Stadium lighting control method and system based on automatic optimization of lamp projection angle

Abstract

The application discloses a stadium lighting control method and system based on automatic optimization of lamp projection angle, and relates to the technical field of lighting control based on wireless communication. The method carries out reverse ray tracing with shielding judgment from each sampling point of the target lighting area to the three-dimensional coordinates of the lamps and lanterns to obtain unshielded light contribution weight; then, with the preset target illuminance distribution as the constraint condition, the normal direction of the light-emitting surface of each lamp is taken as the variable to carry out multivariate optimization, the optimal space vector direction is obtained, and is converted into the target horizontal rotation angle and the target vertical pitch angle; finally, the control instruction is issued to each lamp in parallel through the wireless communication network. The application quantifies the light contribution weight of each lamp to the target lighting area accurately, takes the normal direction of the light-emitting surface as the variable to carry out global multivariate optimization, effectively improves the illuminance uniformity of the target lighting area, and realizes reliable issuance of the angle control instruction through the wireless communication network.

Description

Technical Field

[0001] This invention relates to the field of lighting control technology based on wireless communication, and in particular to a stadium lighting control method and system based on automatic optimization of lamp projection angle. Background Technology

[0002] The projection angle adjustment of existing stadium lighting systems is usually based on theoretical light distribution simulation completed according to the stadium building parameters. The standard installation direction of each lamp is determined in advance. After the on-site construction and deployment are completed, the angle is corrected lamp by lamp by manually checking the local illuminance data. After the debugging is completed, it is run with fixed parameters for a long time. When switching to different lighting tasks, the corresponding preset angle data is called.

[0003] However, during construction, there are errors in measurement and layout, and the welding of steel structures can cause deformation. In addition, there are factors such as the offset of the bracket installation. There is a deviation between the actual installation position of each lamp and its theoretical design coordinates. When dozens or even hundreds of lamps project onto the same target area, the positional deviations of each lamp are superimposed during the beam convergence process, causing the deviation between the actual illuminance distribution and the design expectation to exceed the acceptable range, and the illuminance uniformity is significantly deteriorated. Summary of the Invention

[0004] This invention provides a stadium lighting control method and system based on automatic optimization of lamp projection angle to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a stadium lighting control method based on automatic optimization of lamp projection angle, comprising: S1. Obtain the three-dimensional actual coordinates of each lamp in physical space generated by ultra-wideband positioning; S2. Perform reverse ray tracing from the sampling point to the actual three-dimensional coordinates of each lamp, including occlusion judgment, to obtain the contribution weight of each lamp to the unobstructed light of each sampling point in the target illumination area; S3. Using the preset target illuminance distribution as a constraint, and based on the light contribution weight, the normal direction of the luminous surface of each lamp is used as a variable to perform multivariate optimization to obtain the optimal spatial vector direction; S4. Convert the optimal space vector direction into the target horizontal rotation angle and target vertical pitch angle of the corresponding lamp; S5. Control the projection direction of each lamp according to the target's horizontal rotation angle and vertical pitch angle.

[0006] Preferably, obtaining the three-dimensional actual coordinates of each lamp includes: Based on the arrival time difference sequence of multiple ultra-wideband raw coordinate samples, the time delay drift coefficient of each sample is calculated, and samples with time delay drift coefficients exceeding a preset threshold are removed to obtain a high-confidence coordinate sample set. Based on the architectural structure data of the stadium, spatial boundary constraints are constructed. The three-dimensional coordinates generated by the high-confidence coordinate sample set are compared with the spatial boundary constraints. Coordinates located outside the spatial boundary are eliminated to obtain the actual three-dimensional coordinates.

[0007] Preferably, the reverse ray tracing including occlusion determination includes: Based on the three-dimensional geometric occlusion data of the stadium, light projection and occlusion detection are performed from each sampling point to the luminous surface area of ​​each lamp; The unobstructed light paths detected are combined with the corresponding luminaire emitting surface zoning information to calculate and generate unobstructed light contribution weights. The emitting surface zoning information is used to identify and constrain the light output contribution of different areas of each luminaire emitting surface during multivariate optimization.

[0008] Preferably, the identification and constraint of the light output contribution of different areas of the luminous surface of each lamp includes: When performing multivariate optimization, independent optimization variables are assigned to different luminous surface areas of each lamp based on the luminous surface zoning information; With the goal of minimizing total global energy consumption or suppressing spillover light in a specified area, together with the preset target illuminance distribution, a multi-objective optimization constraint is formed, and the optimal light output contribution of each luminaire's luminous surface area is solved simultaneously.

[0009] Preferably, the reverse ray tracing including occlusion determination further includes: When it is detected that the light path to the target sampling point is blocked by an obstruction in part of the light-emitting surface area of ​​a lamp, an obstruction compensation mark is generated for the unobstructed light-emitting surface area of ​​the lamp. In multivariate optimization, occlusion compensation markers are identified and the optimization variable weights of the corresponding unoccluded areas are increased. By supplementing light in the unoccluded areas, the light loss in the occluded areas is compensated, and the optimal light output contribution of each emitting surface area after compensation is obtained.

[0010] Preferably, the step of performing multivariate optimization by treating the normal direction of the luminous surface of each lamp as a variable includes: For all luminaires, the optimization sensitivity coefficient of each luminaire is calculated based on the spatial distribution entropy value of its corresponding light contribution weight within the target lighting area. Based on the sensitivity coefficient, all lamps are sorted in descending order. The lamps with the highest sensitivity coefficients are extracted from the sorting results to form an active variable set. The normal direction of the luminous surface of the remaining lamps is locked as the current initial direction.

