Lamp color correction method and device, computer equipment and storage medium

Abstract

The embodiment of the invention belongs to the technical field of photoelectricity, and relates to a lamp color correction method which comprises the following steps: acquiring detection data of to-be-corrected lamps of different colors; determining an adjustment strategy corresponding to each to-be-corrected lamp according to the detection data by adopting a preset algorithm; and according to the adjustment strategy, light source parameters of each to-be-corrected lamp are adjusted, and the light source parameters comprise power, color temperature and chromatic aberration. The invention further provides a lamp color correction device, computer equipment and a storage medium. Accurate lamp light source parameter data are obtained through automatic detection equipment, then an accurate adjustment strategy is generated through a preset algorithm, rapid and comprehensive collection of optical information is achieved, a reliable basis is provided for color correction, subjective errors in manual adjustment are avoided, and the efficiency and accuracy of adjustment are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of optoelectronic technology, and in particular to a lamp color correction method, device, computer equipment and storage medium. Background Art

[0002] With the development of science and technology and the improvement of people's living standards, people have higher and higher requirements for the color of lamps. For lamps, different light colors have differences in brightness and light intensity, so it is necessary to perform color correction on lamps to ensure that the differences in brightness and light intensity under different light colors are small. Therefore, color correction is a very important technical link in the field of lamp design.

[0003] Traditional color correction solutions are usually achieved by adjusting the power of the light source in the lamp. For example, the brightness of the red light is adjusted by adjusting the power of the red light, thereby achieving color correction. However, due to the current difficulties in testing the differences between lamps of different colors, it is difficult to accurately debug the color correction parameters of the lamp using this solution. At the same time, the power of the light source in the lamp needs to be adjusted manually, which consumes a lot of time and manpower costs, and the adjustment accuracy is limited, making the efficiency of color correction low. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a lamp color correction method, device, computer equipment and storage medium to solve the technical problem of low efficiency of lamp color correction.

[0005] In order to solve the above technical problems, the embodiment of the present application provides a lamp color correction method, which adopts the following technical solution:

[0006] A lamp color correction method comprises the following steps:

[0007] Obtaining detection data of lamps to be calibrated of different colors;

[0008] Using a preset algorithm, according to the detection data, determining the adjustment strategy corresponding to each of the lamps to be calibrated;

[0009] According to the adjustment strategy, the light source parameters of each of the lamps to be calibrated are adjusted, and the light source parameters include power, color temperature and color difference.

[0010] Furthermore, the step of using a preset algorithm to determine the adjustment strategy corresponding to each of the lamps to be calibrated according to the detection data specifically includes:

[0011] According to the preset algorithm, a target algorithm model is established;

[0012] The detection data is input into the algorithm model, and the adjustment strategy is output according to the algorithm model.

[0013] Furthermore, the preset algorithm includes a grey relational algorithm, and the step of inputting the detection data into the algorithm model and outputting the adjustment strategy according to the algorithm model specifically includes:

[0014] Generating a statistical graph according to the detection data, and analyzing the statistical graph to obtain an analysis series;

[0015] Preprocessing the analysis sequence to obtain a preprocessed target analysis sequence, wherein the target analysis sequence includes a parent sequence and a child sequence;

[0016] Calculate the correlation between each index in the subsequence and the parent sequence;

[0017] The adjustment strategy is determined according to the correlation degree.

[0018] Furthermore, after the step of using a preset algorithm to determine the adjustment strategy corresponding to each of the lamps to be calibrated according to the detection data, the method further includes:

[0019] Displaying the adjustment strategy on a preset display interface, and receiving an adjustment instruction carrying user feedback information triggered by the display interface;

[0020] According to the user feedback information, the light source parameters of each of the lamps to be calibrated are updated.

