Traffic full-color display screen control method and system

Abstract

The invention relates to the technical field of image communication, and provides a traffic full-color display screen control method and system. Big data acquisition is carried out through the Internet of Things chip; filtering the acquired environment data and traffic data in the edge network to generate an environment tensor and a traffic tensor; the quantum dot lighting effect is determined based on the real-time temperature in the environment tensor, a photoelectric response equation is generated based on a preset target brightness matrix, the quantum dot lighting effect and the real-time wind speed in the environment tensor, and a driving current is obtained through solving; the physiological adaptation change rate of the human eye state is determined by integrating the human eye physiological state, the display state of the full-color display screen and the environment tensor so as to trigger a dynamic frequency reduction protection mode; and generating control information based on the driving current and the refresh rate distribution, and sending the control information to a control component of the full-color display screen. Smooth display of a high-dynamic area and low-power-consumption operation of a static area are achieved, and a high-reliability information interaction platform is provided for an intelligent traffic system.

Description

Technical Field

[0001] The present application relates to the field of image communication technology, and in particular to a method and system for controlling a full-color traffic display screen. Background Art

[0002] With the development of smart cities and vehicle-road collaboration technologies, full-color traffic displays have become a core vehicle for real-time information exchange. Their application scenarios have expanded from traditional road signs to dynamic traffic conditions, intelligent navigation prompts, and emergency warnings, requiring displays with high refresh rates and low latency for real-time response. Modern traffic displays integrate video surveillance, sensor data, and navigation system data, visualizing multimodal information through full-color displays. For example, at complex intersections, vehicles can be guided through a combination of dynamic arrows, text prompts, and icons.

[0003] In practical applications, traffic display screens are often deployed outdoors, where they must withstand environmental challenges such as strong direct sunlight, extreme temperatures, humidity, and electromagnetic interference to ensure stable operation in various environments. Traditional displays rely on fixed brightness modes, which can blur information in strong backlight and reduce color saturation in rainy and foggy conditions, resulting in low display control efficiency and display quality. Summary of the Invention

[0004] The present application provides a method and system for controlling a full-color traffic display screen, which can at least to some extent solve the problem of low control efficiency and display effect in the process of controlling a full-color traffic display screen.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the present application.

[0006] According to one aspect of the present application, a method for controlling a full-color traffic display screen is provided, comprising: collecting big data through an Internet of Things chip to obtain environmental data and traffic data; filtering the environmental data and traffic data in an edge network to generate filtered data, and generating an environmental tensor representing the environmental state and a traffic tensor representing the traffic state based on the filtered data; determining the quantum dot light effect based on the real-time temperature in the environmental tensor, generating a photoelectric response equation based on a target brightness matrix set for the full-color display screen, the quantum dot light effect, and the real-time wind speed in the environmental tensor, and solving the photoelectric response equation to obtain a driving current for controlling the full-color display screen; determining the physiological adaptation change rate of the human eye state based on the physiological state of the human eye, the display state of the full-color display screen, and the environmental tensor, and triggering a dynamic frequency reduction protection mode when the physiological adaptation change rate is greater than a set threshold; generating control information based on the driving current and the refresh rate distribution of the dynamic frequency reduction protection mode, and sending the control information to the control component of the full-color display screen.

[0007] In the present application, based on the aforementioned scheme, the big data collection through the Internet of Things chip to obtain environmental data and traffic data includes: collecting big data through the Internet of Things chip; building a sensor network based on the physical address of the Internet of Things chip; and aggregating the environmental data and traffic data collected by the Internet of Things chip through the sensor network, wherein the environmental data includes real-time temperature, real-time wind speed and light intensity, and the traffic data includes traffic data and road data.

[0008] In the present application, based on the aforementioned scheme, the environmental data and traffic data are filtered in the edge network to generate filtered data, and an environmental tensor representing the environmental state and a traffic tensor representing the traffic state are generated based on the filtered data, including: in the edge network, based on a preset noise model, nonlinear filtering is performed on the environmental data and traffic data to generate filtered data; the filtered data are arranged according to a preset spatial position to construct an initial tensor; tensor convolution is performed on the environmental part of the initial tensor to generate an environmental tensor representing the environmental state; traffic flow evolution is performed on the traffic part of the initial tensor to generate a traffic tensor representing the traffic state.

[0009] In the present application, based on the aforementioned scheme, the quantum dot light effect is determined based on the real-time temperature in the environmental tensor, and a photoelectric response equation is generated based on the target brightness matrix set for the full-color display screen, the quantum dot light effect, and the real-time wind speed in the environmental tensor. The photoelectric response equation is solved to obtain the driving current for controlling the full-color display screen, including: determining the quantum dot light effect based on the real-time temperature and temperature sensitivity coefficient in the environmental tensor; obtaining the target brightness matrix set for the full-color display screen, and generating a photoelectric response equation based on the quantum dot light effect, the target brightness matrix, and the real-time wind speed in the environmental tensor; solving the photoelectric response equation to obtain the driving current for controlling the full-color display screen.

[0010] In this application, based on the above scheme, the target brightness matrix set for the full-color display screen is obtained, and the photoelectric response equation is generated based on the quantum dot light effect, the target brightness matrix and the real-time wind speed in the environmental tensor, including: generating a wind impact factor based on the real-time wind speed in the environmental tensor for:

[0011] in, W Indicates the real-time wind speed. represents the critical wind speed; obtaining the target brightness matrix set for the full-color display screen, and generating the photoelectric response equation based on the target brightness matrix, the quantum dot light effect and the wind impact factor:

[0012] in, Indicates the response time constant, which is used to control the inertia of current changes. t Indicates the current moment; represents the diffusion inhibition coefficient; Represents the target brightness matrix set for the full-color display. I represents the driving current, represents the Laplace operator.

