Indoor lighting visible light communication carrier frequency intelligent optimization method and device

Abstract

The present application relates to the technical field of visible light communication, solves the problem that the prior art cannot accurately optimize the carrier frequency of visible light communication according to the changes in the indoor environment, and the communication effect is poor, and provides an indoor lighting visible light communication carrier frequency intelligent optimization method, device and equipment. The method comprises the following steps: using a spectrum sensing algorithm to analyze the environmental spectrum characteristics in the real-time environmental illumination data obtained under the indoor visible light communication scene, and outputting a communication light frequency interval matched with the environmental spectrum characteristics; using a signal-to-noise ratio optimization algorithm to obtain a first communication light frequency corresponding to the highest signal-to-noise ratio in the communication light frequency interval; using a machine learning algorithm to match the first communication light frequency with the historical communication carrier frequency corresponding to the real-time environment according to a preset communication light frequency selection strategy, and when the matching is successful, outputting a target communication carrier frequency. The present application can accurately optimize the carrier frequency of visible light communication and improve the communication effect.

Description

[0001] The present application is a divisional application of the patent application with the title of "Environment-adaptive visible light communication method, device, equipment and medium", application number 202410226775.4, filed on February 29, 2024. TECHNICAL FIELD

[0002] The present application relates to the technical field of visible light communication, in particular to an indoor lighting visible light communication carrier frequency intelligent optimization method and device. BACKGROUND

[0003] A visible light communication system is usually composed of one or more light sources and one or more light sensors. The light source constantly emits coded light signals, i.e. visible light modulation signals, and the light sensor receives and decodes these signals to obtain the information carried thereby. The use of lighting LEDs to achieve visible light communication will become an important supplement to existing communication systems, especially in the field of indoor short-range high-speed wireless communication. When the communication light source is multiplexed with the lighting light source, and other visible light sources exist, the ambient light may interfere with the communication or reduce the system performance, which is one of the main obstacles for visible light communication to enter practical use. Therefore, reducing the impact of ambient light on the visible light communication system and enabling it to be used normally in a complex light environment is the core of visible light communication technology.

[0004] The existing Chinese patent CN107612616A discloses a visible light communication device and method for reducing the interference of strong light sources, which comprises a first light source, a first light-sensitive receiving module, a second light-sensitive receiving module, a second light source, a first polarizer, a second polarizer, a first controller, a second controller, a first connection port and a second connection port. The input end of the first controller is connected to the first light-sensitive receiving module, the input-output control end is connected to the first connection port, and the output end is connected to the first light source. The input end of the second controller is connected to the second light-sensitive receiving module, the input-output control end is connected to the second connection port, and the output end is connected to the second light source. Different light is only allowed to pass through the polarized light with the same polarization direction as the light. The above-mentioned patent modulates the polarization direction of the light signal by installing a polarizer in front of the strong light signal to achieve maximum filtering. However, under the condition of ambient light, such as sunlight, fluorescent lamps or other LED lamps, the light spectrum has specific spectral characteristics, and the spectral overlap may cause interference. Environmental noise also affects the communication signal. Strong ambient light also makes the communication signal difficult to distinguish. In addition, in a high-noise and multi-path transmission environment, the communication connection is unstable. In summary, due to the adverse effects of ambient light on visible light communication, the performance of visible light communication is poor.

[0005] Therefore, how to reduce the interference of ambient light and optimize the performance of visible light communication is a problem to be solved. SUMMARY

[0006] In view of this, the present invention provides an intelligent optimization method and device for visible light communication carrier frequency in indoor lighting, in order to solve the problem of poor visible light communication performance caused by the adverse effects of ambient light on visible light communication in the prior art.

[0007] The technical solution adopted in this invention is:

[0008] In a first aspect, the present invention provides an intelligent optimization method for visible light communication carrier frequency in indoor lighting, the method comprising:

[0009] Using spectral sensing algorithms, the environmental spectral characteristics in real-time ambient light data acquired in indoor visible light communication scenarios are analyzed, and a communication light frequency range that matches the environmental spectral characteristics is output.

[0010] Using a signal-to-noise ratio optimization algorithm, the first communication optical frequency corresponding to the highest signal-to-noise ratio in the communication optical frequency range is obtained;

[0011] Using machine learning algorithms, based on a preset communication optical frequency selection strategy, the first communication optical frequency is matched with the historical communication carrier frequency corresponding to the real-time environment. When a match is found, the target communication carrier frequency is output.

[0012] The method utilizes machine learning algorithms to match the first communication optical frequency with the historical communication carrier frequency corresponding to the real-time environment based on a preset communication optical frequency selection strategy. When a match is found, the output target communication carrier frequency includes:

[0013] Acquire real-time images in the aforementioned real-time environment;

[0014] The real-time image is input into a pre-trained scene recognition model based on a deep learning algorithm to identify a first target scene corresponding to the real-time environment.

[0015] The target scene is matched with a preset set of indoor scenes. When the first target scene is successfully matched with a second target scene in the set of indoor scenes, the communication optical frequency corresponding to the second target scene is output as the historical communication carrier frequency.

