Intelligent workshop illumination control system and method based on Internet of Things

Abstract

The invention discloses an intelligent workshop illumination control system and method based on the Internet of Things, and belongs to the field of illumination control systems.The system constructs a high-precision workshop three-dimensional space model by collecting three-dimensional parameters of workshop buildings, structural beams, shelters and production equipment, and performs simulation calculation by means of a light tracing algorithm; and outputting an optimal lamp layout scheme and a digital construction model with three-dimensional coordinates to realize accurate positioning and installation. The system creatively creates an Internet of Things feedback calibration mechanism, automatically corrects simulation parameters by using actually measured illumination data and stores the simulation parameters in a historical database to realize self-evolution of model precision, and in addition, personnel distribution, natural illumination and MES production data are fused in an operation stage to realize partition self-adaptive constant illumination control and dynamic energy saving, so that the system has a wide application prospect. Full-life-cycle intelligent management from design, construction to operation and maintenance is facilitated, illumination precision and construction efficiency are remarkably improved, and energy consumption is reduced.

Description

A Smart Workshop Lighting Control System and Method Based on the Internet of Things Technical Field

[0001] This invention relates to the field of computer lighting control systems, and more specifically, to a smart workshop lighting control system and method based on the Internet of Things. Background Technology

[0002] As a fundamental supporting facility in industrial production environments, the rationality of workshop lighting design directly affects production efficiency, employee visual comfort, and energy costs. In actual engineering projects, lighting design typically requires a comprehensive assessment of factors such as the workshop's building dimensions, equipment layout, and production process requirements to determine the type, location, and quantity of lighting fixtures. Traditional design methods mainly rely on designers' experience combined with illuminance standards for estimation, or employ simplified calculation methods such as the utilization factor method. These methods can meet basic needs in regular spaces with simple equipment, but when there are numerous production equipment, structural beams, columns, and other obstructions within the workshop, the light propagation path is affected, leading to a deviation between the actual illuminance distribution and the design expectations. This often requires adjustments to the fixture positions or increases in the number of fixtures after construction, resulting in rework and increased costs.

[0003] With the development of computer-aided design technology, some design firms have begun to use lighting simulation software for scheme verification. While existing simulation software can perform ray tracing calculations, it has certain limitations in industrial workshop applications. For example, during the operation phase of the lighting system, current workshop lighting control is mostly based on timed switching or manual control. Even systems with dimming functions rarely incorporate real-time factors such as personnel distribution and natural light for dynamic adjustment. Furthermore, illuminance measurement data collected after construction is usually only used for acceptance testing and is not compared with earlier simulation data for analysis. This makes it difficult to trace the causes of deviations, and historical data is not effectively accumulated for reference in subsequent projects.

[0004] With the intelligent upgrading of the manufacturing industry and the increasing requirements for energy conservation and consumption reduction, there is still room for optimization in terms of design accuracy, construction convenience, intelligent operation and data closed-loop management of workshop lighting systems. It is necessary to explore lighting design and control solutions that are more suitable for industrial scenarios. Therefore, we propose an Internet of Things-based smart workshop lighting control system and method to solve the above-mentioned problems. Summary of the Invention

[0005] 1. Technical problems to be solved

[0006] In view of the problems existing in the prior art, the purpose of this invention is to provide a smart workshop lighting control system and method based on the Internet of Things. It has room for further optimization in terms of design accuracy, construction convenience, intelligent operation and data closed-loop management, making it more convenient for lighting design and control in industrial scenarios.

[0007] 2. Technical Solution

[0008] To solve the above problems, the present invention adopts the following technical solution.

[0009] A smart workshop lighting control system based on the Internet of Things includes:

[0010] The spatial parameter acquisition module is used to acquire a dataset of geometric parameters of the workshop space. The dataset includes workshop size parameters, wall reflectivity parameters, ceiling height parameters, structural beam position coordinates, and three-dimensional coordinates of obstructions.

[0011] The equipment layout parameter module is communicatively connected to the spatial parameter acquisition module. It is used to acquire the equipment identification, three-dimensional position coordinates, external dimensions and surface reflectivity parameters of the production equipment. It integrates the production equipment parameters with the workshop spatial geometric parameter dataset to generate workshop three-dimensional spatial model data.

[0012] The lighting parameter database is communicatively connected to the equipment layout parameter module and is used to store the lighting fixture identification, optical parameters, and economic parameters of the selected lighting fixtures.

[0013] The lighting simulation calculation module is connected to the spatial parameter acquisition module, the equipment layout parameter module, and the lamp parameter database, respectively. It is used to receive the workshop three-dimensional spatial model data and the target illuminance standard value, call the optical parameters in the lamp parameter database, simulate the propagation path of light in the workshop through the ray tracing algorithm, calculate the number of lamps installed, the coordinates of the installation position and the installation angle, and generate illuminance distribution data.

