Intelligent construction method for multi-lamp collaborative angle adjustment in intelligent lighting engineering

Abstract

The invention discloses an intelligent construction method for multi-lamp cooperative angle adjustment of intelligent illumination engineering, and the method comprises the steps: 1, constructing a three-dimensional point cloud model of a construction region, extracting the coordinate data of an obstacle, and generating a light distribution map; 2, constructing an on-site three-dimensional model by adopting three-dimensional modeling software; 3, determining the installation coordinates of each lamp through simulation calculation by using illumination simulation software according to the constructed on-site three-dimensional model, and 4, dividing a construction area into different illumination intensity areas according to a light distribution map, setting different lamp inclination angles in different areas, and determining the installation coordinates of each lamp according to the different lamp inclination angles. 5, sending a cooperative angle adjustment instruction to the target partition lamp through the light control system, and 6, starting a self-learning control algorithm based on the data accumulated in the step 5; the beneficial effects of the invention are that through the self-learning control algorithm, the self-adaptive adjustment of the illumination system is realized, and the intelligent level and user experience of the illumination system are improved.

Description

TECHNICAL FIELD

[0001] The application relates to the field of multi-lamp cooperative angle adjustment, in particular to an intelligent construction method for multi-lamp cooperative angle adjustment of intelligent lighting engineering. BACKGROUND

[0002] With the rapid development of intelligent buildings, smart spaces and the like, the application of intelligent lighting systems is becoming more and more widespread. In traditional lighting engineering, lamp installation often relies on manual experience for angle adjustment, which has problems such as poor lighting uniformity, inability to adapt to complex environmental changes, difficulty in meeting diversified lighting needs, and the like. At the same time, the traditional lighting system lacks a systematic cooperative control mechanism, and efficient linkage between lamps cannot be achieved, resulting in serious energy waste and difficulty in troubleshooting and system maintenance. In addition, with the increasing complexity of building space structures, traditional construction methods are prone to spatial conflicts when dealing with multi-lamp installation and angle adjustment, resulting in low construction efficiency and difficulty in ensuring quality. Therefore, an intelligent and precise multi-lamp cooperative angle adjustment construction method is urgently needed to improve the construction quality and system performance of intelligent lighting engineering. SUMMARY

[0003] The purpose of the present application is to solve the above-mentioned problems, and therefore an intelligent construction method for multi-lamp cooperative angle adjustment of intelligent lighting engineering is proposed.

[0004] The purpose of the present application can be achieved by the following technical solution: an intelligent construction method for multi-lamp cooperative angle adjustment of intelligent lighting engineering, comprising:

[0005] Step one: use a laser range finder to scan the construction area in all directions, obtain the spatial structure dimensions in the horizontal and vertical directions, record the position and size information of the building structure, at the same time, use a three-dimensional scanner to construct a three-dimensional point cloud model of the construction area, extract the coordinate data of the obstacles through point cloud data processing algorithm, including shape, position and height information, deploy multiple light detectors at different positions and heights in the construction area, record the light distribution intensity at different time periods at set time intervals, and generate a light distribution map;

[0006] Step two: based on the collected spatial structure dimensions, obstacle coordinate data and light distribution map, use a three-dimensional modeling software to construct a three-dimensional model of the site;

[0007] Step three: based on the constructed three-dimensional model, use lighting simulation software, combine with the requirements of lighting uniformity and the optical characteristics of the lamps, determine the installation coordinates of each lamp through simulation calculation, when calculating the distance between lamps, use the formula D=KxH / tan(θ / 2), where D is the distance between lamps, K is the correction coefficient, the value range is 0.8-1.2, H is the installation height of the lamp, θ is the light emitting angle of the lamp, and according to the simulation results, optimize and adjust to ensure that the light coverage area of adjacent lamps has no obvious overlap and no lighting blind area;

[0008] Step four: according to the light distribution map, divide the construction area into different light intensity areas, set different lamp inclination angles in different areas, build a wireless communication network, connect the intelligent lamps with the control equipment, and write the control program according to the initial angle and distance parameters of the lamps;

[0009] Step five: send the cooperative angle adjustment instruction to the target partition lamp through the light control system, the instruction contains the target angle, adjustment time and adjustment sequence, use the detection unit integrated with machine vision to monitor the lamp angle in real time, establish a data comparison and analysis mechanism, compare the real-time collected lamp actual angle value and lighting parameters with the expected target, and adjust;

[0010] Step six: based on the data accumulated in step five, start the self-learning control algorithm, and mine different adjustment strategies through deep analysis of historical data.