[0016] Preferably, after using a machine learning algorithm to match the first communication optical frequency with the historical communication carrier frequency corresponding to the real-time environment according to a preset communication optical frequency selection strategy, and outputting the target communication carrier frequency when a match is found, the method further includes:

[0017] Based on the target communication carrier frequency, at least one spectral type that matches the target communication carrier frequency is determined and denoted as the target spectral type;

[0018] acquire a communication performance index within a preset time window, wherein the communication performance index at least includes an optical error rate;

[0019] compare the optical error rate with a preset optical error rate threshold, and adjust a light source parameter when the optical error rate is greater than the optical error rate threshold, wherein the light source parameter at least includes an output power, a modulation mode, and a coding mode.

[0020] Preferably, the real-time ambient light data acquired in the indoor visible light communication scenario is analyzed using a spectrum sensing algorithm to output a communication light frequency interval matching the ambient spectrum characteristics, including:

[0021] obtain light intensity measurement values of different wavelengths from the real-time ambient light data, which are presented in the form of a spectrum;

[0022] preprocess the obtained light intensity measurement values of different wavelengths, which includes smoothing, filtering, and calibration, and output the preprocessed data;

[0023] analyze the preprocessed data using a spectrum sensing algorithm to output a communication light frequency interval matching the ambient spectrum characteristics.

[0024] Preferably, the target scene is matched with a preset indoor scene set, and when the first target scene is successfully matched with a second target scene in the indoor scene set, the communication light frequency corresponding to the second target scene is output as the historical communication carrier frequency, including:

[0025] When the user is detected to enter the living room, i.e., the first target scene is the living room, the living room is matched with a preset indoor scene set, the matched entertainment scene is determined as the second target scene, and the communication light frequency corresponding to the entertainment scene is output as the historical communication carrier frequency;

[0026] When the patient is detected to enter the hospital room, i.e., the first target scene is the hospital, the matched medical monitoring scene is output as the second target scene according to the real-time environment, and the communication light frequency corresponding to the medical monitoring scene is output as the historical communication carrier frequency;

[0027] When the robot is detected to move from one workshop to another, i.e., the first target scene is the workshop, the matched production scene of the current workshop is output as the second target scene, and the communication light frequency corresponding to the production scene is output as the historical communication carrier frequency;

[0028] When the user is detected to roam in the mall, i.e., the first target scene is the mall, the matched store navigation scene is output as the second target scene, and the communication light frequency corresponding to the store navigation scene is output as the historical communication carrier frequency.

[0029] Preferably, the method further comprises, before the obtaining the communication performance indicator in the preset time window, the following steps:

[0030] obtaining the target spectrum type, wherein the target spectrum type comprises at least one of the following: red spectrum, green spectrum, blue spectrum and white spectrum;

[0031] obtaining an encoding strategy and a modulation strategy corresponding to the target spectrum type according to the target spectrum type;

[0032] adjusting an encoding mode of the light source and a modulation mode of the light source to be matched with the target communication carrier frequency according to the encoding strategy and the modulation strategy.

[0033] Preferably, the obtaining the encoding strategy and the modulation strategy corresponding to the target spectrum type according to the target spectrum type comprises:

[0034] if the target spectrum type is the red spectrum, the encoding strategy is to use NRZ encoding and the modulation strategy is to use BPSK modulation;

[0035] if the target spectrum type is the green spectrum, the encoding strategy is to use differential Manchester encoding and the modulation strategy is to use PAM modulation;

[0036] if the target spectrum type is the blue spectrum, the encoding strategy is to use 8B / 10B line encoding and the modulation strategy is to use QAM modulation;

[0037] if the target spectrum type is the white spectrum, the encoding strategy is to use Turbo encoding and the modulation strategy is to use CAP modulation.

[0038] In a second aspect, an electronic device is provided, which comprises at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor implement the method of the first aspect as described in the above embodiments.

[0039] In a third aspect, a storage medium is provided, which has stored thereon computer program instructions, which when executed by a processor implement the method of the first aspect as described in the above embodiments.

[0040] In summary, the present application has the following advantages:

[0041] The application provides an indoor lighting visible light communication carrier frequency intelligent optimization method and device, the method comprises the following steps: using a spectrum sensing algorithm to analyze the environmental spectrum characteristics of real-time environmental illumination data obtained in an indoor visible light communication scene, and outputting a communication light frequency interval matched with the environmental spectrum characteristics; using a signal-to-noise ratio optimization algorithm to obtain a first communication light frequency corresponding to the highest signal-to-noise ratio in the communication light frequency interval; using a machine learning algorithm to match the first communication light frequency with a historical communication carrier frequency corresponding to the real-time environment according to a preset communication light frequency selection strategy, and outputting a target communication carrier frequency when the matching is successful. The application selects the most suitable communication light frequency according to the actual environmental conditions by obtaining real-time environmental illumination data and using an adaptive algorithm. This adaptive adjustment helps to reduce the interference of environmental illumination on communication and improve the communication quality and stability. By adjusting the light source parameters of the target spectrum type according to the preset communication performance indicators, the communication performance is optimized to ensure the best communication quality under different environmental conditions. Selecting the spectrum type matched with the target communication carrier frequency helps to reduce the interference of signals by other light sources, which means that the communication signals are more easily distinguished from the background light source, reducing the data error rate. By adjusting the light source parameters, the energy is effectively utilized. Considering the spectrum type, frequency selection and parameter adjustment comprehensively helps to improve the reliability of the visible light communication system, the user can more reliably perform data transmission, and the data loss and errors are reduced. In summary, the application solves the environmental problems in the indoor visible light communication scene by adaptive adjustment, communication performance optimization and signal interference reduction, improves the performance and reliability of the communication system, adapts to different environmental conditions, and provides better communication experience for the user. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments of the application. For those skilled in the art, other drawings can also be obtained without creative labor on the premise of these drawings, and these are within the protection scope of the application.