[0014] The construction positioning output module is connected to the lighting simulation calculation module. It is used to receive the coordinate data of the lamp installation position, embed the lamp installation position parameters into the workshop three-dimensional space model data, and generate a three-dimensional construction model with lamp installation position annotations.

[0015] The budget output module is connected to the lighting simulation calculation module and the lamp parameter database. It is used to receive data on the number and type of lamps installed, retrieve the corresponding economic parameters from the lamp parameter database, generate budget report data, and generate candidate lighting scheme data at multiple cost levels.

[0016] The IoT feedback calibration module includes several illuminance sensors, a wireless communication unit, and a data comparison unit deployed in the workshop work area. The illuminance sensors are used to collect actual illuminance data in each area after construction is completed. The data comparison unit compares the actual illuminance data with the simulated illuminance data generated by the lighting simulation calculation module. When the deviation value exceeds a preset threshold, a parameter correction command is generated and transmitted to the luminaire parameter database to correct the optical parameters in the luminaire parameter database.

[0017] Furthermore, the lighting simulation calculation module also includes a ray tracing engine, which is used to convert the light distribution curve data of the lamps into light emission vectors, convert the three-dimensional spatial model data of the workshop into a light collision detection mesh, and calculate the light intensity value of each collision point by tracing the collision points between the light rays and the workshop surface, thereby generating illuminance distribution data.

[0018] Furthermore, the construction positioning output module also includes a coordinate annotation generation unit, which is used to generate a unique lamp number for each lamp in the workshop three-dimensional space model and convert the three-dimensional coordinates of the lamp installation position into relative coordinate data relative to the workshop reference point.

[0019] Furthermore, the construction positioning output module also includes a construction drawing export unit, which is used to convert the three-dimensional construction model with the installation position markings of the lamps into construction drawings. The construction drawings include a lamp plan layout, a lamp installation section view, lamp number markings, and a lamp installation position coordinate table.

[0020] Furthermore, the budget output module also includes a scheme comparison unit, which is used to compare the data of multiple candidate lighting schemes at different cost levels, generate comparison data including the number of lamps, total cost, expected illuminance value and investment payback period of each scheme, and display it to the user.

[0021] Furthermore, the IoT feedback calibration module also includes a data storage unit, which stores the actual illuminance data, simulated illuminance data and parameter correction instructions collected each time according to the timestamp, forming a historical calibration database. The historical calibration database is used as a reference for simulation calculation parameters in subsequent workshop lighting renovation or expansion projects.

[0022] Furthermore, it also includes an intelligent control execution module, which is communicatively connected to the drive controller of the lamps. It is used to generate lamp control commands based on the personnel location data collected by the personnel distribution sensor in the workshop and the ambient light data collected by the natural light sensor, and send them to the drive controller of each lamp to adjust the brightness and on / off status of the lamps in each area.

[0023] Furthermore, the intelligent control execution module divides the workshop into several independent lighting areas, with each lighting area using a dot matrix arrangement of lamps. The natural light sensor includes daylight brightness detection units deployed on the sides of the workshop windows and doors. The intelligent control execution module automatically adjusts the operating parameters of the lamps in the corresponding lighting areas based on the real-time daylight brightness data from the sides of the windows and doors.

[0024] A smart workshop lighting control method based on the Internet of Things includes the following steps:

[0025] S1. Obtain the workshop space geometric parameter dataset;

[0026] S2. Obtain the three-dimensional position coordinates and external dimension parameters of the production equipment, and perform coordinate system integration with the workshop space geometric parameter dataset to generate workshop three-dimensional space model data.

[0027] S3. Retrieve the optical and economic parameters of the selected lamps from the lamp parameter database;

[0028] S4. Based on the target illuminance standard value, combined with the workshop 3D spatial model data and the optical parameters of the lamps, the number of lamps installed, the coordinates of their installation positions, and the installation angles are calculated using a ray tracing algorithm, and illuminance distribution data is generated.

[0029] S5. Embed the lamp installation location parameters into the workshop 3D space model data to generate a 3D construction model with lamp installation location annotations.

[0030] S6. Based on the number and type of lighting fixtures installed, and combined with the economic parameters of the lighting fixtures, generate budget report data and generate candidate lighting scheme data for multiple cost levels.

[0031] S7. After construction is completed, the actual illuminance data is collected by the illuminance sensor and compared with the simulated illuminance data. When the deviation value exceeds the preset threshold, the optical parameters in the lamp parameter database are corrected.

[0032] Furthermore, the step of generating the workshop 3D spatial model data also includes communicating with the workshop manufacturing execution system to receive production line adjustment information. When the location of production equipment changes or new equipment is added, the lighting layout scheme is automatically recalculated, and the lighting installation position parameters are updated within the workshop 3D spatial model to generate the modified 3D construction model, budget report data, and construction positioning data.

