Indoor illumination visible light communication carrier frequency intelligent optimization method, device and equipment

Abstract

The invention relates to the technical field of visible light communication, solves the problems that in the prior art, the visible light communication carrier frequency cannot be accurately optimized according to changes in the indoor environment, and the communication effect is poor, and provides an intelligent optimization method, device and equipment for the visible light communication carrier frequency of indoor illumination. The method comprises the following steps: analyzing environment spectral characteristics in real-time environment illumination data acquired in an indoor visible light communication scene by using a spectrum sensing algorithm, and outputting a communication light frequency interval matched with the environment spectral characteristics; obtaining a first communication light frequency corresponding to the highest signal-to-noise ratio in the communication light frequency interval by using a signal-to-noise ratio optimization algorithm; and matching the first communication light frequency with a historical communication carrier frequency corresponding to a real-time environment by using a machine learning algorithm according to a preset communication light frequency selection strategy, and outputting a target communication carrier frequency when the first communication light frequency is matched with the historical communication carrier frequency. According to the method, the visible light communication carrier frequency can be accurately optimized, and the communication effect is improved.

Description

[0001] This invention is a divisional application of the invention patent application filed on February 29, 2024, with the invention name being “Visible light communication method, device, equipment and medium based on environmental adaptation” and application number being 202410226775.4. Technical Field

[0002] The present invention relates to the field of visible light communication technology, and in particular to a method, device and equipment for intelligently optimizing carrier frequency of indoor lighting visible light communication. Background Art

[0003] With the rapid development of information technology, optical communication technology, as a communication method with high speed, high security and large capacity, has gradually become an important part of modern communication. Among many optical communication technologies, indoor visible light communication has become an ideal choice for future smart home, Internet of Things, smart transportation and other fields because it uses existing LED lighting equipment to transmit data, avoids the competition for spectrum resources, and has good anti-interference performance.

[0004] Indoor visible light communication is a communication technology that uses the visible light band in indoor environments for data transmission. Indoor visible light communication usually consists of a light source, a light receiver, a modem, and a control system. By modulating the intensity of the light source, the data is encoded into an optical signal, which is transmitted through the air and received by the receiver, and then decoded into an electrical signal for processing. The advantages of indoor visible light communication include high transmission rate, low power consumption, low latency, and license-free operation. Especially in indoor environments, the use of existing LED lighting equipment not only saves infrastructure investment, but also enables data transmission without interfering with existing wireless communication bands.

[0005] At present, the selection of carrier frequency in indoor visible light communication systems generally relies on experimental data or theoretical calculations, taking into account factors such as the wavelength of ambient light, the spectral characteristics of LED light sources, and interference. Some studies have attempted to optimize the selection of carrier frequency by analyzing the ambient light intensity and spectrum. For example, by utilizing the dimming function of LED lamps to adjust the frequency of transmitted light and maximizing communication performance through interference suppression algorithms. In addition, there are methods to optimize communication quality by adjusting the frequency of the light source to avoid spectral overlap or interference with external interference sources.

[0006] However, most optimization schemes in the prior art rely on static or preset ambient light parameters and do not have timely adaptability to dynamically changing indoor ambient light conditions, such as the difference in illumination between day and night, or changes in light sources of other devices. Existing frequency optimization schemes usually do not fully consider the real-time environmental spectral characteristics and instant changes in communication channels, resulting in inflexible frequency selection of communication systems in complex indoor environments, which affects communication quality and transmission rate.

[0007] Therefore, how to accurately optimize the visible light communication carrier frequency according to the changes in the indoor environment and improve the communication effect is an urgent problem to be solved. Summary of the invention

[0008] In view of this, the present invention provides a method, device and equipment for intelligent optimization of visible light communication carrier frequency for indoor lighting, so as to solve the problem that the prior art cannot accurately optimize the visible light communication carrier frequency according to changes in the indoor environment and the communication effect is poor.

[0009] The technical solution adopted by the present invention is:

[0010] In a first aspect, the present invention provides a method for intelligently optimizing carrier frequency of indoor lighting visible light communication, the method comprising:

[0011] Using the spectrum perception algorithm, the environmental spectrum characteristics in the real-time environmental illumination data obtained in the indoor visible light communication scenario are analyzed, and the communication optical frequency range matching the environmental spectrum characteristics is output;

[0012] Using a signal-to-noise ratio optimization algorithm, obtaining a first communication optical frequency corresponding to a highest signal-to-noise ratio in a communication optical frequency interval;

[0013] Using a machine learning algorithm, according to a preset communication optical frequency selection strategy, the first communication optical frequency is matched with a historical communication carrier frequency corresponding to the real-time environment, and when a match is found, a target communication carrier frequency is output.

