is to perform a standard verification on the code instructions, and send the code instructions that pass the standard verification to the lighting devices in the lighting area to control the lighting devices in the lighting area.

[0055] In a specific implementation, the code instructions are verified in a standardized manner, including: First, the code instructions are syntax-checked using regular expressions and protocol syntax rules to obtain syntax-check results.

[0056] In this embodiment, the generated code instructions can be verified using regular expressions and protocol syntax rules to check whether the instruction format complies with the specifications, such as whether the data length and parameter range are compliant, and obtain the syntax verification results. In actual applications, if a format error is found, such as missing necessary parameters or mismatched parameter types, an error prompt will be triggered and the policy editing link will be returned for correction.

[0057] Then, conflict detection is performed on the code instructions to obtain a conflict detection result.

[0058] In practical applications, a command conflict detection mechanism can be established. By querying the device status database and historical command records, it can analyze whether there is time overlap, area coverage, or parameter contradiction between the code command and the current control task. For example, if "full open" and "close" commands are issued at the same time, the conflict detection results can be obtained. If there is a conflict, a conflict report will be generated, suggesting that the user adjust the policy priority or merge duplicate commands.

[0059] Finally, based on the syntax check results and conflict detection results, the specification verification results are obtained.

[0060] It can be understood that if both the syntax check result and the conflict check result are normal, the standard verification result is standard verification passed; if either the syntax check result or the conflict check result is abnormal or both are abnormal, the standard verification result is standard verification failed.

[0061] In one embodiment, the intelligent lighting control method may further include: The first step is to receive a mode selection instruction input by the user.

[0062] In this embodiment, a variety of alternative lighting modes can be pre-set, including normal inspection mode, night watch mode, emergency repair mode, equipment debugging mode and energy-saving standby mode.

[0063] In normal inspection mode, the brightness can be set to 80% of the maximum brightness, and the color temperature can be set to 4000K. This meets the requirements of power plant equipment inspections and ensures that inspectors can clearly view equipment parameters and operating status. In night watch mode, the brightness can be set to 30% of the maximum brightness, and the color temperature can be set to 2700K. This maintains basic illumination while reducing visual irritation to on-duty personnel. In emergency repair mode, the brightness can be set to 100% and the color temperature to 5000K, providing maximum brightness and optimal color rendering, ensuring efficient and safe repair work. In equipment commissioning mode, the brightness of local areas can be set to 90% of the maximum brightness, and the color temperature can be set to 4500K, enabling technicians to operate and observe equipment details with precision. In energy-saving standby mode, the brightness of unoccupied areas can be set to 10% of the maximum brightness, automatically switching to normal brightness when someone enters, achieving a balance between energy conservation and lighting needs.

[0064] In the second step, in response to the mode selection instruction, a target lighting mode and target lighting parameters corresponding to the target lighting mode are determined.

[0065] It can be understood that the target lighting parameters specifically include lighting brightness values ​​and color temperature values.

[0066] The third step is to control the lighting equipment in the lighting area to operate according to the target lighting parameters.

[0067] In a specific implementation, controlling the lighting devices in the lighting area to operate according to the target lighting parameters specifically includes: First, the actual lighting parameters of the lighting equipment in the lighting area are obtained.

[0068] Then, the actual lighting parameters are subtracted from the target lighting parameters to obtain the parameter deviation value.

[0069] Finally, according to the parameter deviation value, the actual lighting parameters of the lighting equipment are adjusted through the PID controller until the parameter deviation value is within the set range.

[0070] In this embodiment, either the brightness value or the color temperature value of the actual lighting parameter can be used as a closed-loop control basis to achieve closed-loop control of the parameter. Alternatively, two PID controllers can be set to perform closed-loop control of the brightness value and the color temperature value respectively.

[0071] In practical applications, the setting range corresponding to the parameter deviation value can be reasonably set according to the actual control accuracy requirements and is not specifically limited here.

[0072] In one embodiment, after performing hierarchical control on the lighting devices within the lighting area, the method may further include: The first step is to obtain multiple lighting index values ​​corresponding to the lighting area.

[0073] In this embodiment, the multiple lighting index values ​​specifically include: brightness adjustment response time of the lighting equipment, energy consumption fluctuation data, and regional illumination uniformity.

[0074] The brightness adjustment response time refers to the time it takes for a lighting device to complete brightness adjustment after receiving a command. A shorter brightness adjustment response time indicates a stronger dynamic adjustment capability, indicating a more optimal tiered lighting solution. Energy consumption fluctuation data refers to the fluctuation range of energy consumption data per unit time. When energy-saving efficiency is compared with historical data, a slowly changing and downward trend in energy consumption fluctuation data indicates a more optimal tiered lighting solution. Regional illumination uniformity can be calculated using data collected by multiple illumination sensors. The closer the regional illumination uniformity is to 100%, the higher the lighting effect, indicating a more optimal tiered lighting solution.

