Visible light sensor, networking method and device of visible light sensor

Abstract

The embodiment of the application provides a visible light sensor, a networking method and device of the visible light sensor, and belongs to the technical field of visible light communication. The method comprises the following steps: a photoelectric detection module is used for receiving a first visible light signal sent by a candidate visible light sensor; an LED light-emitting module is used for emitting a second visible light signal; a data processing module is electrically connected with the photoelectric detection module, is used for extracting a networking request parameter according to the first visible light signal, generating a networking response parameter according to the networking request signal, and sending the networking response parameter to the candidate visible light sensor through the second visible light signal. The embodiment of the application can complete visible light communication by using an existing visible light sensor, and saves hardware cost and manpower.

Description

Technical Field

[0001] This application relates to the field of visible light communication technology, and in particular to a visible light sensor, a networking method and apparatus for visible light sensors. Background Technology

[0002] Visible light communication technology, as an emerging form of wireless optical communication, combines the flexibility of wireless communication with the anti-interference and low-power advantages of optical communication. In related technologies, indoor visible light sensors typically function only as a unidirectional downlight source and do not integrate light-receiving components such as photodetectors (PDs). In other related technologies, radio frequency sensor networks require dedicated communication nodes and gateways to achieve communication. Furthermore, visible light sensors are usually constant current sources used for stable lighting; if communication signals need to be superimposed, the visible light sensor's circuitry must be modified or additional bias applied, which affects the lighting quality of the visible light sensor and increases modification costs and manpower.

[0003] Therefore, how to utilize existing visible light sensors to achieve visible light communication while saving hardware costs and manpower has become an urgent technical problem to be solved. Summary of the Invention

[0004] The main objective of this application is to propose a visible light sensor, a method and apparatus for networking visible light sensors, which aims to complete visible light communication using existing visible light sensors and save hardware costs and manpower.

[0005] To achieve the above objectives, a first aspect of this application provides a visible light sensor, the visible light sensor comprising: The photoelectric detection module is used to receive the first visible light signal sent by the candidate visible light sensor; LED light-emitting modules are used to emit a second visible light signal; The data processing module, electrically connected to the photoelectric detection module, is used to extract networking request parameters based on the first visible light signal, generate networking response parameters based on the networking request signal, and send the networking response parameters to the candidate visible light sensor via the second visible light signal.

[0006] In some embodiments, the visible light sensor further includes: The sensing module is used to collect environmental data from the visible light sensor; The data processing module is also used to perform fault prediction on the visible light sensor based on the environmental data to obtain fault prediction information, and is also used to drive the LED light-emitting module to send the fault prediction information to the cloud through the second visible light signal based on the fault prediction information.

[0007] To achieve the above objectives, a first aspect of this application proposes a networking method for a visible light sensor, applied to a target visible light sensor, wherein the target visible light sensor is the aforementioned visible light sensor, and the method includes: Receive the first visible light signal sent by the candidate visible light sensor; The network parameter extraction is performed on the first visible light signal to obtain the network request parameters; wherein, the network request parameters include: requested location information, requested working status and requested operating parameters; Select a visible light sensor from the candidate visible light sensors based on the requested location information, the requested working status, and the requested operating parameters; Network response parameters are generated based on the network request parameters of the visible light sensor, and the network response parameters are sent to the selected visible light sensor via a second visible light signal.

[0008] In some embodiments, selecting a visible light sensor from the candidate visible light sensors based on the requested location information, the requested operating status, and the requested operating parameters includes: Obtain the current location information, and determine the communication distance based on the requested location information and the current location information; The communication quality of the candidate visible light sensor is evaluated based on a lightweight communication quality assessment model, the communication distance, the requested operating status, and the requested operating parameters to obtain communication quality assessment data. The selected visible light sensor is selected from the candidate visible light sensors based on the communication quality assessment data.

[0009] In some embodiments, before performing a communication quality assessment on the candidate visible light sensor based on a lightweight communication quality assessment model, the communication distance, the requested operating state, and the requested operating parameters to obtain communication quality assessment data, the method further includes: Local network data is uploaded to the cloud, so that the cloud can optimize the preset original quality assessment model based on the local network data to obtain a preliminary quality assessment model, and perform a compression operation on the preliminary quality assessment model to obtain the communication quality assessment model; wherein, the original quality assessment model is trained by historical network data uploaded by candidate visible light sensors of the same type and in the same area as the target visible light sensor; Receive the communication quality assessment model sent by the cloud.