[0033] 3. Beneficial Effects

[0034] Compared with the prior art, the advantages of this invention are:

[0035] 1. This invention breaks through the limitation of the disconnect between simulation and actual measurement in traditional lighting design. It has created an original Internet of Things feedback calibration mechanism. After the construction is completed, the system uses deployed illuminance sensors to collect actual data and automatically compares it with simulation data. When the deviation exceeds the threshold, the optical parameters in the lamp parameter database are automatically corrected and the correction record is stored in the historical calibration database. This mechanism not only solves the problem of idle acceptance data, but also enables the simulation model to learn and evolve continuously with the accumulation of projects, which can significantly improve the simulation prediction accuracy of subsequent workshop renovation or expansion projects.

[0036] 2. Addressing the pain points of dense equipment and numerous obstructions in industrial workshops, this invention deeply integrates the three-dimensional parameters of production equipment with the parameters of building structure to construct a three-dimensional spatial model of the workshop that includes complete obstruction relationships. Combined with a ray tracing engine, the system can accurately simulate the collision, reflection, and shadow distribution of light on the equipment surface, thereby calculating the optimal number, position, and angle of light fixtures. This effectively avoids the uneven illumination or blind spots caused by traditional simplified algorithms in complex environments, reducing rework and cost waste caused by design deviations from the source.

[0037] 3. This invention uses a construction positioning output module to directly convert the lamp coordinates obtained from simulation calculations into a three-dimensional construction model and standardized drawings with unique numbers and relative coordinate data. Construction personnel can directly perform precise positioning and installation based on the model, reducing the construction error rate and significantly improving construction efficiency and standardization.

[0038] 4. The system has a built-in solution comparison unit that can automatically generate candidate lighting solutions at different cost levels (high, medium, and low) and intuitively display the number of lamps, total cost, expected illuminance, and investment payback period for each solution. This multi-dimensional quantitative comparison helps users scientifically weigh initial investment and long-term operating costs while meeting process illuminance standards, and select the most cost-effective lighting solution.

[0039] 5. The integrated intelligent control execution module enables the system to merge personnel distribution data and natural lighting data in real time. It can divide the workshop into independent areas and achieve constant illuminance control by turning lights on when people are present, turning them off when people leave, and automatically adjusting the brightness according to the intensity of natural light. In addition, the system can also be linked with the workshop manufacturing execution system. When the production line layout is adjusted, it automatically recalculates and updates the lighting control strategy to ensure that lighting resources are always allocated on demand and minimize energy consumption. Attached Figure Description

[0040] Figure 1 is a schematic diagram of the main system architecture of the present invention;

[0041] Figure 2 is a schematic diagram illustrating the functions of some modules and the database of this invention;

[0042] Figure 3 is a schematic diagram of the function of some modules of the present invention;

[0043] Figure 4 is a schematic diagram of the method flow of the present invention.

[0044] Explanation of the labels in the diagram:

[0045] 1. Spatial parameter acquisition module;

[0046] 2. Equipment layout parameter module;

[0047] 3. Lighting fixture parameter database;

[0048] 4. Lighting simulation calculation module; 401. Ray tracing engine;

[0049] 5. Construction positioning output module; 501. Coordinate annotation generation unit; 502. Construction drawing export unit;

[0050] 6. Budget output module; 601. Scheme comparison unit;

[0051] 7. IoT feedback calibration module; 701. Illuminance sensor; 702. Wireless communication unit; 703. Data comparison unit; 704. Data storage unit;

[0052] 8. Intelligent control execution module. Detailed Implementation

[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0054] Example 1:

[0055] Please refer to Figures 1-4. A smart workshop lighting control system based on the Internet of Things includes a spatial parameter acquisition module 1, an equipment layout parameter module 2, a lamp parameter database 3, a lighting simulation calculation module 4, a construction positioning output module 5, a budget output module 6, an Internet of Things feedback calibration module 7, and an intelligent control execution module 8.

[0056] The spatial parameter acquisition module 1 is used to collect a dataset of geometric parameters of the workshop space, specifically including workshop dimensions, wall reflectivity, ceiling height, structural beam location coordinates, and 3D coordinates of obstructions. In practical applications, the spatial parameter acquisition module 1 can acquire workshop space data through laser scanners, 3D measuring instruments, or by importing from a building BIM model. Workshop dimensions include the length, width, and height of the workshop; wall reflectivity is measured with a photometer or obtained by looking up a table based on the wall material type; ceiling height refers to the vertical distance from the lighting fixture installation plane to the work surface; structural beam location coordinates include the beam's cross-sectional dimensions, spatial location, and direction of extension; and the 3D coordinates of obstructions cover the spatial location information of fixed obstacles within the workshop, such as pipes and ventilation equipment. The spatial parameter acquisition module 1 converts the collected data into a 3D Cartesian coordinate system with a corner of the workshop as the origin, providing a benchmark for subsequent modeling.