[0013] In the present application, based on the aforementioned scheme, the physiological state of the human eye, the display state of the full-color display screen and the environmental tensor are integrated to determine the physiological adaptation change rate of the human eye state. When the physiological adaptation change rate is greater than the set threshold, the dynamic frequency reduction protection mode is triggered, including: determining the physiological adaptation change rate of the human eye state according to the physiological time constant of the human eye adaptation state, the display brightness of the full-color display screen, and the ambient light intensity in the environmental tensor; triggering the dynamic frequency reduction protection mode when the physiological adaptation change rate is greater than the set threshold; determining the content complexity field based on the traffic tensor, generating a refresh rate differential equation, and solving it to obtain the refresh rate distribution.

[0014] In the present application, based on the above scheme, the physiological adaptation change rate of the human eye state is determined according to the physiological time constant of the human eye adaptation state, the display brightness of the full-color display screen, and the ambient light intensity in the environmental tensor, including: determining the physiological adaptation change rate of the human eye state according to the physiological time constant of the human eye adaptation state, the display brightness of the full-color display screen, and the ambient light intensity in the environmental tensor for:

[0015] in, The physiological time constant that represents the adaptation state of the human eye, Indicates the display brightness of the full-color display. represents the ambient light intensity in the ambient tensor, g Indicates the preset environmental factors; Indicates the adaptation status of the human eye. A value of 0 indicates no adaptation at all, and a value of 1 indicates complete adaptation.

[0016] According to one aspect of the present application, a traffic full-color display screen control system is provided, comprising: The acquisition unit is used to collect big data through the Internet of Things chip to obtain environmental data and traffic data; a filtering unit configured to filter the environmental data and the traffic data in the edge network to generate filtered data, and generate an environmental tensor representing an environmental state and a traffic tensor representing a traffic state based on the filtered data; a current unit, configured to determine the quantum dot light effect based on the real-time temperature in the environmental tensor, generate a photoelectric response equation based on a target brightness matrix set for the full-color display, the quantum dot light effect, and the real-time wind speed in the environmental tensor, and solve the photoelectric response equation to obtain a driving current for controlling the full-color display; A physiological unit is used to integrate the physiological state of the human eye, the display state of the full-color display screen, and the environmental tensor to determine the physiological adaptation change rate of the human eye state. When the physiological adaptation change rate is greater than a set threshold, a dynamic frequency reduction protection mode is triggered; A control unit is used to generate control information based on the driving current and the refresh rate distribution of the dynamic frequency reduction protection mode, and send the control information to the control component of the full-color display screen.

[0017] According to one aspect of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for controlling a full-color traffic display screen as described in the above embodiment is implemented.

[0018] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the traffic full-color display screen control method as described in the above embodiments.

[0019] According to one aspect of the present application, a computer program product or computer program is provided. The computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the traffic full-color display screen control method provided in the various optional implementations described above.

[0020] The technical effects of the technical solution of this application are as follows: Through IoT chips and sensor networks, efficient collection and integration of environmental and traffic data is achieved. Nonlinear filtering and tensor modeling technologies in edge networks eliminate noise interference and build multidimensional environmental and traffic state models, providing high-precision input for subsequent control and significantly improving perception reliability in complex scenarios.

[0021] The quantum dot light efficiency is dynamically calculated based on real-time temperature and wind speed, and the drive current is generated using the photoelectric response equation. This ensures the display's brightness uniformity and color accuracy in a variety of complex and changing environments. Furthermore, dynamic refresh rate allocation driven by content complexity and physiological adaptation enables smooth display in highly dynamic areas and low-power operation in static areas, balancing performance and energy efficiency.

[0022] By monitoring the rate of change in eye adaptation, dynamic frequency reduction protection is triggered when brightness changes suddenly or ambient light fluctuates dramatically, reducing the risk of visual fatigue. Combined with redundant communication and a closed-loop feedback mechanism, driver parameters are calibrated in real time to avoid single points of failure. This ensures long-term stable operation of the display in harsh conditions such as electromagnetic interference and drastic temperature and humidity fluctuations, improving user experience and device lifespan.

[0023] IoT sensors collect real-time environmental and traffic data, generate environmental and traffic tensors through filtering and modeling, dynamically calculate quantum dot light efficiency and driving current, and combine wind speed compensation to maintain display stability. Real-time monitoring of eye adaptation triggers frequency reduction protection to reduce visual fatigue. Zoned refresh and closed-loop feedback optimize energy efficiency, enhance reliability and adaptability in complex environments, ensure efficient transmission of traffic information, and provide a highly reliable information exchange platform for intelligent transportation systems.

[0024] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0026] Figure 1 The flowchart of the traffic full-color display screen control method in one embodiment of the present application is schematically shown.

[0027] Figure 2 The flowchart for generating the environment tensor and the traffic tensor in one embodiment of the present application is schematically shown.

[0028] Figure 3 The following schematically shows a schematic diagram of a traffic full-color display screen control system in one embodiment of the present application.

[0029] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0030] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0031] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner.In the following description, many specific details are provided so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps etc. can be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0032] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0033] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0034] The implementation details of the technical solution of this application are described in detail below: Figure 1 FIG2 shows a flow chart of a method for controlling a full-color traffic display screen according to an embodiment of the present application. Figure 1 As shown, the traffic full-color display screen control method includes at least steps S110 to S150, which are described in detail as follows: S110 collects big data through IoT chips, acquiring environmental and traffic data.

