Integrated fog measurement type highway safe driving induction device and method

Abstract

The invention relates to the technical field of intelligent traffic, in particular to an integrated fog measurement type highway safe driving guidance device and method, and provides the following scheme that local visibility data is acquired based on a plurality of highway safe driving guidance devices and uploaded to a region controller to generate guidance information. And the regional controller performs research and judgment calibration and gradient calibration on the data, corrects measurement errors and generates road visibility space distribution information by screening calibration anchor points and adopting an exponential attenuation function and iterative gradient calculation. Based on the information, a connectivity algorithm is used to identify a fog area, and in combination with road environment and historical accident data, an optimal induction strategy is matched, and induction information is dynamically adjusted. The fog area detection accuracy is improved, the induction information matching is optimized, and the driving safety and the passing efficiency under the low-visibility condition are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation, and particularly to an integrated fog-detecting highway safe driving guidance device and method. Background Art

[0002] Existing fog area detection technologies mainly rely on single-point fog detection devices or area measurement methods based on linear interpolation. However, in the case of uneven fog area distribution, large measurement device errors, and complex environmental interference, it is difficult to ensure detection accuracy and stability. The single-point measurement method cannot comprehensively reflect the spatial changes of the fog area, and is prone to misjudgment due to individual device failures or measurement deviations. Traditional interpolation methods have limited accuracy in fog area boundary recognition and are difficult to accurately determine the fog area range.

[0003] For example, a Chinese patent with the authorization announcement number CN112885116B discloses a vehicle-road collaborative induction system for highway rain and fog scenarios, including: an intelligent roadside system, including: a roadside information acquisition subsystem, a roadside communication subsystem, and a traffic information publishing subsystem; an intelligent vehicle-mounted system including: a vehicle-mounted information acquisition subsystem, a vehicle-mounted communication subsystem, and a vehicle-mounted warning and control subsystem; a vehicle-road collaborative system for communicating and transmitting data between the intelligent roadside system and the intelligent vehicle-mounted system. This invention enables vehicles and personnel to obtain the driving information and road condition information of other vehicles within a super-visual range; obtains the running speed prediction models for curved roads, ramps, and straight road sections in rain and fog environments; and realizes intelligent induction of vehicle-road collaborative driving on highways in rain and fog environments, thereby improving the traffic efficiency and safety of intelligent highways.

[0004] The above existing technologies all have the problems raised in this background art: it is difficult to ensure detection accuracy and stability in the case of uneven fog area distribution, large measurement device errors, and complex environmental interference. To solve the above problems, this application designs an integrated fog-detecting highway safe driving guidance device and method. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an integrated fog-measuring highway safety driving guidance device and method in view of the deficiencies of the prior art. Based on multiple highway safety driving guidance devices, local visibility data is obtained and uploaded to a regional controller to generate guidance information. Each guidance device is configured with a fog-measuring module, and a discrete time-sharing coding control rule is adopted. Infrared signals are excited by a multi-level power signal pulse train and scattered signals are received to obtain accurate visibility data. The regional controller calibrates and gradient-calibrates the data. By screening calibration anchor points, an exponential decay function and iterative gradient calculation are used to correct measurement errors and generate the spatial distribution information of road visibility. Based on this information, a connectivity algorithm is used to identify fog areas, and combined with road environment and historical accident data, the optimal guidance strategy is matched to dynamically adjust the guidance information. In addition, the highway safety driving guidance device integrates an impact detection sensor, which can monitor the collision state in real time and upload it to the regional controller.

[0006] To achieve the above object, the present invention provides the following technical solutions: An integrated fog-measuring highway safety driving guidance method, applied to a highway safety driving guidance device, the highway safety driving guidance device being communicatively connected to a regional controller, the method comprising: Obtaining a plurality of local visibility data through a plurality of highway safety driving guidance devices, and uploading the local visibility data to the regional controller through corresponding wireless communication terminals, so that the regional controller generates guidance information according to the local visibility data, wherein each highway safety driving guidance device is configured with a fog-measuring module, and the fog-measuring module uses a discrete time-sharing coding control rule to generate a multi-level power signal pulse train, cyclically exciting infrared signals of different levels in the target area and receiving scattered signals in real time to obtain local visibility data; Displaying the guidance information transmitted by the regional controller.

