A smart shooting light adaptive control system based on multi-point photometric monitoring

Abstract

This invention relates to an intelligent adaptive control system for shooting light based on multi-point photometric monitoring, belonging to the field of optical measurement and automatic control technology. The system includes: a photometric sensing unit that collects and preprocesses photometric data from various areas of the shooting scene through a multi-directional, three-dimensional array of acquisition nodes; an anchoring and calculation unit that constructs a four-layer holographic mapping model of the light field based on measured data, matches the target light field parameters, calculates the deviation, and generates a priority-based collaborative control strategy; a collaborative control unit that adjusts the exposure parameters, supplementary light intensity, and color temperature of the shooting equipment according to priority to correct the light field deviation; and an optimization and control unit that iteratively optimizes the model by re-acquiring data, compares the remaining deviations, and executes closed-loop control. This system achieves comprehensive perception, accurate modeling, and intelligent control of the shooting light field, solving the problems of incomplete data acquisition, low control accuracy, and poor scene adaptability in traditional systems.

Description

Technical Field

[0001] This invention belongs to the field of optical measurement and automatic control technology, specifically relating to an intelligent shooting light adaptive control system based on multi-point photometric monitoring. Background Technology

[0002] Currently, existing shooting light control technologies are mainly divided into two categories: manual control and automatic control. Manual control relies on the professional experience of the operator, who manually adjusts the exposure parameters of the shooting equipment and the brightness and color temperature of the supplementary lighting equipment to achieve light adaptation. This method is not only cumbersome and inefficient, but its control accuracy is also greatly affected by the operator's experience. It is difficult to cope with the dynamic changes in the light field in the shooting scene and cannot achieve real-time adaptation of light parameters. Especially in complex scenes or during dynamic shooting, problems such as overexposure, underexposure, light ratio imbalance, and color temperature deviation are prone to occur, affecting the stability and consistency of the shooting image quality.

[0003] While existing automatic light control systems can automatically adjust some parameters, they still have many technical shortcomings and cannot meet the requirements for high-precision, intelligent light control. Specifically, the following problems exist: Firstly, the light field data acquisition method is singular, mostly only performing single-point photometric acquisition on the subject being photographed, ignoring the simultaneous acquisition of multi-dimensional light field data such as environment and background. This results in incomplete light field data acquisition, which cannot fully reflect the light field distribution state at the shooting scene, and thus affects the accuracy of subsequent deviation calculation and control strategies. Secondly, the lack of a sound digital restoration mechanism for light fields and the absence of a precise holographic mapping model for light fields make it impossible to convert the collected photometric data into light field features that can be accurately analyzed and controlled, making it difficult to achieve true restoration and dynamic tracking of the light field at the shooting location. Third, the data preprocessing process is imperfect. The raw photometric data collected is not effectively denoised, calibrated and optimized. Factors such as sensor temperature drift, nonlinear error and instantaneous interference can easily lead to data deviation, which further affects the control accuracy. Fourth, the design of deviation calculation and control strategy is unreasonable. The priority of correction of optical field deviation in each dimension is not clear. The control action lacks coordination and is prone to problems such as control confusion and adjustment lag. It is impossible to achieve accurate and coordinated optimization of the characteristics of each dimension of the optical field. Fifth, the closed-loop control mechanism is imperfect. After the control actions are executed, there is a lack of effective feedback verification and iterative optimization. It is impossible to correct the remaining deviations in a timely manner, making it difficult to maintain the long-term stability of the shooting light field and making it less adaptable to different shooting scenarios.

[0004] Furthermore, as industry standards for film and television lighting continue to improve, the requirements for target light field parameters in different shooting scenarios are becoming increasingly refined. Most existing automatic control systems lack a comprehensive industry standard database, making it impossible to automatically match corresponding target light field parameters according to different shooting scenarios. They also lack flexible parameter adaptation capabilities, making it difficult to meet the diverse shooting needs of various scenarios. At the same time, the control logic of existing systems often remains at the level of surface parameter adjustment, failing to form a complete closed-loop process. This results in low control precision and intelligence levels, making it impossible to achieve adaptive iterative optimization of light parameters. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides an intelligent shooting light adaptive control system based on multi-point photometric monitoring. The objective of this invention can be achieved through the following technical solution: include: The photometric sensing unit acquires raw photometric data of the shooting scene through preset multi-directional three-dimensional photometric acquisition nodes; performs full-link preprocessing on the raw photometric data, and outputs a measured photometric dataset. Anchored calculation unit, based on the measured photometric dataset, constructs a light field holographic mapping model of the shooting scene; matches the target light field parameters of the corresponding scene according to the shooting scene type and film and television lighting industry standards; compares the measured data in the light field holographic mapping model with the target parameters, calculates the deviation of each dimension of the shooting light field features, and generates a priority collaborative control strategy; The collaborative control unit receives the priority collaborative control strategy and executes control actions according to a preset priority order; it corrects the subject illumination deviation by prioritizing the adjustment of relevant exposure parameters of the shooting device; and then performs dynamic optimization of light ratio and color temperature calibration according to a preset supplementary lighting control channel. The control unit is optimized to reacquire photometric data and optimize the holographic mapping model of the light field after the control action takes effect; the remaining deviation of each dimension of the light field is compared and calculated, and the corresponding closed-loop control scheme is executed according to the amplitude level of the remaining deviation.