[0035] In one embodiment of the present application, big data collection is performed through an IoT chip to obtain environmental data and traffic data, including: Big data collection through IoT chips; Building a sensor network based on the physical address of the IoT chip; The environmental data and traffic data collected by the IoT chip are aggregated through the sensor network, wherein the environmental data includes real-time temperature, real-time wind speed and light intensity, and the traffic data includes traffic data and road data.

[0036] In one embodiment of the present application, real-time data capture of the physical environment is achieved by deploying high-precision IoT chips, such as edge computing modules with integrated temperature, humidity, light, and wind speed sensors. Each chip has a built-in low-power wide-area communication protocol that collects environmental parameters at a preset sampling frequency. A structured data stream is generated through a multi-channel data conversion interface. The IoT chip also integrates an on-board radar interface to receive real-time location and speed information from nearby vehicles, forming a fundamental data source for traffic dynamics.

[0037] Leveraging the physical addresses of IoT chips, a hierarchical sensor network topology is constructed. IoT chips are geographically grouped, for example, with a cell every 500 meters, using a low-power wide-area communication protocol gateway. Time-division multiple access (TDMA) is used for conflict-free data transmission. Physical addresses are mapped to network topology coordinates, ensuring reliable data transmission in complex urban environments and reducing bit error rates and packet loss.

[0038] For example, the environmental data in this embodiment may include real-time temperature, real-time wind speed, humidity, particulate matter concentration, and light intensity, etc. Traffic data may include traffic volume, average speed, accident probability, and lane occupancy rate, etc. The sensor network aggregates environmental and traffic data through an edge computing gateway, utilizing a data stream processing platform for efficient data access. After noise reduction using a Kalman filter, the environmental data is correlated with traffic data in real time to generate a fused feature vector. This data is ultimately stored in a distributed time-series database for subsequent control algorithm invocation, enabling dynamic optimization of display parameters.

[0039] S120 , in the edge network, filtering the environmental data and the traffic data to generate filtered data, and generating an environmental tensor representing an environmental state and a traffic tensor representing a traffic state based on the filtered data.

[0040] In this embodiment, after acquiring environmental data and traffic data, the acquired data is filtered to remove noise and generate filtered data. The filtered data is then used to generate an environmental tensor and a traffic tensor. The environmental tensor represents the current environmental state of the full-color traffic display, while the traffic tensor represents the current traffic state of the displayed road.

[0041] like Figure 2As shown, in one embodiment of the present application, filtering is performed on the environmental data and traffic data to generate filtered data, and based on the filtered data, an environmental tensor representing the environmental state and a traffic tensor representing the traffic state are generated, including: S210, in the edge network, performing nonlinear filtering on the environmental data and the traffic data based on a preset noise model to generate filtered data; S220, arranging the filtered data according to a preset spatial position to construct an initial tensor; S230, performing tensor convolution processing on the environment part of the initial tensor to generate an environment tensor representing the environment state; S240: Perform traffic flow evolution on the traffic part in the initial tensor to generate a traffic tensor representing the traffic state.

[0042] In one embodiment of the present application, nonlinear noise filtering is performed on each sensor signal type (e.g., temperature, humidity) within the edge network. For example, a temperature sensor may generate periodic interference due to circuit thermal noise. This interference is eliminated by modeling noise characteristics (e.g., noise frequency and amplitude). Nonlinear filtering is performed on environmental and traffic data based on a pre-defined noise model.

[0043] Specifically, based on k Noise Model for Sensor-like Signals ,in, Indicates the characteristic frequency of the sensor, such as the temperature sensor =0.1Hz, humidity sensor =0.05Hz. The noise model indicates that the sensor signal is subject to periodic interference (such as power supply fluctuations) and Gaussian white noise. Based on the noise model of various data, nonlinear filtering is performed to eliminate the periodic noise and random disturbance in the sensor signal and obtain filtered data.

[0044] In one embodiment of the present application, the filtered data is arranged according to a preset spatial position to construct an initial tensor. The five types of sensor data are fused in the spatial dimension to eliminate spatial measurement deviations caused by differences in sensor installation positions, such as illumination measurement errors caused by occlusion of sensors in edge areas.

[0045] Specifically, the denoised five-dimensional sensor data is sorted by spatial position N×M The environment part of the initial tensor is then convolved to generate an environment tensor representing the environment state. C for:

[0046] in, A, BRepresent the environment part in the initial tensor, n , m Indicates the dimension identifier, i , j represents the preset tensor field length and width, Represents the standard deviation of the Gaussian kernel. Data from the five sensor types are fused in the spatiotemporal dimensions to eliminate spatial inconsistencies between sensors, such as measurement bias caused by occlusion in edge regions. The tensor convolution results are then spatiotemporally normalized to generate a continuous and smooth environment tensor. This normalization factor adjusts for differences in the temporal response of different sensors, such as a humidity sensor responding slower than a temperature sensor, ensuring spatiotemporal data consistency.

[0047] For example, the denoised environment tensor is a three-dimensional tensor, where the first dimension represents five environmental parameters, including temperature, humidity, light, particulate matter, and wind speed; the second and third dimensions represent spatial grid coordinates. For example, a 512×512 grid corresponds to a geographic spatial resolution of 0.01 degrees by 0.01 degrees. Each grid point in the three-dimensional tensor contains the precise value of the environmental parameter, such as the temperature at a certain location is 25.3°C and the light intensity is 1200 lux.