[0011] Further, the control program further comprises a permission management module, which sets different user permissions, including administrator permission, ordinary user permission, and maintenance personnel permission. The administrator can comprehensively set the system, adjust parameters and manage users, the ordinary user can perform lighting scene switching operation, and the maintenance personnel can view system running state, fault information and perform device maintenance related operation.

[0012] Through the hierarchical permission management and identity verification mechanism, the system running risk caused by misoperation or malicious tampering is effectively avoided, ensuring the stability of system parameters and configuration, at the same time, according to the user function distribution operation permission, the work efficiency of different post personnel can be improved, and the management confusion caused by permission redundancy is reduced.

[0013] Further, the control program further comprises a fault diagnosis module, which locates the fault lamps and fault types by analyzing the communication signal strength, lamp current data and feedback working state information in real time.

[0014] With real-time data monitoring and analysis, the faulty lamps and their types can be quickly and accurately located, greatly reducing troubleshooting time, significantly improving fault handling efficiency, reducing operation and maintenance costs, and ensuring the stable operation of the intelligent lighting system.

[0015] Further, the image recognition algorithm of the detection unit adopts a deep learning model based on a convolutional neural network, which is trained with image samples under different types of lamps, different angles, and lighting conditions, achieving fast processing and analysis of lamp angle images.

[0016] The powerful feature extraction and pattern recognition capabilities of deep learning enable the image recognition algorithm to adapt to complex and variable lighting environments and lamp types, effectively improving the accuracy and efficiency of lamp angle detection, avoiding errors and lags in manual measurement, and providing reliable data support for coordinated angle adjustment of lamps.

[0017] Further, the self-learning control algorithm also has a real-time updating function. When a new lighting scene or environmental parameter changes is detected, relevant data is automatically collected and included in the learning range, and the lighting strategy is re-optimized.

[0018] The system is given dynamic adaptive ability, which can adjust the lighting strategy in real time according to environmental changes, avoid one-size-fits-all fixed control mode, continuously accumulate and optimize lighting strategy, improve energy utilization efficiency, and provide users with more comfortable and more actual demand lighting environment.

[0019] Further, the coordinated angle adjustment instruction adopts a priority management mechanism, and different priorities are set for coordinated instructions according to different lighting scenes and emergency levels.

[0020] Through the priority management mechanism, the system can prioritize key instructions in complex and variable lighting scenes, ensuring the lighting needs of important areas or emergency situations, avoiding adjustment confusion caused by instruction conflicts, and improving the orderliness and timeliness of lamp coordination.

[0021] Further, step three also includes using building information modeling technology to perform collision detection on lamp installation coordinates, importing lamp models and building structure models into building information modeling software, and using collision detection functions to comprehensively check the spatial conflict between lamps and other building pipelines.

[0022] Collision detection using building information modeling technology in the early stages of construction can identify and solve spatial conflict problems between lamps and building pipelines in advance, avoiding rework and material waste during construction, optimizing lamp installation layout, improving construction efficiency and installation quality, and ensuring the smooth implementation of intelligent lighting projects.

[0023] Further in the step one, when collecting the light distribution intensity, the light detector also synchronously records the ambient temperature and humidity data, and integrates the ambient temperature and humidity information through the three-dimensional model in step two.

[0024] Combined with the ambient temperature and humidity information, more comprehensive data support is provided for lamp selection.

[0025] Compared with the prior art, the beneficial effects of the present application are:

[0026] The laser range finder and three-dimensional scanner are used for on-site data collection, a three-dimensional model containing light distribution information is constructed, the on-site physical environment is accurately restored, and comprehensive and accurate data basis is provided for lamp selection, positioning and angle adjustment, so that construction errors caused by insufficient environmental awareness are avoided.