[0007] The highway safety driving guidance device is further configured with an impact detection sensor, and the method further comprises: Monitoring the collision state of the highway safety driving guidance device through the impact detection sensor, and reporting a collision event to the regional controller through the wireless communication terminal.

[0008] The monitoring of the collision state of the highway safety driving guidance device includes: Collecting vibration signals of the device body; Performing feature extraction on the vibration signals to determine whether it is a valid impact event; When it is determined to be a valid impact event, the highway safety driving guidance device generates event data including the collision location, time, impact intensity and the operating state of the guidance device, and uploads it to the regional controller through the wireless communication terminal.

[0009] The fog area detection method includes: Receiving local visibility data uploaded by multiple highway safety driving induction devices, where each highway safety driving induction device is configured with a fog detection module. The fog detection module uses a discrete time-division coding control rule to generate a multi-level power signal pulse train, cyclically excites infrared signals of different levels in the target area, and receives scattered signals in real time to obtain local visibility data; Calibrating the local visibility data to generate road visibility spatial distribution information; Generating fog area information according to the road visibility spatial distribution information, where the fog area information includes the fog area range and the fog area visibility; Generating induction information according to the fog area information.

[0010] Calibrating the local visibility data includes: Based on historical visibility and device reliability, screening calibration anchor points from multiple highway safety driving induction devices; Sending a calibration activation instruction to the calibration anchor points to generate an associated data packet, where the associated data packet includes local visibility data and temperature and humidity parameters; According to the associated data packet, calibrating the local visibility data of the remaining highway safety driving induction devices through a gradient calculation method to generate locally visibility data corrected by gradient calibration.

[0011] Calibrating the local visibility data of the remaining highway safety driving induction devices through a gradient calculation method includes: Based on the position coordinates of the calibration anchor points and the corresponding local visibility data, constructing a discretized visibility spatial gradient field, where the initial calibration value of the visibility of each remaining highway safety driving induction device is calculated through an exponential decay function according to its Euclidean distance from the calibration anchor points and the visibility difference; According to the visibility spatial gradient field, performing multiple iterative gradient calculations on the visibility difference between nodes until the number of iterations is reached, where in each iteration, the visibility values of each node are calibrated according to the gradient direction and distance weight of adjacent nodes.

[0012] Generating the fog area information includes: According to the road visibility spatial distribution information, screening out target nodes with visibility less than the visibility threshold according to a preset visibility threshold; Connecting the target nodes through a connectivity algorithm to generate a low visibility area; Performing boundary fitting on each low visibility area, and generating fog area information according to the area information within the boundary.

[0013] Generating the induction information includes: Classify the danger level of the foggy area according to the fog area information; According to the danger level of the fog area, combined with the road type, vehicle density and historical accident information, select the corresponding induction information in the preset strategy library.

[0014] A highway safe driving induction device, the highway safe driving induction device includes: A fog measurement module, configured to generate a multi-level power signal pulse train through a discrete time-division coding control rule, circularly drive an infrared emitting tube, excite the infrared signal in the target area and receive the scattered signal in real time to calculate the local visibility data; A wireless communication terminal, communicatively connected to the area controller, for uploading local visibility data, impact detection data and receiving induction information; An induction execution module, including an LED array with adjustable brightness and a direction indicator light, for generating a dynamic light band or direction guidance according to the induction information sent by the area controller; An impact detection module, configured with a vibration sensor and an embedded processor, for collecting the vibration signal of the device body, judging whether it is an effective impact event through a time-frequency domain feature extraction algorithm, and generating event data including the collision position, time and impact intensity when it is determined to be an effective impact.

[0015] An area controller, the area controller includes: A data receiving module, for receiving local visibility data, temperature and humidity parameters and impact event data uploaded by multiple highway safe driving induction devices; A processor, configured to perform the following operations: Screen and calibrate anchor points according to historical visibility volatility and device reliability, construct a spatial gradient field based on the visibility data and temperature and humidity parameters of the anchor points, calibrate the visibility data of non-calibrated anchor point devices through a non-linear attenuation model and a weight fusion algorithm, and trigger a secondary infrared signal verification for the visibility abnormal area; Perform connectivity clustering analysis on the calibrated visibility data, fit the dynamic fog area boundary in combination with the real-time wind speed vector and the road topology structure, and generate fog area information including the fog area range and visibility level; Calculate the risk coefficient according to the fog area visibility level, diffusion speed and road type, match the LED brightness, flashing frequency and direction guidance rules from the multi-modal strategy library, and synchronize them to the external traffic management system through the cooperative control interface; A cooperative control interface, for synchronizing fog area information and induction strategies to the external traffic management system, and receiving meteorological prediction data to optimize calibration parameters.