[0043] Figure 1 The flowchart of the whole work of the visible light communication method based on environmental adaptation in the embodiment 1 of the application;

[0044] Figure 2 The flowchart of determining the target communication carrier frequency by using the preset adaptive algorithm in the embodiment 1 of the application;

[0045] Figure 3 The flowchart of matching the first communication light frequency with the historical communication carrier frequency in the embodiment 1 of the application;

[0046] Figure 4A flowchart for adjusting the coding mode and the modulation mode of the light source in the embodiment 1 of the present application is shown in the figure;

[0047] Figure 5 A flowchart for obtaining the coding strategy and the modulation strategy corresponding to the target spectrum type in the embodiment 1 of the present application is shown in the figure;

[0048] Figure 6 A flowchart for adjusting the light source parameters in the embodiment 1 of the present application is shown in the figure;

[0049] Figure 7 A structure block diagram of the visible light communication device based on environment adaptation in the embodiment 2 of the present application is shown in the figure;

[0050] Figure 8 A structure diagram of the electronic device in the embodiment 3 of the present application is shown in the figure. DETAILED DESCRIPTION

[0051] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. It should be noted that, in this document, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between the entities or operations. In the description of the present application, it should be understood that the orientations or positional relationships indicated by the terms “center”, “upper”, “lower”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inner”, “outer” and the like are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. Moreover, the terms “include”, “contain” or any other variants thereof are intended to cover non-exclusive inclusion, so that the processes, methods, articles or devices including a series of elements not only include those elements, but also include other elements not explicitly listed or inherent to such processes, methods, articles or devices. Without more limitations, the elements defined by the statement “include” do not exclude the presence of other identical elements in the processes, methods, articles or devices including the elements. If there is no conflict, the embodiments of the present application and the various features in the embodiments can be combined with each other, and are all within the protection scope of the present application.

[0052] Embodiment 1

[0053] Please refer to Figure 1 The embodiment 1 of the present application discloses a visible light communication method based on environment adaptation, which comprises:

[0054] S1: obtaining real-time ambient light data in an indoor visible light communication scenario;

[0055] Specifically, a light sensor suitable for an indoor visible light communication scenario is selected, wherein the indoor visible light communication scenario at least includes a museum, an airport, a shopping mall, a hospital, an office building and a factory. Different light sensors have different sensitivities and working ranges, ensuring that the selected sensor can cover the visible light spectrum range (usually in the range of 300-700 nanometers); the selected light sensor is installed at the location of the communication device or system so as to be able to capture the required ambient light data. The sensor should be exposed to the main communication area in the room to ensure accurate capture of the characteristics of the communication environment. The obtained real-time ambient light data at least includes light intensity, wavelength distribution and spectral information, which can be further processed and analyzed to determine the real-time ambient light conditions.

[0056] S2: determining a target communication carrier frequency that meets the real-time environmental requirements by using a preset adaptive algorithm according to the real-time ambient light data;

[0057] Specifically, a target communication carrier frequency that meets the real-time environmental requirements is determined by using a preset adaptive algorithm according to the real-time ambient light data; the adaptive algorithm at least includes the following algorithms: a spectral sensing algorithm, a signal-to-noise ratio optimization algorithm and a machine learning algorithm. The spectral sensing algorithm is used to analyze the spectral characteristics in the real-time ambient light data, which can identify the intensity and distribution of light of different wavelengths to determine the most suitable communication frequency range; by understanding the environmental spectrum, the communication frequency can be selected to minimize the overlap with the ambient light, reduce interference and improve signal quality; the signal-to-noise ratio optimization algorithm is used to evaluate the signal and noise levels in the real-time environment and select the communication frequency with the highest signal-to-noise ratio, which helps to improve the communication quality and reduce the bit error rate; by selecting the frequency with the highest signal-to-noise ratio, better communication performance can be obtained, i.e. higher signal strength relative to noise; the machine learning algorithm uses patterns and trends in the real-time ambient light data to predict the most suitable communication frequency, learns the impact of environmental light on communication according to historical data and makes real-time decisions. The machine learning algorithm is adaptive and can adapt to different environmental conditions, providing more intelligent frequency selection to meet real-time requirements. By comprehensively using these adaptive algorithms, the best communication light frequency can be dynamically selected according to environmental conditions to minimize the adverse effects of ambient light on communication, which helps to improve the performance, stability and adaptability of visible light communication and ensures reliable communication connection under different environmental conditions.