[0027] Combined with the lighting simulation software and optical characteristic parameters, the lamp installation coordinates and initial angle are determined to ensure the lighting uniformity and improve the practicability and user experience of the lighting system.

[0028] The integrated machine vision detection unit and deep learning image recognition algorithm are used to monitor the lamp angle, a data comparison and analysis mechanism is established to measure the lamp angle, accurate monitoring of the lamp angle is realized, the control program parameters are corrected in time, the lamp angle adjustment is accurate, and the lighting effect is guaranteed to meet the standard.

[0029] Through the self-learning control algorithm, the scene memory and real-time updating function are provided to mine the best lighting strategy, realize the adaptive adjustment of the lighting system, and improve the intelligent level and user experience of the lighting system. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to facilitate the understanding of those skilled in the art, the present application will be further described below in conjunction with the drawings.

[0031] Figure 1 The flowchart of the intelligent lighting engineering multi-lamp collaborative angle adjustment intelligent construction method of the present application. DETAILED DESCRIPTION

[0032] The technical solutions of the present application will be described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0033] Please refer to Figure 1 The intelligent lighting engineering multi-lamp collaborative angle adjustment intelligent construction method comprises:

[0034] Step one: use laser range finder to scan the construction area comprehensively, obtain the spatial structure size in horizontal and vertical directions, record the position and size information of the building structure, at the same time, use three-dimensional scanner to build three-dimensional point cloud model of the construction area, extract the coordinate data of obstacles including shape, position and height information through point cloud data processing algorithm, deploy multiple light detectors at different positions and heights in the construction area, record the light distribution intensity at different time intervals according to the set time interval, continuously monitor for not less than 24 hours, and generate light distribution map;

[0035] Step two: based on the above collected spatial structure size, obstacle coordinate and light distribution map data, use three-dimensional modeling software to build the three-dimensional model of the site, which not only contains the building structure and obstacles, but also integrates the light distribution information to present the lighting environment of the site in a visual way;

[0036] Step three: based on the built three-dimensional model of the site, use lighting simulation software, combine with the requirements of lighting uniformity and the optical characteristic parameters of lamps and lanterns such as light emitting angle and luminous flux, determine the installation coordinates of each lamp through simulation calculation, when calculating the distance between lamps, consider the space height, lighting demand and lamp illumination range, use formula calculation, the formula is D=K×H / tan(θ / 2), wherein D is the distance between lamps, K is the correction coefficient, the value range is 0.8-1.2, H is the installation height of lamp, θ is the light emitting angle of lamp, and the simulation results are optimized and adjusted to ensure that the light coverage area of adjacent lamps has no obvious overlap and no lighting blind area;

[0037] Step four: according to the light distribution map, the construction area is divided into different light intensity areas, for low light area, the initial inclination angle of lamp is set to 15°-30°, so that the light can be more concentrated to irradiate the area, and the lighting effect is enhanced; for high light area, the initial inclination angle is set to 5°-15° to maintain the uniformity of the whole lighting, build wireless communication network to connect the lamps and light control system, according to the initial angle and distance parameters of the lamps, use programming software to write control program, in the program, set the angle adjustment step length to 0.1° / step to ensure the accuracy of angle adjustment; set the adjustment response delay ≤100ms to ensure that the system can quickly respond to the angle adjustment instruction, integrate fault diagnosis module in the control program, which can quickly locate the fault lamps and fault types through real-time analysis of communication signal strength, lamp current data and feedback working state information, realize the abnormal alarm function with fault positioning accuracy ≤1m, when detecting fault, the system automatically records fault information and tries to repair itself by restarting and reconnecting, if it cannot be repaired, send alarm notice to the management personnel in time to prompt the fault position and type for manual intervention repair;

[0038] Step five: send the cooperative angle adjustment instruction to the target partition lamp through the light control system, which contains the target angle, adjustment time and adjustment sequence;