[0010] In some embodiments, the cloud optimizes a preset original quality assessment model based on the local network data to obtain a preliminary quality assessment model, and performs a compression operation on the preliminary quality assessment model to obtain the communication quality assessment model, including: The cloud platform optimizes the original quality assessment model based on the local network data to obtain the preliminary quality assessment model. The cloud platform performs distillation processing on the preliminary quality assessment model to obtain candidate quality assessment models; The cloud platform performs structured pruning on the candidate quality assessment models to obtain the selected quality assessment model. The cloud platform performs quantification on the selected quality assessment model to obtain the communication quality assessment model.

[0011] In some embodiments, selecting the chosen visible light sensor from the candidate visible light sensors based on the communication quality assessment data includes: Preliminary visible light sensors are selected from the candidate visible light sensors based on a preset quality threshold and the communication quality evaluation data; The preliminary visible light sensors are sorted in ascending order based on the communication quality assessment data to obtain sensor serial numbers; The selected visible light sensor is selected from the preliminary visible light sensors according to the preset quantity and the sensor serial number.

[0012] In some embodiments, the method further includes: Collect environmental data from the target visible light sensor; Based on the second visible light signal and the environmental data, the target visible light sensor is subjected to fault risk prediction to obtain fault risk prediction data. Based on the fault risk prediction data, a target early warning operation is performed on the target visible light sensor.

[0013] In some embodiments, the step of predicting the failure risk of the target visible light sensor based on the second visible light signal and the environmental data to obtain failure risk prediction data includes: The communication parameters of the second visible light signal are predicted to obtain the predicted communication parameters. Based on the predicted communication parameters, a fault risk prediction is performed on the target visible light sensor to obtain a first predicted fault risk value; Based on the environmental data, a second predicted failure risk value is obtained by predicting the failure risk of the target visible light sensor. The first predicted fault risk value and the second predicted fault risk value are concatenated to obtain the fault risk prediction data.

[0014] To achieve the above objectives, a third aspect of this application provides a networking device for a visible light sensor. The device is applied to a target visible light sensor, which is the aforementioned visible light sensor. The device includes: The data processing module is used to implement the networking method of the above-mentioned visible light sensor.

[0015] The visible light sensor, visible light sensor networking method, and apparatus proposed in this application receive the first visible light signal from candidate visible light sensors and extract the requested location information, requested working status, and requested operating parameters. Based on multi-dimensional networking request parameters, it can accurately screen matching visible light sensors and generate targeted networking response parameters as feedback with a second visible light signal. This achieves efficient and adaptive networking among visible light sensors, significantly improving the accuracy and flexibility of networking matching. It avoids communication delays and resource waste caused by blind connections or parameter mismatches. At the same time, it utilizes the inherent advantages of visible light communication to ensure low interference, high reliability, and real-time response capabilities in complex environments, saving manpower and hardware costs for communication networking. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the visible light sensor provided in an embodiment of this application; Figure 2 This is a flowchart of a network method for visible light sensors provided in an embodiment of this application; Figure 3 yes Figure 2 The flowchart of step S203 in the process; Figure 4 yes Figure 3 The flowchart of step S303 in the process; Figure 5 This is a flowchart of a networking method for visible light sensors provided in another embodiment of this application; Figure 6 This is a flowchart of a networking method for visible light sensors provided in another embodiment of this application; Figure 7 This is a flowchart of a networking method for visible light sensors provided in another embodiment of this application; Figure 8 yes Figure 7 The flowchart of step S702 in the process; Figure 9 This is a schematic diagram of the visible light sensor provided in this application embodiment, which is a lighting tube. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0018] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0020] First, let's analyze some of the terms used in this application: Visible light sensor: an electronic device that converts visible light signals into electrical signals, used to detect changes in light intensity, color, and ambient light.

[0021] Visible light communication technology is a wireless optical communication technology that uses the high-speed flickering signals emitted by visible light sources such as light-emitting diodes (LEDs) to transmit information. Its basic principle lies in utilizing the excellent modulation performance and extremely short response time of LEDs to modulate digital signals onto a light beam, and then using high-frequency switching imperceptible to the human eye to achieve data encoding and transmission.

[0022] Model lightweighting is a technique that improves deployment efficiency and resource utilization while maintaining performance by reducing model parameters and computational load.

[0023] Among related technologies, wireless sensor networks, as a crucial cornerstone of IoT technology, are playing an increasingly important role in fields such as industrial monitoring, environmental monitoring, smart homes, and health monitoring. Traditional wireless sensor networks are based on radio frequency communication technology, requiring additional cabling and dedicated radio frequency base stations, resulting in high wireless communication costs and high manpower consumption.