[0048] In one embodiment of the present application, the chaotic characteristics of traffic flow are simulated by nonlinear differential equations, and the traffic flow evolution is performed on the traffic part in the initial tensor to generate the evolution equation:

[0049] in, represents the traffic state vector, where Q Represents a time window t The traffic volume in the area, V represents the average speed; A represents the probability of an accident based on historical accident data; Indicates the preset control parameters, represents the random disturbance parameter of the simulated traffic flow, Represents the norm operation.

[0050] After generating the evolution equation, solve the evolution equation to obtain the traffic tensor representing the traffic state , used as a prediction of future set time This breaks through the limitations of traditional linear models and provides early warning of sudden traffic changes, such as triggering a flashing display screen warning during high-accident periods.

[0051] In this process, a sensor network built using IoT chips collects environmental and traffic data in real time and performs nonlinear filtering. This effectively eliminates noise interference, improves data reliability and accuracy, and provides high-quality input for subsequent environmental and traffic state modeling. Physical address mapping within the sensor network ensures data source traceability, enhancing the system's anti-interference capabilities and deployment flexibility. The filtered data is then processed through tensor convolution and traffic flow evolution to generate environmental and traffic tensors. The environmental tensor integrates multidimensional environmental parameters to accurately quantify the impact of the external environment on the display screen; the traffic tensor dynamically represents traffic flow trends. This multidimensional fusion modeling approach overcomes the limitations of traditional single-parameter control and supports adaptive decision-making in complex scenarios.

[0052] S130, determining the quantum dot light effect based on the real-time temperature in the environmental tensor, generating a photoelectric response equation based on the target brightness matrix set for the full-color display screen, the quantum dot light effect, and the real-time wind speed in the environmental tensor, and solving the photoelectric response equation to obtain a driving current for controlling the full-color display screen.

[0053] In one embodiment of the present application, the quantum dot light effect in the current environment is determined based on the real-time temperature in the environmental tensor, and a photoelectric response equation is generated based on the target brightness matrix set for the full-color display screen, the quantum dot light effect, and the real-time wind speed in the environmental tensor. The photoelectric response equation is solved to obtain the driving current for controlling the full-color display screen.

[0054] In one embodiment of the present application, the quantum dot light effect is determined based on the real-time temperature in the environmental tensor, a photoelectric response equation is generated based on a target brightness matrix set for a full-color display screen, the quantum dot light effect, and the real-time wind speed in the environmental tensor, and the photoelectric response equation is solved to obtain a driving current for controlling the full-color display screen, including: Determining the quantum dot light efficiency based on the real-time temperature and the temperature sensitivity coefficient in the environmental tensor; Obtaining a target brightness matrix set for a full-color display screen, and generating a photoelectric response equation based on the quantum dot light effect, the target brightness matrix, and the real-time wind speed in the environmental tensor; The photoelectric response equation is solved to obtain the driving current for controlling the full-color display screen.

[0055] In one embodiment of the present application, the quantum dot light efficiency is determined based on the real-time temperature in the environmental tensor and the temperature sensitivity coefficient. for:

[0056] in, Indicates normal temperature ( ) under the benchmark quantum dot light efficiency, represents the temperature sensitivity coefficient, which means, T Indicates the real-time temperature.

[0057] In one embodiment of the present application, a target brightness matrix set for a full-color display screen is obtained, and a photoelectric response equation is generated based on the quantum dot light effect, the target brightness matrix, and the real-time wind speed in the environmental tensor, including: Generate wind impact factor based on the real-time wind speed in the environmental tensor for:

[0058] in, W Indicates the real-time wind speed. Indicates the critical wind speed.

[0059] The target brightness matrix set for the full-color display is obtained. Based on the target brightness matrix, the quantum dot light efficiency, and the wind impact factor, the photoelectric response equation is generated as follows:

[0060] in, Indicates the response time constant, which is used to control the inertia of current changes. t Indicates the current moment; represents the diffusion inhibition coefficient; Represents the target brightness matrix set for the full-color display. I represents the driving current, represents the Laplace operator.

[0061] After generating the photoelectric response equation, the photoelectric response equation is solved to obtain the drive current that controls the full-color display. Specifically, the finite difference method is used to discretize the equation into a space-time grid, and the steady-state distribution of the current field is iteratively calculated. The drive current is output as a two-dimensional matrix, which is used to represent the drive current value required for each pixel. For example, when the target brightness of a pixel is 2000 nits, the drive current is 25mA.

[0062] This process calculates the quantum dot light efficiency in real time based on ambient temperature and incorporates wind speed factors to generate a photoelectric response equation. By solving this equation, the drive current is dynamically adjusted to ensure the display maintains brightness uniformity and color accuracy even in high temperatures or strong winds. This effectively compensates for light efficiency degradation caused by environmental changes, extending the life of the quantum dot material while reducing energy consumption.

[0063] S140, comprehensively considering the physiological state of the human eye, the display state of the full-color display screen, and the environmental tensor, to determine the physiological adaptation change rate of the human eye state. When the physiological adaptation change rate is greater than a set threshold, a dynamic frequency reduction protection mode is triggered.

[0064] In actual applications, the display state of a full-color display screen often affects human eye perception, and the visual state of the human eye also affects the efficiency of information display in the full-color display screen. In this embodiment, the physiological state of the human eye, the display state of the full-color display screen, and the environmental tensor are comprehensively considered to determine the physiological adaptation change rate of the human eye state. When the physiological adaptation change rate is greater than the set threshold, the dynamic frequency reduction protection mode is triggered.