[0006] Specifically, the process of obtaining the raw luminance data of the shooting scene is as follows: Based on the preset multi-directional three-dimensional layout of photometric acquisition nodes, each node is aligned with the environment, subject, and background area of ​​the shooting scene; All nodes synchronously acquire light intensity and spectral color temperature data for each region; The collected data are categorized and archived according to region, and the categorized data are integrated and submitted to generate raw photometric data.

[0007] Specifically, the light field holographic mapping model includes a parameter input layer, a data processing layer, a feature mapping layer, and a model output layer. The specific construction process is as follows: The total input is a measured photometric dataset, and the output is a real-time updatable holographic mapping model of the light field. The parameter input layer receives the measured photometric dataset and performs format regularization and normalization processing on the multi-dimensional photometric data of the environment, subject, and background, establishing a standardized data transmission protocol; the data processing layer performs noise reduction, purification, and feature analysis on the input data, extracting the core features of light intensity and color temperature; the feature mapping layer constructs a holographic mapping relationship between light field features and spatial location, restoring the spatial distribution of the light field; the model output layer generates a dynamically iterative holographic mapping model of the light field.

[0008] Specifically, the process of matching the target light field parameters of the corresponding scene is as follows: Identify the type of shooting scene selected by the user and retrieve the corresponding lighting control reference parameters from the film and television lighting industry standard system; Based on the actual working conditions at the shooting location, the lighting control reference parameters were adjusted accordingly, and the rationality and synergy of the reference parameters were verified during the adjustment process.

[0009] Specifically, the process of calculating the deviation of each dimension of the captured light field is as follows: Based on the measured illumination data from various dimensions, the measured data are classified and sorted according to the dimensions of illumination characteristics; The classified measured data are compared with the target light field parameters of the corresponding dimension one by one in all dimensions, and the specific difference between the measured data and the target parameters in each dimension is calculated. The differences in each dimension are labeled and archived according to feature type. All labeled dimensional differences are summarized, and the feature deviations in each dimension reflecting the state of the light field deviation are calculated and generated.

[0010] Specifically, the process of generating the priority collaborative control strategy is as follows: Based on the weight of the influence of the deviation of each dimension of the shooting light field on the shooting effect, the priority order of correction of each dimension deviation is determined; The deviation dimensions that have a significant impact on the shooting effect are listed as priority correction items, and corresponding parameter adjustment schemes are formulated according to the amplitude and type of deviation of each dimension. All parameter adjustment schemes are integrated according to priority to generate a priority-based coordinated control strategy.

[0011] Specifically, the process of executing the control actions according to a preset priority order is as follows: Based on the priority-based coordinated control strategy, the priority of deviation correction for each dimension is analyzed. The parameter adjustment actions for each dimension are triggered sequentially based on their priority order. When performing the preceding core deviation adjustment, the subsequent adjustment action is set to the pending trigger state; After the preceding adjustments are completed and the preset control effect is achieved, the next dimension of control action is initiated. During the execution, the adjustment status is monitored in real time and data changes are fed back.

[0012] Specifically, the process of correcting the subject illuminance deviation is as follows: Access the exposure parameter adjustment permissions of the shooting equipment to determine the direction and magnitude of the subject's illumination deviation; Adjust the relevant exposure parameters of the shooting equipment based on the judgment results; by adjusting the exposure parameters, change the amount of light entering the equipment, thereby controlling the exposure level of the main area of ​​the image; During calibration, the subject's illuminance data is acquired in real time, and the calibrated data is compared with the target parameters until the subject's illuminance deviation is calibrated to the preset range.

[0013] Specifically, the process of performing dynamic light ratio optimization and color temperature calibration is as follows: A signal transmission and command execution link for supplementary lighting regulation is established through a preset supplementary lighting control channel; Obtain the specific amplitude of the light ratio deviation, adjust the output intensity of the supplementary light according to the degree of deviation, and regulate the ratio between the main light and the ambient light; Determine the direction of color temperature deviation and adjust the spectral properties of the supplementary light output according to the deviation status; By adjusting the spectral characteristics of the supplementary light, the overall color temperature of the scene is calibrated and matched with the target parameters.

[0014] Specifically, the process of reacquiring photometric data and optimizing the light field holographic mapping model is as follows: Once the previous control measures take effect, the photometric data acquisition node will be restarted. Real-time photometric data were recollected from each region according to the original photometric data acquisition specifications, and the recollected data was synchronously input into the light field holographic mapping model. Update the illumination distribution data of each region in the light field holographic mapping model and re-acquire the illumination mapping relationship of each dimension.