[0024] Based on this, this application provides a visible light sensor, a networking method and apparatus for visible light sensors, aiming to construct a visible light sensor integrating a photoelectric detection module, an LED light-emitting module and a data processing module. Only the photoelectric detection module needs to be added to the LED light-emitting module, and the data processing module completes the networking based on a first visible light signal and a second visible light signal, achieving low-cost optical communication networking. Therefore, this embodiment uses the visible light sensor as an optical communication node, and communication transmission can be achieved after the networking is completed. This embodiment does not require additional communication cables or the deployment of radio frequency base stations; the sensor network can be constructed using existing lighting facilities, significantly reducing system deployment costs.

[0025] The visible light sensor, the networking method and the apparatus for the visible light sensor provided in this application are specifically described through the following embodiments. First, the visible light sensor in the embodiments of this application is described.

[0026] Please refer to Figure 1 , Figure 1 This application provides a visible light sensor comprising a photoelectric detection module, an LED light-emitting module, and a data processing module. The photoelectric detection module receives a first visible light signal from a candidate visible light sensor; the LED light-emitting module emits a second visible light signal; and the data processing module, electrically connected to the photoelectric detection module, extracts network request parameters based on the first visible light signal, generates network response parameters based on the network request signal, and sends the network response parameters to the candidate visible light sensor via the second visible light signal. This application integrates the photoelectric detection module, LED light-emitting module, and data processing module onto a single visible light sensor, and sequentially executes a closed-loop operation of "receiving the first visible light signal → extracting network request parameters → generating network response parameters → sending the network response parameters via the second visible light signal." This allows any candidate visible light sensor to complete bidirectional handshake and parameter negotiation with only one optical signal interaction, automatically confirming each other's identity, hopping, and link quality without relying on radio frequency assistance or manual configuration. Furthermore, by using existing visible light sensors as optical communication nodes, only the photoelectric detection module and data processing module need to be added to the existing optical communication nodes to complete optical communication, achieving low-cost sensor network deployment.

[0027] In some embodiments, the LED light-emitting module can be a lighting tube, which serves as a communication node in the networking system. The lighting tubes facilitate networking between different lighting fixtures, enabling the construction of a smart home lighting system. In this embodiment, the lighting tube structure used is as follows: Figure 9As shown, the lighting tube includes a bracket 100, a connecting component, and a lamp body 300; the lamp body 300 includes a transparent cover and a lamp plate located inside the transparent cover. When in operation, the lamp beads on the lamp plate emit light through the transparent cover, realizing the accurate transmission of the second visible light signal.

[0028] Furthermore, in this embodiment, the bracket 100 and the lamp body 300 of the lighting tube are detachably connected, which facilitates the installation of the photoelectric detection module and the data processing module in the bracket, so as to turn the traditional lighting tube into a visible light sensor that can realize optical communication networking.

[0029] In some embodiments, the visible light sensor further includes a sensing module electrically connected to a data processing module. The sensing module is used to collect environmental data from the visible light sensor. The data processing module is also used to perform fault prediction on the visible light sensor based on the environmental data to obtain fault prediction information, and to drive an LED light-emitting module to send the fault prediction information to the cloud via a second visible light signal based on the fault prediction information. The visible light sensor constructed in this embodiment can also predict faults in advance based on environmental data and transmit fault prediction information to the cloud via a second visible light signal emitted by the LED light-emitting module, providing early fault feedback and reducing the impact of visible light sensor faults on optical communication signal transmission.

[0030] Specifically, the environmental data includes at least one of the following: temperature, humidity, vibration data, current value, and light intensity value. This embodiment does not limit the environmental data. The data processing module of this embodiment sets up a lightweight fault prediction model to extract environmental features from the environmental data. Fault prediction information is obtained by using the fault prediction model and environmental features to predict faults in the visible light sensor. In addition, key indicator values ​​can be extracted from the environmental data, and the key indicator values ​​can be compared with preset thresholds to determine fault prediction information. The fault prediction information is then transmitted to the cloud via a second visible light signal, reducing the impact of individual visible light sensor failures on visible light communication quality and improving the quality of visible light communication.

[0031] Figure 2 This is an optional flowchart of a visible light sensor networking method provided in the embodiments of this application. Figure 2 The method may include, but is not limited to, steps S201 to S204.

[0032] Step S201: Receive the first visible light signal sent by the candidate visible light sensor; Step S202: Extract network parameters from the first visible light signal to obtain network request parameters; wherein, the network request parameters include: request location information, request working status and request operation parameters; Step S203: Select the visible light sensor from the candidate visible light sensors based on the requested location information, requested working status, and requested operating parameters; Step S204: Generate network response parameters based on the network request parameters of the visible light sensor, and send the network response parameters to the selected visible light sensor via the second visible light signal.