[0039] For example, in a large conference room scenario, to achieve the effect of gradually brightening and adjusting the angle of the light before the meeting starts, the lamp can be set to adjust from the initial angle to a specific target angle within 5 seconds, and adjust in a certain order to avoid discomfort caused by light flicker;

[0040] A detection unit integrated with machine vision is used to monitor the angle of the lamp in real time. The detection unit is equipped with a camera and is installed in a position that can fully capture the lamp. The real-time image of the lamp captured by the camera is processed through an image recognition algorithm. The image recognition algorithm uses a deep learning model and is trained with a large number of lamp angle image samples. The detection error of the lamp angle is ≤0.3°, which can accurately calculate the actual angle value of the lamp. At the same time, a light detector continuously and synchronously records the lighting parameters on site, including light intensity, color temperature, and color rendering index;

[0041] A data comparison and analysis mechanism is established to compare the actual angle value and lighting parameters of the lamp collected in real time with the expected target. When the actual angle deviates from the target angle by >1° or the lighting parameters do not meet the preset standards, such as a light intensity deviation of >±10%, a color temperature deviation of >±500K, and a color rendering index lower than the preset value, the control program parameter correction mechanism is triggered. The system automatically analyzes the reasons for the deviation, such as communication delay, lamp failure, and environmental interference, and adjusts the relevant parameters in the control program according to the reasons, such as optimizing the communication protocol, increasing the number of retries, and adjusting the angle compensation value, to ensure the accuracy of the lamp angle adjustment and the standardization of the lighting effect;

[0042] Step six: based on the data accumulated in the test phase, start the self-learning control algorithm. This algorithm analyzes historical adjustment records, lighting effect data, and user operation logs to find the best combination of lamp angle parameters and adjustment strategies in different lighting scenarios;

[0043] For example, the system records that in the daily office scene, the lamp angle of a certain area needs to be adjusted to a certain range from 10 am to 12 pm every day to meet the reading needs of personnel, and the self-learning algorithm will automatically remember the angle parameters in this scene and automatically apply this optimization strategy in the same time period in the future, realizing intelligent adaptive adjustment of the lighting system. The self-learning control algorithm includes a scene memory module that can store angle parameter combinations of ≥10 typical lighting scenes and supports one-key calling, making it easy for managers to quickly switch lighting scenes according to actual needs and conduct comprehensive stability testing on the optimized system. Simulate more than three typical working conditions, including strong light interference such as direct sunlight, load fluctuation such as simultaneous opening of a large number of electrical equipment, and sudden power failure recovery scene. In the strong light interference test, by introducing a simulated strong light source into the test area, it is observed whether the system can automatically adjust the lamp angle to reduce the contrast of strong light reflection and ensure lighting comfort;

[0044] In the load fluctuation test, by controlling the opening and closing of the simulated load device, the stability of the system communication and lamp control is monitored. In the sudden power failure recovery scene test, the situation of power failure after power failure is simulated to verify that the system takes ≤30s from power failure to recovery coordination adjustment function, ensuring that the lighting system can operate stably and reliably under various complex working conditions;

[0045] The control program in step four also includes a permission management module that sets different user permissions, including administrator permissions, ordinary user permissions, and maintenance personnel permissions. Administrators can make comprehensive settings, parameter adjustments, and user management of the system; ordinary users can perform lighting scene switching operations; maintenance personnel can view system operation status, fault information, and perform device maintenance-related operations. Different permission users log in to the system through their own independent accounts and need to perform identity verification when logging in to ensure the security and standardization of system operation and prevent unauthorized personnel from maliciously modifying or misoperating the system.

[0046] The image recognition algorithm of the detection unit in step five uses a deep learning model based on a convolutional neural network. This model is trained on a large number of image samples of different types of lamps, different angles, and lighting conditions, with a training sample size of not less than 10,000 groups. During the training process, model parameters are continuously optimized to improve the recognition accuracy and robustness of the model for lamp angles. The model is deployed on an edge computing device to realize fast processing and analysis of lamp angle images, reduce data transmission delay, and ensure real-time and accurate feedback of lamp angle information, providing accurate data support for coordinated angle adjustment.