[0015] Specifically, the process of comparing and calculating the residual deviation of the optical field in each dimension is as follows: Using the optimized light field holographic mapping model as the data base, we extract the measured illumination data of each dimension after adjustment and updating. The updated data is categorized and sorted according to the dimensions of illumination features, and the categorized data is compared with the scene target light field parameters again in all dimensions. The remaining deviation values ​​after adjusting the illumination data in each dimension are calculated, and the remaining deviation values ​​are labeled and archived according to feature type. All labeled values ​​are then summarized.

[0016] Specifically, the process of implementing the corresponding closed-loop control scheme is as follows: The amplitude level of the residual deviation of each dimension of the calculated light field is determined, and the corresponding level is divided according to the amplitude of the deviation. The determined level is matched with the preset control rules, and the corresponding control execution plan is retrieved. When the deviation exceeds the preset range, the deviation calculation and parameter fine-tuning process is repeated. When the deviation converges to the preset range, all operating parameters are immediately locked, scene light field data is continuously acquired, and dynamic monitoring is entered.

[0017] The beneficial effects of this invention are as follows: (1) By setting up a photometric sensing unit with multi-directional stereo acquisition and a layered architecture anchoring and calculation unit, it is possible to simultaneously acquire the light intensity and spectral color temperature data of multiple areas of the environment, subject and background in the shooting scene. The original data is preprocessed in the whole link to remove drift error and instantaneous interference. Then, relying on the four-layer structure of parameter input, data processing, feature mapping and model output, a light field holographic mapping model is constructed to fully restore the spatial distribution and dynamic change characteristics of the light field on site. This achieves comprehensive acquisition of light field information, accurate data purification and digital holographic representation of the light field, fundamentally solving the problems of single acquisition method, data distortion and inability to fully reproduce the light field on site, and improving the accuracy and completeness of light field perception and modeling. (2) By setting up a priority-driven collaborative control unit and a closed-loop iterative optimization control unit, the correction order can be determined according to the degree of deviation impact. Priority is given to correcting the core deviation of the main illumination, and then the light ratio optimization and color temperature calibration are completed in a coordinated manner to ensure that the control actions are orderly and coordinated, without conflict or lag. At the same time, after the control, data is re-collected, the model is iteratively updated and the remaining deviation is compared. Closed-loop fine-tuning or parameter locking monitoring is performed according to the deviation level. Combined with film and television lighting standards, scene target parameters are automatically matched to form a full-process intelligent closed loop of data collection, calculation, control feedback and optimization. This significantly improves the accuracy, adaptability and scene adaptability of light control, effectively ensures the long-term stability of the shooting light field, and improves the consistency and reliability of image quality. Attached Figure Description

[0018] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0019] Figure 1 This is a system architecture diagram of an intelligent shooting light adaptive control system based on multi-point photometric monitoring according to the present invention; Figure 2 This is a data flow diagram of an intelligent shooting light adaptive control system based on multi-point photometric monitoring according to the present invention; Figure 3 This is a schematic diagram illustrating an implementation of an intelligent shooting light adaptive control system based on multi-point photometric monitoring according to the present invention. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0021] Please see Figure 1-3 An intelligent shooting light adaptive control system based on multi-point photometric monitoring; include: The photometric sensing unit acquires raw photometric data of the shooting scene through preset multi-directional three-dimensional photometric acquisition nodes; performs full-link preprocessing on the raw photometric data, and outputs a measured photometric dataset. Anchored calculation unit, based on the measured photometric dataset, constructs a light field holographic mapping model of the shooting scene; matches the target light field parameters of the corresponding scene according to the shooting scene type and film and television lighting industry standards; compares the measured data in the light field holographic mapping model with the target parameters, calculates the deviation of each dimension of the shooting light field features, and generates a priority collaborative control strategy; The collaborative control unit receives the priority collaborative control strategy and executes control actions according to a preset priority order; it corrects the subject illumination deviation by prioritizing the adjustment of relevant exposure parameters of the shooting device; and then performs dynamic optimization of light ratio and color temperature calibration according to a preset supplementary lighting control channel. The control unit is optimized to reacquire photometric data and optimize the holographic mapping model of the light field after the control action takes effect; the remaining deviation of each dimension of the light field is compared and calculated, and the corresponding closed-loop control scheme is executed according to the amplitude level of the remaining deviation.

[0022] In this embodiment, the raw photometric data specifically includes: initial illumination data that has not been preprocessed and is synchronously acquired by photometric acquisition nodes arranged in a multi-directional three-dimensional layout. Specifically, it includes the illumination intensity data of the environmental area, the illumination intensity data of the main body area, the illumination intensity data of the background area, and the overall spectral color temperature data of the scene.