[0033] In the embodiments of this application, steps S201 to S204 involve the target visible sensor receiving a first visible light signal from a candidate visible light sensor. The data processing module then performs comprehensive extraction and logical judgment of the requested location information, requested working status, and requested operating parameters. The selected visible light sensor is then selected from the candidate visible light sensors, and network response parameters are generated accordingly. These network response parameters are then sent to the selected visible light sensor via a second visible light signal. Therefore, this embodiment of the application completes the networking through visible light sensors, completely distributing the traditional centralized topology decision-making process, which relies on a central gateway or manual preset, to each visible light sensor node. This mechanism, on the one hand, allows visible light communication nodes to complete the selection of visible light communication nodes and link confirmation without reporting networking request parameters to the gateway, significantly reducing the number of uplink signaling and the computational load on the gateway, effectively avoiding data flooding and collision congestion at the gateway for network-wide networking requests. On the other hand, by introducing multi-dimensional screening criteria of "location + working status + operating parameters", it ensures that each networking handshake can be finely matched based on the actual physical layout, real-time working conditions of nodes, and service requirements, avoiding frequent link breaks and invalid relays caused by blind link building. Thus, a highly reliable, self-decision-making, and dynamically adaptable distributed networking is achieved with low overhead of a single optical interaction, greatly shortening the self-organizing convergence time of large-scale networks and enabling the network topology to be autonomously optimized in real time as node status changes.

[0034] As parking lots integrate with intelligent systems, and their lighting systems become increasingly intelligent, a key challenge lies in the weak location signals within parking lots. Drivers often have to navigate around for a long time to find a parking space. This embodiment addresses this by applying a visible light sensor networking method to parking guidance scenarios, improving the parking experience for drivers in indoor parking lots. Specifically, the visible light sensors are the indoor parking lot's lighting tubes. When a new lighting tube is installed, it sends a networking request, specifying its location, operating status, and parameters. The main controller then determines if the new tube meets the network access requirements. If so, it sends networking response parameters to the new tube, determining its network address. Therefore, after each lighting tube completes its networking, when the main controller receives a parking instruction from the indoor parking lot, it first locates available parking spaces and forms a guidance route based on the parking lot entrance and available spaces. Then, based on the guidance route and the network address of each lighting tube, it controls the lighting tubes along the route to illuminate, thus providing parking guidance. Therefore, completing the lighting network in advance makes subsequent indoor parking guidance and control more convenient and accurate, thus improving the indoor parking experience.

[0035] In step S201 of some embodiments, the visible light sensor networking method of this application embodiment is applied to the target visible light sensor. When the target visible light sensor is in a network-ready state, it receives first visible light signals from other candidate visible light sensors in other network-ready states. It should be noted that the first visible light signal represents the light signal received by the target visible light sensor.

[0036] In step S202 of some embodiments, the first visible light signal is demodulated, decoded, and parsed to obtain parsed data, and network-related structured parameters are extracted from the parsed data as network request parameters. Specifically, the network request parameters include, but are not limited to: request location information, request operating status, and request operation parameters. The request location information is the location information of the candidate visible light sensor, the request operating status characterizes the operating condition and availability of the candidate visible light sensor, and the request operation parameters characterize the communication capability and operating configuration of the candidate visible light sensor, which is used to evaluate the network feasibility between the candidate visible light sensor and the target visible light sensor.

[0037] In step S203 of some embodiments, the present application embodiments evaluate each candidate visible light sensor by combining the requested location information, requested operating status and requested operating parameters, and select the selected visible light sensor from the candidate visible light sensors based on the evaluation results.

[0038] Please see Figure 3 In some embodiments, step S203 may include, but is not limited to, steps S301 to S303: Step S301: Obtain the current location information and determine the communication distance based on the requested location information and the current location information; Step S302: Based on the lightweight communication quality assessment model, communication distance, requested working status, and requested operating parameters, the communication quality of the candidate visible light sensor is assessed to obtain communication quality assessment data. Step S303: Select the visible light sensor from the candidate visible light sensors based on the communication quality assessment data.

[0039] In step S301 of some embodiments, the target visible light sensor configuration positioning module of this embodiment collects the current position information of the target visible light sensor in real time, or sends a positioning request to the cloud via a second visible light signal, and receives the current position information sent by the cloud via the second visible light signal. This embodiment does not limit the method of obtaining the current position information. Subsequently, the data processing module substitutes the requested position information and the current position information into the distance calculation formula to calculate the spatial straight-line distance between the target visible light sensor and the candidate visible light sensor as the communication distance. The communication distance is used to evaluate the communication quality between the candidate visible light sensor and the target visible light sensor.