[0057] The equipment layout parameter module 2 communicates with the spatial parameter acquisition module 1 to acquire the equipment identification, 3D position coordinates, external dimensions, and surface reflectivity parameters of the production equipment. It then fuses the production equipment parameters with the workshop spatial geometric parameter dataset using a coordinate system to generate a 3D spatial model of the workshop. The equipment layout parameter module 2 can obtain equipment layout information by communicating with the workshop Manufacturing Execution System (MES) or by manually inputting equipment parameters. The equipment identification distinguishes different models of production equipment; the 3D position coordinates refer to the equipment's location data in the workshop coordinate system; the external dimensions include the equipment's length, width, height, and outline shape; and the surface reflectivity parameter is determined based on the equipment's shell material, with metal surfaces typically having a reflectivity of 0.3-0.6 and painted surfaces typically having a reflectivity of 0.4-0.7. After fusing the equipment parameters with the spatial parameters using a coordinate system, the equipment layout parameter module 2 generates a complete 3D spatial model data containing the workshop's architectural structure and equipment layout. This model data is represented using a mesh, with the mesh precision adjustable from 10cm to 50cm according to simulation requirements.

[0058] The luminaire parameter database 3 communicates with the equipment layout parameter module 2 to store the luminaire identification, optical parameters, and economic parameters of the selected luminaires. The luminaire identification includes information such as luminaire model, manufacturer, and product series; optical parameters include luminaire power, luminous flux, light distribution curve, beam angle, color temperature, and color rendering index, with the light distribution curve data stored in polar or rectangular coordinates to describe the light intensity distribution of the luminaire at different angles; economic parameters include luminaire unit price, installation cost, expected service life, and maintenance cost. The luminaire parameter database 3 supports dynamic updates of luminaire parameters; when new luminaire products are added to the database or existing luminaire parameters are revised, the database can be updated synchronously in real time.

[0059] The lighting simulation calculation module 4 is connected to the spatial parameter acquisition module 1, the equipment layout parameter module 2, and the luminaire parameter database 3. It receives the workshop's 3D spatial model data and the target illuminance standard value, calls the optical parameters in the luminaire parameter database 3, simulates the propagation path of light within the workshop using a ray tracing algorithm, calculates the number of luminaires installed, their coordinates, and installation angles, and generates illuminance distribution data. The target illuminance standard value is determined based on the workshop's production process requirements; generally, it is 500-750 lx for precision machining workshops, 300-500 lx for ordinary assembly workshops, and 100-200 lx for storage areas. The lighting simulation calculation module 4 initially estimates the number of luminaires based on the workshop area, target illuminance value, and luminaire luminous flux, and then determines the optimal luminaire layout scheme through iterative optimization.

[0060] The construction positioning output module 5 is connected to the lighting simulation calculation module 4. It receives the coordinate data of the lamp installation positions and embeds the lamp installation position parameters into the workshop's three-dimensional spatial model data to generate a three-dimensional construction model with lamp installation position annotations. The three-dimensional construction model output by the construction positioning output module 5 can be directly used for construction briefings and on-site positioning. Construction personnel can view the three-dimensional model through mobile terminals to obtain the precise installation position of each lamp.

[0061] The budget output module 6 is connected to the lighting simulation calculation module 4 and the luminaire parameter database 3. It receives data on the number and type of luminaires installed, retrieves corresponding economic parameters from the luminaire parameter database 3, generates budget report data, and generates candidate lighting schemes at multiple cost levels. The budget output module 6 can generate lighting schemes at three cost levels: high, medium, and low. High-cost schemes use high-performance luminaires, offering high illuminance uniformity and good energy efficiency, but with a large initial investment. Low-cost schemes use economical luminaires, with a small initial investment but higher operating energy consumption. Medium-cost schemes strike a balance between cost and performance.

[0062] The IoT feedback calibration module 7 includes several illuminance sensors 701 deployed in the workshop work area, a wireless communication unit 702, and a data comparison unit 703. Illuminance sensors 701 are used to collect actual illuminance data for each area after construction is completed. Illuminance sensors 701 are digital illuminance sensors with a measurement range of 0-2000 lx and an accuracy of ±5%. The data is uploaded to the data comparison unit 703 via wired or wireless means. The wireless communication unit 702 uses wireless communication protocols such as WiFi, ZigBee, or LoRa to achieve data communication between the illuminance sensors 701 and the system host. The data comparison unit 703 compares the actual illuminance data with the simulated illuminance data generated by the lighting simulation calculation module 4. When the deviation exceeds a preset threshold, a parameter correction command is generated and transmitted to the luminaire parameter database 3 to correct the optical parameters in the luminaire parameter database 3. The preset threshold is generally set to ±10% of the simulated illuminance value. When the actual illuminance deviates from the simulated illuminance beyond this range, the system automatically analyzes the cause of the deviation, which may be due to differences between the actual luminous flux of the lamp and the nominal value, inaccurate measurement of wall reflectivity, or insufficient modeling of the equipment shading effect, and then corrects the corresponding parameters.