[0047] The self-learning control algorithm in step six also has a real-time updating function, when the system detects a new lighting scene or environmental parameter change, such as seasonal replacement leading to natural light intensity change, construction area function change, automatically collects relevant data, and incorporates it into the learning range, re-optimizes the lighting strategy, for example, in summer sunlight, the system automatically adjusts the lamp angle and brightness according to the new light distribution, to adapt to environmental changes, while updating the new adjustment strategy to the scene memory module, so that the system can continuously maintain the best lighting control effect and improve user experience.

[0048] The cooperative angle adjustment instruction in step five adopts a priority management mechanism, and different priorities are set for the cooperative instructions according to different lighting scenes and emergency levels;

[0049] For example, in an emergency such as a fire, the emergency lighting switching instruction is given the highest priority, and the system executes the instruction first to ensure that the emergency lighting mode can be switched quickly in an emergency; for daily lighting scene switching instructions, a lower priority is set, and when the system receives multiple instructions at the same time, the instructions are executed in order according to the priority, ensuring that the system can efficiently and accurately respond to critical instructions in a complex instruction environment, and ensuring the stability of the lighting system and the reliability of the emergency function.

[0050] Step two also includes using building information modeling technology to detect collisions of lamp installation coordinates, importing lamp models and building structure models into building information modeling software, using collision detection functions to comprehensively check whether there is a space conflict between the lamps and other building pipelines such as water pipes, wire pipes, and ventilation pipes, and recording the collision position and conflict type in detail during the detection process, and adjusting the lamp installation coordinates in time to avoid the lamps being unable to be installed or affecting the normal operation of other systems after installation due to space conflicts during construction. Through the application of building information modeling technology, potential problems are discovered and solved in advance, construction efficiency is improved, and engineering quality is ensured.

[0051] The present application is based on the intelligent lighting engineering multi-lamp cooperative angle adjustment construction method, and the innovation mainly lies in data acquisition and modeling, which uses a combination of laser range finders and three-dimensional scanners to realize the surveying and mapping of the spatial structure and obstacles in the construction area, and to construct a three-dimensional model containing physical environment and light distribution information, providing multi-dimensional data support for construction;

[0052] Multiple light detectors are deployed to monitor and generate detailed light distribution maps, covering natural light and existing lighting conditions, breaking the traditional single data acquisition mode;

[0053] Lamp positioning and angle adjustment optimization, a lamp spacing calculation method based on the formula D=K×H / tan(θ / 2) is proposed, and combined with lighting simulation software optimization, to ensure that the light coverage has no overlap and blind area;

[0054] According to the light intensity partition, the initial tilt angle of the lamp is set, and the lighting comfort and effectiveness are improved;

[0055] The test and optimization mechanism is broken through, the detection unit integrated with machine vision is introduced, the real-time monitoring of the lamp angle is realized based on the deep learning model, the comparison and analysis mechanism is established combined with the data of the light detector, and the control parameters are dynamically corrected;

[0056] The self-learning control algorithm is designed, the best lighting strategy is mined through historical data analysis, ≥10 groups of scene memory and one-key calling are supported, the real-time updating function is provided, and the environmental changes are adapted;

[0057] The construction process is innovatively supplemented, the building information model technology is used for collision detection of the lamp installation coordinates in the construction process, the space conflict with the building pipeline is avoided in advance, and the construction efficiency and quality are improved.

[0058] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details and limit the application to the specific embodiments described. Obviously, many modifications and changes can be made according to the content of the specification. The specification selects and describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and use the application. The application is limited by the claims and their entire scope and equivalents.