[0014] Preferably, in the method of using a machine learning algorithm, according to a preset communication optical frequency selection strategy, matching the first communication optical frequency with a historical communication carrier frequency corresponding to a real-time environment, when a match is found, outputting the target communication carrier frequency further includes:

[0015] According to the target communication carrier frequency, determining at least one spectrum type matching the target communication carrier frequency, recorded as a target spectrum type;

[0016] Acquire the communication performance indicator within a preset time window, wherein the communication performance indicator at least includes: an optical bit error rate;

[0017] The optical bit error rate is compared with a preset optical bit error rate threshold, and when the optical bit error rate is greater than the optical bit error rate threshold, the light source parameters are adjusted, wherein the light source parameters at least include: output power, modulation mode and coding mode.

[0018] Preferably, the use of a spectral perception algorithm to analyze the ambient spectral characteristics in the real-time ambient lighting data acquired in an indoor visible light communication scenario and outputting a communication optical frequency interval that matches the ambient spectral characteristics includes:

[0019] Obtaining light intensity measurements of different wavelengths from the real-time ambient light data, wherein the light intensity measurements are presented in the form of a spectrum;

[0020] Preprocessing the acquired light intensity measurement values ​​of different wavelengths, wherein the preprocessing includes smoothing, filtering and calibration, and outputting the preprocessed data;

[0021] The preprocessed data is analyzed using a spectral perception algorithm to output a communication optical frequency range that matches the ambient spectral characteristics.

[0022] Preferably, the using of a machine learning algorithm, according to a preset communication optical frequency selection strategy, matches the first communication optical frequency with a historical communication carrier frequency corresponding to the real-time environment, and when a match is found, outputting a target communication carrier frequency comprises:

[0023] Acquiring a real-time image in the real-time environment;

[0024] Inputting the real-time image into a pre-trained scene recognition model trained based on a deep learning algorithm to identify a first target scene corresponding to the real-time environment;

[0025] 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 optical frequency corresponding to the second target scene is output as the historical communication carrier frequency.

[0026] Preferably, the matching of the target scene with a preset indoor scene set, when the first target scene successfully matches a second target scene in the indoor scene set, outputting the communication optical frequency corresponding to the second target scene as the historical communication carrier frequency comprises:

[0027] When it is detected that the user enters the living room, that is, the first target scene is the living room, the living room is matched with the preset indoor scene set, 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;

[0028] When it is detected that a patient enters a hospital room, that is, the first target scene is a hospital, the matched medical monitoring scene is used as the second target scene according to the real-time environment, and the communication optical frequency corresponding to the medical monitoring scene is output as the historical communication carrier frequency;

[0029] When it is detected that the robot moves from one workshop to another, that is, the first target scene is the workshop, the production scene of the current workshop matched is used as the second target scene, and the communication optical frequency corresponding to the production scene is output as the historical communication carrier frequency;

[0030] When it is detected that the user is roaming in a shopping mall, that is, the first target scene is a 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.

[0031] Preferably, before acquiring the communication performance indicator within the preset time window, the method further includes:

[0032] Acquire the target spectrum type, wherein the target spectrum type includes at least one of the following: red spectrum, green spectrum, blue spectrum and white spectrum;

[0033] According to the target spectrum type, acquiring a coding strategy and a modulation strategy corresponding to the target spectrum type;

[0034] According to the coding strategy and the modulation strategy, the coding mode and the modulation mode of the light source are adjusted to match the target communication carrier frequency respectively.

[0035] Preferably, the acquiring, according to the target spectrum type, a coding strategy and a modulation strategy corresponding to the target spectrum type comprises:

[0036] If the target spectrum type is red spectrum, the encoding strategy is to use NRZ encoding, and the modulation strategy is to use BPSK modulation;

[0037] If the target spectrum type is green spectrum, the encoding strategy is to use differential Manchester encoding, and the modulation strategy is to use PAM modulation;

[0038] If the target spectrum type is blue spectrum, the coding strategy is to use 8B / 10B line coding, and the modulation strategy is to use QAM modulation;

[0039] If the target spectrum type is a white spectrum, the encoding strategy is to use Turbo encoding, and the modulation strategy is to use CAP modulation.

[0040] In a second aspect, the present invention provides a device for intelligently optimizing carrier frequency of indoor lighting visible light communication, the device comprising:

[0041] The spectrum analysis module is used to analyze the environmental spectrum characteristics in the real-time environmental illumination data obtained in the indoor visible light communication scenario by using the spectrum perception algorithm, and output the communication optical frequency range that matches the environmental spectrum characteristics;

[0042] A signal-to-noise ratio optimization module, used to obtain a first communication optical frequency corresponding to a highest signal-to-noise ratio in a communication optical frequency interval by using a signal-to-noise ratio optimization algorithm;

[0043] The communication carrier frequency matching module is used to use 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 when a match is found, output a target communication carrier frequency.

[0044] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising: 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 in the above-mentioned embodiment.