[0021] Furthermore, after the step of obtaining the detection data of the lamps to be calibrated of different colors, the method further includes:

[0022] When there is abnormal detection data in the detection data, the alarm information corresponding to the abnormal detection data is sent to the preset administrator user terminal

[0023] In order to solve the above technical problems, the embodiment of the present application further provides a lamp color correction device, which adopts the following technical solution:

[0024] A lamp color correction device, comprising:

[0025] An acquisition module, used to acquire detection data of lamps to be calibrated of different colors;

[0026] A determination module, used to determine the adjustment strategy corresponding to each of the lamps to be calibrated according to the detection data by using a preset algorithm;

[0027] The adjustment module is used to adjust the light source parameters of each of the lamps to be calibrated according to the adjustment strategy, and the light source parameters include power, color temperature and color difference.

[0028] Furthermore, the determination module includes:

[0029] A model building unit, used to build a target algorithm model according to the preset algorithm;

[0030] The model execution unit is used to input the detection data into the algorithm model and output the adjustment strategy according to the algorithm model.

[0031] Furthermore, the preset algorithm includes a grey relational algorithm, and the model execution unit includes:

[0032] An analysis subunit, used for generating a statistical graph according to the detection data, and analyzing the statistical graph to obtain an analysis series;

[0033] A preprocessing subunit, used to preprocess the analysis sequence to obtain a target analysis sequence after preprocessing, wherein the target analysis sequence includes a parent sequence and a child sequence;

[0034] A calculation subunit, used for calculating the correlation between each index in the subsequence and the parent sequence;

[0035] A determination subunit is used to determine the adjustment strategy according to the correlation degree.

[0036] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0037] A computer device comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the lamp color correction method as described above when executing the computer-readable instructions.

[0038] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0039] A computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the lamp color correction method described above are implemented.

[0040] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0041] The lamp color correction method disclosed in the present application obtains the detection data of each lamp to be corrected of different colors; uses a preset algorithm to determine the adjustment strategy corresponding to each lamp to be corrected according to the detection data; and adjusts the light source parameters of each lamp to be corrected according to the adjustment strategy, and the light source parameters include power, color temperature and color difference. The present application obtains accurate lamp light source parameter data through automated detection equipment, and then generates an accurate adjustment strategy through a preset algorithm, so as to realize the rapid and comprehensive collection of optical information, provide a reliable basis for color correction, avoid subjective errors in manual adjustment, and significantly improve the efficiency and accuracy of adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0044] Figure 2 is a flow chart of an embodiment of a lamp color correction method according to the present application;

[0045] Figure 3 is a schematic structural diagram of an embodiment of a lamp color correction device according to the present application;

[0046] Figure 4 yes Figure 3 A structural schematic diagram of a specific implementation of the determination module shown;

[0047] Figure 5 yes Figure 4 A structural schematic diagram of a specific implementation of the model execution unit shown;

[0048] Figure 6 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0050] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0051] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0052] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0053] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0054] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0055] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0056] It should be noted that the lamp color correction method provided in the embodiment of the present application is generally executed by a server, and accordingly, the lamp color correction device is generally arranged in the server.

[0057] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0058] Continue to refer Figure 2 , shows a flow chart of an embodiment of a lamp color correction method according to the present application. The lamp color correction method comprises the following steps:

[0059] Step S201, obtaining detection data of lamps to be calibrated of different colors.

[0060] In this embodiment, the electronic device (eg, Figure 1 The server shown in the figure) can send or receive data through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0061] In this embodiment, a multi-channel photoelectric detection device is used to perform optical detection on the lamp to be calibrated. The device integrates multiple types of light detectors, including red, green, and blue basic color light detectors and a comprehensive light intensity detector. The device tests the lamps one by one to obtain key data such as the current light intensity, color temperature, and color difference of each color light source. These data are transmitted to the lamp management system in the form of digital signals for subsequent algorithm processing.

[0062] Step S202: using a preset algorithm to determine the adjustment strategy corresponding to each of the lamps to be calibrated according to the detection data.