[0011] Preferably, the step of using the normal direction of the luminous surface of each lamp as a variable for multivariate optimization further includes: During the iterative process of multivariate optimization, the change in the direction of the normal direction of each lamp in the active variable set between two adjacent iterations is collected to generate the convergence state marker of each lamp. When it is detected that the convergence state markers of all lamps in the active variable set indicate that they have reached a stable state, the locking state of the remaining lamps is released, the normal direction of the luminous surface of the remaining lamps is added to the variable space, and the iterative solution continues until the convergence state markers of all lamps indicate that they have reached a stable state, and the optimal space vector direction is obtained.

[0012] Preferably, controlling the projection direction of each lamp includes: Based on the target horizontal rotation angle and target vertical pitch angle of each lamp, the lamps are grouped into regions according to their spatial distribution in physical space, and angle command packets corresponding to each region are generated. For each angle command packet, the verification sequence of all angle commands in the packet is extracted, the verification sequence is appended to the corresponding angle command packet, and then sent in parallel to the lamps in the corresponding area through the wireless communication network.

[0013] Preferably, controlling the projection direction of each lamp further includes: When issuing angle command packets, the lamps located at the geometric center of each region are selected from each region based on the actual three-dimensional coordinates of each lamp as the response reference lamps for that region. Send advance execution commands to the response reference lights in each area, driving the response reference lights to perform angular rotations before other lights in the same area; Collect the actual pointing data after the reference light completes its rotation, compare the actual pointing data with the corresponding target horizontal rotation angle and target vertical pitch angle, and generate the command execution verification result for this area; When the command execution verification results are consistent, a synchronous execution command is sent to the other lights in the same area, driving the other lights to rotate at the same angle.

[0014] To address the aforementioned problems, this invention also provides a stadium lighting control system based on automatic optimization of lamp projection angle, the system comprising: The 3D coordinate acquisition module is used to acquire the actual 3D coordinates of each lamp in physical space generated by ultra-wideband positioning; The light contribution weight determination module is used to perform reverse ray tracing from the sampling point to the actual three-dimensional coordinates of each lamp, including occlusion judgment, to obtain the unobstructed light contribution weight of each lamp to each sampling point in the target illumination area. The projection direction optimization module is used to perform multivariate optimization based on the light contribution weight and the normal direction of the emitting surface of each lamp as a variable, constrained by the preset target illuminance distribution, to obtain the optimal spatial vector direction. Angle conversion module is used to convert the optimal spatial vector direction into the target horizontal rotation angle and target vertical pitch angle of the corresponding lamp; The lighting projection control module is used to control the projection direction of each lamp according to the target's horizontal rotation angle and vertical pitch angle.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention obtains the actual physical coordinates of each lamp through ultra-wideband positioning to replace the theoretical design coordinates, eliminating input errors caused by construction and installation deviations; combined with reverse ray tracing and multivariate optimization, it achieves the global optimal matching of the projection angles of all lamps, effectively improving the illuminance uniformity of the target lighting area.

[0016] 2. This invention, by deploying a wireless communication network to send angle control commands to each lamp, can effectively overcome the attenuation and reflection interference of the metal structure in large venues on wireless signals, ensuring that each lamp node reliably receives and synchronously executes the angle control commands.

[0017] 3. By calculating the optimization sensitivity coefficient through the spatial distribution entropy value of light contribution weight, high-impact lamps are selected to form an active variable set. Combined with a phased unlocking and iteration convergence mechanism, the variable scale and iteration overhead of the optimization process can be reduced while ensuring global optimization accuracy, thus significantly improving the convergence speed of multivariate optimization. Attached Figure Description

[0018] Figure 1 The flowchart shows the stadium lighting control method based on automatic optimization of lamp projection angle provided by the present invention.

[0019] Figure 2 This is a block diagram of the stadium lighting control system based on automatic optimization of lamp projection angle provided by the present invention. Detailed Implementation

[0020] The technical solutions of 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] Reference Figure 1 The diagram shown is a flowchart illustrating a stadium lighting control method based on automatic optimization of lamp projection angle according to an embodiment of the present invention. In this embodiment, the stadium lighting control method based on automatic optimization of lamp projection angle includes: S1. Obtain the actual three-dimensional coordinates of each lamp in physical space generated by ultra-wideband positioning.

[0022] Obtaining the actual three-dimensional coordinates of each lamp includes: Based on the arrival time difference sequence of multiple ultra-wideband raw coordinate samples, the time delay drift coefficient of each sample is calculated, and samples with time delay drift coefficients exceeding a preset threshold are removed to obtain a high-confidence coordinate sample set. Based on the architectural structure data of the stadium, spatial boundary constraints are constructed. The three-dimensional coordinates generated by the high-confidence coordinate sample set are compared with the spatial boundary constraints. Coordinates located outside the spatial boundary are eliminated to obtain the actual three-dimensional coordinates.

[0023] As a preferred embodiment of the present invention, for example, multiple ultra-wideband positioning base stations are deployed around the catwalk of the top lighting fixtures in an indoor multi-purpose stadium. Ultra-wideband positioning tags are fixed on the outer shell of each stadium lighting fixture. The signals of each positioning tag are continuously collected at a fixed acquisition frequency to generate multiple sets of arrival time difference sequences corresponding to each lighting fixture. Each set of arrival time difference sequences corresponds to an ultra-wideband original coordinate sample.

[0024] The time difference of arrival sequence refers to the time difference between the arrival of the positioning signal emitted by the same tag at different base stations. It is a continuous data group arranged in the order of sampling time and is the basic data source for solving spatial coordinates.