[0063] The intelligent control execution module 8 communicates with the luminaire drive controllers. Based on personnel location data collected by the personnel distribution sensor and ambient light data collected by the natural light sensor, it generates luminaire control commands and sends them to the drive controllers of each luminaire to adjust the brightness and on / off status of the luminaires in each area. The intelligent control execution module 8 supports luminaire drive controllers including 0-10V dimming, DALI digital dimming, and PWM dimming, and can automatically adapt the control protocol according to the luminaire type.

[0064] Example 2:

[0065] In view of the above embodiment 1, further description is provided, referring to Figures 1-4, this embodiment provides a detailed explanation of the specific working principles of the lighting simulation calculation module 4 and the construction positioning output module 5.

[0066] The lighting simulation calculation module 4 also includes a ray tracing engine 401. The ray tracing engine 401 is used to convert the light distribution curve data of the lamps into light emission vectors, convert the three-dimensional space model data of the workshop into a light collision detection mesh, and calculate the light intensity value of each collision point by tracing the collision points of the light rays with the workshop surface, thereby generating illuminance distribution data. The workflow of Ray Tracing Engine 401 is as follows: First, based on the light distribution curve data of the luminaires, a large number of light emission vectors are generated on the luminaire's emitting surface. Each ray carries a certain luminous flux, and the number of rays is set according to the simulation accuracy requirements, generally 1000-10000 rays per luminaire. Second, the 3D spatial model data of the workshop is converted into a ray collision detection mesh, with the mesh unit size set to 10cm×10cm×10cm. Each mesh unit is labeled with its surface material and reflectivity attributes. Then, the propagation path of each ray in the workshop is tracked. When a ray collides with the mesh surface, the direction of the reflected ray and the luminous flux attenuation are calculated based on the surface reflectivity. The reflected ray continues to be tracked until the luminous flux attenuates below a threshold or reaches the maximum number of reflections. Finally, the luminous flux received by each mesh unit is counted, converted into illuminance values, and workshop illuminance distribution data is generated. Ray Tracing Engine 401 supports multiple reflection calculations, and the maximum number of reflections is generally set to 3-5 times to balance calculation accuracy and calculation time.

[0067] The construction positioning output module 5 also includes a coordinate annotation generation unit 501. This unit generates a unique lamp number for each lamp in the workshop's three-dimensional spatial model and converts the three-dimensional coordinates of the lamp's installation position into relative coordinate data relative to a workshop reference point. The lamp number uses a coding rule such as "L-area number-serial number," for example, "L-A01-001" represents lamp number 1 in area A. The coordinate annotation generation unit 501 converts the absolute coordinates of the lamp into relative coordinates relative to a workshop reference point. The reference point is typically a ground point in a corner of the workshop, and the relative coordinate format is (X, Y, Z), where X and Y are horizontal position coordinates, and Z is the installation height coordinate. This relative coordinate data facilitates on-site positioning for construction personnel. They only need to start from the reference point, measure the corresponding distances along the X and Y directions, and then determine the installation height to complete the lamp positioning.

[0068] The construction positioning output module 5 also includes a construction drawing export unit 502. This unit converts the 3D construction model with lighting fixture installation location markings into construction drawings. These drawings include a lighting fixture plan view, a lighting fixture installation section view, lighting fixture number markings, and a lighting fixture installation location coordinate table. The lighting fixture plan view is a top view showing the distribution and numbering of the lighting fixtures on the workshop floor plan; the lighting fixture installation section view is a side view showing the installation height, hoisting method, and relationship with the surrounding structure; the lighting fixture number markings clearly indicate the number of each lighting fixture on the drawings; and the lighting fixture installation location coordinate table lists the number, coordinates, and installation method of all lighting fixtures in tabular form. The construction drawing export unit 502 supports exporting to multiple formats such as DWG and PDF, which can be directly used for construction and archiving.

[0069] The budget output module 6 also includes a scheme comparison unit 601. This unit compares data from multiple candidate lighting schemes at different cost levels, generating comparative data including the number of luminaires, total cost, expected illuminance, and payback period for each scheme, which is then displayed to the user. The comparative data generated by the scheme comparison unit 601 is presented in tabular or chart form, allowing users to intuitively compare the advantages and disadvantages of each scheme. Total cost includes luminaire procurement cost, installation cost, and estimated 10-year operating energy consumption cost; expected illuminance includes indicators such as average illuminance and illuminance uniformity; payback period refers to the time required for the increased investment in the higher-end scheme compared to the lower-end scheme to be recovered through energy-saving benefits. The scheme comparison unit 601 also supports user-defined weights, comprehensively scoring based on the user's emphasis on indicators such as cost, illuminance, and energy saving, and recommending the optimal scheme.