[0058] In an embodiment, referring to Figure 2 , the S2 comprises:

[0059] S21: using the spectrum sensing algorithm, analyzing the environmental spectrum characteristics in the real-time environmental lighting data, and outputting the communication light frequency interval matching the environmental spectrum characteristics;

[0060] Specifically, the light intensity measurement values of different wavelengths are obtained from the collected real-time environmental lighting data, usually in the form of a spectrum; the obtained light intensity measurement values of different wavelengths are preprocessed to eliminate noise and unnecessary fluctuations, including smoothing, filtering and calibration, to ensure the accuracy and stability of the data; the spectrum sensing algorithm is used to analyze the data, first to identify different wavelengths of light and determine their relative intensity; according to the known communication frequency and wavelength characteristics, identify the communication light frequency matching the environmental spectrum characteristics, determine the most suitable communication frequency interval to minimize the interference of environmental lighting while maintaining good signal quality, output the communication light frequency interval matching the environmental spectrum characteristics. These frequency intervals will be used to adjust the light source to achieve visible light communication, the spectrum sensing algorithm helps to select the most suitable communication light frequency in real-time environment to reduce the interference of environmental lighting, improve the communication quality and stability.

[0061] S22: using the signal-to-noise ratio optimization algorithm, obtaining the first communication light frequency corresponding to the highest signal-to-noise ratio in the communication light frequency interval;

[0062] Specifically, within the communication light frequency interval matching the environmental spectrum characteristics, the signal-to-noise ratio (SNR) is calculated, where SNR is usually calculated in the following way:

[0063] SNR = 10log10(Ps / Pn)

[0064] Where Ps represents the signal power and Pn represents the noise power. In visible light communication, the signal is usually the transmitted light signal, and the noise comes from the environmental lighting. The highest SNR frequency selection: find the frequency with the highest SNR value in the entire frequency interval, which will be the optimal communication light frequency. Usually, the algorithm will choose the first frequency encountered whose SNR reaches the maximum value, and output the first communication light frequency corresponding to the highest SNR found. This algorithm helps to select the best communication frequency to ensure the highest signal quality, thereby reducing the impact of noise on communication. Through the signal-to-noise ratio optimization algorithm, the performance of the visible light communication system is optimized, especially in the case of environmental lighting interference, by selecting the frequency with the highest SNR, a more reliable communication connection can be achieved.

[0065] S23: using the machine learning algorithm, according to the preset communication light frequency selection strategy, matching the first communication light frequency with the historical communication carrier frequency corresponding to the real-time environment, and outputting the target communication carrier frequency when matching.

[0066] Specifically, real-time ambient light data is collected, and historical communication carrier frequencies are recorded. The collected real-time ambient light data and recorded historical communication carrier frequencies are used to train a machine learning model. In real-time environments, current ambient light data is input into the machine learning model to predict the most suitable communication light frequency. The model compares the current environmental data with historical data to find the frequency that best matches the historical communication carrier frequency. The target communication carrier frequency predicted by the machine learning model is output as the current communication frequency. The machine learning algorithm can intelligently select the best communication light frequency based on historical communication data and changes in real-time environmental conditions. By utilizing the capabilities of the machine learning model to adapt to different environments, the performance and stability of communication can be improved, helping to address the negative impact of ambient light on visible light communication and providing more reliable communication connections.

[0067] In an embodiment, referring to Figure 3 , the S23 comprises:

[0068] S231: Acquire real-time images in the real-time environment;

[0069] Specifically, an image sensor suitable for a specific application is selected, such as selecting sensor types (e.g. CCD, CMOS, etc.) and resolution (number of pixels) and other parameters. The selected image sensor is installed in the desired location to ensure that it can capture the desired real-time images, which may involve physical installation and calibration to ensure the accuracy of the images. Corresponding acquisition devices, such as cameras, video cameras or computer interfaces, are used to acquire real-time image data. These devices can be connected to the image sensor and acquire images through a cable or wireless connection. Real-time images are processed to improve image quality or extract specific information.

[0070] S232: Input the real-time images into a pre-trained scene recognition model trained based on a deep learning algorithm to identify the first target scene corresponding to the real-time environment;

[0071] Specifically, a deep learning model that has been trained on a scene recognition task is selected, which at least includes a convolutional neural network such as VGG, ResNet, Inception; the captured real-time image is input into the selected deep learning model, ensuring that the image matches the input requirements of the model, and the image is preprocessed such as scaling, standardization, etc. The deep learning model will extract features in the image and perform a classification task. The model will analyze the features in the image, such as texture, color, shape, etc., to determine which scene category the image belongs to. According to the output of the model, the first target scene corresponding to the real-time environment is determined. The model will return a probability distribution indicating the probability of the image belonging to each possible scene category. The category with the highest probability is selected as the first target scene for subsequent application or system use. By using a deep learning model to help automatically identify the real-time environment, this is very useful for automated systems, intelligent monitoring and other applications. The deep learning model can efficiently process large amounts of image data, providing accurate scene recognition results.