[0023] In this embodiment, the shooting scene types and film and television lighting industry standards specifically include: The shooting scene type is the category of on-site scene selected by the user that is suitable for different shooting needs and purposes; Film and television lighting industry standards are the industry-wide technical specifications for film and television shooting lighting, the benchmark requirements for lighting control, the reference criteria for light and shadow ratio, and the standardized lighting parameter system adapted to various shooting scenarios.

[0024] In this embodiment, the relevant exposure parameters specifically include: adjustable operating parameters in the shooting device used to control the amount of light entering the image and the degree of exposure, mainly core control parameters used to change the intensity of light entering the image, adjust the illuminance of the main area, and control the brightness of the image.

[0025] In this embodiment, the photometric data specifically includes: various types of light-related data acquired and processed by the system in the entire process of acquisition, preprocessing, modeling, comparison, and feedback, covering the initial photometric data acquired in the original acquisition, the measured photometric data after preprocessing, the updated photometric data after adjustment and reacquisition, as well as various types of light intensity data and spectral color temperature data.

[0026] In this embodiment, the illumination feature dimension specifically includes: core illumination attribute categories used to characterize the light field quality at the shooting location, specifically divided into subject illuminance feature dimension, light ratio relationship feature dimension, and color temperature consistency feature dimension, which are the main basis for the system to perform deviation calculation and hierarchical control.

[0027] In this embodiment, the illumination distribution data of each region specifically includes: illumination intensity information, spectral color temperature information, and inter-regional illumination correlation information of the environmental region, the main region, and the background region recorded in the light field holographic mapping model, as well as the spatial distribution and mapping relationship data of illumination in each region on the shooting site.

[0028] In this embodiment, the scene light field data specifically includes: global illumination comprehensive data covering the entire shooting scene, specifically including luminance data of each region, illumination feature data of each dimension, deviation data before and after adjustment, dynamic light field data after model iteration and update, and real-time light field change data for continuous monitoring.

[0029] Specifically, the process of obtaining the raw luminance data of the shooting scene is as follows: Based on the preset multi-directional three-dimensional layout of photometric acquisition nodes, each node is aligned with the environment, subject, and background area of ​​the shooting scene; All nodes synchronously acquire light intensity and spectral color temperature data for each region; The collected data are categorized and archived according to region, and the categorized data are integrated and submitted to generate raw photometric data.

[0030] Specifically, the light field holographic mapping model includes a parameter input layer, a data processing layer, a feature mapping layer, and a model output layer. The specific construction process is as follows: The total input is a measured photometric dataset, and the output is a real-time updatable holographic mapping model of the light field. The parameter input layer receives the measured photometric dataset and performs format regularization and normalization processing on the multi-dimensional photometric data of the environment, subject, and background, establishing a standardized data transmission protocol; the data processing layer performs noise reduction, purification, and feature analysis on the input data, extracting the core features of light intensity and color temperature; the feature mapping layer constructs a holographic mapping relationship between light field features and spatial location, restoring the spatial distribution of the light field; the model output layer generates a dynamically iterative holographic mapping model of the light field.

[0031] Specifically, the process of matching the target light field parameters of the corresponding scene is as follows: Identify the type of shooting scene selected by the user and retrieve the corresponding lighting control reference parameters from the film and television lighting industry standard system; Based on the actual working conditions at the shooting location, the lighting control reference parameters were adjusted accordingly, and the rationality and synergy of the reference parameters were verified during the adjustment process.

[0032] Specifically, the process of calculating the deviation of each dimension of the captured light field is as follows: Based on the measured illumination data from various dimensions, the measured data are classified and sorted according to the dimensions of illumination characteristics; The classified measured data are compared with the target light field parameters of the corresponding dimension one by one in all dimensions, and the specific difference between the measured data and the target parameters in each dimension is calculated. The differences in each dimension are labeled and archived according to feature type. All labeled dimensional differences are summarized, and the feature deviations in each dimension reflecting the state of the light field deviation are calculated and generated.

[0033] Specifically, the process of generating the priority collaborative control strategy is as follows: Based on the weight of the influence of the deviation of each dimension of the shooting light field on the shooting effect, the priority order of correction of each dimension deviation is determined; The deviation dimensions that have a significant impact on the shooting effect are listed as priority correction items, and corresponding parameter adjustment schemes are formulated according to the amplitude and type of deviation of each dimension. All parameter adjustment schemes are integrated according to priority to generate a priority-based coordinated control strategy.

[0034] Specifically, the process of executing the control actions according to a preset priority order is as follows: Based on the priority-based coordinated control strategy, the priority of deviation correction for each dimension is analyzed. The parameter adjustment actions for each dimension are triggered sequentially based on their priority order. When performing the preceding core deviation adjustment, the subsequent adjustment action is set to the pending trigger state; After the preceding adjustments are completed and the preset control effect is achieved, the next dimension of control action is initiated. During the execution, the adjustment status is monitored in real time and data changes are fed back.