[0063] In this embodiment, the lamp management system analyzes and processes the input detection data according to a preset adjustment algorithm. The adjustment algorithm generates an adjustment strategy based on the deviation value in the detection data (such as the red light intensity is too low, the blue light is too warm). For example, when it is detected that the red light intensity of a lamp is 20% lower than the standard value, the algorithm will generate an adjustment strategy, including increasing the power of the red light source, while fine-tuning the green and blue light sources to maintain overall brightness consistency.

[0064] Step S203: adjusting the light source parameters of each of the lamps to be calibrated according to the adjustment strategy, wherein the light source parameters include power, color temperature and color difference.

[0065] In this embodiment, according to the adjustment strategy, the automatic control device adjusts the light source parameters of the lamp, such as power, color temperature and color difference, and monitors the adjustment effect in real time through the optical signal feedback loop to ensure that the adjusted light color output of the lamp meets the design standard.

[0066] This application obtains accurate lamp light source parameter data through automated detection equipment, and then generates accurate adjustment strategies through preset algorithms to achieve fast and comprehensive collection of optical information, providing a reliable basis for color correction, avoiding subjective errors in manual adjustment, and significantly improving the efficiency and accuracy of adjustment.

[0067] In some optional implementations of this embodiment, the above step of using a preset algorithm to determine the adjustment strategy corresponding to each of the lamps to be calibrated according to the detection data specifically includes:

[0068] According to the preset algorithm, a target algorithm model is established;

[0069] The detection data is input into the algorithm model, and the adjustment strategy is output according to the algorithm model.

[0070] In this embodiment, a gray correlation algorithm is selected, and the data of historical light source parameters and target output values ​​are used to construct an adjustment algorithm model. The model can dynamically analyze the contribution of each light source to the overall output light color based on the detection data of different lamps, and then identify the main causes of light color deviation. Specifically, the detection data is visualized to generate statistical charts of the red, green and blue light intensity of the lamp; the standard value sequence (parent sequence) and the current value sequence (subsequence) of the lamp are extracted from the statistical chart to form a target analysis series; the gray correlation algorithm is used to calculate the correlation between each light source parameter in the subsequence and the target value of the parent sequence. The lower the correlation value, the greater the deviation of the parameter from the target output; according to the correlation analysis results, an accurate adjustment strategy is formulated. For example, when the correlation of the green light source is significantly lower than that of other colors, the system will recommend increasing the power of the green light source and adjusting other light sources to maintain light color balance.

[0071] This application improves the scientificity and reliability of the generation of adjustment strategies by establishing an algorithm model, avoiding the subjective problems of relying on experience-based adjustments.

[0072] In some optional implementations of this embodiment, the preset algorithm includes a grey relational algorithm, and the step of inputting the detection data into the algorithm model and outputting the adjustment strategy according to the algorithm model specifically includes:

[0073] Generating a statistical graph according to the detection data, and analyzing the statistical graph to obtain an analysis series;

[0074] Preprocessing the analysis sequence to obtain a preprocessed target analysis sequence, wherein the target analysis sequence includes a parent sequence and a child sequence;

[0075] Calculate the correlation between each index in the subsequence and the parent sequence;

[0076] The adjustment strategy is determined according to the correlation degree.

[0077] In this embodiment, the detection data is presented in a visual manner (such as a curve graph) to facilitate the analysis of the changing trend of the optical parameters; the analysis series is the basic data for subsequent algorithm processing, including the light source parameters of the lamp. The parent sequence is the target optical parameter sequence, and the child sequence is the detection optical parameter sequence; data preprocessing includes normalization operations so that different light source parameters can be calculated in the same dimension. The correlation reflects the degree to which the detection data deviates from the target value. For example, the color temperature parameter has a low correlation, which means that the deviation from the target value is large and needs to be adjusted first. Parameters with low correlation are marked as priority adjustment objects, and specific adjustment instructions are generated (such as the color temperature needs to be increased by 5%).