[0016] Compared with the prior art, the beneficial effects of the present invention are: The present invention realizes high-precision correction of visibility data by screening and calibrating anchor points, combining the exponential decay function and iterative gradient calculation, minimizing the measurement error globally, and ensuring the reliability of fog area recognition. At the same time, the connectivity algorithm is used to accurately delimit the fog area boundary, and combined with historical accident data, road types and traffic flow density, the induction strategy is dynamically matched to improve the pertinence of induction information. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Other features, objects and advantages of the present invention will become more apparent by reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 FIG. is a schematic flow chart of an integrated fog-measuring highway safe driving induction method according to Embodiment 1 of the present invention; Figure 2 FIG. is a schematic data transmission diagram of Embodiment 1 of the present invention; Figure 3 FIG. is a visibility detection flow chart of Embodiment 1 of the present invention; Figure 4 FIG. is a simplified schematic diagram of an infrared ray emitted by a visibility detection device according to Embodiment 1 of the present invention; Figure 5 FIG. is a schematic flow chart of an integrated fog-measuring highway safe driving induction method according to Embodiment 2 of the present invention; Figure 6 FIG. is a schematic induction information transmission diagram of Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0019] Embodiment 1

[0020] Please refer to Figure 1 , an embodiment provided by the present invention: an integrated fog-measuring highway safe driving induction method, which is applied to a highway safe driving induction device, and the highway safe driving induction device is communicatively connected to a regional controller. The specific steps of the method are as follows: S1: Obtain a plurality of local visibility data through a plurality of highway safe driving induction devices; In this embodiment, multiple highway safety driving guidance devices are arranged along a specific section of the highway. Each highway safety driving guidance device is equipped with a fog detection module. The fog detection module adopts a discrete time-sharing coding control rule and generates a multi-level power signal pulse train through a preset visibility detection device interval sequence to synchronously excite the infrared signal in the target area. For different interval areas, the emission angle and light intensity level of the infrared signal will be dynamically adjusted, and the scattered signal is collected at the receiving end to achieve precise measurement of the local visibility. This method can improve the stability and anti-interference ability of the measurement data, and ensure the reliability of the detection results even in strong fog weather or complex lighting conditions.

[0021] S2: Upload the local visibility data to the area controller through the corresponding wireless communication terminal, so that the area controller generates guidance information based on the local visibility data; In this embodiment, the highway safety driving guidance device establishes a data connection with the area controller through the wireless communication terminal and uploads the locally collected visibility data in real time. After receiving the visibility data of multiple highway safety driving guidance devices, the area controller performs spatio-temporal calibration and distributed gradient calibration on the data, and combines the traffic flow data and historical meteorological information to generate high-precision road visibility spatial distribution information. On this basis, the area controller further analyzes the coverage range of the fog area and the change of visibility gradient, and determines the fog area level according to the preset fog area division standard, and matches the corresponding guidance strategy. It can effectively reduce the single-point measurement error and improve the accuracy of fog area detection, so that the guidance information can better conform to the actual situation of the road.

[0022] S3: Display the guidance information transmitted by the area controller; In this embodiment, based on the generated guidance information, the area controller issues an instruction to the highway safety driving guidance device through the wireless communication terminal to make the guidance device display the corresponding guidance information. The specific display methods include that the LED variable message sign displays the fog area level and the recommended vehicle speed, the warning light flashes dynamically to remind, the voice broadcasts to prompt the driver of the fog area condition ahead, or sends a guidance signal to the vehicle terminal through the vehicle network. The display method of the guidance information is adaptively adjusted according to the severity of the fog area, the road structure and the traffic flow. For example, in the case of severe thick fog, the update frequency of the guidance information will be increased, and more refined driving guidance can be issued in combination with the in-vehicle navigation system. This method can effectively improve the driver's perception ability of the visibility condition of the road ahead, reduce traffic accidents caused by insufficient visibility, and improve the highway traffic efficiency and driving safety.