[0075] The second step is to determine the indicator weight corresponding to each lighting indicator value based on the scene information of the lighting area.

[0076] For example, in the lighting scenario of a main control room, a core monitoring area of ​​a power plant, visual comfort must be ensured for operators working long hours. Uniform illumination reduces visual fatigue, and fast brightness adjustment response time helps cope with ambient light fluctuations. Energy consumption is relatively less important. Therefore, in this scenario, the weight of the indicator for regional illumination uniformity can be set to 40%, the weight of the indicator for brightness adjustment response time can be set to 35%, and the weight of the indicator for energy consumption fluctuation data can be set to 25%.

[0077] In the lighting scenario of a work shop, where equipment operates frequently, rapid brightness adjustment ensures operational safety in emergencies. At the same time, energy consumption must be controlled to reduce operating costs, and illumination uniformity requirements are relatively low. Therefore, in this scenario, the indicator weight for brightness adjustment response time can be set to 40%, the indicator weight for energy consumption fluctuation data can be set to 35%, and the indicator weight for regional illumination uniformity can be set to 25%.

[0078] In warehouse lighting scenarios, where daily personnel activity is minimal, energy conservation is key; maintaining a certain level of illumination uniformity facilitates item locating; and the requirement for a short response time for brightness adjustment is minimal. Therefore, in this scenario, the indicator weight for energy consumption fluctuation data can be set to 45%, the indicator weight for regional illumination uniformity can be set to 30%, and the indicator weight for brightness adjustment response time can be set to 25%.

[0079] For office lighting scenarios, where employees work for long periods of time, a comfortable lighting environment is crucial. Energy conservation and rapid response to changes in natural light are also essential. Therefore, in this scenario, the weight of the regional illumination uniformity indicator can be set to 35%, the weight of the energy consumption fluctuation indicator can be set to 30%, and the weight of the brightness adjustment response time indicator can be set to 35%.

[0080] The third step is to normalize the multiple lighting index values ​​and perform weighted calculation on the normalized multiple lighting index values ​​according to the index weights to obtain the lighting quality evaluation value.

[0081] The fourth step is to conduct a quality assessment on the graded lighting scheme based on the lighting quality assessment value to obtain the quality assessment result.

[0082] In this embodiment, multiple quality levels can be set, and different quality levels correspond to different lighting quality evaluation value ranges. Alternatively, a quality standard value can be set, and the currently obtained lighting quality evaluation value can be compared with the quality standard value. When the lighting quality evaluation value is lower than the quality standard value, and the current quality evaluation result is that the lighting quality does not meet the standard, an early warning prompt can be issued to remind on-site personnel to manually control it, or the graded lighting plan can be dynamically adjusted until the quality evaluation result is that the lighting quality meets the standard.

[0083] In some embodiments, a standard range for normal operating conditions can be established based on historical operating data of lighting equipment. The actual operating conditions of the lighting equipment can be monitored. When one or more operating parameters of the lighting equipment are detected to be outside the standard range, a warning can be issued to alert personnel in the lighting area of ​​the potential for a potential fault.

[0084] Based on the same general inventive concept, the present invention also protects an intelligent lighting control system. The intelligent lighting control system provided by the present invention is described below. The intelligent lighting control system described below and the intelligent lighting control method described above can be referenced to each other.

[0085] like Figure 2 As shown, the intelligent lighting control system provided by the embodiment of the present invention specifically includes: The acquisition module 210 is used to obtain the actual operating status of the lighting equipment, the ambient light intensity and the activity status of people in the lighting area to obtain the measured original data.

[0086] The prediction module 220 is used to pre-process the measured raw data and input the pre-built lighting demand prediction model to obtain the demand prediction results of the lighting area in the future period.

[0087] The determination module 230 is used to obtain scene information of the lighting area in the future time period, and determine the basic lighting strategy, brightness adjustment strategy and color temperature transition strategy of the lighting area in the future time period based on the scene information and demand prediction results to obtain a hierarchical lighting plan.

[0088] The control module 240 is used to perform hierarchical control on the lighting equipment in the lighting area according to the hierarchical lighting scheme.

[0089] Regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the relevant method and will not be elaborated again here.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An intelligent lighting control method, characterized in that: include: Obtain the actual operating status of lighting equipment, ambient light intensity, and personnel activity status in the lighting area to obtain measured raw data; Preprocessing the measured raw data and inputting it into a pre-built lighting demand forecasting model to obtain demand forecast results for the lighting area in the future period; Obtaining scene information of the lighting area in a future time period, and determining a basic lighting strategy, a brightness adjustment strategy, and a color temperature transition strategy for the lighting area in the future time period based on the scene information and the demand forecast result, to obtain a hierarchical lighting plan; According to the hierarchical lighting scheme, the lighting equipment in the lighting area is controlled in a hierarchical manner.