[0040] In step S302 of some embodiments, this embodiment sets up a lightweight communication quality assessment model, which is a pre-trained mathematical model. Communication distance, requested working status, and requested running parameters are input into the communication quality assessment model to perform communication quality assessment and obtain communication quality assessment data. Specifically, the communication quality assessment model of this embodiment determines a first score based on the communication distance, a second score based on the requested working status, and a third score based on the requested running parameters. The first, second, and third scores are then weighted and summed or nonlinearly mapped to obtain the communication quality assessment data.

[0041] In step S303 of some embodiments, this embodiment sets filtering rules in advance, and selects the visible light sensor from the candidate visible light sensors according to the preset filtering specifications and communication quality evaluation data, so as to select the selected visible light sensor with the best communication quality, complete the networking between the selected visible light sensor and the target visible light sensor, and improve the networking quality and success rate.

[0042] In steps S301-S303 of the embodiments of this application, each visible light sensor, upon receiving a networking request from a candidate node, can independently complete the entire decision-making chain from distance measurement and comprehensive quality assessment to optimal target selection without relying on a gateway or cloud. Furthermore, the embodiments of this application introduce a lightweight communication quality assessment model to perform comprehensive quality evaluation on candidate visible light sensors, and quantify, rank, and optimally select these indicators to ensure that the selected target nodes are not only spatially reachable but also highly compatible with the current node in terms of working status and operating parameters.

[0043] Please see Figure 4 In some embodiments, step S303 may include, but is not limited to, steps S401 to S403: Step S401: Select preliminary visible light sensors from the candidate visible light sensors based on the preset quality threshold and communication quality evaluation data; Step S402: Sort the preliminary visible light sensors in ascending order according to the communication quality assessment data to obtain the sensor serial number; Step S403: Select the visible light sensor from the preliminary visible light sensors according to the preset quantity and sensor serial number.

[0044] In step S401 of some embodiments, the communication quality evaluation data of each candidate visible light sensor is compared with a preset quality threshold to obtain a comparison result, and the preset quality threshold can be dynamically adjusted according to the current network environment. In this embodiment, the comparison result characterizes candidate visible light sensors whose communication quality evaluation data exceeds the preset quality threshold as preliminary visible light sensors. It should be noted that if no preliminary visible light sensor can be selected from the candidate visible light sensors according to the preset quality threshold, the preset quality threshold is lowered until at least one preliminary visible light sensor is selected.

[0045] In steps S402 and S403 of some embodiments, if the number of preliminary visible light sensors is greater than one, the preliminary visible light sensors that have passed the initial screening are sorted in ascending order according to their communication quality assessment data. That is, the better the communication quality assessment data, the larger the sorting number; the worse the communication quality assessment data, the smaller the sorting number. After sorting, each preliminary visible light sensor is assigned a unique sensor number, and the number directly reflects the relative quality of the preliminary visible light sensor. Then, a preset number of preliminary visible light sensors before the sensor number sorting are selected as the visible light sensors. It should be noted that the number of preliminary visible light sensors is greater than the preset number. For example, if the preset number is two, the two preliminary visible light sensors with the largest sensor numbers are selected as the selected visible light sensors; if the preset number is one, only the preliminary visible light sensor with the best sorting is selected as the selected visible light sensor.

[0046] In steps S401-S403 as shown in this application, the complex global optimization search is replaced by mathematical operations of "threshold + sorting + truncation". Under the premise of ensuring that the selected visible light sensors meet the requirements of rationality and optimality, the computation time and memory usage are controlled within the range that the resource-constrained MCU can easily bear, which significantly improves the efficiency and accuracy of neighbor selection in large-scale self-organizing networks.

[0047] Please see Figure 5 In some embodiments, prior to step S302, the networking method for visible light sensors may also include, but is not limited to, steps S501 to S502: Step S501: Upload the local network data to the cloud so that the cloud can optimize the preset original quality assessment model based on the local network data to obtain a preliminary quality assessment model, and perform a compression operation on the preliminary quality assessment model to obtain a communication quality assessment model; wherein, the original quality assessment model is trained by historical network data uploaded by candidate visible light sensors of the same type and in the same area as the target visible light sensor; Step S502: Receive the communication quality assessment model sent from the cloud.

[0048] In step S501 of some embodiments, to improve the evaluation accuracy of the communication quality assessment model, local networking data uploaded by candidate visible light sensors of the same type and region as the target visible light sensor is collected in advance by the cloud. The original quality assessment model is then optimized in the cloud based on the local networking data to obtain a preliminary quality assessment model. Specifically, the preset original quality assessment model is retrained or fine-tuned to obtain the preliminary quality assessment model. Then, the cloud performs a compression operation on the preliminary quality assessment model to obtain the communication quality assessment model. It should be noted that the compression operation includes at least one of the following: knowledge distillation, pruning, and quantization.