[0045] In a fourth aspect, an embodiment of the present invention further provides a storage medium on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method of the first aspect in the above-mentioned embodiment is implemented.

[0046] In summary, the beneficial effects of the present invention are as follows:

[0047] The present invention provides a method, device and equipment for intelligent optimization of indoor lighting visible light communication carrier frequency. The method includes: using a spectral perception algorithm to analyze the environmental spectral characteristics in real-time environmental lighting data obtained in an indoor visible light communication scenario, and outputting a communication optical frequency interval that matches the environmental spectral characteristics; using a signal-to-noise ratio optimization algorithm to obtain a first communication optical frequency corresponding to the highest signal-to-noise ratio in the communication optical frequency interval; using a machine learning algorithm, according to a preset communication optical frequency selection strategy, matching the first communication optical frequency with a historical communication carrier frequency corresponding to the real-time environment, and when a match is achieved, outputting a target communication carrier frequency. The present invention effectively solves the problem that the visible light communication carrier frequency cannot be accurately optimized for indoor environmental changes in the prior art by introducing a combination of spectral perception algorithm, signal-to-noise ratio optimization algorithm and machine learning algorithm; first, the spectral perception algorithm analyzes the real-time ambient light data to identify the spectral characteristics of the ambient light, thereby determining the communication optical frequency interval that matches the ambient spectral characteristics; this process ensures that the communication system can automatically adjust its operating frequency interval according to the actual ambient light conditions, avoiding the spectrum interference that may be caused by static frequency setting in the traditional scheme; then, the signal-to-noise ratio optimization algorithm finds the frequency point with the highest signal-to-noise ratio in the selected communication optical frequency interval, and determines the optimal communication optical frequency. This optimization process significantly improves the communication quality and reduces signal attenuation and interference; finally, based on the machine learning algorithm, combined with the preset communication frequency selection strategy, the optimal frequency is matched with the historical communication carrier frequency in real time, ensuring that the communication can be adaptively adjusted according to historical data, thereby achieving efficient carrier frequency selection. When the new communication frequency matches the historical frequency, the target communication carrier frequency will be output, ensuring the accuracy and flexibility of the communication frequency selection. This optimization solution based on real-time spectral analysis and adaptive adjustment of historical data can effectively respond to changes in indoor lighting, optimize frequency selection, improve the stability and transmission performance of the communication system, and solve the problem of poor communication effects of traditional solutions in dynamically changing environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings required for use in the embodiment of the present invention will be briefly introduced below. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work, and these are all within the protection scope of the present invention.

[0049] Figure 1 This is a schematic diagram of the overall working process of the visible light communication method based on environment adaptation in Embodiment 1 of the present invention;

[0050] Figure 2 This is a flow chart of determining a target communication carrier frequency using a preset adaptive algorithm in Embodiment 1 of the present invention;

[0051] Figure 3 This is a schematic diagram of a process for matching the first communication optical frequency with the historical communication carrier frequency in Embodiment 1 of the present invention;

[0052] Figure 4 This is a schematic diagram of a process for adjusting the encoding mode and modulation mode of a light source in Embodiment 1 of the present invention;

[0053] Figure 5 This is a schematic diagram of a process for obtaining a coding strategy and a modulation strategy corresponding to a target spectrum type in Embodiment 1 of the present invention;

[0054] Figure 6 This is a schematic diagram of a process for adjusting light source parameters in Embodiment 1 of the present invention;

[0055] Figure 7 is a structural block diagram of a visible light communication device based on environment adaptation in Embodiment 2 of the present invention;

[0056] Figure 8 It is a schematic diagram of the structure of the electronic device in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described in conjunction with the drawings in the embodiment of the present invention. It should be noted that, in this article, relational 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 these entities or operations. In the description of the present invention, it should be understood that the orientation or position relationship indicated by the terms "center", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method, article or device. In the absence of further restrictions, the elements defined by the phrase "comprising..." do not exclude the existence of other identical elements in the process, method, article or device comprising the elements. If there is no conflict, the embodiments of the present invention and the various features in the embodiments can be combined with each other, all within the protection scope of the present invention.

[0058] Example 1

[0059] See also Figure 1 Embodiment 1 of the present invention discloses a visible light communication method based on environment adaptation, the method comprising:

[0060] S1: Obtain real-time ambient light data in indoor visible light communication scenarios;

[0061] Specifically, select a light sensor suitable for indoor visible light communication scenarios, wherein the indoor visible light communication scenarios include at least: museums, airports, shopping malls, hospitals, office buildings and factories. Different light sensors have different sensitivities and working ranges, ensuring that the selected sensor can cover the visible spectrum range (usually in the range of 300-700 nanometers); install the selected light sensor at the location of the communication equipment or system so as to capture the required ambient lighting data. The sensor should be exposed to the main communication area indoors to ensure that the characteristics of the communication environment are accurately captured. The real-time ambient lighting data obtained includes at least light intensity, wavelength distribution and spectral information. These data can be further processed and analyzed to determine the real-time ambient lighting conditions.