[0035] Specifically, the process of correcting the subject illuminance deviation is as follows: Access the exposure parameter adjustment permissions of the shooting equipment to determine the direction and magnitude of the subject's illumination deviation; Adjust the relevant exposure parameters of the shooting equipment based on the judgment results; by adjusting the exposure parameters, change the amount of light entering the equipment, thereby controlling the exposure level of the main area of ​​the image; During calibration, the subject's illuminance data is acquired in real time, and the calibrated data is compared with the target parameters until the subject's illuminance deviation is calibrated to the preset range.

[0036] Specifically, the process of performing dynamic light ratio optimization and color temperature calibration is as follows: A signal transmission and command execution link for supplementary lighting regulation is established through a preset supplementary lighting control channel; Obtain the specific amplitude of the light ratio deviation, adjust the output intensity of the supplementary light according to the degree of deviation, and regulate the ratio between the main light and the ambient light; Determine the direction of color temperature deviation and adjust the spectral properties of the supplementary light output according to the deviation status; By adjusting the spectral characteristics of the supplementary light, the overall color temperature of the scene is calibrated and matched with the target parameters.

[0037] Specifically, the process of reacquiring photometric data and optimizing the light field holographic mapping model is as follows: Once the previous control measures take effect, the photometric data acquisition node will be restarted. Real-time photometric data were recollected from each region according to the original photometric data acquisition specifications, and the recollected data was synchronously input into the light field holographic mapping model. Update the illumination distribution data of each region in the light field holographic mapping model and re-acquire the illumination mapping relationship of each dimension.

[0038] Specifically, the process of comparing and calculating the residual deviation of the optical field in each dimension is as follows: Using the optimized light field holographic mapping model as the data base, we extract the measured illumination data of each dimension after adjustment and updating. The updated data is categorized and sorted according to the dimensions of illumination features, and the categorized data is compared with the scene target light field parameters again in all dimensions. The remaining deviation values ​​after adjusting the illumination data in each dimension are calculated, and the remaining deviation values ​​are labeled and archived according to feature type. All labeled values ​​are then summarized.

[0039] Specifically, the process of implementing the corresponding closed-loop control scheme is as follows: The amplitude level of the residual deviation of each dimension of the calculated light field is determined, and the corresponding level is divided according to the amplitude of the deviation. The determined level is matched with the preset control rules, and the corresponding control execution plan is retrieved. When the deviation exceeds the preset range, the deviation calculation and parameter fine-tuning process is repeated. When the deviation converges to the preset range, all operating parameters are immediately locked, scene light field data is continuously acquired, and dynamic monitoring is entered.

[0040] In this embodiment, the photometric sensing unit uses three photometric acquisition nodes, arranged in a multi-directional three-dimensional layout of 2 on top and 1 in front, which are precisely aligned with the environment, subject, and background areas of the shooting scene. All nodes synchronously acquire data from each area at a sampling frequency of 10Hz. Specifically, they acquire light intensity data of 50-1000 lux for the environment area, 80-1200 lux for the subject area, 50-800 lux for the background area, and spectral color temperature data of 3200K-7500K for the entire scene. After acquisition, the data is classified and archived according to the area, and then integrated and merged to generate the original photometric data.

[0041] In this embodiment, the multi-directional three-dimensional layout refers to arranging at least three or more light intensity acquisition nodes for a typical shooting scene (including the subject, foreground, background, top lighting area, and side lighting area). A recommended layout is a "2 top, 1 front" layout: two nodes are located above the shooting area, pointing towards the subject and the main environmental area respectively, for acquiring top lighting and overall ambient light data; one node is located coaxially or adjacent to the shooting device, pointing towards the subject, for acquiring illuminance data of the subject's front; the number of nodes is determined based on the ability to cover key areas of light ratio transition in the scene (such as the boundary between the subject and background, and the light areas of the main light and fill light).

[0042] In this embodiment, the four-layer architecture of the light field holographic mapping model is specifically set as follows: The parameter input layer normalizes the measured photometric data into normalized values ​​of 0-1 and establishes a standardized data transmission protocol with a baud rate of 9600; the data processing layer uses a mean filtering algorithm to denoise and purify the input data, with the filtering window set to 5, and extracts the core features of light intensity and color temperature; the feature mapping layer constructs the mapping relationship between the light field and spatial position according to a 10cm×10cm spatial grid, and uses an inverse distance weighted interpolation algorithm to calculate and reconstruct the light feature values ​​at any position within the shooting area based on the spatial coordinates and photometric data of each acquisition node; the model output layer generates a dynamically iterative model, updating the model parameters every 0.5 seconds to ensure that the mapping accuracy error does not exceed 5%.