[0049] In step S502 of some embodiments, after the cloud completes model optimization and compression, it sends the updated communication quality assessment model as a data packet to the target visible light sensor and candidate visible light sensors through the gateway. Each visible light sensor receives the communication quality assessment model through a photoelectric detection module or a wired interface and stores it in the Flash memory of the data processing module, replacing the original old version of the communication quality assessment model for use in subsequent networking processes.

[0050] In steps S501 to S502 of this embodiment, by leveraging the powerful computing capabilities of the cloud and the training advantages of massive cross-node historical networking data, a lightweight communication quality assessment model highly adapted to the current environment of the same region and type is continuously obtained, without the need to perform a complex model training process on the resource-constrained local MCU. The trained communication quality assessment model is then compressed before being transmitted to the target visible light sensor, saving storage space on the target visible light sensor. Therefore, this embodiment not only eliminates the reliance on manual parameter tuning but also ensures that each visible light sensor always runs the optimal version of the assessment model within its own hardware limitations. This continuously improves the accuracy of network selection, the success rate of network link establishment, and the robustness of the entire network against environmental changes at the system level, providing architectural-level support for the performance self-optimization of the visible light sensor network throughout its entire lifecycle.

[0051] Please see Figure 6 In some embodiments, the cloud optimizes the preset original quality assessment model based on local network data to obtain a preliminary quality assessment model, and performs a compression operation on the preliminary quality assessment model to obtain a communication quality assessment model, including but not limited to steps S601 to S603: Step S601: The cloud optimizes the original quality assessment model based on the local network data to obtain a preliminary quality assessment model; Step S602: The cloud performs distillation processing on the preliminary quality assessment model to obtain candidate quality assessment models; Step S603: The cloud performs structured pruning on the candidate quality assessment models to obtain the selected quality assessment model; Step S604: The cloud performs quantitative processing on the selected quality assessment model to obtain the communication quality assessment model.

[0052] In steps S602 to S604 of some embodiments, as disclosed above, the compression operation includes at least one of the following: knowledge distillation, pruning, and quantization. It should be noted that the knowledge distillation operation distills the knowledge of the optimized preliminary quality assessment model into a student model with only 1 / 5 to 1 / 10 the number of parameters of the preliminary quality assessment model, generating a candidate quality assessment model. The structured pruning operation removes smaller neural connections in the candidate quality assessment model, further simplifying the structure. The quantization operation quantizes the model parameters of the selected quality assessment model from 32-bit floating-point to 8-bit or 4-bit integers, reducing the model size to less than 1 / 4 of the original, thus obtaining the communication quality assessment model.

[0053] It should be noted that the binary size of the communication quality assessment model after compression in this embodiment is usually controlled between 50KB and 500KB, the memory required for inference is no more than 50KB, and the amount of computation for a single assessment is no more than 100,000 multiply-accumulate operations.

[0054] In steps S601 to S604 of this embodiment, the optimized preliminary quality assessment model is significantly better than the initial general model in terms of accuracy. The subsequent compression operation reduces the number of parameters and computational complexity of the model to a level that is strictly matched with the local MCU resources, ensuring that even on a low-cost visible light sensor with less than 2MB of storage space and a main frequency of less than 100MHz, a single evaluation inference can be completed with a sub-millisecond latency.

[0055] Please see Figure 7 In some embodiments, the networking method for visible light sensors may also include, but is not limited to, steps S701 to S703: Step S701: Collect environmental data from the target visible light sensor; Step S702: Based on the second visible light signal and environmental data, perform fault risk prediction on the target visible light sensor to obtain fault risk prediction data; Step S703: Perform a target early warning operation on the target visible light sensor based on the fault risk prediction data.

[0056] In step S701 of some embodiments, after the target visible light sensor successfully establishes a network with the selected visible light sensor, it continuously collects environmental data at the location of the target visible light sensor according to a preset sampling period using its own multiple sensors. These multiple sensors include at least one of the following: such as a temperature sensor, a humidity sensor, a light intensity sensor, a vibration sensor, and a current / voltage monitoring circuit.

[0057] In step S702 of some embodiments, a pre-set fault risk prediction model is input by combining the second visible light signal emitted by the target visible light sensor and environmental data to comprehensively assess the future fault risk of the target visible light sensor and output quantified fault risk prediction data. The higher the fault risk prediction data, the more likely the target visible light sensor is affected by environmental factors and its own communication quality, indicating a potential fault risk or lifespan risk.