[0062] S2: Determine a target communication carrier frequency that meets the real-time environment requirements using a preset adaptive algorithm based on the real-time environment illumination data;

[0063] Specifically, according to the real-time ambient light data, a preset adaptive algorithm is used to determine the target communication carrier frequency that meets the real-time environmental requirements; the adaptive algorithm includes at least the following algorithms: spectrum perception algorithm, signal-to-noise ratio optimization algorithm and machine learning algorithm. The spectrum perception algorithm is used to analyze the spectral characteristics in the real-time ambient light data, and can identify the intensity and distribution of light of different wavelengths to determine the most suitable communication frequency range; by understanding the ambient 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 communication quality and reduce bit error rate; by selecting the frequency with the highest signal-to-noise ratio, better communication performance can be obtained, that is, higher signal strength relative to noise; the machine learning algorithm uses the patterns and trends in the real-time ambient light data to predict the most suitable communication frequency, learns the impact of ambient light on communication based on historical data, and makes real-time decisions. The machine learning algorithm is adaptive and can adapt to different environmental conditions and provide smarter frequency selection to meet real-time requirements. The combined use of these adaptive algorithms can dynamically select the optimal communication optical frequency based on environmental conditions to minimize the adverse effects of ambient light on communication. This helps to improve the performance, stability and adaptability of visible light communication and ensure reliable communication connections under different environmental conditions.

[0064] In one embodiment, see Figure 2 , said S2 comprises:

[0065] S21: Analyze the ambient spectral characteristics in the real-time ambient light data using the spectral perception algorithm, and output a communication optical frequency interval that matches the ambient spectral characteristics;

[0066] Specifically, light intensity measurements of different wavelengths are obtained from the collected real-time ambient light data, usually presented in the form of a spectrum; the obtained light intensity measurements of different wavelengths are preprocessed to eliminate noise and unnecessary fluctuations, and the preprocessing includes smoothing, filtering and calibration to ensure the accuracy and stability of the data; the data is analyzed using a spectral perception algorithm to first identify light of different wavelengths and determine their relative intensities; based on the known communication frequency and wavelength characteristics, the communication optical frequency that matches the ambient spectral characteristics is identified, and the most suitable communication frequency interval is determined to minimize interference with ambient light while maintaining good signal quality, and the communication optical frequency interval that matches the ambient spectral characteristics is output. These frequency intervals will be used to adjust the light source to achieve visible light communication, and the spectral perception algorithm helps to select the most suitable communication optical frequency in a real-time environment to reduce the interference of ambient light and improve communication quality and stability.

[0067] S22: using the signal-to-noise ratio optimization algorithm, obtaining a first communication optical frequency corresponding to the highest signal-to-noise ratio in the communication optical frequency interval;

[0068] Specifically, the signal-to-noise ratio (SNR) is calculated within the communication optical frequency interval that matches the ambient spectral characteristics, wherein the SNR is usually calculated in the following manner:

[0069] SNR=10log10(Ps / Pn)

[0070] Among them, Ps represents signal power and Pn represents noise power. In visible light communication, the signal is usually the transmitted optical signal, while the noise comes from the ambient light. The highest SNR frequency selection: in the entire frequency range, find the frequency with the highest SNR value, which will be the optimal communication optical frequency. Usually, the algorithm will select the first frequency encountered, whose SNR reaches the maximum value, and output the first communication optical 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 when it is interfered by ambient light. By selecting the frequency with the highest SNR, a more reliable communication connection can be achieved.

[0071] S23: Using the machine learning algorithm, according to 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, and when a match is found, the target communication carrier frequency is output.

[0072] Specifically, real-time ambient lighting data is collected, and historical communication carrier frequencies are recorded. The collected real-time ambient lighting data and the recorded historical communication carrier frequencies are used to train a machine learning model. In a real-time environment, the current ambient lighting data is input to allow the machine learning model to predict the most suitable communication optical frequency. The model will compare the current ambient data with the historical data, find the frequency that best matches the historical communication carrier frequency, and output the target communication carrier frequency predicted by the machine learning model as the current communication frequency. The machine learning algorithm can intelligently select the optimal communication optical frequency based on changes in historical communication data and real-time environmental conditions. By leveraging the ability of the machine learning model to adapt to different environments, the performance and stability of communication can be improved, which helps to solve the adverse effects of ambient light on visible light communications and provide more reliable communication connections.