[0070] Example 3:

[0071] Based on the above embodiments 1-2, and with reference to Figures 1-4, this embodiment provides a detailed description of the specific working principles of the IoT feedback calibration module 7 and the intelligent control execution module 8.

[0072] The IoT feedback calibration module 7 also includes a data storage unit 704. This unit stores the actual illuminance data, simulated illuminance data, and parameter correction instructions collected each time, tagged with timestamps, forming a historical calibration database. This database serves as a reference for simulation calculation parameters in subsequent workshop lighting renovation or expansion projects. The data storage unit 704 uses a relational database or time-series database to store data. Each data record includes fields such as timestamp, sensor number, location coordinates, actual illuminance value, simulated illuminance value, deviation value, parameter correction type, and correction amount. The historical calibration database can be used to analyze the light decay patterns of lamps. For example, if a certain model of lamp experiences a 5% luminous flux decay after one year of use and a 10% decay after three years, this data can be used in simulation calculations for subsequent projects, allowing for early consideration of light decay factors and making the simulation results closer to actual operating conditions. The historical calibration database also supports data query and statistical analysis functions. Users can query historical data by time range, location, lamp type, and other criteria to generate analysis reports such as illuminance change trend charts and deviation distribution charts.

[0073] The intelligent control execution module 8 divides the workshop into several independent lighting zones. Each zone uses a dot-matrix arrangement of luminaires. The natural light sensor includes daylight intensity detection units deployed on the sides of the workshop windows and doors. Based on real-time daylight intensity data from the windows and doors, the intelligent control execution module 8 automatically adjusts the operating parameters of the luminaires in the corresponding lighting zones. The workshop zone division is determined based on production processes and personnel activity patterns. Generally, the same work area or production line is divided into one lighting zone, and the luminaires in each zone can be controlled independently. Dot-matrix arrangement refers to luminaires arranged in a regular grid, with the spacing between adjacent luminaires determined based on their light distribution and installation height, typically 1-1.5 times the installation height. The daylight intensity detection unit of the natural light sensor uses photodiodes or silicon photocells as photosensitive elements, with a measurement range of 0-100,000 lx, accurately detecting natural light intensity. Based on the data from the daylight intensity detection units, the intelligent control execution module 8 automatically reduces the brightness of luminaires in the corresponding areas or turns off some luminaires when natural light is sufficient, and automatically increases the brightness of luminaires when natural light is insufficient, achieving constant illuminance control. For example, in areas near windows where natural light is strong during the day, the brightness of the lights can be reduced to 30%-50%, while the interior areas of the workshop still need to maintain a higher level of brightness.

[0074] The intelligent control execution module 8 also generates lighting control commands based on personnel location data collected by personnel distribution sensors within the workshop. Personnel distribution sensors can employ infrared human body sensors, video analytics cameras, or WiFi positioning to detect the presence of personnel in various areas of the workshop. When no one is present in a lighting area, the intelligent control execution module 8 automatically turns off or dims the lights in that area; when personnel are detected entering, the lights are automatically turned on or brightened. A delay mechanism is implemented between the personnel distribution sensors and the lighting control to prevent frequent switching of lights due to brief periods of personnel absence; the delay time is typically set to 5-15 minutes. The intelligent control execution module 8 supports various control strategies, including timed control, illuminance control, personnel sensing control, and combined control. Users can select appropriate control strategies or customize control logic based on the actual usage conditions of the workshop.

[0075] The intelligent control execution module 8 connects to the luminaire's drive controller via wired or wireless communication. Wired communication methods include DALI bus and 0-10V control lines, suitable for new workshops or renovations where conditions permit. Wireless communication methods include ZigBee, Bluetooth Mesh, and WiFi, suitable for renovation projects where wiring is difficult. The intelligent control execution module 8 supports group control of luminaires, allowing luminaires within the same lighting area to be grouped together and receive unified control commands. It also allows for independent control of individual luminaires, enabling refined lighting management.

[0076] Example 4:

[0077] Please refer to Figures 1-4. A smart workshop lighting control method based on the Internet of Things, including a system applied to any one of Embodiments 1-3, includes the following steps:

[0078] S1. Obtain the workshop space geometric parameter dataset. Collect workshop size parameters, wall reflectivity parameters, ceiling height parameters, structural beam position coordinates, and three-dimensional coordinates of obstructions using a laser scanner or BIM model import method. Control the data acquisition accuracy within ±5cm, and the wall reflectivity measurement deviation should not exceed ±0.05.