Claims

1. An intelligent construction method for coordinated angle adjustment of multiple luminaires in intelligent lighting engineering, characterized in that, include: Step 1: Using a laser rangefinder, a full-range scan of the construction area is performed to obtain the spatial structural dimensions in the horizontal and vertical directions, and to record the location and size information of the building structure. At the same time, a 3D scanner is used to construct a 3D point cloud model of the construction area. Through point cloud data processing algorithms, the coordinate data of obstacles are extracted, including their shape, location and height information. Multiple light detectors are deployed at different locations and heights in the construction area to record the light distribution intensity at different time intervals according to the set time intervals, and to generate a light distribution map. Step 2: Based on the collected spatial structure dimensions, obstacle coordinate data, and light distribution map, a 3D model of the site is constructed using 3D modeling software; Step 3: Based on the constructed 3D model of the site, lighting simulation software is used to determine the installation coordinates of each lamp through simulation calculation, taking into account the lighting uniformity requirements and the optical characteristic parameters of the lamps. When calculating the lamp spacing, the formula is used: D=K×H / tan(θ / 2), where D is the lamp spacing, K is the correction coefficient with a value range of 0.8-1.2, H is the lamp installation height, and θ is the lamp beam angle. The simulation results are then optimized and adjusted to ensure that the light coverage areas of adjacent lamps have neither significant overlap nor lighting blind spots. Step 4: Based on the light distribution map, divide the construction area into different light intensity zones, set different lamp tilt angles for different zones, build a wireless communication network, connect the smart lamps to the control equipment, and write control programs based on the initial angle and spacing parameters of the lamps; Step 5: Send coordinated angle adjustment commands to the target zone lights through the lighting control system. The commands include the target angle, adjustment duration, and adjustment sequence. Use an integrated machine vision detection unit to monitor the light angle in real time, establish a data comparison and analysis mechanism, compare the real-time collected actual angle values ​​and lighting parameters of the lights with the expected target, and make adjustments accordingly. Step Six: Based on the data accumulated in Step Five, start the self-learning control algorithm and explore different adjustment strategies through in-depth analysis of historical data.

2. The intelligent construction method for multi-lamp coordinated angle adjustment in intelligent lighting engineering according to claim 1, characterized in that, The control program also includes a permission management module, which sets different user permissions, including administrator permissions, ordinary user permissions, and maintenance personnel permissions. Administrators can perform comprehensive system settings, parameter adjustments, and user management. Ordinary users can perform lighting scene switching operations. Maintenance personnel can view system operating status, fault information, and perform equipment maintenance-related operations.

3. The intelligent construction method for multi-lamp coordinated angle adjustment in intelligent lighting engineering according to claim 1, characterized in that, The control program integrates a fault diagnosis module, which analyzes the communication signal strength, lamp current data and feedback working status information in real time to locate faulty lamps and fault types.

4. The intelligent construction method for multi-lamp coordinated angle adjustment in intelligent lighting engineering according to claim 1, characterized in that, The image recognition algorithm of the detection unit adopts a deep learning model based on convolutional neural networks. This model is trained with image samples of different types of lamps, different angles and lighting conditions to achieve rapid processing and analysis of lamp angle images.

5. The intelligent construction method for multi-lamp coordinated angle adjustment in intelligent lighting engineering according to claim 1, characterized in that, The self-learning control algorithm also has a real-time update function. When a new lighting scene or environmental parameter change is detected, it automatically collects relevant data and incorporates it into the learning scope to re-optimize the lighting strategy.

6. The intelligent construction method for multi-lamp coordinated angle adjustment in intelligent lighting engineering according to claim 1, characterized in that, The coordinated angle adjustment command adopts a priority management mechanism, setting different priorities for the coordinated command according to different lighting scenarios and urgency levels.

7. The intelligent construction method for multi-lamp coordinated angle adjustment in intelligent lighting engineering according to claim 1, characterized in that, Step three also includes using building information modeling (BIM) technology to perform collision detection on the installation coordinates of the lighting fixtures. The lighting fixture model and the building structure model are imported into the BIM software, and the collision detection function is used to comprehensively check the spatial conflicts between the lighting fixtures and other building pipelines.

8. The intelligent construction method for multi-lamp coordinated angle adjustment in intelligent lighting engineering according to claim 1, characterized in that, In step one, while collecting the light distribution intensity, the light detector also simultaneously records the ambient temperature and humidity data, and integrates the ambient temperature and humidity information through the three-dimensional model in step two.

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