[0073] In one embodiment, see Figure 3 , the S23 comprises:

[0074] S231: Acquire a real-time image in the real-time environment;

[0075] Specifically, select an image sensor suitable for a specific application, such as selecting parameters such as sensor type (such as CCD, CMOS, etc.) and resolution (number of pixels), install the selected image sensor in the required position to ensure that it can capture the required real-time image, which may involve physical installation and calibration to ensure the accuracy of the image; use corresponding acquisition devices, such as cameras, camcorders or computer interfaces to obtain real-time image data, which can be connected to the image sensor and acquire images through cables or wireless connections, and process the real-time images to improve image quality or extract specific information.

[0076] S232: Inputting the real-time image into a pre-trained scene recognition model trained based on a deep learning algorithm to identify a first target scene corresponding to the real-time environment;

[0077] Specifically, a deep learning model that has been trained on scene recognition tasks is selected, and the deep learning model includes at least a convolutional neural network, such as VGG, ResNet, and 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 preprocessing the image, such as scaling and standardization, etc. The deep learning model will extract features in the image and perform classification tasks. The model will analyze 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 that the image belongs to each possible scene category, and the category with the highest probability is selected as the first target scene for subsequent applications or systems. By using a deep learning model to help automatically identify the real-time environment, it is very useful for automation systems, intelligent monitoring and other applications. The deep learning model can efficiently process a large amount of image data, thereby providing accurate scene recognition results.

[0078] S233: Match the target scene with a preset indoor scene set, and when the first target scene successfully matches a second target scene in the indoor scene set, output the communication optical frequency corresponding to the second target scene as the historical communication carrier frequency.

[0079] Specifically, the first target scene is identified and output through the above-mentioned deep learning model, and the first target scene is matched with each scene in a preset indoor scene set. Predefined scene labels or specific scene features are used for comparison to determine whether the first target scene successfully matches a scene in the indoor scene set, and the model output is compared with the indoor scene label or feature to determine the match. If the match is successful, the communication optical frequency corresponding to the second target scene is output according to the matched indoor scene. This frequency is usually pre-set based on historical communication data, and the output communication optical frequency is recorded as the historical communication carrier frequency for subsequent communication use. For example: in a smart home scenario, when a user is detected entering the living room (first target scenario), it will automatically match with the preset indoor scene set. If the living room matches the "entertainment" scene (second target scenario), the communication optical frequency will be automatically adjusted to adapt to the data transmission between entertainment devices, such as TV, audio and smart home devices; in a medical scenario, when a patient is receiving treatment in a hospital room (first target scenario), it will be matched to the "medical monitoring" scene (second target scenario) according to the real-time environment. Medical equipment can intelligently select the communication optical 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. When they move from one workshop (first target scenario) to another, the system can automatically match the "production" scene (second target scenario) of the current workshop, which helps optimize the communication settings to adapt to the data transmission needs between production equipment. In an indoor navigation system, a user may roam in a shopping mall (first target scenario) and then be matched to the "store navigation" scene (second target scenario). At this time, the communication optical frequency is automatically selected to provide information and offers related to store navigation. The communication optical frequency is intelligently selected according to the environmental scenario to meet the needs of different applications. The adaptive and automated approach helps improve the performance of the communication system while simplifying the configuration and management of users and devices.

[0080] S3: Determine, according to the target communication carrier frequency, at least one spectrum type that matches the target communication carrier frequency, recorded as a target spectrum type;

[0081] Specifically, according to the selected target communication carrier frequency, find the spectrum type that matches the frequency range. For example, for the red spectrum communication frequency (620-750nm), choose to use red LED as the target spectrum type, and for the blue spectrum communication frequency (450-495nm), 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 a stable communication connection.

[0082] In one embodiment, see Figure 4, the S4 also includes:

[0083] S401: Acquire the target spectrum type, wherein the target spectrum type includes at least one of the following: red spectrum, green spectrum, blue spectrum and white spectrum;

[0084] S402: According to the target spectrum type, obtaining a coding strategy and a modulation strategy corresponding to the target spectrum type;

[0085] Specifically, various spectral types usually require specific coding strategies to convert digital data into optical signals. The coding strategies include at least Manchester coding, 4B / 5B coding, and 8B / 10B coding. The appropriate coding strategy is selected according to the characteristics of the target spectral type; the modulation strategy is used to adjust the intensity or phase of the optical signal to transmit digital data. Different spectral types require different modulation strategies.

[0086] In one embodiment, see Figure 5 , the S402 includes:

[0087] S4021: If the target spectrum type is red spectrum, the encoding strategy is to use NRZ encoding, and the modulation strategy is to use BPSK modulation;

[0088] Specifically, if the target spectrum type is red spectrum, the NRZ coding strategy and BPSK modulation strategy are adopted, which helps to achieve efficient data transmission in red spectrum communication; NRZ coding is used to convert digital data into high and low levels of optical signals, while BPSK modulation allows binary modulation in phase, which can achieve stable data transmission in red spectrum communication and improve the signal's anti-interference ability to adapt to different environmental conditions.