[0072] S233: match the target scene with a preset indoor scene set, when the first target scene matches a second target scene in the indoor scene set, output the communication light frequency corresponding to the second target scene as the historical communication carrier frequency.

[0073] Specifically, the first target scene is identified and output by the deep learning model described above, and each scene in the preset indoor scene set is matched with the first target scene. The predefined scene label or specific scene feature is compared to determine whether the first target scene matches a certain scene in the indoor scene set. The model output is compared with the indoor scene label or feature to determine the match. If the match is successful, the communication light frequency corresponding to the second target scene is output according to the matched indoor scene. This frequency is usually pre-set according to historical communication data, and the output communication light frequency is recorded as the historical communication carrier frequency for subsequent communication. For example: in a smart home scene, when the user enters the living room (first target scene), it will automatically match with the preset indoor scene set. If the living room matches the "entertainment" scene (second target scene), the communication light frequency will be automatically adjusted to adapt to the data transmission between entertainment devices such as TV, sound and smart home devices; in a medical scene, when a patient is treated in a hospital room (first target scene), it is matched to the "medical monitoring" scene (second target scene) according to the real-time environment, and the medical device can intelligently select the communication light frequency according to this scene to ensure high-quality data transmission and real-time monitoring; in a factory environment, robots or sensors can perform tasks in different workshops, and when they move from one workshop (first target scene) to another, the system can automatically match the "production" scene (second target scene) of the current workshop, which helps to optimize the communication settings to adapt to the data transmission needs between production devices. In an indoor navigation system, the user may roam in a shopping mall (first target scene) and then be matched to the "store navigation" scene (second target scene), at which time the communication light frequency is automatically selected to provide information and discounts related to store navigation. According to the environmental scene, the communication light frequency is intelligently selected to meet the needs of different applications, and the adaptive and automated method helps to improve the performance of the communication system while simplifying the configuration and management of users and devices.

[0074] S3: According to the target communication carrier frequency, at least one spectrum type matching the target communication carrier frequency is determined, denoted as target spectrum type;

[0075] Specifically, according to the selected target communication carrier frequency, the spectrum type matching the frequency range is found. For example, for red spectrum communication frequency (620-750 nm), red LED is selected as the target spectrum type, and for blue spectrum communication frequency (450-495 nm), blue LED may be the most suitable spectrum type, which ensures that the transmitter of the communication system matches the selected frequency range to achieve stable communication connection.

[0076] In an embodiment, please refer to Figure 4, the S4 further comprises:

[0077] S401: obtaining the target spectrum type, wherein the target spectrum type comprises at least one of the following: red spectrum, green spectrum, blue spectrum and white spectrum;

[0078] S402: obtaining the encoding strategy and the modulation strategy corresponding to the target spectrum type according to the target spectrum type;

[0079] Specifically, various spectrum types usually require specific encoding strategies to convert digital data into optical signals, and the encoding strategies at least include Manchester encoding, 4B / 5B encoding and 8B / 10B encoding. The appropriate encoding strategy is selected according to the characteristics of the target spectrum type; the modulation strategy is used to adjust the intensity or phase of the optical signal to transmit digital data, and different spectrum types require different modulation strategies.

[0080] In an embodiment, referring to Figure 5 , the S402 comprises:

[0081] S4021: if the target spectrum type is a red spectrum, the encoding strategy is to use NRZ encoding, and the modulation strategy is to use BPSK modulation;

[0082] Specifically, if the target spectrum type is a red spectrum, the NRZ encoding strategy and the BPSK modulation strategy are used, which helps to realize efficient transmission of data in red spectrum communication; the NRZ encoding is used to convert digital data into high and low levels of optical signals, and the BPSK modulation method allows binary modulation in phase, which can realize stable data transmission in red spectrum communication and improve the anti-interference performance of the signal to adapt to different environmental conditions.

[0083] S4022: if the target spectrum type is a green spectrum, the encoding strategy is to use differential Manchester encoding, and the modulation strategy is to use PAM modulation;

[0084] Specifically, if the target spectrum type is a green spectrum, differential Manchester encoding is selected as the encoding strategy, and the PAM modulation strategy is used, which helps to realize high-quality transmission of data in green spectrum communication; differential Manchester encoding is used to provide clock synchronization of data, and PAM modulation method uses different light intensity levels to represent data, thereby improving the reliability of data transmission. This combination helps to realize higher transmission stability in green spectrum communication and reduce the data transmission error rate.

[0085] S4023: if the target spectrum type is a blue spectrum, the encoding strategy is to use 8B / 10B line encoding, and the modulation strategy is to use QAM modulation;

[0086] Specifically, when the target spectrum type is blue spectrum, the 8B / 10B line coding is selected as the encoding strategy, and the QAM modulation strategy, which is very effective for blue spectrum communication. 8B / 10B encoding is a data encoding method used to provide data reliability and synchronization, while QAM modulation combines amplitude and phase modulation, allowing more data to be transmitted within a limited bandwidth. This combination of strategies helps to achieve high-speed data transmission in blue spectrum communication and reduces the risk of signal distortion.