[0058] In step S703 of some embodiments, the target warning operation includes, but is not limited to: local audible and visual warning operation, remote alarm operation, and network shutdown operation. If the fault risk prediction data exceeds a preset risk threshold, the risk level is determined based on the fault risk prediction data, and the target warning operation is selected from the candidate warning operations based on the risk level.

[0059] In steps S701-S703 of this application, a closed-loop link is constructed: "real-time environmental data acquisition → multi-dimensional risk prediction by fusing second visible light signal quality → hierarchical and classified early warning and handling." This enables each visible light sensor to have autonomous health monitoring and fault prediction capabilities. Then, the hierarchical early warning operation automatically triggered based on the fault risk prediction data can not only remind maintenance personnel to intervene in maintenance in a timely manner and avoid data loss caused by sudden network outages, but also delay the aging process by proactively reducing workload and prevent downstream subnetting from being disconnected due to the sudden failure of the visible light sensor.

[0060] Please see Figure 8 In some embodiments, step S702 may include, but is not limited to, steps S801 to S804: Step S801: Predict the communication parameters of the second visible light signal to obtain the predicted communication parameters; Step S802: Based on the predicted communication parameters, perform fault risk prediction on the target visible light sensor to obtain the first predicted fault risk value; Step S803: Based on environmental data, perform fault risk prediction on the target visible light sensor to obtain a second predicted fault risk value; Step S804: The first predicted fault risk value and the second predicted fault risk value are concatenated to obtain fault risk prediction data.

[0061] In step S801 of some embodiments, communication parameters are predicted based on physical layer measurement data of the second visible light signal emitted by the current node during transmission, and quantifiable predicted communication parameters are output. These predicted communication parameters include, but are not limited to: predicted signal-to-noise ratio, predicted bit error rate, predicted transmit power, and predicted interference intensity.

[0062] In steps S802 and S803 of some embodiments, the predicted communication parameters are input into a local first fault risk prediction sub-model to predict the fault risk and obtain a first predicted fault risk value. The collected environmental data is then input into a second fault risk prediction sub-model to predict the fault risk and obtain a second predicted fault risk value. It should be noted that the first predicted fault risk value characterizes the degree of fault risk assessed by the target visible light sensor from the perspective of the communication link, and its value range is typically 0~1 or 0~100. A higher value indicates a greater risk of communication link failure or severe degradation. The second predicted fault risk value characterizes the degree of fault risk assessed by the target visible light sensor from the perspective of the working environment, and also uses a quantization scale of 0~1 or 0~100.

[0063] In step S804 of some embodiments, the splicing method can be direct splicing, weighted average, taking the maximum value, etc. This embodiment does not limit the splicing method.

[0064] In this embodiment, steps S801-S804 involve independently assessing the risk of the target visible light sensor based on predicted communication parameters and environmental data, and then merging the two risk values ​​into fault risk prediction data. This "decoupled prediction and merged judgment" design avoids the root cause confusion caused by a single model conflating communication degradation with environmental anomalies. Furthermore, by preserving the traceability of the two independent risk values, maintenance personnel can quickly determine the dominant fault factor based on their relative levels. This significantly shortens the fault investigation time while improving prediction accuracy, achieving highly reliable, interpretable, and accurate prediction and assessment of the health status of visible light sensor nodes.

[0065] This application embodiment receives the first visible light signal from candidate visible light sensors and extracts the requested location information, requested working status, and requested operating parameters. Based on multi-dimensional network request parameters, it can accurately screen matching visible light sensors and generate targeted network response parameters as feedback via a second visible light signal. This achieves efficient and adaptive networking among visible light sensors, significantly improving the accuracy and flexibility of network matching. It avoids communication delays and resource waste caused by blind connections or parameter mismatches. At the same time, it utilizes the inherent advantages of visible light communication to ensure low interference, high reliability, and real-time response capabilities during the networking process in complex environments.

[0066] This application embodiment also provides a networking device for visible light sensors, which can implement the above-described networking method for visible light sensors. The device includes: The data processing module is used to implement the networking method of the visible light sensor described above.

[0067] The specific implementation of the networking device for the visible light sensor is basically the same as the specific implementation of the networking method for the visible light sensor described above, and will not be repeated here.

[0068] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0069] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0070] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0071] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0072] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0073] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0075] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0077] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A visible light sensor, characterized in that, The visible light sensor includes: The photoelectric detection module is used to receive the first visible light signal sent by the candidate visible light sensor; LED light-emitting modules are used to emit a second visible light signal; The data processing module, electrically connected to the photoelectric detection module, is used to extract networking request parameters based on the first visible light signal, generate networking response parameters based on the networking request signal, and send the networking response parameters to the candidate visible light sensor via the second visible light signal.