[0023] Please refer to Figure 2, Schematic diagram of data transmission according to an embodiment of the present invention, which is composed of several highway safety driving induction devices. Visibility data is collected by the highway safety driving induction devices and then transmitted to the area controller. The numbers of the devices increase from small to large along the direction of the lane.

[0024] Please refer to Figure 3 , Flowchart of visibility detection according to an embodiment of the present invention. The specific steps of S1 are as follows: S1.1: Preset a visibility detection device interval sequence. Based on the visibility detection device interval sequence and the preset discrete time-sharing coding control rule, generate a coding pulse train corresponding to each interval; S1.2: Based on the coding pulse train corresponding to each interval, synchronously obtain a multi-level power signal pulse train corresponding to each interval through a signal power amplification device; Further, in this embodiment, the process of obtaining the multi-level power signal pulse train corresponding to each interval includes: The preset visibility detection device interval sequence includes N visibility detection device intervals, and N reference clock pulse train sequences are generated by combining a excitation frequency of 38KHz and a control clock; Further, in this embodiment, the deployment interval of the visibility detection device is set with an interval length of 20 meters, and the visibility detection device is deployed according to the path direction to obtain a visibility detection device interval sequence.

[0025] Based on the N reference clock pulse train sequences and a counter, obtain the period length T corresponding to each reference clock pulse train, the high-level window width within each period, and the adjacent low-level window width; Based on the period length T corresponding to the N reference clock pulse train sequences and the high-level window width within each period, obtain the standard duty cycle of the N reference clock pulse train sequences; Set the instantaneous power level interval of the signal excitation, and each instantaneous power level increases in turn, and obtain the signal emission information sequence corresponding to the maximum signal detection intensity detected in each historical visibility detection , where represents the instantaneous power corresponding to the pulse train emitted by the nth visibility detection device at the current t moment, which directly determines the emission intensity of the infrared signal; in a low visibility scenario, aerosol scattering and medium absorption will significantly weaken the signal intensity. Therefore, it is necessary to increase the instantaneous power (for example, from 20mW to 80mW) to penetrate the environmental interference and ensure that the receiving end can capture the effective echo signal. In this embodiment, by setting different power levels at different duty cycles, while increasing the probability of the signal being detected, the corresponding energy consumption points are reduced; Denotes the duty cycle corresponding to the pulse train emitted by the nth visibility detection device at the current time t, which is used to adjust the balance between energy consumption and signal duration. The smaller the duty cycle, the lower the average power consumption; Denotes the energy conversion efficiency of the power amplifier of the nth visibility detection device, which characterizes the effectiveness of energy conversion. Improving the efficiency can reduce energy loss and allows for higher under the same , Denotes the average power corresponding to the pulse emitted by the nth visibility detection device within a single cycle; Denotes the signal-to-noise ratio corresponding to the pulse train emitted by the nth visibility detection device at the current time t, Denotes the noise power spectral density of the environmental noise on the pulse train emitted by the nth visibility detection device, which characterizes the environmental interference intensity (such as rain and snow scattering, electromagnetic noise). For example, heavy rain weather will cause a sudden increase in the noise power. At this time, it is necessary to dynamically increase the instantaneous power or adjust the emission angle to maintain the signal-to-noise ratio above the detection threshold (such as SNR≥10dB); if the noise mainly comes from heat sources (such as high-temperature environments), it is necessary to shorten the noise sampling time of the low-level window to reduce the cumulative effect of noise. By this variable, the duty cycle and instantaneous power of the emitted pulse train can be more precisely balanced, while reducing energy consumption and increasing the signal-to-noise ratio of the transmitted power, so that the average power consumption corresponding to each emission cycle remains at a very low level; Denotes the high-level window width corresponding to the pulse train emitted by the nth visibility detection device at the current time t, Denotes the low-level window width corresponding to the pulse train emitted by the nth visibility detection device at the current time t, Denotes the emission angle gain factor corresponding to the infrared signal emitted by the nth visibility detection device at the current time t, which determines the radiation concentration of the infrared signal in a specific direction. By dynamically adjusting the emission angle (for example, narrowing from 60° to 30°), the system can focus the energy on the target area (such as the road monitoring direction) and reduce the ineffective scattering loss. For example, narrow-angle emission can concentrate energy to penetrate the fog layer in foggy weather, while wide-angle emission is suitable for large-range high-visibility monitoring; Denotes the thermal noise value corresponding to the nth visibility detection device, which is the environmental thermal noise and is related to the temperature; Denotes the probability that the infrared signal emitted by the nth visibility detection device at the mth instantaneous power level is detected; Furthermore, in this embodiment , where Denotes the emission angle; Based on the signal emission information sequence, the signal excitation instantaneous power level interval, the average power threshold corresponding to the emission pulse within a single period, and the upper and lower limits of the high-level window width corresponding to the pulse train, construct a high-level width-instantaneous power-maximum signal-to-noise ratio search equation and the corresponding search constraint conditions. While satisfying the constraint conditions, maximize the probability that the infrared signal emitted by the visibility detection device at each instantaneous power level is detected; Further, the high-level width-instantaneous power-maximum signal-to-noise ratio search equation is constructed by those skilled in the art according to the above variables and the multi-objective linear algorithm; Further, the corresponding constraint conditions of the present invention are specifically: , where 、 are the upper and lower limits of the signal excitation instantaneous power level interval. Here, the corresponding power levels are divided according to the density of the corresponding emission signals during the actual detection process. Specifically, those skilled in the art make specific divisions according to specific requirements; , ; Further, the average power threshold corresponding to the emission pulse within a single period in this embodiment is 0.009W; Based on the high-level width-instantaneous power-maximum signal-to-noise ratio search equation and the corresponding search constraint conditions, combine the multi-objective optimization discrimination algorithm and the bidirectional search rule to perform search and solution, obtain the high-level window width and the corresponding instantaneous power level that meet the conditions, and perform differential coding annotation on the high-level window width that meets the conditions.