[0087] S4024: If the target spectrum type is white spectrum, the encoding strategy is to use Turbo encoding, and the modulation strategy is to use CAP modulation.

[0088] Specifically, if the target spectrum type is white spectrum, Turbo encoding is selected as the encoding strategy, and CAP modulation strategy, which helps to achieve high fault tolerance and high-speed transmission in white spectrum communication. Turbo encoding is a powerful error correction encoding method that can reduce the error rate in data transmission, while CAP modulation combines amplitude and phase modulation, suitable for high-speed visible light communication. This combination of strategies helps to achieve high-performance and reliable data transmission in white spectrum communication.

[0089] S403: According to the encoding strategy and modulation strategy, the encoding method of the light source and the modulation method of the light source are adjusted to match the target communication carrier frequency.

[0090] Specifically, according to the encoding strategy and modulation strategy, the encoding method of the light source and the modulation method of the light source are adjusted to match the target communication carrier frequency. Since the selection of encoding and modulation strategies is based on the characteristics of different spectrum types and communication requirements, it helps to optimize the performance of the communication system, improve the quality of data transmission, reduce the error rate, and thus achieve more reliable visible light communication.

[0091] S4: According to the preset communication performance index, adjust the light source parameters of the target spectrum type, and when the adjusted light source parameters match the target communication carrier frequency, control the light source to emit visible light signals to the receiver.

[0092] Specifically, the preset communication performance indicators are obtained, including data transmission rate, bit error rate, signal-to-noise ratio, etc. According to the performance indicators, the parameters of the light source are adjusted, including light source intensity, light source wavelength, light source modulation depth, etc. These parameters can affect the quality and reliability of the signal. The adjusted light source parameters need to match the target communication carrier frequency to ensure data transmission within the selected frequency range, which helps to achieve consistency in communication. By continuously monitoring the performance of the light source, it is ensured that it meets the preset communication performance indicators. If the performance does not meet the requirements, the light source parameters need to be further adjusted. Once the light source parameters match the target communication carrier frequency and meet the performance indicators, the control light source emits a visible light signal, which will be transmitted to the receiver to complete data transmission. By dynamically adjusting the parameters of the light source, the communication performance requirements are met, and the best communication quality is achieved under different conditions. This adaptive method helps to cope with changes in the environment and changes in communication needs to ensure that the performance of the visible light communication system is always in the best state.

[0093] In an embodiment, referring to Figure 6 , the S4 comprises:

[0094] S41: Obtain the communication performance indicators within the preset time window, wherein the communication performance indicators at least include the optical bit error rate;

[0095] Specifically, it is crucial to obtain the communication performance indicators within the preset time window, which includes the optical bit error rate. The optical bit error rate is an important measure of communication performance, reflecting the error rate in data transmission.

[0096] S42: Compare the optical bit error rate with the preset optical bit error rate threshold. When the optical bit error rate is greater than the optical bit error rate threshold, adjust the light source parameters, including output power, modulation mode and encoding mode.

[0097] Specifically, the optical error rate is continuously monitored to understand the communication quality in real time, including periodic error rate detection and calculation, comparing the actual optical error rate with the preset optical error rate threshold, if the actual optical error rate is greater than the threshold, it means that the communication performance is reduced, and measures need to be taken; once the optical error rate exceeds the threshold, the light source parameters are adjusted, including output power, modulation mode and coding mode; the adjustment of output power can be realized by increasing or reducing the intensity of the light source to ensure that the signal reaches the appropriate intensity level of the receiver, which can help to improve the signal quality; if the error rate is still high, the modulation mode and coding mode need to be changed to improve the anti-interference and reliability of the signal; after adjusting the light source parameters, the optical error rate is monitored again to ensure that the performance has been improved, if the optical error rate is still higher than the threshold, the parameters need to be further adjusted. The above process is usually adaptive, the light source parameters are continuously monitored and adjusted to maintain good communication quality. By comparing the optical error rate with the threshold and automatically adjusting the light source parameters, the communication quality is maintained at the required level and changes in the environment and communication requirements are addressed, which helps to achieve stable and high-quality visible light communication.

[0098] Embodiment 2

[0099] See Figure 7 Embodiment 2 of the present application also provides a visible light communication device based on environmental adaptation, the device comprises:

[0100] An ambient light data acquisition module is configured to acquire real-time ambient light data in an indoor visible light communication scenario.

[0101] An adaptive adjustment module is configured to determine a target communication carrier frequency that meets real-time environmental requirements by using a preset adaptive algorithm according to the real-time ambient light data.

[0102] A spectrum type determination module is configured to determine at least one spectrum type that matches the target communication carrier frequency, denoted as a target spectrum type, according to the target communication carrier frequency.

[0103] A light source parameter adjustment module is configured to adjust the light source parameters of the target spectrum type according to a preset communication performance index, and control the light source to emit a visible light signal to a receiver when the adjusted light source parameters match the target communication carrier frequency.