[0043] In this embodiment, the end-to-end preprocessing includes the following steps: Timestamp alignment and synchronization: High-precision timestamps are added to the raw data packets reported by all acquisition nodes, and alignment is performed based on the system master clock to ensure that the time synchronization of multiple data streams is within 1 millisecond; Format normalization and standardization: Raw electrical signals or digital readings from sensors of different models or ranges are uniformly converted into illuminance values ​​in lux and color temperature values ​​in Kelvin (K) through their respective calibration curves. Subsequently, all illuminance values ​​are normalized to the [0, 1] interval, with the normalization reference being the maximum range of the sensor; Noise reduction and outlier removal: Moving average filtering (window size of 5-10 sampling points) is used to smooth the time-series data to suppress random noise. At the same time, a dynamic threshold based on statistical principles (such as ±3 times the standard deviation) is set, and instantaneous data points exceeding the threshold are regarded as outliers and removed. Then, linear interpolation is used to complete the data at that time point.

[0044] In this embodiment, when matching the target light field parameters of the corresponding scene, the film and television lighting industry standard system is built in. The lighting control benchmark parameters retrieved for indoor portrait shooting scenes are 800-1000 lux for the main body illuminance, 1:2 light ratio, and 5600K color temperature. The benchmark parameters for outdoor landscape shooting scenes are 1000-1200 lux for the main body illuminance, 1:3 light ratio, and 6500K color temperature. Based on the actual working conditions on site, the benchmark parameters are adjusted by ±50 lux, ±0.2 light ratio, and ±300K. After adjustment, the parameter synergy is verified to ensure that the parameter deviation does not exceed 10%.

[0045] In this embodiment, when calculating the deviation of each dimension of the captured light field, the measured data are sorted and classified according to three lighting feature dimensions: subject illuminance, light ratio, and color temperature. The difference method is used to compare the data with the target parameters one by one, and the calculation accuracy is retained to one decimal place. The subject illuminance deviation calculation threshold is set to ±30 lux, the light ratio deviation threshold is set to ±0.1, and the color temperature deviation threshold is set to ±200K. After the difference values ​​of each dimension are labeled and archived, the deviation dataset is generated and used for subsequent control strategy generation.

[0046] In this embodiment, when the collaborative control unit performs control actions, the preset priority order is subject illuminance correction > light ratio optimization > color temperature calibration. This priority order is determined based on the following shooting principles: correct subject exposure is the foundation for a usable image (highest weight), light ratio affects the image's three-dimensionality and focus (second highest weight), and color temperature deviation within a certain range can be corrected in post-processing (relatively low weight). The relevant exposure parameters of the shooting device are adjusted first, with ISO sensitivity ranging from 100 to 3200, shutter speed ranging from 1 / 10000s to 30s, and aperture ranging from f / 1.4 to f / 22. After the subject illuminance deviation is adjusted to within ±30 lux, the fill light intensity is adjusted through the fill light control channel, with an adjustment range of 0-1000 lux and a color temperature adjustment range of 3200K-7500K.

[0047] In this embodiment, when the optimized control unit executes the closed-loop control scheme, it waits for a 1-second delay after the control action takes effect before starting the photometric acquisition node to re-acquire data. The re-acquisition frequency remains at 10Hz. The calculated remaining deviation is divided into three amplitude levels. When the deviation is >50 lux (or >0.2 light ratio, >300K), the deviation calculation and parameter fine-tuning are repeated. The fine-tuning step size is 10 lux, 0.05 light ratio, and 100K, and the number of iterations does not exceed 5. When the deviation is ≤30 lux (or ≤0.1 light ratio, ≤200K), the equipment operating parameters are locked, and scene light field data is continuously acquired at a frequency of 5Hz, entering the dynamic monitoring state.