[0026] Further, the process of performing differential coding annotation in this embodiment includes: Generate an unpredictable pulse interval sequence based on the improved Logistic map combined with the pulse train corresponding to the duty cycle; Further, the improved Logistic map in this embodiment adopts the Cubic-Logistic hybrid mapping algorithm; Superimpose orthogonal subcarriers on the fundamental frequency of the pulse interval sequence to form a spectral fingerprint, obtain the differential coding of the corresponding pulse train and the corresponding decoding information, and synchronously pre-store the corresponding decoding information at the receiving end.

[0027] Further, the process of the bidirectional search rule in this embodiment includes: Based on the upper and lower limits of the high-level window width and the signal excitation instantaneous power level interval, take the upper limit of the high-level window width corresponding to the standard duty cycle as the search starting point, search downward for each high-level window width value, and for each high-level window width, obtain the duty cycle corresponding to each high-level window width; Based on the duty cycle corresponding to each high-level window width, search from the lower limit to the upper limit of the interval in the instantaneous power level interval and emission angle interval of the signal excitation, and obtain the power level sequence corresponding to each duty cycle that satisfies the average power threshold and the emission angle corresponding to the maximum probability of the emitted infrared signal being detected at the current duty cycle under the condition of satisfying the average power threshold of the emission pulse within a single period.

[0028] S1.3. Based on the multi-level power signal pulse train, through a preset hierarchical power control model combined with a preset one-to-one mapping table of emission angle interval and signal power level-visibility, circularly excite infrared signals with different light intensities and emission angles in the target area of each interval area; Furthermore, the all-digital CMOS logic circuit adopted inside the visibility detection device in this embodiment does not use a single-chip microcomputer processor, so the corresponding anti-interference ability is extremely strong, and it can accurately generate pulse signals and cyclically change the modulated emission intensity of infrared rays.

[0029] S1.4. Through the signal receiving device configured by the visibility detection device, collect in real time the infrared signals with different light intensities and emission angles scattered in the target area, and synchronously output the signal power level through a preset filtering discrimination evaluation model, and match and discriminate whether there are infrared signals corresponding to the corresponding power level in the infrared signals collected in each interval. The specific filtering discrimination evaluation model can be set through existing filtering algorithms.

[0030] Furthermore, the preset emission angle interval in this embodiment is [39°, 41°], please refer to Figure 4 is a simplified diagram of the infrared ray emission of the visibility detection device, where A is the emission angle, and the two circles corresponding to 1 and 2 are the corresponding infrared signal emission ports, and Q represents the target detection area; The signal receiving device configured by the visibility detection device is a conical total reflection condenser, which has a large light input and a small overall size of the device, greatly reducing the use cost.