[0065] In one embodiment of the present application, the physiological state of the human eye, the display state of the full-color display screen, and the environmental tensor are comprehensively considered to determine the physiological adaptation change rate of the human eye state. When the physiological adaptation change rate is greater than a set threshold, a dynamic frequency reduction protection mode is triggered, including: Determining a physiological adaptation change rate of a human eye state according to a physiological time constant of a human eye adaptation state, the display brightness of the full-color display screen, and the ambient light intensity in the environmental tensor; When the physiological adaptation change rate is greater than a set threshold, a dynamic frequency reduction protection mode is triggered; A content complexity field is determined based on the traffic tensor, a refresh rate differential equation is generated, and the refresh rate distribution is obtained by solving the equation.

[0066] In one embodiment of the present application, the physiological adaptation change rate of the human eye state is determined based on the physiological time constant of the human eye adaptation state, the display brightness of the full-color display screen, and the ambient light intensity in the environmental tensor. for:

[0067] in, The physiological time constant that represents the adaptation state of the human eye, Indicates the display brightness of the full-color display. represents the ambient light intensity in the ambient tensor, g Indicates the preset environmental factors; Indicates the adaptation status of the human eye. A value of 0 indicates no adaptation at all, and a value of 1 indicates complete adaptation.

[0068] After calculating the physiological adaptation change rate, when the physiological adaptation change rate is greater than the set threshold, the dynamic frequency reduction protection mode is triggered. During the dynamic frequency reduction protection mode, the refresh rate differential equation is generated based on the average moving speed of the vehicle in the traffic tensor as the content complexity field:

[0069] in, D Represents the diffusion coefficient, which controls the smooth transition of refresh rate in space, such as the refresh rate difference between adjacent areas does not exceed 10Hz; v Indicates the dynamic change rate of the image, such as the speed of a vehicle;k Indicates the suppression factor that limits the power consumption in the high refresh rate area, C Represents the content complexity field generated from the traffic tensor, which is used to quantify the dynamic characteristics of the image. Indicates the refresh rate, t Indicates time, x After generating the refresh rate differential equation, the finite element method is used to discretize the equation and the Newton iteration method is used to solve the steady-state refresh rate distribution.

[0070] For example, when a vehicle passes through a certain area at high speed, the gradient of C increases, driving the refresh rate in that area to 240Hz, ensuring no artifacts in dynamic information. If the content complexity term approaches zero, the refresh rate is smoothly reduced to 60Hz through the diffusion term, saving energy. If the refresh rate in that area is too high (e.g., 200Hz), the energy consumption suppression term prevents further increase, keeping power consumption within a safe threshold.

[0071] The above process uses a diffusion factor to ensure a smooth transition between refresh rates in adjacent areas, avoiding screen tearing. A content-driven factor increases the refresh rate in high-motion areas based on the dynamic complexity of the image, such as 240Hz in traffic areas. An energy suppression factor prevents overheating or power surges caused by local high refresh rates, balancing performance and energy efficiency. The rate of change in eye adaptation is calculated in real time by integrating the physiological state of the human eye, display brightness, and ambient light intensity. When a sudden brightness change or a dramatic change in ambient light is detected, dynamic frequency reduction protection mode is triggered to smoothly adjust the refresh rate and brightness gradient. This significantly reduces visual fatigue and improves viewing comfort, especially at tunnel entrances and exits or in day-night scenes.

[0072] S150 , generating control information based on the driving current and the refresh rate distribution of the dynamic frequency reduction protection mode, and sending the control information to a control component of the full-color display screen.

[0073] In one embodiment of the present application, a spatiotemporal mapping engine generates control instructions for a full-color display screen based on the drive current and refresh rate distribution. Specifically, the drive current is converted into a high-precision pulse-width modulation signal. For example, high-brightness areas correspond to high-duty cycle pulses, while low-brightness areas have a reduced duty cycle to save energy. The refresh rate distribution is dynamically allocated according to the sub-areas of the screen. The control information is encapsulated in blocks by region, embedded with a checksum and timestamp, forming a standardized data stream to ensure the integrity of the instructions and the synchronization of the timing.

[0074] The generated control information is transmitted to the display control unit via a high-speed hybrid interface. Dynamic zone commands are preferentially transmitted via fiber optic channels; static zone data is transmitted via a low-power interface. The control unit, equipped with a high-performance processor, parses the data stream and drives the quantum dot LED array, adjusting the voltage output based on the current value while simultaneously generating scan timing signals. The hardware module also integrates a temperature sensor to monitor the screen's operating status in real time and dynamically compensate for current deviations to ensure display stability.

[0075] The control unit and the display's built-in photoelectric sensor network monitor actual brightness and color output in real time and compare them against target values. If a local brightness deviation or color temperature shift is detected, the system immediately generates an error signal and transmits it back to the control center, triggering online adjustments to the drive current and refresh rate. For example, if high temperatures cause a drop in brightness in a specific area, the drive current in that area is automatically increased to compensate. If a sudden change in ambient light causes visual discomfort, the refresh rate is dynamically reduced and the brightness transition is smoothed. Through a closed-loop feedback mechanism, the system rapidly completes the entire process, from data acquisition to parameter optimization, ensuring a consistently stable and reliable display.

[0076] The above process encodes the drive current and refresh rate distribution and transmits it to the control component, enabling high-speed, low-latency command issuance via a multi-protocol interface. The control component dynamically adjusts parameters based on real-time feedback data, forming a closed-loop control system. Redundant design and fault-tolerant mechanisms ensure stable operation under extreme conditions, preventing display anomalies caused by single point failures.