[0025] Then, for each original coordinate sample, the arrival time difference sequence is processed by extracting the time difference values ​​of multiple consecutive sampling times, calculating the time difference fluctuation value between adjacent sampling times, and then the fluctuation value is compared with the average time difference value of the sample sequence to obtain the time delay drift coefficient of each sample.

[0026] The time delay drift coefficient is used to characterize the signal time delay stability of a single positioning sample. The larger the coefficient, the stronger the signal interference and the lower the reliability of the positioning result. In indoor environments, this interference mostly comes from signal multipath reflection from the ceiling steel structure and equipment enclosure.

[0027] Positioning samples can be collected in an open calibration area without large obstructions in the venue through preliminary site calibration tests. The normal fluctuation range of the time delay drift coefficient under no obvious interference environment can be statistically analyzed, and the upper limit of this range can be taken as the preset threshold.

[0028] If the time delay drift coefficient of a certain sample exceeds the preset threshold, it is determined that the sample is affected by multipath reflection from the steel structure inside the building or interference from temporary equipment, resulting in positioning distortion. The sample is then removed from the original coordinate sample set, and the remaining samples together constitute a high-confidence coordinate sample set.

[0029] The high-confidence coordinate sample set is a set of original coordinate samples that meet the positioning confidence requirements after interference filtering, and is used for subsequent accurate three-dimensional coordinate calculation.

[0030] Then, for each set of samples in the high-confidence coordinate sample set, the straight-line distance from the tag to each base station is calculated based on the time difference of arrival, and the spatial position is calculated by the trilateration principle to generate multiple sets of three-dimensional coordinates corresponding to each lamp.

[0031] Based on the construction data of the stadium's building structure, the actual spatial dimensions and location information of the overhead lighting installation walkway and the ceiling load-bearing truss were extracted to construct the spatial boundary constraints of the legal installation area for the lighting fixtures.

[0032] Spatial boundary constraints are used to limit the legal spatial range of the physical existence of the lighting fixtures, filter out abnormal positioning results that do not conform to the actual installation logic, and prevent the calculated coordinates from falling into the ceiling cavity or the competition area of ​​the venue outside the catwalk.

[0033] Each of the calculated three-dimensional coordinates is compared with the legal spatial range defined by the spatial boundary constraints.

[0034] If a certain three-dimensional coordinate is outside the range defined by the spatial boundary constraints, the coordinate is determined to be an abnormal positioning result and is removed from the valid samples.

[0035] Finally, the average of the remaining valid three-dimensional coordinates is calculated to obtain the actual three-dimensional coordinates of each lamp in physical space.

[0036] S2. Perform reverse ray tracing from the sampling point to the actual three-dimensional coordinates of each lamp, including occlusion judgment, to obtain the contribution weight of each lamp to the unobstructed light of each sampling point in the target illumination area.

[0037] Inverse ray tracing that includes occlusion detection includes: Based on the three-dimensional geometric occlusion data of the stadium, light projection and occlusion detection are performed from each sampling point to the luminous surface area of ​​each lamp; The unobstructed light paths detected are combined with the corresponding luminaire emitting surface zoning information to calculate and generate unobstructed light contribution weights. The emitting surface zoning information is used to identify and constrain the light output contribution of different areas of each luminaire emitting surface during multivariate optimization.

[0038] This includes identifying and constraining the light output contribution of different areas of the luminous surface of each lamp, including: When performing multivariate optimization, independent optimization variables are assigned to different luminous surface areas of each lamp based on the luminous surface zoning information; With the goal of minimizing total global energy consumption or suppressing spillover light in a specified area, together with the preset target illuminance distribution, a multi-objective optimization constraint is formed, and the optimal light output contribution of each luminaire's luminous surface area is solved simultaneously.

[0039] Inverse ray tracing, which includes occlusion detection, also includes: When it is detected that the light path to the target sampling point is blocked by an obstruction in part of the light-emitting surface area of ​​a lamp, an obstruction compensation mark is generated for the unobstructed light-emitting surface area of ​​the lamp. In multivariate optimization, occlusion compensation markers are identified and the optimization variable weights of the corresponding unoccluded areas are increased. By supplementing light in the unoccluded areas, the light loss in the occluded areas is compensated, and the optimal light output contribution of each emitting surface area after compensation is obtained.

[0040] As a preferred embodiment of the present invention, for example, the standard basketball court floor of an indoor stadium is used as the target lighting area, and sampling points are evenly distributed on the surface of the court floor at fixed intervals, with each sampling point corresponding to an illuminance calculation point in the court.

[0041] First, based on the building information model of the stadium, the geometric contour data of the main truss structure of the ceiling, the central suspended LED screen, the sound array around the venue, and the lighting catwalk support are extracted and organized to form three-dimensional geometric obstruction data. This data records the spatial location and external dimensions of all physical structures in the venue that may block the propagation of light.

[0042] Then, light projection and occlusion detection are performed from each sampling point to the luminous surface area corresponding to the three-dimensional actual coordinates of each lamp. The calculation is carried out by reverse ray tracing, that is, the starting point of the light is set at the sampling point in the target lighting area, and the ending point of the light is pointed to the luminous surface of the lamp. This method can perform optical path calculation only for the target lighting area, effectively reducing the amount of calculation and improving the preprocessing efficiency before optimization.

[0043] In practice, for the correspondence between a single sampling point and a single lamp, a detection ray is emitted from the sampling point to the light-emitting surface area of ​​the lamp, and the overlap relationship between the spatial position of the ray and the three-dimensional geometric occlusion data is compared segment by segment along the ray propagation path.