[0104] Specifically, the visible light communication device based on environmental adaptation provided in this embodiment of the invention includes: an ambient light data acquisition module for acquiring real-time ambient light data in an indoor visible light communication scenario; an adaptive adjustment module for determining a target communication carrier frequency that meets real-time environmental requirements based on the real-time ambient light data and using a preset adaptive algorithm; a spectral type determination module for determining at least one spectral type that matches the target communication carrier frequency, denoted as the target spectral type; and a light source parameter adjustment module for adjusting the light source parameters of the target spectral type according to preset communication performance indicators. When the adjusted light source parameters match the target communication carrier frequency, the light source is controlled to emit a visible light signal to the receiver. This device acquires real-time ambient light data and uses an adaptive algorithm to select the most suitable communication light frequency based on actual environmental conditions. This adaptive adjustment helps reduce interference from ambient light, improving communication quality and stability. By adjusting the light source parameters of the target spectral type according to preset communication performance indicators, it optimizes communication performance to ensure optimal communication quality under different environmental conditions. Selecting a spectral type that matches the target communication carrier frequency helps reduce interference from other light sources, meaning the communication signal is easier to distinguish from background light sources, reducing data error rates. Adjusting light source parameters enables efficient energy utilization. By comprehensively considering spectral type, frequency selection, and parameter adjustment, it helps improve the reliability of the visible light communication system, allowing users to transmit data more reliably and reducing data loss and errors. In summary, this invention effectively solves environmental problems in indoor visible light communication scenarios through adaptive adjustment, communication performance optimization, and signal interference reduction, improving the performance and reliability of the communication system, adapting to different environmental conditions, and providing users with a better communication experience.

[0105] Example 3

[0106] In addition, combined Figure 1 The environment-adaptive visible light communication method described in Embodiment 1 of the present invention can be implemented by an electronic device. Figure 8 A schematic diagram of the hardware structure of the electronic device provided in Embodiment 3 of the present invention is shown.

[0107] Electronic devices may include processors and memory storing computer program instructions.

[0108] Specifically, the processor may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement embodiments of the present invention.

[0109] The memory can include mass storage for data or instructions. By way of example, and not limitation, the memory can include a Hard Disk Drive (HDD), floppy disk drive, flash memory, optical disc, magneto-optical disc, magnetic tape, or Universal Serial Bus (USB) drive or a combination of two or more of these. The memory can be removable and / or non-removable (or fixed) as appropriate. The memory can be internal or external as appropriate. In certain embodiments, the memory is non-volatile solid-state memory. In certain embodiments, the memory includes read-only memory (ROM). Where appropriate, this ROM can be mask- programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these.

[0110] The processor implements the above-described any one of the environment-adaptive visible light communication methods by reading and executing computer program instructions stored in the memory.

[0111] In one example, the electronic device can further include a communication interface and a bus. Wherein, as shown in Figure 8 the processor, the memory, the communication interface are connected through the bus and complete the communication between each other.

[0112] The communication interface is mainly used to realize the communication between the modules, devices, units and / or equipment in the embodiments of the application.

[0113] The bus includes hardware, software or both to couple the components of the device to each other. By way of example, and not limitation, the bus can include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or another suitable bus or interconnect, or a combination of two or more of these. Where appropriate, the bus can include one or more buses. Although the present embodiments describe and show a particular bus, the present application contemplates any suitable bus or interconnect.

[0114] Embodiment 4

[0115] In addition, in combination with the environment-adaptive visible light communication method in the above-described embodiment 1, the embodiment 4 of the present application can also provide a computer-readable storage medium for implementation. The computer-readable storage medium has computer program instructions stored thereon; the computer program instructions are executed by a processor to implement any one of the environment-adaptive visible light communication methods in the above-described embodiments.

[0116] In summary, the embodiment of the present application provides an environment-adaptive visible light communication method, device, equipment and medium.

[0117] It should be noted that the present application is not limited to the specific configurations and processes described above and shown in the drawings. For the sake of brevity, detailed descriptions of well-known methods are omitted herein. In the above-described embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order of the steps, after understanding the spirit of the present application.

[0118] The functional blocks shown in the structural block diagrams described above can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, etc. When implemented in software, the elements of the present application are program or code segments used to perform the required tasks. The program or code segments can be stored in a machine-readable medium or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. The "machine-readable medium" can include any medium capable of storing or transmitting information. Examples of the machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via a computer network such as the Internet, an intranet, etc.

[0119] It should also be noted that the exemplary embodiments mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps can be executed in the order mentioned in the embodiments, or in an order different from the embodiments, or several steps can be executed simultaneously.