[0077] In the technical solution of this application, big data is collected through an Internet of Things chip to obtain environmental data and traffic data; in the edge network, the environmental data and traffic data are filtered to generate filtered data, and based on the filtered data, an environmental tensor representing the environmental state and a traffic tensor representing the traffic state are generated; the quantum dot light efficiency is determined based on the real-time temperature in the environmental tensor, and a photoelectric response equation is generated based on the target brightness matrix set for the full-color display, the quantum dot light efficiency, and the real-time wind speed in the environmental tensor. The photoelectric response equation is solved to obtain the driving current for controlling the full-color display; the physiological adaptation change rate of the human eye state is determined by integrating the physiological state of the human eye, the display state of the full-color display, and the environmental tensor. When the physiological adaptation change rate is greater than a set threshold, a dynamic frequency reduction protection mode is triggered; control information is generated based on the driving current and the refresh rate distribution of the dynamic frequency reduction protection mode, and the control information is sent to the control component of the full-color display. The above process collects environmental and traffic data in real time through Internet of Things sensors, generates environmental and traffic tensors through filtering modeling, dynamically calculates the quantum dot light efficiency and driving current, and combines wind speed compensation to maintain display stability. Real-time monitoring of eye adaptation triggers frequency reduction protection to reduce visual fatigue. Partitioned refresh and closed-loop feedback optimize energy efficiency, enhance reliability and adaptability in complex environments, ensure efficient communication of traffic information, and provide a highly reliable information exchange platform for intelligent transportation systems.

[0078] The following describes an embodiment of a device of the present application, which can be used to implement the method for controlling a full-color traffic display screen described in the aforementioned embodiment of the present application. It is understood that the device can be a computer program (including program code) running on a computer device, such as application software; the device can be used to perform the corresponding steps of the method provided in the embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method for controlling a full-color traffic display screen described in the aforementioned embodiment of the present application.

[0079] Figure 3 A block diagram of a traffic full-color display screen control system according to an embodiment of the present application is shown.

[0080] Reference Figure 3 As shown, a traffic full-color display screen control system according to one embodiment of the present application includes: An acquisition unit 310 is configured to collect big data through an IoT chip to acquire environmental data and traffic data; A filtering unit 320 is configured to filter the environmental data and the traffic data in the edge network to generate filtered data, and generate an environmental tensor representing an environmental state and a traffic tensor representing a traffic state based on the filtered data; a current unit 330 for determining the quantum dot light effect based on the real-time temperature in the environmental tensor, generating a photoelectric response equation based on a target brightness matrix set for the full-color display, the quantum dot light effect, and the real-time wind speed in the environmental tensor, and solving the photoelectric response equation to obtain a driving current for controlling the full-color display; The physiological unit 340 is used to integrate the physiological state of the human eye, the display state of the full-color display screen, and the environmental tensor to determine the physiological adaptation change rate of the human eye state. When the physiological adaptation change rate is greater than a set threshold, a dynamic frequency reduction protection mode is triggered; The control unit 350 is configured to generate control information based on the driving current and the refresh rate distribution of the dynamic frequency reduction protection mode, and send the control information to the control component of the full-color display screen.

[0081] In the present application, based on the aforementioned scheme, the big data collection through the Internet of Things chip to obtain environmental data and traffic data includes: collecting big data through the Internet of Things chip; building a sensor network based on the physical address of the Internet of Things chip; and aggregating the environmental data and traffic data collected by the Internet of Things chip through the sensor network, wherein the environmental data includes real-time temperature, real-time wind speed and light intensity, and the traffic data includes traffic data and road data.

[0082] In the present application, based on the aforementioned scheme, the environmental data and traffic data are filtered in the edge network to generate filtered data, and an environmental tensor representing the environmental state and a traffic tensor representing the traffic state are generated based on the filtered data, including: in the edge network, based on a preset noise model, nonlinear filtering is performed on the environmental data and traffic data to generate filtered data; the filtered data are arranged according to a preset spatial position to construct an initial tensor; tensor convolution is performed on the environmental part of the initial tensor to generate an environmental tensor representing the environmental state; traffic flow evolution is performed on the traffic part of the initial tensor to generate a traffic tensor representing the traffic state.

[0083] In the present application, based on the aforementioned scheme, the quantum dot light effect is determined based on the real-time temperature in the environmental tensor, and a photoelectric response equation is generated based on the target brightness matrix set for the full-color display screen, the quantum dot light effect, and the real-time wind speed in the environmental tensor. The photoelectric response equation is solved to obtain the driving current for controlling the full-color display screen, including: determining the quantum dot light effect based on the real-time temperature and temperature sensitivity coefficient in the environmental tensor; obtaining the target brightness matrix set for the full-color display screen, and generating a photoelectric response equation based on the quantum dot light effect, the target brightness matrix, and the real-time wind speed in the environmental tensor; solving the photoelectric response equation to obtain the driving current for controlling the full-color display screen.

[0084] In this application, based on the above scheme, the target brightness matrix set for the full-color display screen is obtained, and the photoelectric response equation is generated based on the quantum dot light effect, the target brightness matrix and the real-time wind speed in the environmental tensor, including: generating a wind impact factor based on the real-time wind speed in the environmental tensor for:

[0085] in, W Indicates the real-time wind speed. represents the critical wind speed; obtaining the target brightness matrix set for the full-color display screen, and generating the photoelectric response equation based on the target brightness matrix, the quantum dot light effect and the wind impact factor:

[0086] in, Indicates the response time constant, which is used to control the inertia of current changes. t Indicates the current moment; represents the diffusion inhibition coefficient; Represents the target brightness matrix set for the full-color display. I represents the driving current, represents the Laplace operator.