[0044] If the light propagation path intersects with the geometric surface of any obstruction, it is determined that the light path is cut off by the obstruction, and the corresponding luminous surface area of ​​the lamp cannot contribute effective illumination to the sampling point. In indoor venues, such obstructions mostly originate from the partial obstruction of the central screen and the ceiling truss.

[0045] If the light propagation path does not intersect with any obstruction in space, the light path is determined to be an unobstructed valid path and can be included in the effective illumination contribution range of the luminaire to the sampling point.

[0046] It should be noted that the light-emitting surface of each lamp is pre-divided into multiple independent sub-regions, and corresponding light-emitting surface partition information is generated. The light-emitting surface partition information records the spatial position, area ratio, and correspondence of independent control channels of each light-emitting sub-region. This information is used to identify and constrain the light output contribution of different regions of the light-emitting surface of each lamp during multi-variable optimization.

[0047] The unobstructed light paths detected are combined with the corresponding luminaire emitting surface zoning information to calculate and generate the unobstructed light contribution weight.

[0048] In the specific calculation, the effective light quantity of each luminous surface area of ​​a single lamp corresponding to a single sampling point is first counted. Then, combined with the light propagation distance attenuation law, the light attenuation ratio is calculated based on the straight-line distance from the sampling point to the corresponding luminous surface area. Finally, the contribution weight of each luminous surface area of ​​each lamp to the unobstructed light of each sampling point in the target lighting area is obtained. This weight directly reflects the contribution ratio of the corresponding luminous area to the illuminance value of the sampling point under unobstructed conditions.

[0049] When performing multivariate optimization, independent optimization variables are assigned to different light-emitting surface areas of each lamp based on the light-emitting surface zoning information. Each optimization variable corresponds to the light output ratio of a light-emitting surface area. Each variable is adjusted independently to achieve separate constraints on the light output contribution of different light-emitting areas.

[0050] With the goal of minimizing total global energy consumption or suppressing spillover light in a specified area, together with the preset target illuminance distribution, a multi-objective optimization constraint is formed, and the optimal light output contribution of each luminaire's luminous surface area is solved simultaneously.

[0051] Specifically, the preset target illuminance distribution can be pre-set according to the lighting standards of the corresponding indoor competition level, clearly defining the target illuminance value, uniformity and glare restriction requirements for each point in the target lighting area. The target for suppressing spill light can be set for the spectator seating area outside the venue or the substitute bench area next to the venue, limiting the maximum allowable illuminance value of the area.

[0052] During the simultaneous solution process, the optimization variables of each luminous surface region are used as the adjustment objects. Under the premise of meeting the target illuminance distribution requirements, the variable values ​​are iteratively adjusted in the direction of reducing total energy consumption or reducing spilled light, and finally the basic optimal light output contribution of each luminous surface region is obtained.

[0053] When it is detected that the light path to the target sampling point is blocked by an obstruction in part of the light-emitting surface area of ​​a certain lamp, an obstruction compensation mark is generated for the unobstructed light-emitting surface area of ​​the lamp. This mark is used to record the local obstruction state of the lamp and the corresponding unobstructed area range, providing an identification basis for subsequent compensation calculation.

[0054] In multivariate optimization, occlusion compensation markers are identified and the optimization variable weights of the corresponding unoccluded areas are increased. By supplementing light in the unoccluded areas, the light loss in the occluded areas is compensated, and the optimal light output contribution of each emitting surface area after compensation is obtained.

[0055] In practice, the compensation and adjustment process is executed according to the set quantitative logic. First, the total luminous flux loss corresponding to all blocked light-emitting surface areas within the same lamp is calculated, and then the total physical maximum output luminous flux of all unblocked light-emitting surface areas within the same lamp is calculated.

[0056] The ratio of the total luminous flux loss to the total maximum output luminous flux is used as the base for weighting, and then a base multiplier is added to this base to form the final compensation adjustment multiplier.

[0057] It should be noted that, as a preferred embodiment of the present invention, the above-mentioned weight adjustment and light enhancement process for occlusion compensation can be expressed by the following quantitative formula:

[0058] In the formula, Indicates the first The optimal light output contribution weight of each unobstructed luminous surface region after compensation and adjustment; This represents the result obtained by solving the initial multi-objective optimization constraints without considering occlusion factors. The basic optimal light output contribution weight of an unobstructed luminous surface region; This represents the set of all luminous surface areas in the same luminaire whose light path to the target sampling point is interrupted by an obstruction. It represents the set of all luminous surface areas in the same luminaire whose light path to the target sampling point is not interrupted by any obstruction; Indicates the first The luminous flux loss value corresponding to the basic optimal light output contribution weight obtained in the initial multi-objective optimization for each blocked luminous surface region; Indicates the first The physical maximum output luminous flux value of an unobstructed luminous surface area; This indicates the universal quantification of the subsequent variable within the scope of its set, that is, the quantification of the set. Each unblocked light-emitting surface area in This formula is applicable to all calculations.

[0059] The above formula can be used to determine the total luminous flux loss in the blocked area. As the value increases, the compensation multiplier term on the right side of the formula increases synchronously, and the weight of the unobstructed area after compensation increases accordingly. As a result, it automatically matches the greater illumination compensation requirements, ensuring that the illumination of the target sampling point is maintained at the preset target level; when the total maximum output luminous flux of the unobstructed area is increased... When the light output is increased, the increase in compensation ratio corresponding to the same amount of light loss decreases, thus avoiding excessive increase in light output, which would lead to energy waste and excessive local illuminance.