[0089] 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;

[0090] Specifically, if the target spectrum type is green spectrum, differential Manchester coding will be selected as the coding strategy, and PAM modulation strategy will help to achieve high-quality data transmission in green spectrum communication. Differential Manchester coding 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 achieve higher transmission stability and reduce data transmission error rate in green spectrum communication.

[0091] S4023: If the target spectrum type is a blue spectrum, the coding strategy is to use 8B / 10B line coding, and the modulation strategy is to use QAM modulation;

[0092] Specifically, when the target spectrum type is blue spectrum, 8B / 10B line coding is selected as the coding strategy, and QAM modulation strategy is selected, which is very effective for blue spectrum communication. 8B / 10B coding is a data coding 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 reduce the risk of signal distortion.

[0093] 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.

[0094] Specifically, if the target spectrum type is white spectrum, selecting Turbo coding as the coding strategy, together with the CAP modulation strategy, helps achieve high fault tolerance and high-speed transmission in white spectrum communication. Turbo coding is a powerful error correction coding method that can reduce the error rate in data transmission, while the CAP modulation method combines amplitude and phase modulation and is suitable for high-speed visible light communication. This combination of strategies helps achieve high performance and reliable data transmission in white spectrum communication.

[0095] S403: According to the coding strategy and the modulation strategy, the coding method and the modulation method of the light source are adjusted to match the target communication carrier frequency respectively.

[0096] Specifically, according to the coding strategy and modulation strategy, the coding mode of the light source and the modulation mode of the light source are adjusted to match the target communication carrier frequency. Since the selection of coding and modulation strategies is based on the characteristics of different spectral types and communication requirements, it helps to optimize the performance of the communication system, improve the data transmission quality, and reduce the error rate, thereby achieving more reliable visible light communication.

[0097] S4: According to preset communication performance indicators, the light source parameters of the target spectrum type are adjusted, and 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.

[0098] Specifically, a preset communication performance index is obtained, and the communication performance index at least includes: data transmission rate, bit error rate, signal-to-noise ratio, etc. According to the performance index, the system will adjust the parameters of the light source, including light source intensity, light source wavelength, light source modulation depth, etc. The adjustment of 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 that data transmission is carried out within the selected frequency range, which helps to achieve communication consistency. By continuously monitoring the performance of the light source to ensure that it meets the preset communication performance index, 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 index, the light source is controlled to emit a visible light signal, which will be transmitted to the receiver to complete the data transmission. By dynamically adjusting the parameters of the light source to meet the communication performance requirements and achieve the best communication quality under different conditions. This adaptive method helps to cope with environmental changes and changes in communication needs to ensure that the performance of the visible light communication system is always in the best state.

[0099] In one embodiment, see Figure 6 , said S4 comprises:

[0100] S41: Acquire the communication performance indicator within a preset time window, wherein the communication performance indicator at least includes: an optical bit error rate;

[0101] Specifically, it is critical to obtain communication performance indicators within a preset time window, where the communication performance indicators include optical bit error rate, which is an important measurement indicator of communication performance and reflects the error rate in data transmission.

[0102] S42: Compare the optical bit error rate with a preset optical bit error rate threshold, and when the optical bit error rate is greater than the optical bit error rate threshold, adjust the light source parameters, wherein the light source parameters include at least: output power, modulation mode and coding mode.

[0103] Specifically, the optical bit error rate is continuously monitored to understand the communication quality in real time, including regular bit error rate detection and calculation, comparing the actual optical bit error rate with the preset optical bit error rate threshold. If the actual optical bit error rate is greater than the threshold, it means that the communication performance is reduced and measures need to be taken; once the optical bit 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 achieved by increasing or decreasing the intensity of the light source to ensure that the signal reaches the appropriate intensity level of the receiver, which can help improve the signal quality; if the bit 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 bit error rate is monitored again to ensure that the performance has improved. If the optical bit error rate is still higher than the threshold, the parameters need to be further adjusted. The above process is usually adaptive, and the light source parameters will be continuously monitored and adjusted to maintain good communication quality. By comparing the optical bit error rate with the threshold and automatically adjusting the light source parameters, the communication quality is kept at the required level and responds to environmental changes and changes in communication needs, which helps to achieve stable and high-quality visible light communication.

[0104] Example 2

[0105] See also Figure 7 Embodiment 2 of the present invention further provides a visible light communication device based on environment adaptation, the device comprising:

[0106] Ambient lighting data acquisition module, used to obtain real-time ambient lighting data in indoor visible light communication scenarios;

[0107] An adaptive adjustment module, used to determine a target communication carrier frequency that meets the real-time environmental requirements based on the real-time environmental illumination data using a preset adaptive algorithm;

[0108] A spectrum type determination module, used to determine at least one spectrum type matching the target communication carrier frequency according to the target communication carrier frequency, recorded as a target spectrum type;

[0109] The light source parameter adjustment module is used to adjust the light source parameters of the target spectrum type according to preset communication performance indicators, and when the adjusted light source parameters match the target communication carrier frequency, control the light source to emit a visible light signal to the receiver.