[0048] In this embodiment, taking an indoor professional portrait photography scene as an example, the specific process is as follows: After the system starts up, the photometric sensing unit begins to work. This unit is equipped with 'a' photometric acquisition nodes, which are installed in a multi-directional three-dimensional layout and are respectively aimed at the main area, environment area and background area of ​​the shooting scene. All acquisition nodes start up synchronously at a sampling frequency of dHz, and synchronously acquire the light intensity data and spectral color temperature data of each area. The light intensity data acquired in the main area ranges from e to f, the environment area from g to h, the background area from i to j, and the overall spectral color temperature data of the scene ranges from k to l. After the data acquisition is completed, the photometric sensing unit performs full-link preprocessing on the raw photometric data, sequentially performing format normalization, normalization and noise reduction operations, removing abnormal data, and finally outputting a standardized measured photometric dataset to provide data support for the anchoring calculation unit. After receiving the measured photometric dataset from the photometric sensing unit, the anchoring and calculation unit initiates the construction process of the light field holographic mapping model. This model adopts a four-layer architecture, specifically: the parameter input layer receives the measured photometric dataset, establishes a standardized data transmission protocol with a baud rate of n, and realizes ordered data transmission; the data processing layer uses a filtering algorithm to denoise and purify the input data with a filtering window p, and extracts the core features of light intensity and color temperature; the feature mapping layer constructs a holographic mapping relationship between light field features and spatial position according to a q×q spatial grid, and restores the on-site light field distribution; the model output layer generates a dynamically iterative light field holographic mapping model, which updates parameters every r seconds, and the mapping accuracy error does not exceed s. After the light field holographic mapping model is constructed, the anchoring and solving unit automatically identifies the current indoor portrait shooting scene type and retrieves the lighting control benchmark parameters that are suitable for the scene from the system's built-in film and television lighting industry standard system, specifically the main body illuminance t, light ratio u:v, and color temperature w; Subsequently, based on the actual working conditions at the shooting location, the reference parameters were adjusted according to the magnitude of x, and the rationality and synergy of the parameters were verified to determine the final target light field parameters. Then, the anchoring calculation unit compared the measured data in the light field holographic mapping model with the target light field parameters in all dimensions, calculated the feature deviation of each dimension, determined the correction order according to the priority of subject illuminance > light ratio > color temperature, generated a priority synergistic control strategy, and sent it to the synergistic control unit. After receiving the control strategy, the collaborative control unit executes control actions according to the preset priority: First, it retrieves the exposure parameter adjustment permissions of the shooting device and adjusts the relevant exposure parameters of the shooting device, including ISO sensitivity adjustment range from y to z, shutter speed adjustment range from 1 / a second to b second, and aperture adjustment range from f / c to f / d, until the subject illumination deviation is corrected to within the range of e; then, through the preset fill light control channel, it adjusts the fill light output intensity (adjustment range from f to g), optimizes the ratio of subject light to ambient light to achieve a light ratio of u:v, and adjusts the spectral properties of the fill light output (color temperature adjustment range from k to l) to calibrate the overall color temperature of the scene to w, completing all control actions; After the coordinated control action is completed, the optimization and control unit starts working. First, it waits for a delay of h seconds to ensure that the control action is fully effective and the light field state tends to stabilize. Then, it restarts a photometric acquisition nodes to re-acquire real-time photometric data of each region at a sampling frequency of dHz. The re-acquired data is synchronously input into the light field holographic mapping model to complete the iterative optimization of the model. Afterward, it compares and calculates the remaining deviation of each dimension of the light field characteristics, divides it into p levels according to the deviation amplitude, and matches the corresponding closed-loop control scheme: if the remaining deviation exceeds the range of q, it repeats the deviation calculation and parameter fine-tuning process with a fine-tuning step size of r, with no more than s iterations; if the remaining deviation converges to the range of q, it locks all system operating parameters and continuously collects scene light field data at a frequency of tHz, entering a dynamic monitoring state to ensure the stability of the light field during shooting.

[0049] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A smart shooting light adaptive control system based on multi-point photometric monitoring, characterized in that, include: The photometric sensing unit acquires the raw photometric data of the shooting scene through preset multi-directional three-dimensional photometric acquisition nodes; The raw photometric data undergoes end-to-end preprocessing to output a measured photometric dataset; Anchored calculation unit, based on the measured photometric dataset, constructs a light field holographic mapping model of the shooting scene; matches the target light field parameters of the corresponding scene according to the shooting scene type and film and television lighting industry standards; compares the measured data in the light field holographic mapping model with the target parameters, calculates the deviation of each dimension of the shooting light field features, and generates a priority collaborative control strategy; The coordinated control unit receives the priority coordinated control strategy and executes control actions according to a preset priority order. By prioritizing the adjustment of relevant exposure parameters of the shooting equipment, the subject's illumination deviation is corrected; then, according to the preset fill light control channel, dynamic optimization of light ratio and color temperature calibration are performed. The control unit is optimized to reacquire photometric data and optimize the holographic mapping model of the light field after the control action takes effect; the remaining deviation of each dimension of the light field is compared and calculated, and the corresponding closed-loop control scheme is executed according to the amplitude level of the remaining deviation.

2. The system according to claim 1, characterized in that, The specific process for obtaining the raw luminance data of the shooting scene is as follows: Based on the preset multi-directional three-dimensional layout of photometric acquisition nodes, each node is aligned with the environment, subject, and background area of ​​the shooting scene; All nodes synchronously acquire light intensity and spectral color temperature data for each region; The collected data are categorized and archived according to region, and the categorized data are integrated and submitted to generate raw photometric data.

3. The system according to claim 1, characterized in that, The light field holographic mapping model includes a parameter input layer, a data processing layer, a feature mapping layer, and a model output layer. The specific construction process is as follows: The total input is a measured photometric dataset, and the output is a real-time updatable holographic mapping model of the light field. The parameter input layer receives the measured photometric dataset, performs format regularization and normalization on the multi-dimensional photometric data of environment, subject, and background, and establishes a standardized data transmission protocol; the data processing layer performs noise reduction, purification and feature analysis on the input data, and extracts the core features of light intensity and color temperature. The feature mapping layer constructs a holographic mapping relationship between light field features and spatial location, restoring the spatial distribution of the light field; the model output layer generates a dynamically iterative holographic mapping model of the light field.