[0060] By using the above method, it is ensured that the weight after compensation is always no less than the basic optimal weight, and the upper limit of compensation is naturally limited by the maximum output capability of the hardware in the unshaded area, so that invalid optimization solutions that exceed the physical constraints of the lamps will not be generated.

[0061] Among them, the basic optimal light output contribution weight comes from the initial multi-objective optimization solution without considering occlusion factors, and is the optimal light output ratio of each emitting surface region under ideal unoccluded conditions.

[0062] The set of blocked light-emitting surface areas is obtained by statistical analysis of the blocking detection results, summarizing all light-emitting surface areas in the same lamp whose light paths to the target sampling point are cut off.

[0063] The set of unobstructed light-emitting surface areas is also obtained from the statistics of obstruction detection results, summarizing all light-emitting surface areas in the same lamp that keep the light path to the target sampling point unobstructed.

[0064] The luminous flux loss value is calculated from the basic optimal light output contribution weight of the shaded area, corresponding to the luminous flux value that the area should have originally output, representing the scale of luminous flux loss caused by shading.

[0065] The maximum physical output luminous flux is determined by the hardware parameters of the luminaire itself, corresponding to the maximum light output capability that a single luminous surface area can achieve. This serves as an upper limit constraint for compensation and improvement, preventing the luminaire from exceeding its physical limits after compensation.

[0066] Understandably, the core significance of this quantitative compensation logic lies in adaptively adjusting the light output weight of the unblocked area based on the actual light loss caused by shading and the upper limit of the output capacity of the unblocked area. Without exceeding the hardware limitations of the luminaire, it makes up for the illuminance gap caused by shading, ensuring that the illuminance of the target lighting area meets the standard and is uniform.

[0067] The greater the total luminous flux loss in the shaded area, the higher the compensation adjustment ratio, and the greater the contribution weight of the compensated light output in the unshaded area, thus matching the greater illuminance compensation requirements.

[0068] When the total maximum output luminous flux of the unobstructed area is greater, the increase in compensation ratio corresponding to the same amount of loss is smaller, so as to avoid excessive increase in light output, which would lead to energy waste and excessive local illuminance.

[0069] It is important to emphasize that indoor stadiums have dense overhead structures, and equipment such as the central screen and sound array can easily cause localized light path obstruction. This obstruction compensation mechanism can specifically address common indoor venue issues such as missing light spots and uneven illumination, and has a more significant optimization effect compared to outdoor scenarios.

[0070] Finally, the unobstructed light contribution weights and compensation adjustment rules for each luminous surface area of ​​all lamps are summarized to form a complete basis for optical path contribution calculation, supporting the accurate solution of the subsequent multivariate optimization process.

[0071] S3. Using the preset target illuminance distribution as a constraint, and based on the light contribution weight, the normal direction of the luminous surface of each lamp is used as a variable to perform multivariate optimization to obtain the optimal spatial vector direction.

[0072] Among them, the direction of the normal to the luminous surface of each lamp is used as a variable for multivariate optimization, including: For all luminaires, the optimization sensitivity coefficient of each luminaire is calculated based on the spatial distribution entropy value of its corresponding light contribution weight within the target lighting area. Based on the sensitivity coefficient, all lamps are sorted in descending order. The lamps with the highest sensitivity coefficients are extracted from the sorting results to form an active variable set. The normal direction of the luminous surface of the remaining lamps is locked as the current initial direction.

[0073] This includes using the normal direction of the luminous surface of each lamp as a variable for multivariate optimization, and also includes: During the iterative process of multivariate optimization, the change in the direction of the normal direction of each lamp in the active variable set between two adjacent iterations is collected to generate the convergence state marker of each lamp. When it is detected that the convergence state markers of all lamps in the active variable set indicate that they have reached a stable state, the locking state of the remaining lamps is released, the normal direction of the luminous surface of the remaining lamps is added to the variable space, and the iterative solution continues until the convergence state markers of all lamps indicate that they have reached a stable state, and the optimal space vector direction is obtained.

[0074] As a preferred embodiment of the present invention, for example, a standard basketball court in an indoor multi-purpose stadium is used as the target lighting area. First, a preset target illuminance distribution is set according to the lighting standards of the corresponding competition level, and the target illuminance values ​​and uniformity requirements of each sampling point in the court are clarified. This distribution serves as a hard constraint condition for the multivariate optimization process.

[0075] Based on the contribution weight of each lamp to the unobstructed light at each sampling point obtained from the aforementioned steps, the normal direction of the emitting surface of each lamp is taken as an independent adjustable variable, and the multivariate optimization solution process is initiated.

[0076] First, calculate the spatial distribution entropy value of the corresponding light contribution weight of each lamp in the target lighting area.

[0077] The spatial distribution entropy value is used to characterize the dispersion of the light contribution of a single lamp across all sampling points. In the specific calculation, the weight ratio of the light contribution of a single lamp at all sampling points is first calculated, and then the entropy value is obtained based on the uniformity of the ratio. The more sampling points the light contribution covers and the more uniform the distribution, the higher the corresponding entropy value.

[0078] Then, the optimization sensitivity coefficient of each lamp is calculated based on the spatial distribution entropy value of each lamp. The higher the entropy value, the larger the optimization sensitivity coefficient, which means that the adjustment of the normal direction of the lamp has a stronger influence on the overall illuminance distribution, and the more significant the benefit of optimization adjustment.

[0079] Based on the obtained optimization sensitivity coefficient, all lamps are sorted in descending order according to the coefficient value.