[0110] Specifically, a visible light communication device based on environmental adaptation provided by an embodiment of the present invention is adopted, and the device includes: an environmental lighting data acquisition module, which is used to acquire real-time environmental lighting data in an indoor visible light communication scenario; an adaptive adjustment module, which is used to determine a target communication carrier frequency that meets the real-time environmental requirements based on the real-time environmental lighting data and a preset adaptive algorithm; a spectrum type determination module, which is used to determine at least one spectrum type that matches the target communication carrier frequency based on the target communication carrier frequency, recorded as a target spectrum type; a light source parameter adjustment module, which is used to adjust the light source parameters of the target spectrum type based on preset communication performance indicators, and when the adjusted light source parameters match the target communication carrier frequency, control the light source to emit a visible light signal to the receiver. The device obtains real-time ambient light data and uses an adaptive algorithm to select the most suitable communication optical frequency according to the actual environmental conditions. This adaptive adjustment helps to reduce the interference of ambient light on communication and improve communication quality and stability. By adjusting the light source parameters of the target spectrum type according to preset communication performance indicators, the communication performance is optimized to ensure the best communication quality under different environmental conditions. Selecting a spectrum type that matches the target communication carrier frequency helps to reduce the interference of other light sources on the signal, which means that the communication signal is easier to distinguish from the background light source and reduces the data error rate. By adjusting the light source parameters, energy is effectively utilized. Comprehensive consideration of spectrum type, frequency selection and parameter adjustment helps to improve the reliability of the visible light communication system, and users can transmit data more reliably and reduce data loss and errors. In short, the present invention effectively solves the environmental problems in indoor visible light communication scenarios through measures such as 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 users with a better communication experience.

[0111] Example 3

[0112] In addition, combined Figure 1 The environment-adaptive visible light communication method described in Embodiment 1 of the present invention may be implemented by an electronic device. Figure 8 A schematic diagram of the hardware structure of an electronic device provided in Embodiment 3 of the present invention is shown.

[0113] The electronic device may include a processor and a memory storing computer program instructions.

[0114] Specifically, the processor may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present invention.

[0115] The memory may include a large capacity memory for data or instructions. By way of example and not limitation, the memory may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory may be inside or outside a data processing device. In a specific embodiment, the memory is a non-volatile solid-state memory. In a specific embodiment, the memory includes a read-only memory (ROM). In appropriate cases, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM) or a flash memory or a combination of two or more of these.

[0116] The processor implements any one of the visible light communication methods based on environment adaptation in the above embodiments by reading and executing computer program instructions stored in the memory.

[0117] In one example, the electronic device may further include a communication interface and a bus. Figure 8 As shown, the processor, memory, and communication interface are connected via a bus and communicate with each other.

[0118] The communication interface is mainly used to implement communication between the modules, devices, units and / or equipment in the embodiments of the present invention.

[0119] The bus includes hardware, software or both, and the parts of the device are coupled to each other. For example, but not limitation, the bus may include an accelerated graphics port (AGP) or other graphics bus, an enhanced industrial standard architecture (EISA) bus, a front-side bus (FSB), a hypertransport (HT) interconnection, an industrial standard architecture (ISA) bus, an infinite bandwidth interconnection, a low pin count (LPC) bus, a memory bus, a microchannel 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 standard association local (VLB) bus or other suitable bus or a combination of two or more of these. In suitable cases, the bus may include one or more buses. Although the embodiments of the present invention describe and illustrate specific buses, the present invention contemplates any suitable bus or interconnection.

[0120] Example 4

[0121] In addition, in combination with the visible light communication method based on environment adaptation in the above embodiment 1, embodiment 4 of the present invention can also provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the visible light communication methods based on environment adaptation in the above embodiments is implemented.

[0122] In summary, the embodiments of the present invention provide a visible light communication method, apparatus, device and medium based on environment adaptation.

[0123] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is 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 between the steps after understanding the spirit of the present invention.

[0124] The functional blocks shown in the above-described block diagram 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 function card, etc. When implemented in software, the elements of the present invention are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media 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 segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0125] It should also be noted that the exemplary embodiments mentioned in the present invention describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiments, or in a different order from the embodiments, or several steps can be performed simultaneously.

[0126] The above is only a specific implementation of the present invention. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the system, module and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be covered within the protection scope of the present invention.