4. The system according to claim 1, characterized in that, The specific process of matching the target light field parameters of the corresponding scene is as follows: Identify the type of shooting scene selected by the user and retrieve the corresponding lighting control reference parameters from the film and television lighting industry standard system; Based on the actual working conditions at the shooting location, the lighting control reference parameters were adjusted accordingly, and the rationality and synergy of the reference parameters were verified during the adjustment process.

5. The system according to claim 1, characterized in that, The specific process for calculating the deviation of each dimension of the captured light field is as follows: Based on the measured illumination data from various dimensions, the measured data are classified and sorted according to the dimensions of illumination characteristics; The classified measured data are compared with the target light field parameters of the corresponding dimension one by one in all dimensions, and the specific difference between the measured data and the target parameters in each dimension is calculated. The differences in each dimension are labeled and archived according to feature type. All labeled dimensional differences are summarized, and the feature deviations in each dimension reflecting the state of the light field deviation are calculated and generated.

6. The system according to claim 1, characterized in that, The specific process of generating the priority-based collaborative regulation strategy is as follows: Based on the weight of the influence of the deviation of each dimension of the shooting light field on the shooting effect, the priority order of correction of each dimension deviation is determined; The deviation dimensions that have a significant impact on the shooting effect are listed as priority correction items, and corresponding parameter adjustment schemes are formulated according to the amplitude and type of deviation of each dimension. All parameter adjustment schemes are integrated according to priority to generate a priority-based coordinated control strategy.

7. The system according to claim 1, characterized in that, The specific process of executing the control actions according to the preset priority order is as follows: Based on the priority-based coordinated control strategy, the priority of deviation correction for each dimension is analyzed. The parameter adjustment actions for each dimension are triggered sequentially based on their priority order. When performing the preceding core deviation adjustment, the subsequent adjustment action is set to the pending trigger state; After the preceding adjustments are completed and the preset control effect is achieved, the next dimension of control action is initiated. During the execution, the adjustment status is monitored in real time and data changes are fed back.

8. The system according to claim 1, characterized in that, The specific process for correcting the subject's illuminance deviation is as follows: Access the exposure parameter adjustment permissions of the shooting equipment to determine the direction and magnitude of the subject's illumination deviation; Adjust the relevant exposure parameters of the shooting equipment based on the judgment results; by adjusting the exposure parameters, change the amount of light entering the equipment, thereby controlling the exposure level of the main area of ​​the image; During calibration, the subject's illuminance data is acquired in real time, and the calibrated data is compared with the target parameters until the subject's illuminance deviation is calibrated to the preset range.

9. The system according to claim 1, characterized in that, The specific process for performing dynamic light ratio optimization and color temperature calibration is as follows: A signal transmission and command execution link for supplementary lighting regulation is established through a preset supplementary lighting control channel; Obtain the specific amplitude of the light ratio deviation, adjust the output intensity of the supplementary light according to the degree of deviation, and regulate the ratio between the main light and the ambient light; Determine the direction of color temperature deviation and adjust the spectral properties of the supplementary light output according to the deviation status; By adjusting the spectral characteristics of the supplementary light, the overall color temperature of the scene is calibrated and matched with the target parameters.

10. The system according to claim 1, characterized in that, The specific process of reacquiring photometric data and optimizing the light field holographic mapping model is as follows: Once the previous control measures take effect, the photometric data acquisition node will be restarted. Real-time photometric data were recollected from each region according to the original photometric data acquisition specifications, and the recollected data was synchronously input into the light field holographic mapping model. Update the illumination distribution data of each region in the light field holographic mapping model and re-acquire the illumination mapping relationship of each dimension.

11. The system according to claim 1, characterized in that, The specific process for comparing and calculating the residual deviation of the optical field features in each dimension is as follows: Using the optimized light field holographic mapping model as the data base, we extract the measured illumination data of each dimension after adjustment and updating. The updated data is categorized and sorted according to the dimensions of illumination features, and the categorized data is compared with the scene target light field parameters again in all dimensions. The remaining deviation values ​​after adjusting the illumination data in each dimension are calculated, and the remaining deviation values ​​are labeled and archived according to feature type. All labeled values ​​are then summarized.

12. The system according to claim 1, characterized in that, The specific process for implementing the corresponding closed-loop control scheme is as follows: The amplitude level of the residual deviation of each dimension of the calculated light field is determined, and the corresponding level is divided according to the amplitude of the deviation. The determined level is matched with the preset control rules, and the corresponding control execution plan is retrieved. When the deviation exceeds the preset range, the deviation calculation and parameter fine-tuning process is repeated. When the deviation converges to the preset range, all operating parameters are immediately locked, scene light field data is continuously acquired, and dynamic monitoring is entered.

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