[0078] It should be noted that the grey correlation algorithm is an algorithm for analyzing the degree of association of various factors within the system, and is suitable for processing small samples, uncertain or incomplete information data. In the present embodiment, the algorithm is used to evaluate the degree of deviation between the detection data (subsequence) and the target standard value (parent sequence). In addition, a particle swarm optimization algorithm or a genetic algorithm can also be used. The particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence, which simulates the collaborative behavior of a flock of birds or a school of fish when searching for food. It searches for the optimal solution in the solution space through multiple "particles", and is suitable for solving multi-parameter optimization problems. A genetic algorithm is an optimization algorithm that simulates the biological evolution process, and realizes a global search of the solution space through operations such as "selection", "crossover" and "mutation". The algorithm uses randomness and genetic rules to find the optimal solution.

[0079] This application uses a grey correlation algorithm to quickly evaluate the degree of color deviation of lamps and focus on optimizing key parameters, thereby improving the accuracy and efficiency of lamp color correction.

[0080] In some optional implementations of this embodiment, after the step of using a preset algorithm to determine the adjustment strategy corresponding to each of the lamps to be calibrated according to the detection data, the method further includes:

[0081] Displaying the adjustment strategy on a preset display interface, and receiving an adjustment instruction carrying user feedback information triggered by the display interface;

[0082] According to the user feedback information, the light source parameters of each of the lamps to be calibrated are updated.

[0083] In this embodiment, the display interface is used to present the correction scheme, including the adjusted power, color temperature and color difference parameters. The user can enter feedback in the interface, such as whether he is satisfied or proposes fine-tuning requirements. User feedback is used to optimize the adjustment strategy, such as reducing the sensitivity of a certain light source parameter or increasing the adjustment range. The updated light source parameters will be reapplied to the lamp to achieve dynamic optimization.

[0084] By introducing a user feedback link, the correction method in this application is not only automated, but also increases flexibility and user participation, so that the correction effect is more in line with actual needs.

[0085] In some optional implementations of this embodiment, after the above step of acquiring the detection data of the lamps to be calibrated of different colors, the following steps are further included:

[0086] When there is abnormal detection data in the detection data, alarm information corresponding to the abnormal detection data is sent to a preset administrator user terminal.

[0087] In this embodiment, the system monitors the detection data in real time. For example, when the light source power, color temperature or color difference parameters exceed the set threshold, it is judged as abnormal and the abnormal information will be sent to the administrator through the alarm system (such as push notification or SMS).

[0088] Through abnormal detection and alarm functions, this application can promptly detect and eliminate lamp failures, avoid affecting the use effect due to optical performance deviation, and thus improve the reliability and safety of the system.

[0089] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0090] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0091] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0092] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0093] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a lamp color correction device, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0094] like Figure 3 As shown, the lamp color correction device 300 described in this embodiment includes: an acquisition module 301, a determination module 302 and an adjustment module 303. Among them:

[0095] An acquisition module 301 is used to acquire detection data of lamps to be calibrated of different colors;

[0096] A determination module 302 is used to determine, using a preset algorithm, an adjustment strategy corresponding to each of the lamps to be calibrated according to the detection data;

[0097] The adjustment module 303 is used to adjust the light source parameters of each of the lamps to be calibrated according to the adjustment strategy, and the light source parameters include power, color temperature and color difference.

[0098] The lamp color correction device provided in the present application obtains accurate lamp light source parameter data through automated detection equipment, and then generates accurate adjustment strategies through preset algorithms, thereby achieving fast and comprehensive collection of optical information, providing a reliable basis for color correction, avoiding subjective errors in manual adjustment, and significantly improving the efficiency and accuracy of adjustment.