[0087] In the present application, based on the aforementioned scheme, the physiological state of the human eye, the display state of the full-color display screen and the environmental tensor are integrated to determine the physiological adaptation change rate of the human eye state. When the physiological adaptation change rate is greater than the set threshold, the dynamic frequency reduction protection mode is triggered, including: determining the physiological adaptation change rate of the human eye state according to the physiological time constant of the human eye adaptation state, the display brightness of the full-color display screen, and the ambient light intensity in the environmental tensor; triggering the dynamic frequency reduction protection mode when the physiological adaptation change rate is greater than the set threshold; determining the content complexity field based on the traffic tensor, generating a refresh rate differential equation, and solving it to obtain the refresh rate distribution.

[0088] In the present application, based on the above scheme, the physiological adaptation change rate of the human eye state is determined according to the physiological time constant of the human eye adaptation state, the display brightness of the full-color display screen, and the ambient light intensity in the environmental tensor, including: determining the physiological adaptation change rate of the human eye state according to the physiological time constant of the human eye adaptation state, the display brightness of the full-color display screen, and the ambient light intensity in the environmental tensor for:

[0089] in, The physiological time constant that represents the adaptation state of the human eye, Indicates the display brightness of the full-color display. represents the ambient light intensity in the ambient tensor, g Indicates the preset environmental factors; Indicates the adaptation status of the human eye. A value of 0 indicates no adaptation at all, and a value of 1 indicates complete adaptation.

[0090] In the technical solution of this application, big data is collected through an Internet of Things chip to obtain environmental data and traffic data; in the edge network, the environmental data and traffic data are filtered to generate filtered data, and based on the filtered data, an environmental tensor representing the environmental state and a traffic tensor representing the traffic state are generated; the quantum dot light efficiency is determined based on the real-time temperature in the environmental tensor, and a photoelectric response equation is generated based on the target brightness matrix set for the full-color display, the quantum dot light efficiency, and the real-time wind speed in the environmental tensor. The photoelectric response equation is solved to obtain the driving current for controlling the full-color display; the physiological adaptation change rate of the human eye state is determined by integrating the physiological state of the human eye, the display state of the full-color display, and the environmental tensor. When the physiological adaptation change rate is greater than a set threshold, a dynamic frequency reduction protection mode is triggered; control information is generated based on the driving current and the refresh rate distribution of the dynamic frequency reduction protection mode, and the control information is sent to the control component of the full-color display. The above process collects environmental and traffic data in real time through Internet of Things sensors, generates environmental and traffic tensors through filtering modeling, dynamically calculates the quantum dot light efficiency and driving current, and combines wind speed compensation to maintain display stability. Real-time monitoring of eye adaptation triggers frequency reduction protection to reduce visual fatigue. Partitioned refresh and closed-loop feedback optimize energy efficiency, enhance reliability and adaptability in complex environments, ensure efficient communication of traffic information, and provide a highly reliable information exchange platform for intelligent transportation systems.

[0091] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown.

[0092] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0093] In this embodiment, the computer system includes a central processing unit (CPU) 401, which can execute various appropriate actions and processes based on programs stored in a read-only memory (ROM) 402 or programs loaded from a storage unit 408 into a random access memory (RAM) 403, such as the traffic full-color display screen control method described in the above embodiment. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output interface 405 is also connected to the bus 404.

[0094] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, mouse, and the like; an output section 407 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage section 408 including devices such as a hard disk; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. Removable media 411, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 410 as needed, so that computer programs read from the removable media can be installed in the storage section 408 as needed.

[0095] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product that includes a computer program carried on a computer-readable medium, the computer program including a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 409 and / or installed from a removable medium 411. When the computer program is executed by the central processing unit 401, the various functions defined in the system of the present application are performed.

[0096] It should be noted that the computer-readable medium described in the embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0098] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0099] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0100] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not be incorporated into the electronic device. The computer-readable medium carries one or more programs, and when executed by the electronic device, the electronic device implements the traffic full-color display screen control method described in the above embodiments.

[0101] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0102] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0103] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0104] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A traffic full-color display screen control method, characterized in that: include: Collect big data through IoT chips to obtain environmental and traffic data; In the edge network, filtering the environmental data and the traffic data to generate filtered data, and generating an environmental tensor representing an environmental state and a traffic tensor representing a traffic state based on the filtered data; determining a quantum dot light effect based on the real-time temperature in the environmental tensor, generating a photoelectric response equation based on a target brightness matrix set for the full-color display screen, the quantum dot light effect, and the real-time wind speed in the environmental tensor, and solving the photoelectric response equation to obtain a driving current for controlling the full-color display screen; The physiological state of the human eye, the display state of the full-color display screen, and the environmental tensor are comprehensively considered to determine the physiological adaptation change rate of the human eye state. When the physiological adaptation change rate is greater than a set threshold, a dynamic frequency reduction protection mode is triggered. Control information is generated based on the driving current and the refresh rate distribution of the dynamic frequency reduction protection mode, and the control information is sent to a control component of the full-color display screen.

2. The traffic full-color display screen control method according to claim 1 is characterized in that: Big data collection is performed through IoT chips to obtain environmental and traffic data, including: Big data collection through IoT chips; Building a sensor network based on the physical address of the IoT chip; The environmental data and traffic data collected by the IoT chip are aggregated through the sensor network, wherein the environmental data includes real-time temperature, real-time wind speed and light intensity, and the traffic data includes traffic data and road data.