[0031] S1.5. If it exists, then based on the infrared signal of the current power level of each interval obtained by detection and the one-to-one mapping table of signal power level-visibility, obtain the visibility corresponding to each interval and perform real-time update and display; Furthermore, in this embodiment, the steps for obtaining the visibility corresponding to each interval include: Based on the power level sequence corresponding to each duty cycle that satisfies the average power threshold, through the hierarchical power control model, generate infrared rays of the power level sequence corresponding to each duty cycle from small to large, and circularly emit them at the emission angle corresponding to the maximum probability of being detected at the current duty cycle, and obtain infrared rays with different power levels with specific coding; Based on the signal receiving devices configured for each visibility detection device, the signals in the corresponding target area are collected in real time, and through the filtering algorithm, combined with the marked encoding and the pre-stored decoding information, the signals in the real-time collected target area are filtered and it is determined whether there is an infrared ray signal with a distinct encoding in the collected target area signals; If so, the information of the infrared ray signal with a distinct encoding obtained by discrimination is input into the matching detection algorithm, combined with the signal power level-visibility one-to-one mapping table, to perform power level matching of the infrared ray signal with a distinct encoding, and obtain the visibility of the current interval area corresponding to the matching level; Furthermore, the signal power level-visibility one-to-one mapping table in this embodiment is constructed by those skilled in the art based on historical experimental detection data of different weather conditions and specific road areas in combination with a hash table; If not, according to the infrared ray signals under each duty cycle corresponding power level, they are cyclically transmitted, collected and detected for matching in ascending order of power level until the visibility of the current interval area is detected; Furthermore, the process of performing power level matching of the infrared ray signal with a distinct encoding in this embodiment includes: According to the obtained infrared ray signal information by discrimination, non-target frequency band noise is filtered through a Butterworth band-pass filter to obtain the infrared signal to be matched; Based on the infrared signal to be matched and combined with the pre-stored decoding information, the infrared ray signal matching the pre-stored decoding information (such as Logistic chaotic sequence) is extracted; Map the distinct encoding corresponding to the matched infrared ray signal to the signal power level-visibility one-to-one mapping table in the hash bucket, and compensate the power level corresponding to the discriminated infrared ray signal through a preset power reduction coefficient; Here, the key in the distinct encoding is the CRC-16 check value of the encoding fingerprint, and the value is the power level; Furthermore, the power reduction coefficient in this embodiment is obtained through the analysis of historical measured data under different environments and visibilities, and is stored in the above signal power level-visibility one-to-one mapping table in one-to-one correspondence with the corresponding power level.

[0032] Furthermore, the CRC-16 check value in this embodiment is generated by an improved Logistic mapping, including 256 groups of chaotic sequences (length 64), and each group of chaotic sequences corresponds to a unique CRC-16 check value; Based on the distinct encoding corresponding to the compensated matched infrared ray signal, traverse the signal power level-visibility one-to-one mapping table. If there is a corresponding power level during the traversal, the corresponding visibility is obtained through inversion.

[0033] Further, the cyclic emission in this embodiment is to continuously emit the corresponding infrared rays from low to high according to the power level sequence corresponding to each duty cycle to detect the visibility of the corresponding area.

[0034] The road safety driving guidance device is further equipped with an impact detection sensor, and the method further includes: Monitoring the collision state of the road safety driving guidance device through the impact detection sensor and reporting the collision event to the area controller through the wireless communication terminal.

[0035] Monitoring the collision state of the road safety driving guidance device includes: Collecting the vibration signal of the device body; Performing feature extraction on the vibration signal to determine whether it is a valid impact event; When it is determined to be a valid impact event, the road safety driving guidance device generates event data including the collision position, time, impact intensity, and the operating state of the guidance device, and uploads it to the area controller through the wireless communication terminal.

[0036] Embodiment 2

[0037] Please refer to Figure 5 , the present invention provides an embodiment: an integrated fog-detecting road safety driving guidance method applied to an area controller, and the fog area detection method includes: A1: Receiving the local visibility data uploaded by multiple road safety driving guidance devices; In this embodiment, multiple road safety driving guidance devices are arranged along the road and upload the local visibility data to the area controller in real time through the wireless communication terminal. The fog detection module configured in each road safety driving guidance device uses the discrete time-division coding control rule to generate a multi-level power signal pulse train, excite infrared signals of different levels at a set time interval, and synchronously receive the scattered signals in the target area to measure the local visibility. Ensure the timing consistency of the visibility data, avoid the overall data deviation caused by single-point errors or measurement lags, and thus improve the accurate assessment of the real-time state of the fog area.