[0080] By conducting optimization efficiency tests on similar venues in the early stages, the optimal selection ratio that balances solution speed and solution accuracy can be statistically analyzed, thereby determining the number of top-ranked lighting fixtures to be extracted from the ranking results.

[0081] The lamps with the highest sensitivity coefficients are extracted from the ranking results to form an active variable set. The normal direction of the luminous surface of the lamps in the set is used as an adjustable variable for optimization in the iterative calculation.

[0082] The normal direction of the luminous surface of the remaining lamps is locked as the current initial direction and is not included in the variable adjustment range for optimization.

[0083] It should be noted that this variable selection method can significantly reduce the variable dimensionality in the early stages of optimization, avoid the computational redundancy caused by iterating all variables simultaneously, and effectively improve the convergence speed of optimization solutions.

[0084] During the iterative process of multivariate optimization, the change in the direction of the normal direction of each lamp in the active variable set is continuously collected between two adjacent iterations.

[0085] By conducting preliminary angle accuracy tests, the impact threshold of the adjustment amount of the normal direction on the illumination result can be statistically determined. When the change in direction is less than this threshold, the fluctuation of the corresponding illumination can be ignored, and this value can be used as the convergence judgment threshold.

[0086] If the directional change of a single lamp in adjacent iterations is less than the convergence threshold, a convergence state marker indicating that the lamp has reached a stable state is generated; otherwise, an unstable marker is generated.

[0087] When the convergence status flags of all lamps in the active variable set indicate that a stable state has been reached, the locking status of the remaining lamps is released.

[0088] The normal direction of the luminous surface of the remaining lamps is added to the optimization variable space, and the lamps in the original active variable set are used together for iterative solution.

[0089] During the iteration process, the changes in adjacent iteration directions of all lamps are collected synchronously, and the convergence status markers of the corresponding lamps are updated.

[0090] The optimization iteration process terminates when all the convergence state markers of all the lamps indicate that they have reached a stable state, and the optimal spatial vector direction corresponding to each lamp is output.

[0091] It is important to emphasize that this phased iterative optimization method can not only quickly approach the global optimal solution by prioritizing the adjustment of high-sensitivity lights, but also ensure the accuracy of the solution by fine-tuning the subsequent low-sensitivity lights. This can significantly improve the optimization efficiency in scenarios with a large number of indoor venue lights.

[0092] S4. Convert the optimal spatial vector direction into the target horizontal rotation angle and target vertical pitch angle of the corresponding lamp.

[0093] As a preferred embodiment of the present invention, for example, for adjustable angle floodlights installed on the catwalk at the top of an indoor stadium, a local rotation reference coordinate system is first established with the mounting base of each light fixture as the origin. The horizontal rotation zero position direction is preset to the initial orientation pointing to the geometric center of the field, and the vertical pitch zero position direction is preset to the emission orientation parallel to the horizontal plane. This reference corresponds one-to-one with the zero position scale of the rotation drive mechanism of the light fixture itself.

[0094] It should be noted that the optimal spatial vector direction obtained in the aforementioned steps is vector data representing the direction of the luminaire's luminous surface normal in the global three-dimensional spatial coordinate system. This vector points to the optimal direction of light projection, starting from the luminaire's own installation position.

[0095] Then, the optimal spatial vector direction is projected vertically onto the horizontal plane, and the vertical component of the vector is removed to obtain the corresponding horizontal projection vector.

[0096] The horizontal projection vector is compared with the horizontal zero-position reference direction of the lamp, and the deflection angle and deflection direction between the two directions are calculated. The deflection direction corresponds to the clockwise or counterclockwise direction of the lamp's horizontal rotation. Finally, the target horizontal rotation angle of the lamp is obtained, which directly corresponds to the angle and direction of rotation of the lamp's horizontal axis.

[0097] Then, extract the vertical component of the optimal spatial vector direction, calculate the tilt angle between the vector and the horizontal plane, and combine the vertical deflection of the vector to distinguish between the elevation angle and the depression angle. Finally, obtain the target vertical pitch angle of the corresponding lamp. This angle directly corresponds to the angle and pitch direction that the lamp's pitch axis needs to rotate.

[0098] Understandably, this three-dimensional vector decomposition mapping method can directly convert the optimal orientation obtained by spatial optimization into two independent angular parameters that the dual-axis rotation mechanism of the lamp can recognize and execute, thus eliminating the parameter barrier between the three-dimensional vector and the physical actuator.

[0099] Finally, the target horizontal rotation angle and target vertical pitch angle corresponding to each lamp are matched and stored one by one with the unique number of the lamp, which serves as the direct basis for subsequent control of the lamp projection direction.

[0100] S5. Control the projection direction of each lamp according to the target's horizontal rotation angle and vertical pitch angle.

[0101] Controlling the projection direction of each lamp includes: Based on the target horizontal rotation angle and target vertical pitch angle of each lamp, the lamps are grouped into regions according to their spatial distribution in physical space, and angle command packets corresponding to each region are generated. For each angle command packet, the verification sequence of all angle commands in the packet is extracted, the verification sequence is appended to the corresponding angle command packet, and then sent in parallel to the lamps in the corresponding area through the wireless communication network.

[0102] Controlling the projection direction of each light fixture also includes: When issuing angle command packets, the lamps located at the geometric center of each region are selected from each region based on the actual three-dimensional coordinates of each lamp as the response reference lamps for that region. Send advance execution commands to the response reference lights in each area, driving the response reference lights to perform angular rotations before other lights in the same area; Collect the actual pointing data after the reference light completes its rotation, compare the actual pointing data with the corresponding target horizontal rotation angle and target vertical pitch angle, and generate the command execution verification result for this area; When the command execution verification results are consistent, a synchronous execution command is sent to the other lights in the same area, driving the other lights to rotate at the same angle.