2. The visible light sensor according to claim 1, characterized in that, The visible light sensor also includes: The sensing module is used to collect environmental data from the visible light sensor; The data processing module is also used to perform fault prediction on the visible light sensor based on the environmental data to obtain fault prediction information, and is also used to drive the LED light-emitting module to send the fault prediction information to the cloud through the second visible light signal based on the fault prediction information.

3. A networking method for visible light sensors, characterized in that, The method is applied to a target visible light sensor, wherein the target visible light sensor is the visible light sensor according to any one of claims 1 to 2, and the method comprises: Receive the first visible light signal sent by the candidate visible light sensor; The network parameter extraction is performed on the first visible light signal to obtain the network request parameters; wherein, the network request parameters include: requested location information, requested working status and requested operating parameters; Select a visible light sensor from the candidate visible light sensors based on the requested location information, the requested working status, and the requested operating parameters; Network response parameters are generated based on the network request parameters of the visible light sensor, and the network response parameters are sent to the selected visible light sensor via a second visible light signal.

4. The method according to claim 3, characterized in that, The step of selecting a visible light sensor from the candidate visible light sensors based on the requested location information, the requested working status, and the requested operating parameters includes: Obtain the current location information, and determine the communication distance based on the requested location information and the current location information; The communication quality of the candidate visible light sensor is evaluated based on a lightweight communication quality assessment model, the communication distance, the requested operating status, and the requested operating parameters to obtain communication quality assessment data. The selected visible light sensor is selected from the candidate visible light sensors based on the communication quality assessment data.

5. The method according to claim 4, characterized in that, Before performing a communication quality assessment on the candidate visible light sensor based on a lightweight communication quality assessment model, the communication distance, the requested operating state, and the requested operating parameters to obtain communication quality assessment data, the method further includes: Local network data is uploaded to the cloud, so that the cloud can optimize the preset original quality assessment model based on the local network data to obtain a preliminary quality assessment model, and perform a compression operation on the preliminary quality assessment model to obtain the communication quality assessment model; wherein, the original quality assessment model is trained by historical network data uploaded by candidate visible light sensors of the same type and in the same area as the target visible light sensor; Receive the communication quality assessment model sent by the cloud.

6. The method according to claim 5, characterized in that, The cloud platform optimizes the preset original quality assessment model based on the local network data to obtain a preliminary quality assessment model, and performs a compression operation on the preliminary quality assessment model to obtain the communication quality assessment model, including: The cloud platform optimizes the original quality assessment model based on the local network data to obtain the preliminary quality assessment model. The cloud platform performs distillation processing on the preliminary quality assessment model to obtain candidate quality assessment models; The cloud platform performs structured pruning on the candidate quality assessment models to obtain the selected quality assessment model. The cloud platform performs quantification on the selected quality assessment model to obtain the communication quality assessment model.

7. The method according to claim 4, characterized in that, The step of selecting the chosen visible light sensor from the candidate visible light sensors based on the communication quality assessment data includes: Preliminary visible light sensors are selected from the candidate visible light sensors based on a preset quality threshold and the communication quality evaluation data; The preliminary visible light sensors are sorted in ascending order based on the communication quality assessment data to obtain sensor serial numbers; The selected visible light sensor is selected from the preliminary visible light sensors according to the preset quantity and the sensor serial number.

8. The method according to any one of claims 3 to 7, characterized in that, The method further includes: Collect environmental data from the target visible light sensor; Based on the second visible light signal and the environmental data, the target visible light sensor is subjected to fault risk prediction to obtain fault risk prediction data. Based on the fault risk prediction data, a target early warning operation is performed on the target visible light sensor.

9. The method according to claim 8, characterized in that, The step of predicting the fault risk of the target visible light sensor based on the second visible light signal and the environmental data to obtain fault risk prediction data includes: The communication parameters of the second visible light signal are predicted to obtain the predicted communication parameters. Based on the predicted communication parameters, a fault risk prediction is performed on the target visible light sensor to obtain a first predicted fault risk value; Based on the environmental data, a second predicted failure risk value is obtained by predicting the failure risk of the target visible light sensor. The first predicted fault risk value and the second predicted fault risk value are concatenated to obtain the fault risk prediction data.

10. A networking device for visible light sensors, characterized in that, The device is applied to a target visible light sensor, wherein the target visible light sensor is the visible light sensor according to any one of claims 1 to 2, and the device comprises: A data processing module, wherein the data processing module is used to implement the networking method of the visible light sensor according to any one of claims 3 to 9.

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