[0099] In some optional implementations of this embodiment, the determining module 302 is further configured to:

[0100] According to the preset algorithm, a target algorithm model is established;

[0101] The detection data is input into the algorithm model, and the adjustment strategy is output according to the algorithm model.

[0102] The lamp color correction device provided in the present application improves the scientificity and reliability of the generation of the adjustment strategy by establishing an algorithm model, thereby avoiding the subjective problem of relying on experience-based adjustment.

[0103] In some optional implementations of this embodiment, the preset algorithm includes a grey relational degree algorithm, and the determination module 302 is further used to:

[0104] Generating a statistical graph according to the detection data, and analyzing the statistical graph to obtain an analysis series;

[0105] Preprocessing the analysis sequence to obtain a preprocessed target analysis sequence, wherein the target analysis sequence includes a parent sequence and a child sequence;

[0106] Calculate the correlation between each index in the subsequence and the parent sequence;

[0107] The adjustment strategy is determined according to the correlation degree.

[0108] The lamp color correction device provided in the present application can quickly evaluate the degree of lamp color deviation through a grey correlation algorithm, and focus on optimizing key parameters, thereby improving the accuracy and efficiency of lamp color correction.

[0109] In some optional implementations of this embodiment, the determining module 302 is further configured to:

[0110] Displaying the adjustment strategy on a preset display interface, and receiving an adjustment instruction carrying user feedback information triggered by the display interface;

[0111] According to the user feedback information, the light source parameters of each of the lamps to be calibrated are updated.

[0112] The lamp color correction device provided in the present application not only realizes automation of the correction method by introducing a user feedback link, but also increases flexibility and user participation, thereby making the correction effect more in line with actual needs.

[0113] In some optional implementations of this embodiment, the determining module 302 is further configured to:

[0114] When there is abnormal detection data in the detection data, alarm information corresponding to the abnormal detection data is sent to a preset administrator user terminal.

[0115] The lamp color correction device provided in the present application can detect and eliminate lamp failures in a timely manner through abnormal detection and alarm functions, avoid affecting the use effect due to optical performance deviation, and thus improve the reliability and safety of the system.

[0116] See also Figure 4 , which is a structural diagram of a specific implementation of the determination module, the calculation module includes a model building unit 3021 and a model execution unit 3022. Among them, the model building unit 3021 is used to establish a target algorithm model according to the preset algorithm; the model execution unit 3022 is used to input the detection data into the algorithm model and output the adjustment strategy according to the algorithm model.

[0117] The lamp color correction device provided in the present application improves the scientificity and reliability of the generation of the adjustment strategy by establishing an algorithm model, thereby avoiding the subjective problem of relying on experience-based adjustment.

[0118] See also Figure 5 , is a structural diagram of a specific implementation of the model execution unit. In some optional implementations of this embodiment, the model execution unit 3022 includes:

[0119] The analysis subunit 30221 is used to generate a statistical graph according to the detection data, and analyze the statistical graph to obtain an analysis series;

[0120] A preprocessing subunit 30222 is used to preprocess the analysis sequence to obtain a target analysis sequence after preprocessing, wherein the target analysis sequence includes a parent sequence and a child sequence;

[0121] A calculation subunit 30223, used for calculating the correlation between each index in the subsequence and the parent sequence;

[0122] The determination subunit 30224 is used to determine the adjustment strategy according to the correlation degree.

[0123] The lamp color correction device provided in the present application can quickly evaluate the degree of lamp color deviation through a grey correlation algorithm, and focus on optimizing key parameters, thereby improving the accuracy and efficiency of lamp color correction.

[0124] To solve the above technical problems, the present application also provides a computer device. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0125] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0126] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0127] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the lamp color correction method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0128] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions or process data stored in the memory 41, such as computer-readable instructions for running the lamp color correction method.

[0129] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0130] The computer equipment provided in this application obtains accurate lamp light source parameter data through automated detection equipment, and then generates accurate adjustment strategies through preset algorithms, thereby achieving fast and comprehensive collection of optical information, providing a reliable basis for color correction, avoiding subjective errors in manual adjustment, and significantly improving the efficiency and accuracy of adjustment.