[0038] A2: Calibrating the local visibility data to generate the road visibility spatial distribution information; In this embodiment, to improve the credibility of visibility data, the regional controller performs calibration processing on the received local visibility data. First, based on historical visibility data and the reliability index of the device, calibration anchor points are selected, and a calibration activation instruction is sent to the calibration anchor points to generate associated data packets, which include local visibility data and environmental temperature and humidity parameters. Then, using the gradient calculation method, based on the visibility data of the calibration anchor points, a visibility spatial gradient field is constructed, and the visibility data of the remaining highway safety driving guidance devices are calibrated. Through multiple iterations of gradient calculation, the visibility data of each highway safety driving guidance device converge to a reasonable range, generating road visibility spatial distribution information that better conforms to the actual road conditions. This method effectively reduces the influence of single-point measurement errors and improves the accuracy and stability of fog area detection.

[0039] A3: Generate fog area information based on the road visibility spatial distribution information; Wherein the fog area information includes the fog area range and the fog area visibility; In this embodiment, based on the calibrated road visibility spatial distribution information, the regional controller screens the data of each measurement point, extracts the nodes with visibility lower than the set threshold, and uses the connectivity algorithm to cluster these target nodes to identify the low visibility area. Subsequently, the boundary fitting algorithm is used to smooth the boundary of the low visibility area to generate complete fog area information, including the fog area range, the fog area center point, and the average fog area visibility. In this way, the distortion of the fog area shape caused by the gaps between measurement points or local outliers can be effectively avoided, making the fog area detection result more in line with the actual road conditions and improving the accuracy of fog area identification.

[0040] A4: Generate induction information based on the fog area information; In this embodiment, to effectively guide the safe passage of vehicles, the regional controller matches the optimal induction strategy in the preset strategy library according to the fog area danger level, combined with the road type, vehicle density, and historical accident information. For mild fog areas, speed limit reminders, lane change instructions, etc. may be used for guidance, while for severe fog areas, hierarchical early warnings, traffic flow control, etc. may be initiated. According to the dynamic changes in the fog area range and traffic flow, the regional controller can adjust the induction information in real time and issue induction instructions through various means such as LED variable message signs, voice broadcast devices, and V2X vehicle networking, ensuring that drivers can obtain driving guidance information in a timely manner. Through this hierarchical induction and real-time adjustment, traffic accidents caused by low visibility can be effectively reduced, and the road traffic efficiency under complex meteorological conditions can be improved.

[0041] In existing fog area detection technologies, fog measurement devices are usually independently deployed to measure the road visibility, and the visibility distribution of the entire road is estimated by single-point or linear interpolation methods. However, since the formation of fog areas is greatly affected by factors such as terrain, wind speed, and humidity, the data collected by a single fog measurement device often cannot truly reflect the overall situation of the fog area, resulting in large measurement errors. Due to the interference of factors such as ambient light, temperature, and humidity on the fog measurement device, its measurement accuracy may deviate, and these deviations will directly affect the final fog area detection result.

[0042] In this embodiment, taking the fog area detection as a specific application scenario, calibration anchor points are selected based on historical visibility data and device reliability. These anchor point devices perform reference calibration on local visibility data through specific algorithms. Compared with the existing method of directly using the data of fog measurement devices to determine the fog area, in this embodiment, by constructing a visibility spatial gradient field and using the gradient calculation method to correct the data of the remaining fog measurement devices, the measurement errors between different devices are dynamically adjusted, thereby significantly improving the accuracy of local visibility data. It is equivalent to performing adaptive correction during the measurement process. Even if individual fog measurement devices are interfered by external factors, their data can still be incorporated into the overall fog area detection system after being corrected by the gradient field, avoiding error accumulation.

[0043] Please refer to Figure 6 , the schematic diagram of induced information transmission in the embodiment of the present invention. After the area controller detects fog area A, it transmits induced information to the driving induction devices within fog area A.

[0044] The specific steps of A2 are as follows: A2.1: Based on historical visibility and device reliability, select calibration anchor points from multiple highway safety driving induction devices; In this embodiment, in order to ensure the accuracy and stability of visibility data, a method based on historical visibility data and device reliability assessment is adopted to select calibration anchor points from multiple highway safety driving induction devices. The selection of calibration anchor points is not just random selection, but a comprehensive judgment based on multiple factors.