[0103] As a preferred embodiment of the present invention, for example, for all the lighting fixtures installed on the circular catwalk at the top of an indoor multi-purpose stadium, the target horizontal rotation angle and target vertical pitch angle corresponding to each lamp are first obtained.

[0104] Based on the physical spatial distribution of each lamp's actual three-dimensional coordinates, the lamps are divided into multiple continuous area groups along the bridle path, with each group corresponding to a section of the bridle path in the venue.

[0105] For each area group, the target angle data of all lamps in the group are integrated with the corresponding lamp numbers to generate an angle instruction package corresponding to each area. Each angle instruction package contains only the angle control data of the lamps in the corresponding area.

[0106] For each angle command packet, extract the numerical sequence of all angle commands within the packet, and generate a corresponding verification sequence according to a fixed arrangement rule. This verification sequence is used by the command receiver to verify the integrity and accuracy of the data transmission.

[0107] The verification sequence is appended to the end of the corresponding angle instruction packet, and the angle instruction packet is sent in parallel to the corresponding lighting receiver in each area through the dedicated wireless communication network deployed in the venue.

[0108] It should be noted that the parallel distribution method of regional groups can reduce the data load of a single communication link, improve the efficiency of distributing commands to multiple lighting fixtures, and adapt to the synchronous control needs of a large number of lighting fixtures in indoor venues.

[0109] When issuing angle command packets, the geometric center position of the spatial coordinates of all lamps in each area is calculated based on the actual three-dimensional coordinates of each lamp. Then, the lamp closest to the geometric center position is selected and used as the response reference lamp for that area.

[0110] Send advance execution commands to the response reference lights in each area, driving the response reference lights to rotate their angles ahead of other lights in the same area, thus completing the pointing adjustment in advance.

[0111] The actual pointing data after the reference lamp completes its rotation is collected. This data is directly read by the angle detection element built into the lamp and includes the actual horizontal rotation angle and the actual vertical pitch angle.

[0112] The actual pointing data collected is compared one by one with the target's horizontal rotation angle and vertical pitch angle corresponding to the reference light, and the command execution verification result for this area is generated based on the degree of deviation.

[0113] The allowable range of angle deviation under normal operating conditions can be statistically determined through preliminary lamp precision calibration tests, and this can be used as the basis for verification and comparison.

[0114] When the command execution verification results are consistent, it indicates that the angle command data transmission in this area is accurate and the reference lamp is executing normally. Then, a synchronous execution command is sent to the other lamps in the same area to drive the other lamps to perform angle rotation simultaneously.

[0115] If the command execution verification results are inconsistent, it is determined that there is a transmission deviation or equipment execution failure in the command for that area. The angle command packet for the corresponding area is regenerated and the reference lamp verification process is executed again until the verification results are consistent.

[0116] Understandably, adopting the response reference lamp pre-execution verification method can identify instruction errors and equipment failures in advance before batch execution, avoid large-scale lamp malfunctions, and ensure the reliability and consistency of indoor venue lighting adjustments.

[0117] Finally, after all the lights in the area have completed their angle rotation, confirm that the projection direction of all the lights matches the optimal direction obtained through optimization, and complete the automatic control process of the entire venue lighting.

[0118] like Figure 2 The diagram shown is a modular structure diagram of the stadium lighting control system based on automatic optimization of lamp projection angle provided by the present invention, which includes: The three-dimensional coordinate acquisition module 100 is used to acquire the actual three-dimensional coordinates of each lamp in physical space generated by ultra-wideband positioning. The light contribution weight determination module 200 is used to perform reverse ray tracing from the sampling point to the three-dimensional actual coordinates of each lamp, including occlusion judgment, to obtain the unobstructed light contribution weight of each lamp to each sampling point in the target illumination area. The projection direction optimization module 300 is used to perform multivariate optimization based on the light contribution weight, with the preset target illuminance distribution as a constraint, and the normal direction of the emitting surface of each lamp as a variable, to obtain the optimal spatial vector direction. Angle conversion module 400 is used to convert the optimal space vector direction into the target horizontal rotation angle and target vertical pitch angle of the corresponding lamp. The lighting projection control module 500 is used to control the projection direction of each lamp according to the target's horizontal rotation angle and vertical pitch angle.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A stadium lighting control method based on automatic optimization of lamp projection angle, characterized in that, The method includes: S1. Obtain the three-dimensional actual coordinates of each lamp in physical space generated by ultra-wideband positioning; S2. Perform reverse ray tracing from the sampling point to the actual three-dimensional coordinates of each lamp, including occlusion judgment, to obtain the contribution weight of each lamp to the unobstructed light of each sampling point in the target illumination area; S3. Using the preset target illuminance distribution as a constraint, and based on the light contribution weight, the normal direction of the luminous surface of each lamp is used as a variable to perform multivariate optimization to obtain the optimal spatial vector direction; S4. Convert the optimal space vector direction into the target horizontal rotation angle and target vertical pitch angle of the corresponding lamp; S5. Control the projection direction of each lamp according to the target's horizontal rotation angle and vertical pitch angle.

2. The stadium lighting control method based on automatic optimization of lamp projection angle as described in claim 1, characterized in that, The process of obtaining the three-dimensional actual coordinates of each lamp includes: Based on the arrival time difference sequence of multiple ultra-wideband raw coordinate samples, the time delay drift coefficient of each sample is calculated, and samples with time delay drift coefficients exceeding a preset threshold are removed to obtain a high-confidence coordinate sample set. Based on the architectural structure data of the stadium, spatial boundary constraints are constructed. The three-dimensional coordinates generated by the high-confidence coordinate sample set are compared with the spatial boundary constraints. Coordinates located outside the spatial boundary are eliminated to obtain the actual three-dimensional coordinates.