[0131] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the lamp color correction method as described above.

[0132] The computer-readable storage medium provided in this application obtains accurate lamp light source parameter data through automated detection equipment, and then generates accurate adjustment strategies through preset algorithms, thereby achieving fast and comprehensive collection of optical information, providing a reliable basis for color correction, avoiding subjective errors in manual adjustment, and significantly improving the efficiency and accuracy of adjustment.

[0133] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0134] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A lamp color correction method, characterized in that: The steps include: Obtaining detection data of lamps to be calibrated of different colors; Using a preset algorithm, according to the detection data, determining the adjustment strategy corresponding to each of the lamps to be calibrated; According to the adjustment strategy, the light source parameters of each of the lamps to be calibrated are adjusted, and the light source parameters include power, color temperature and color difference.

2. The lamp color correction method according to claim 1, characterized in that: The step of using a preset algorithm to determine the adjustment strategy corresponding to each of the lamps to be calibrated according to the detection data specifically includes: According to the preset algorithm, a target algorithm model is established; The detection data is input into the algorithm model, and the adjustment strategy is output according to the algorithm model.

3. The lamp color correction method according to claim 2, characterized in that: The preset algorithm includes a grey relational algorithm, and the step of inputting the detection data into the algorithm model and outputting the adjustment strategy according to the algorithm model specifically includes: Generating a statistical graph according to the detection data, and analyzing the statistical graph to obtain an analysis series; Preprocessing the analysis sequence to obtain a preprocessed target analysis sequence, wherein the target analysis sequence includes a parent sequence and a child sequence; Calculate the correlation between each index in the subsequence and the parent sequence; The adjustment strategy is determined according to the correlation degree.

4. The lamp color correction method according to claim 1, characterized in that: After the step of using a preset algorithm to determine the adjustment strategy corresponding to each of the lamps to be calibrated according to the detection data, the method further includes: Displaying the adjustment strategy on a preset display interface, and receiving an adjustment instruction carrying user feedback information triggered by the display interface; According to the user feedback information, the light source parameters of each of the lamps to be calibrated are updated.

5. The lamp color correction method according to any one of claims 1 to 4, characterized in that: After the step of obtaining the detection data of the lamps to be calibrated of different colors, the method further includes: When there is abnormal detection data in the detection data, alarm information corresponding to the abnormal detection data is sent to a preset administrator user terminal.

6. A lamp color correction device, characterized in that: include: An acquisition module, used to acquire detection data of lamps to be calibrated of different colors; A determination module, used to determine the adjustment strategy corresponding to each of the lamps to be calibrated according to the detection data by using a preset algorithm; The adjustment module is used to adjust the light source parameters of each of the lamps to be calibrated according to the adjustment strategy, and the light source parameters include power, color temperature and color difference.

7. The lamp color correction device according to claim 6, characterized in that: The determination module comprises: A model building unit, used to build a target algorithm model according to the preset algorithm; The model execution unit is used to input the detection data into the algorithm model and output the adjustment strategy according to the algorithm model.

8. The lamp color correction device according to claim 7, characterized in that: The preset algorithm includes a grey relational algorithm, and the model execution unit includes: An analysis subunit, used for generating a statistical graph according to the detection data, and analyzing the statistical graph to obtain an analysis series; A preprocessing subunit, used to preprocess the analysis sequence to obtain a target analysis sequence after preprocessing, wherein the target analysis sequence includes a parent sequence and a child sequence; A calculation subunit, used for calculating the correlation between each index in the subsequence and the parent sequence; A determination subunit is used to determine the adjustment strategy according to the correlation degree.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the lamp color correction method according to any one of claims 1 to 5 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the lamp color correction method according to any one of claims 1 to 5 are implemented.

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