2. The intelligent lighting control method according to claim 1, characterized in that: The lighting demand prediction model includes: The input layer is used to receive the pre-processed measured raw data; A convolutional neural network layer is used to extract local spatial features from the preprocessed measured raw data, perform dimensionality reduction on the local spatial features to obtain a two-dimensional feature map, and expand the two-dimensional feature map into a one-dimensional feature vector; A long short-term memory network layer is used to convert the one-dimensional feature vector into a data sequence in the form of a time series, perform time series feature extraction and memory processing on the data sequence, and obtain a spatiotemporal fusion feature vector; The output layer is used to output the demand forecast result based on the spatiotemporal fusion feature vector.

3. The intelligent lighting control method according to claim 1, characterized in that: Based on the scene information and the demand forecast result, a basic lighting strategy, a brightness adjustment strategy, and a color temperature transition strategy for the lighting area in a future period are determined, including: Set basic lighting templates, brightness adjustment templates, and color temperature transition templates for different time periods, different lighting scenes, and different lighting requirements, and build a template database; According to the scene information and the demand prediction result, matching the target basic lighting template, target brightness adjustment template, and target color temperature transition template corresponding to the lighting area in the future time period from the template database; The target basic template is used as the basic lighting strategy of the lighting area in the future period, the target brightness adjustment template is used as the brightness adjustment strategy of the lighting area in the future period, and the target color temperature transition template is used as the color temperature transition strategy of the lighting area in the future period.

4. The intelligent lighting control method according to claim 1, characterized in that: Controlling lighting equipment within the lighting area according to the hierarchical lighting scheme includes: Performing text decomposition on the hierarchical lighting scheme to obtain key text information; Mapping the key text information into corresponding code instructions according to a preset communication protocol; The code instructions are subjected to standard verification, and the code instructions that pass the standard verification are sent to the lighting devices in the lighting area to control the lighting devices in the lighting area.

5. The intelligent lighting control method according to claim 4, characterized in that: Performing standard verification on the code instructions, including: Performing syntax verification on the code instruction using regular expressions and protocol syntax rules to obtain a syntax verification result; Performing conflict detection on the code instructions to obtain a conflict detection result; A specification verification result is obtained based on the syntax check result and the conflict detection result.

6. The intelligent lighting control method according to claim 1, characterized in that: The method further comprises: Receive a mode selection instruction input by a user; In response to the mode selection instruction, determining a target lighting mode and target lighting parameters corresponding to the target lighting mode; The lighting equipment in the lighting area is controlled to operate according to the target lighting parameters.

7. The intelligent lighting control method according to claim 6, characterized in that: Controlling the lighting equipment within the lighting area to operate according to the target lighting parameters includes: Acquire actual lighting parameters of lighting equipment in the lighting area; Subtracting the actual lighting parameter from the target lighting parameter to obtain a parameter deviation value; According to the parameter deviation value, the actual lighting parameter of the lighting device is adjusted by the PID controller until the parameter deviation value is within the set range.

8. The intelligent lighting control method according to claim 1, characterized in that: After performing hierarchical control on the lighting devices within the lighting area, the method further includes: Obtaining multiple lighting index values ​​corresponding to the lighting area; Determining the indicator weight corresponding to each lighting indicator value based on the scene information of the lighting area; Normalizing the multiple lighting index values, and performing a weighted operation on the normalized multiple lighting index values ​​according to the index weights to obtain a lighting quality evaluation value; A quality assessment is performed on the graded lighting scheme according to the lighting quality assessment value to obtain a quality assessment result.

9. The intelligent lighting control method according to claim 8, characterized in that: The multiple lighting index values ​​include: brightness adjustment response time of lighting equipment, energy consumption fluctuation data and regional illumination uniformity.

10. An intelligent lighting control system, characterized in that: include: The acquisition module is used to obtain the actual operating status of the lighting equipment, the ambient light intensity and the activity status of people in the lighting area to obtain the measured raw data; A prediction module is used to pre-process the measured raw data and input it into a pre-built lighting demand prediction model to obtain a demand prediction result for the lighting area in the future period; a determination module, configured to obtain scene information of the lighting area in a future time period, and determine, based on the scene information and the demand forecast result, a basic lighting strategy, a brightness adjustment strategy, and a color temperature transition strategy for the lighting area in the future time period, to obtain a hierarchical lighting solution; A control module is used to perform hierarchical control on the lighting equipment in the lighting area according to the hierarchical lighting scheme.

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