3. The stadium lighting control method based on automatic optimization of lamp projection angle as described in claim 1, characterized in that, The reverse ray tracing including occlusion detection includes: Based on the three-dimensional geometric occlusion data of the stadium, light projection and occlusion detection are performed from each sampling point to the luminous surface area of ​​each lamp; The unobstructed light paths detected are combined with the corresponding luminaire emitting surface zoning information to calculate and generate unobstructed light contribution weights. The emitting surface zoning information is used to identify and constrain the light output contribution of different areas of each luminaire emitting surface during multivariate optimization.

4. The stadium lighting control method based on automatic optimization of lamp projection angle as described in claim 3, characterized in that, The identification and constraint of the light output contribution of different areas of the luminous surface of each lamp includes: When performing multivariate optimization, independent optimization variables are assigned to different luminous surface areas of each lamp based on the luminous surface zoning information; With the goal of minimizing total global energy consumption or suppressing spillover light in a specified area, together with the preset target illuminance distribution, a multi-objective optimization constraint is formed, and the optimal light output contribution of each luminaire's luminous surface area is solved simultaneously.

5. The stadium lighting control method based on automatic optimization of lamp projection angle as described in claim 3 or 4, characterized in that, The reverse ray tracing including occlusion detection also includes: When it is detected that the light path to the target sampling point is blocked by an obstruction in part of the light-emitting surface area of ​​a lamp, an obstruction compensation mark is generated for the unobstructed light-emitting surface area of ​​the lamp. In multivariate optimization, occlusion compensation markers are identified and the optimization variable weights of the corresponding unoccluded areas are increased. By supplementing light in the unoccluded areas, the light loss in the occluded areas is compensated, and the optimal light output contribution of each emitting surface area after compensation is obtained.

6. The stadium lighting control method based on automatic optimization of lamp projection angle as described in claim 1, characterized in that, The method of using the normal direction of the luminous surface of each lamp as a variable for multivariate optimization includes: For all luminaires, the optimization sensitivity coefficient of each luminaire is calculated based on the spatial distribution entropy value of its corresponding light contribution weight within the target lighting area. Based on the sensitivity coefficient, all lamps are sorted in descending order. The lamps with the highest sensitivity coefficients are extracted from the sorting results to form an active variable set. The normal direction of the luminous surface of the remaining lamps is locked as the current initial direction.

7. The stadium lighting control method based on automatic optimization of lamp projection angle as described in claim 6, characterized in that, The method of using the normal direction of the luminous surface of each lamp as a variable for multivariate optimization also includes: During the iterative process of multivariate optimization, the change in the direction of the normal direction of each lamp in the active variable set between two adjacent iterations is collected to generate the convergence state marker of each lamp. When it is detected that the convergence state markers of all lamps in the active variable set indicate that they have reached a stable state, the locking state of the remaining lamps is released, the normal direction of the luminous surface of the remaining lamps is added to the variable space, and the iterative solution continues until the convergence state markers of all lamps indicate that they have reached a stable state, and the optimal space vector direction is obtained.

8. The stadium lighting control method based on automatic optimization of lamp projection angle as described in claim 1, characterized in that, The control of the projection direction of each lamp includes: Based on the target horizontal rotation angle and target vertical pitch angle of each lamp, the lamps are grouped into regions according to their spatial distribution in physical space, and angle command packets corresponding to each region are generated. For each angle command packet, the verification sequence of all angle commands in the packet is extracted, the verification sequence is appended to the corresponding angle command packet, and then sent in parallel to the lamps in the corresponding area through the wireless communication network.

9. The stadium lighting control method based on automatic optimization of lamp projection angle as described in claim 8, characterized in that, The control of the projection direction of each lamp also includes: When issuing angle command packets, the lamps located at the geometric center of each region are selected from each region based on the actual three-dimensional coordinates of each lamp as the response reference lamps for that region. Send advance execution commands to the response reference lights in each area, driving the response reference lights to perform angular rotations before other lights in the same area; Collect the actual pointing data after the reference light completes its rotation, compare the actual pointing data with the corresponding target horizontal rotation angle and target vertical pitch angle, and generate the command execution verification result for this area; When the command execution verification result is consistent, a synchronous execution command is sent to the other lamps in the same area, driving the other lamps to rotate at the same angle.

10. A stadium lighting control system based on automatic optimization of lamp projection angle, used to implement the stadium lighting control method based on automatic optimization of lamp projection angle as described in any one of claims 1-9, characterized in that, The system includes: The 3D coordinate acquisition module is used to acquire the actual 3D coordinates of each lamp in physical space generated by ultra-wideband positioning; The light contribution weight determination module is used to perform reverse ray tracing from the sampling point to the actual three-dimensional coordinates of each lamp, including occlusion judgment, to obtain the unobstructed light contribution weight of each lamp to each sampling point in the target illumination area. The projection direction optimization module is used to perform multivariate optimization based on the light contribution weight and the normal direction of the emitting surface of each lamp as a variable, constrained by the preset target illuminance distribution, to obtain the optimal spatial vector direction. Angle conversion module is used to convert the optimal spatial vector direction into the target horizontal rotation angle and target vertical pitch angle of the corresponding lamp; The lighting projection control module is used to control the projection direction of each lamp according to the target's horizontal rotation angle and vertical pitch angle.

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