Claims

1. A method for intelligent optimization of carrier frequency for indoor lighting visible light communication, characterized in that: The method comprises: Using the spectrum perception algorithm, the environmental spectrum characteristics in the real-time environmental illumination data obtained in the indoor visible light communication scenario are analyzed, and the communication optical frequency range matching the environmental spectrum characteristics is output; Using a signal-to-noise ratio optimization algorithm, obtaining a first communication optical frequency corresponding to a highest signal-to-noise ratio in a communication optical frequency interval; Using a machine learning algorithm, according to a preset communication optical frequency selection strategy, the first communication optical frequency is matched with a historical communication carrier frequency corresponding to the real-time environment, and when a match is found, a target communication carrier frequency is output.

2. The method for intelligent optimization of indoor lighting visible light communication carrier frequency according to claim 1, characterized in that: In the method of using the machine learning algorithm, according to a preset communication optical frequency selection strategy, matching the first communication optical frequency with a historical communication carrier frequency corresponding to a real-time environment, and outputting a target communication carrier frequency when a match is found, further comprising: According to the target communication carrier frequency, determining at least one spectrum type matching the target communication carrier frequency, recorded as a target spectrum type; Acquire the communication performance indicator within a preset time window, wherein the communication performance indicator at least includes: an optical bit error rate; The optical bit error rate is compared with a preset optical bit error rate threshold, and when the optical bit error rate is greater than the optical bit error rate threshold, the light source parameters are adjusted, wherein the light source parameters at least include: output power, modulation mode and coding mode.

3. The method for intelligent optimization of carrier frequency for indoor lighting visible light communication according to claim 1, characterized in that: The spectrum sensing algorithm is used to analyze the environmental spectrum characteristics in the real-time environmental illumination data obtained in the indoor visible light communication scene, and the communication light frequency range that matches the environmental spectrum characteristics is output, including: Obtaining light intensity measurements of different wavelengths from the real-time ambient light data, wherein the light intensity measurements are presented in the form of a spectrum; Preprocessing the acquired light intensity measurement values ​​of different wavelengths, wherein the preprocessing includes smoothing, filtering and calibration, and outputting the preprocessed data; The preprocessed data is analyzed using a spectral perception algorithm to output a communication optical frequency range that matches the ambient spectral characteristics.

4. The method for intelligent optimization of carrier frequency for indoor lighting visible light communication according to claim 1, characterized in that: The using of the machine learning algorithm, according to a preset communication optical frequency selection strategy, matches the first communication optical frequency with a historical communication carrier frequency corresponding to the real-time environment, and when a match is found, outputting a target communication carrier frequency includes: Acquiring a real-time image in the real-time environment; Inputting the real-time image into a pre-trained scene recognition model trained 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 indoor scene set, and when the first target scene is successfully matched with a second target scene in the indoor scene set, the communication optical frequency corresponding to the second target scene is output as the historical communication carrier frequency.

5. The method for intelligent optimization of carrier frequency for indoor lighting visible light communication according to claim 4, characterized in that: The matching of the target scene with a preset indoor scene set, and when the first target scene successfully matches a second target scene in the indoor scene set, outputting the communication optical frequency corresponding to the second target scene as the historical communication carrier frequency comprises: When it is detected that the user enters the living room, that is, the first target scene is the living room, the living room is matched with the preset indoor scene set, 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 it is detected that a patient enters a hospital room, that is, the first target scene is a hospital, the matched medical monitoring scene is used as the second target scene according to the real-time environment, and the communication optical frequency corresponding to the medical monitoring scene is output as the historical communication carrier frequency; When it is detected that the robot moves from one workshop to another, that is, the first target scene is the workshop, the production scene of the current workshop matched is used as the second target scene, and the communication optical frequency corresponding to the production scene is output as the historical communication carrier frequency; When it is detected that the user is roaming in a shopping mall, that is, the first target scene is a 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.

6. The method for intelligent optimization of carrier frequency for indoor lighting visible light communication according to claim 2, characterized in that: Before acquiring the communication performance indicator within the preset time window, the method further includes: Acquire the target spectrum type, wherein the target spectrum type includes at least one of the following: red spectrum, green spectrum, blue spectrum and white spectrum; According to the target spectrum type, acquiring a coding strategy and a modulation strategy corresponding to the target spectrum type; According to the coding strategy and the modulation strategy, the coding mode and the modulation mode of the light source are adjusted to match the target communication carrier frequency respectively.

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

8. An intelligent optimization device for indoor lighting visible light communication carrier frequency, characterized in that: The device comprises: The spectrum analysis module is used to analyze the environmental spectrum characteristics in the real-time environmental illumination data obtained in the indoor visible light communication scene by using the spectrum perception algorithm, and output the communication light frequency range that matches the environmental spectrum characteristics; A signal-to-noise ratio optimization module, used to obtain a first communication optical frequency corresponding to a highest signal-to-noise ratio in a communication optical frequency interval by using a signal-to-noise ratio optimization algorithm; The communication carrier frequency matching module is used to use 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 when a match is found, output a target communication carrier frequency.

9. An electronic device, characterized in that: include: At least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, implement the method according to any one of claims 1 to 7.

10. A storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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