[0120] The above description is merely a specific embodiment of the present invention. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A method for intelligent optimization of visible light communication carrier frequency for indoor lighting, characterized in that, The method includes: Using spectral sensing algorithms, the environmental spectral characteristics in real-time ambient light data acquired in indoor visible light communication scenarios are analyzed, and a communication light frequency range that matches the environmental spectral characteristics is output. Using a signal-to-noise ratio optimization algorithm, the first communication optical frequency corresponding to the highest signal-to-noise ratio in the communication optical frequency range is obtained; Using machine learning algorithms, based on a preset communication optical frequency selection strategy, the first communication optical frequency is matched with the historical communication carrier frequency corresponding to the real-time environment. When a match is found, the target communication carrier frequency is output. The method utilizes machine learning algorithms to match the first communication optical frequency with the historical communication carrier frequency corresponding to the real-time environment based on a preset communication optical frequency selection strategy. When a match is found, the output target communication carrier frequency includes: Acquire real-time images in the aforementioned real-time environment; The real-time image is input into a pre-trained scene recognition model based on a deep learning algorithm to identify a first target scene corresponding to the real-time environment. The target scene is matched with a preset set of indoor scenes. When the first target scene is successfully matched with a second target scene in the set of indoor scenes, the communication optical frequency corresponding to the second target scene is output as the historical communication carrier frequency.

2. The intelligent optimization method for visible light communication carrier frequency in indoor lighting according to claim 1, characterized in that, Following the step of using a machine learning algorithm to match the first communication optical frequency with the historical communication carrier frequency corresponding to the real-time environment according to a preset communication optical frequency selection strategy, and outputting the target communication carrier frequency when a match is found, the process further includes: Based on the target communication carrier frequency, at least one spectral type that matches the target communication carrier frequency is determined and denoted as the target spectral type; Obtain communication performance indicators within a preset time window, wherein the communication performance indicators include at least: optical bit error rate; The optical bit error rate is compared with a preset optical bit error rate threshold. When the optical bit error rate is greater than the optical bit error rate threshold, the light source parameters are adjusted. The light source parameters include at least: output power, modulation method and encoding method.

3. The intelligent optimization method for visible light communication carrier frequency in indoor lighting according to claim 1, characterized in that, The method of using a spectral sensing algorithm to analyze the environmental spectral characteristics in real-time ambient lighting data acquired in indoor visible light communication scenarios, and outputting a communication light frequency range that matches the environmental spectral characteristics, includes: Light intensity measurements at different wavelengths are obtained from the real-time ambient light data, and the light intensity measurements are presented in spectral form. The acquired light intensity measurements at different wavelengths are preprocessed, including smoothing, filtering, and calibration, and the preprocessed data is output. The preprocessed data is analyzed using a spectral sensing algorithm to output a communication optical frequency range that matches the spectral characteristics of the environment.

4. The intelligent optimization method for visible light communication carrier frequency in indoor lighting according to claim 1, characterized in that, The step of matching the target scene with a preset set of indoor scenes, and when the first target scene successfully matches a second target scene in the set of indoor scenes, outputting the communication optical frequency corresponding to the second target scene as the historical communication carrier frequency, includes: When a user is detected entering the living room, i.e., the first target scene is the living room, the living room is matched with a set of preset indoor scenes, the matched entertainment scene is determined as the second target scene, and the communication optical frequency corresponding to the entertainment scene is output as the historical communication carrier frequency. When a patient is detected entering a hospital room, i.e., the first target scenario is a hospital, the matched medical monitoring scenario is used as the second target scenario based on the real-time environment, and the communication optical frequency corresponding to the medical monitoring scenario is output as the historical communication carrier frequency. When it is detected that the robot has moved from one workshop to another, i.e., the first target scene is a workshop, the production scene of the current workshop is matched as the second target scene, and the communication optical frequency corresponding to the production scene is output as the historical communication carrier frequency. When a user is detected roaming in a shopping mall, i.e., the first target scene is the shopping mall, the matched store navigation scene is used as the second target scene, and the communication optical frequency corresponding to the store navigation scene is output as the historical communication carrier frequency.

5. The intelligent optimization method for visible light communication carrier frequency in indoor lighting according to claim 2, characterized in that, Before obtaining the communication performance metrics within the preset time window, the method further includes: The target spectral type is obtained, wherein the target spectral type includes at least one of the following: red spectrum, green spectrum, blue spectrum, and white spectrum; Based on the target spectral type, obtain the coding strategy and modulation strategy corresponding to the target spectral type; Based on the encoding and modulation strategies, the encoding and modulation methods of the light source are adjusted to match the target communication carrier frequency.

6. The intelligent optimization method for visible light communication carrier frequency in indoor lighting according to claim 5, characterized in that, The step of obtaining the coding strategy and modulation strategy corresponding to the target spectral type includes: If the target spectral type is red spectrum, then the encoding strategy is NRZ encoding and the modulation strategy is BPSK modulation; If the target spectral type is green spectrum, then the coding strategy is differential Manchester coding and the modulation strategy is PAM modulation. If the target spectral type is blue spectrum, then the coding strategy is to use 8B / 10B line coding, and the modulation strategy is to use QAM modulation; If the target spectral type is white spectrum, then the encoding strategy is Turbo encoding and the modulation strategy is CAP modulation.

7. An electronic device, characterized in that, include: At least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method as described in any one of claims 1-6.

8. A storage medium storing computer program instructions thereon, characterized in that, The method as described in any one of claims 1-6 is implemented when the computer program instructions are executed by the processor.

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