3. The traffic full-color display screen control method according to claim 1, characterized in that: In the edge network, filtering the environmental data and the traffic data to generate filtered data, and generating an environmental tensor representing an environmental state and a traffic tensor representing a traffic state based on the filtered data, including: In the edge network, based on the preset noise model, nonlinear filtering is performed on the environmental data and traffic data to generate filtered data; Arrange the filtered data according to a preset spatial position to construct an initial tensor; Performing tensor convolution processing on the environment portion of the initial tensor to generate an environment tensor representing the environment state; Traffic flow evolution is performed on the traffic part in the initial tensor to generate a traffic tensor representing the traffic state.

4. The traffic full-color display screen control method according to claim 1, characterized in that: The method includes determining a quantum dot light effect based on the real-time temperature in the environmental tensor, generating a photoelectric response equation based on a target brightness matrix set for the full-color display screen, the quantum dot light effect, and the real-time wind speed in the environmental tensor, and solving the photoelectric response equation to obtain a driving current for controlling the full-color display screen, including: Determining the quantum dot light efficiency based on the real-time temperature and the temperature sensitivity coefficient in the environmental tensor; Obtaining a target brightness matrix set for a full-color display screen, and generating a photoelectric response equation based on the quantum dot light effect, the target brightness matrix, and the real-time wind speed in the environmental tensor; The photoelectric response equation is solved to obtain the driving current for controlling the full-color display screen.

5. The traffic full-color display screen control method according to claim 4 is characterized in that: Obtain a target brightness matrix set for a full-color display screen, and generate a photoelectric response equation based on the quantum dot light effect, the target brightness matrix, and the real-time wind speed in the environmental tensor, including: Generate wind impact factor based on the real-time wind speed in the environmental tensor for: in, W Indicates the real-time wind speed. Indicates the critical wind speed; The target brightness matrix set for the full-color display screen is obtained. Based on the target brightness matrix, the quantum dot light efficiency, and the wind impact factor, the photoelectric response equation is generated as follows: in, Indicates the response time constant, which is used to control the inertia of current changes. t Indicates the current moment; represents the diffusion inhibition coefficient; Represents the target brightness matrix set for the full-color display. I represents the driving current, represents the Laplace operator.

6. The traffic full-color display screen control method according to claim 1, characterized in that: The physiological state of the human eye, the display state of the full-color display, and the environmental tensor are integrated to determine the physiological adaptation change rate of the human eye state. When the physiological adaptation change rate is greater than a set threshold, a dynamic frequency reduction protection mode is triggered, including: Determining a physiological adaptation change rate of a human eye state according to a physiological time constant of a human eye adaptation state, the display brightness of the full-color display screen, and the ambient light intensity in the environmental tensor; When the physiological adaptation change rate is greater than a set threshold, a dynamic frequency reduction protection mode is triggered; A content complexity field is determined based on the traffic tensor, a refresh rate differential equation is generated, and the refresh rate distribution is obtained by solving the equation.

7. The traffic full-color display screen control method according to claim 6, characterized in that: Determining the physiological adaptation change rate of the human eye state according to the physiological time constant of the human eye adaptation state, the display brightness of the full-color display screen, and the ambient light intensity in the environmental tensor includes: Determine the physiological adaptation change rate of the human eye state based on the physiological time constant of the human eye adaptation state, the display brightness of the full-color display screen, and the ambient light intensity in the environmental tensor for: in, The physiological time constant that represents the adaptation state of the human eye, Indicates the display brightness of the full-color display. represents the ambient light intensity in the ambient tensor, g Indicates the preset environmental factors; Indicates the adaptation status of the human eye. A value of 0 indicates no adaptation at all, and a value of 1 indicates complete adaptation.

8. A traffic full-color display screen control system, characterized in that: include: The acquisition unit is used to collect big data through the Internet of Things chip to obtain environmental data and traffic data; a filtering unit configured to filter the environmental data and the traffic data in the edge network to generate filtered data, and generate an environmental tensor representing an environmental state and a traffic tensor representing a traffic state based on the filtered data; a current unit, configured to determine the quantum dot light effect based on the real-time temperature in the environmental tensor, generate a photoelectric response equation based on a target brightness matrix set for the full-color display, the quantum dot light effect, and the real-time wind speed in the environmental tensor, and solve the photoelectric response equation to obtain a driving current for controlling the full-color display; A physiological unit is used to integrate the physiological state of the human eye, the display state of the full-color display screen, and the environmental tensor to determine the physiological adaptation change rate of the human eye state. When the physiological adaptation change rate is greater than a set threshold, a dynamic frequency reduction protection mode is triggered; A control unit is used to generate control information based on the driving current and the refresh rate distribution of the dynamic frequency reduction protection mode, and send the control information to the control component of the full-color display screen.

9. The traffic full-color display screen control system according to claim 8, characterized in that: Big data collection is performed through IoT chips to obtain environmental and traffic data, including: Big data collection through IoT chips; Building a sensor network based on the physical address of the IoT chip; The environmental data and traffic data collected by the IoT chip are aggregated through the sensor network, wherein the environmental data includes real-time temperature, real-time wind speed and light intensity, and the traffic data includes traffic data and road data.

10. The traffic full-color display screen control system according to claim 8, characterized in that: In the edge network, filtering the environmental data and the traffic data to generate filtered data, and generating an environmental tensor representing an environmental state and a traffic tensor representing a traffic state based on the filtered data, including: In the edge network, based on the preset noise model, nonlinear filtering is performed on the environmental data and traffic data to generate filtered data; Arrange the filtered data according to a preset spatial position to construct an initial tensor; Performing tensor convolution processing on the environment portion of the initial tensor to generate an environment tensor representing the environment state; Traffic flow evolution is performed on the traffic part in the initial tensor to generate a traffic tensor representing the traffic state.

Images