[0079] S2. Obtain the three-dimensional position coordinates and external dimensions of the production equipment, and fuse them with the workshop spatial geometric parameter dataset to generate workshop three-dimensional spatial model data. Obtain production equipment information from the workshop manufacturing execution system (MES) or equipment ledger, including equipment identification, three-dimensional position coordinates, external dimensions, and equipment surface reflectivity parameters. Unify the equipment parameters with the spatial parameters to generate gridded workshop three-dimensional spatial model data.

[0080] S3. Retrieve the optical and economic parameters of the candidate luminaires from the luminaire parameter database. Based on the workshop's illuminance requirements and budget constraints, select suitable luminaire models from the luminaire parameter database and retrieve their optical parameters such as luminous flux, light distribution curve, and power, as well as their economic parameters such as unit price and service life.

[0081] S4. Based on the target illuminance standard value, combined with the workshop's 3D spatial model data and the optical parameters of the luminaires, the number of luminaires to be installed, their installation location coordinates, and installation angles are calculated using a ray tracing algorithm, and illuminance distribution data is generated. After setting the target illuminance standard value, the lighting simulation calculation module optimizes the luminaire layout through iterative calculations to ensure that the illuminance distribution meets the standard requirements while minimizing the number of luminaires.

[0082] S5. Embed the lamp installation location parameters into the workshop's 3D spatial model data to generate a 3D construction model with lamp installation location annotations. The construction positioning output module generates a unique number and relative coordinates for each lamp, producing a 3D construction model that can be used by construction personnel.

[0083] S6. Based on the quantity and type of lighting fixtures, and combined with their economic parameters, generate a budget report and multiple candidate lighting schemes at different cost levels. The budget output module generates lighting schemes at high, medium, and low cost levels, and the scheme comparison unit compares and analyzes each scheme for the user to choose from.

[0084] S7. After construction is completed, actual illuminance data is collected by an illuminance sensor and compared with simulated illuminance data. When the deviation exceeds a preset threshold, the optical parameters in the luminaire parameter database are corrected. The IoT feedback calibration module performs illuminance detection after construction. When the deviation between actual and simulated illuminance exceeds ±10%, the cause of the deviation is analyzed and relevant parameters are corrected, forming a data closed loop.

[0085] Furthermore, the step of generating the workshop 3D spatial model data also includes communicating with the workshop manufacturing execution system to receive production line adjustment information. When the location of production equipment changes or new equipment is added, the system automatically recalculates the lighting layout scheme and updates the lighting installation position parameters within the workshop 3D spatial model, generating the revised 3D construction model, budget report data, and construction positioning data. When adjustments to the workshop production line lead to changes in equipment layout, the system automatically receives the change information, recalculates the lighting simulation, and updates the lighting layout scheme to ensure that the lighting system always matches the production layout.

[0086] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concepts, should be covered within the scope of protection of the present invention.

Claims

1. A smart workshop lighting control system based on the Internet of Things, characterized in that, include: The spatial parameter acquisition module (1) is used to acquire the workshop spatial geometric parameter dataset, which includes workshop size parameters, wall reflectivity parameters, ceiling height parameters, structural beam position coordinates and three-dimensional coordinates of obstructions; the equipment layout parameter module (2) is connected to the spatial parameter acquisition module (1) and is used to acquire the equipment identification, three-dimensional position coordinates, external dimensions and equipment surface reflectivity parameters of the production equipment, and to integrate the production equipment parameters with the workshop spatial geometric parameter dataset to generate workshop three-dimensional spatial model data; The lighting parameter database (3) is connected to the equipment layout parameter module (2) and is used to store the lighting fixture identification, optical parameters and economic parameters of the selected lighting fixtures; the lighting simulation calculation module (4) is connected to the spatial parameter acquisition module (1), the equipment layout parameter module (2) and the lighting parameter database (3) respectively, and is used to receive the workshop three-dimensional spatial model data and the target illuminance standard value, call the optical parameters in the lighting parameter database (3), simulate the propagation path of light in the workshop through the ray tracing algorithm, calculate the number of lighting fixtures installed, the coordinates of the installation position and the installation angle, and generate the lighting simulation calculation module (4). Degree distribution data; Construction positioning output module (5), connected to the lighting simulation calculation module (4), is used to receive the coordinate data of the lamp installation position, embed the lamp installation position parameters into the workshop three-dimensional space model data, and generate a three-dimensional construction model with lamp installation position markings; Budget output module (6), connected to the lighting simulation calculation module (4) and the lamp parameter database (3), is used to receive the lamp installation quantity and type data, retrieve the corresponding economic parameters from the lamp parameter database (3), generate budget report data, and generate multiple candidate lighting scheme data at different cost levels; The IoT feedback calibration module (7) includes several illuminance sensors (701), a wireless communication unit (702), and a data comparison unit (703) deployed in the workshop work area. The illuminance sensors (701) are used to collect actual illuminance data of each area after construction is completed. The data comparison unit (703) compares the actual illuminance data with the simulated illuminance data generated by the lighting simulation calculation module (4). When the deviation value exceeds the preset threshold, a parameter correction instruction is generated and transmitted to the lamp parameter database (3) to correct the optical parameters in the lamp parameter database (3).

2. The smart workshop lighting control system based on the Internet of Things according to claim 1, characterized in that: The lighting simulation calculation module (4) also includes a ray tracing engine (401), which is used to convert the light distribution curve data of the lamp into light emission vectors, convert the three-dimensional space model data of the workshop into a light collision detection grid, and calculate the light intensity value of each collision point by tracing the collision point between the light and the workshop surface, thereby generating illuminance distribution data.

3. The smart workshop lighting control system based on the Internet of Things according to claim 1, characterized in that: The construction positioning output module (5) also includes a coordinate annotation generation unit (501), which is used to generate a unique lamp number for each lamp in the workshop three-dimensional space model and convert the three-dimensional coordinates of the lamp installation position into relative coordinate data relative to the workshop reference point.

4. The smart workshop lighting control system based on the Internet of Things according to claim 1, characterized in that: The construction positioning output module (5) also includes a construction drawing export unit (502), which is used to convert a three-dimensional construction model with lighting fixture installation position markings into construction drawings. The construction drawings include lighting fixture plan layout, lighting fixture installation section view, lighting fixture number markings, and lighting fixture installation position coordinate table.

5. A smart workshop lighting control system based on the Internet of Things according to claim 1, characterized in that: The budget output module (6) also includes a scheme comparison unit (601), which is used to compare the data of multiple candidate lighting schemes at different cost levels, generate comparison data including the number of lamps, total cost, expected illuminance value and investment payback period of each scheme, and display it to the user.

6. The smart workshop lighting control system based on the Internet of Things according to claim 1, characterized in that: The IoT feedback calibration module (7) also includes a data storage unit (704). The data storage unit (704) is used to store the actual illuminance data, simulated illuminance data and parameter correction instructions collected each time according to the timestamp to form a historical calibration database. The historical calibration database is used as a reference for the simulation calculation parameters of subsequent workshop lighting renovation or expansion projects.

7. A smart workshop lighting control system based on the Internet of Things according to claim 1, characterized in that: It also includes an intelligent control execution module (8), which is connected in communication with the drive controller of the lamps. It is used to generate lamp control commands and send them to the drive controller of each lamp based on the personnel location data collected by the personnel distribution sensor in the workshop and the ambient light data collected by the natural light sensor, so as to adjust the brightness and on / off status of the lamps in each area.

8. A smart workshop lighting control system based on the Internet of Things according to claim 7, characterized in that: The intelligent control execution module (8) divides the workshop into several independent lighting areas. Each lighting area uses a dot matrix arrangement of lamps. The natural light sensor includes a daylight brightness detection unit deployed on the side of the workshop window and the side of the door. The intelligent control execution module (8) automatically adjusts the working parameters of the lamps in the corresponding lighting areas according to the real-time daylight brightness data on the side of the window and the side of the door.

9. A smart workshop lighting control method based on the Internet of Things, comprising a system applied to any one of claims 1-8, characterized in that, Includes the following steps: S1. Obtain the workshop space geometric parameter dataset; S2. Obtain the 3D position coordinates and external dimensions of the production equipment, and fuse them with the workshop spatial geometric parameter dataset to generate a 3D spatial model of the workshop. S3. Retrieve the optical and economic parameters of the candidate luminaires from the luminaire parameter database. S4. Based on the target illuminance standard value, combined with the workshop 3D spatial model data and luminaire optical parameters, calculate the number of luminaires to be installed, their installation location coordinates, and installation angle using a ray tracing algorithm, and generate illuminance distribution data. S5. Embed the luminaire installation location parameters into the workshop 3D spatial model data to generate a 3D construction model with luminaire installation location annotations. S6. Based on the number and type of luminaires to be installed, combined with the luminaire economic parameters, generate budget report data and generate candidate lighting scheme data for multiple cost levels. S7. After construction is completed, the actual illuminance data is collected by the illuminance sensor and compared with the simulated illuminance data. When the deviation value exceeds the preset threshold, the optical parameters in the lamp parameter database are corrected.

10. The IoT-based smart workshop lighting control method according to claim 9, characterized in that, The step of generating the workshop 3D spatial model data also includes communicating with the workshop manufacturing execution system to receive production line adjustment information. When the location of production equipment changes or new equipment is added, the lighting layout scheme is automatically recalculated, and the lighting installation position parameters are updated within the workshop 3D spatial model to generate the modified 3D construction model, budget report data, and construction positioning data.

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