[0045] Specifically, first, analyze the historical measurement data of each highway safe driving guidance device under different meteorological conditions, calculate the long-term measurement error deviation, and eliminate those devices with large measurement fluctuations or large errors to ensure the high credibility of the calibration anchor points. Secondly, evaluate the working stability of each device, including parameters such as the failure rate of the device, data transmission stability, and communication delay, and select devices with stable data transmission, low false alarm rate, and small long-term error as calibration anchor points. In addition, geographical factors need to be considered. For example, the calibration anchor points should be evenly distributed within the target monitoring area to avoid distortion of the overall calibration result due to over-concentration of the calibration anchor points. After determining the calibration anchor points, the area controller will assign special identifiers to these devices so that the data of these devices can be preferentially referenced during the subsequent calibration process.

[0046] A2.2: Send a calibration activation instruction to the calibration anchor point to generate an associated data packet, where the associated data packet includes local visibility data and temperature and humidity parameters; In this embodiment, when the area controller determines the calibration anchor points, it will send a calibration activation instruction to these calibration anchor point devices to make them enter a special calibration mode. In the calibration mode, the calibration anchor points not only collect visibility data but also synchronously collect environmental information such as temperature and humidity parameters to form a complete associated data packet.

[0047] Specifically, since the formation of fog is significantly affected by temperature and humidity conditions, under the same meteorological conditions, the density, coverage, and change rate of fog will be different. If temperature and humidity factors are not considered and only visibility data is relied on for calibration, it may lead to large errors in fog area identification.

[0048] Furthermore, in the present application, the visibility data and temperature and humidity parameters are synchronously collected in the form of an associated data packet, and metadata such as measurement timestamps and device geographical locations are attached to the data packet to ensure the consistency of the data in time and space.

[0049] Preferably, in order to prevent errors in measurement data due to individual differences in devices, the calibration anchor point device will continuously measure visibility and temperature and humidity data multiple times in the calibration mode and use the weighted average method to obtain the final measurement value to reduce the influence of accidental factors on the measurement accuracy. All calibration data will be transmitted to the area controller through a wireless communication terminal and synchronously sent to adjacent non-calibration anchor point devices to provide highly credible reference data to support the subsequent gradient calibration process. Ensure the stability of visibility measurement and the spatio-temporal consistency of the data, thereby improving the accuracy of subsequent fog area identification.

[0050] A2.3: According to the associated data packet, calibrate the local visibility data of the remaining highway safe driving guidance devices through a gradient calculation method to generate locally visibility data corrected by gradient calibration; In this embodiment, after the area controller receives the associated data packet uploaded by the calibration anchor point, it will perform gradient calibration on the data of the remaining highway safety driving guidance devices based on the calibration data. The core idea of gradient calibration is to use the data of the calibration anchor point as a reference, and combine the measurement data of the non-calibration anchor point devices to gradually adjust their measurement values to minimize the measurement errors of all devices, where the associated data packet includes calibration data.

[0051] Specifically, the area controller first constructs a visibility space gradient field, and the core variables of this gradient field include the position coordinates of the calibration anchor point, the measured visibility value, and the corresponding temperature and humidity parameters. For each non-calibration anchor point device, the area controller will calculate the spatial distance between it and the calibration anchor point, analyze the measurement differences between the two in the historical measurement data, and use the exponential decay weight function to correct its initial measurement value to ensure that the visibility data will not have large deviations due to individual differences in devices. In addition, after the first round of gradient correction is completed, iterative optimization will be performed, that is, after each round of calibration, the data of the non-calibration anchor point device will recalculate the difference with the nearest calibration anchor point and further adjust its own measurement value until the overall measurement error is lower than the set threshold to ensure that the data of all devices are within a reasonable range. It not only considers the spatial distance relationship between devices, but also combines historical data and meteorological conditions for multi-level error correction to make the calibration result more stable. For example, in the traditional method, if the visibility data measured by a certain device is significantly lower than that of other devices, this data may be directly excluded or averaged, which may lead to the failure to correctly identify the true local low visibility area. In this embodiment, by introducing a gradient correction model, the data of this device will not be simply excluded, but will be dynamically adjusted in combination with the data of the surrounding calibration anchor points, making the visibility measurement result more reasonable.

[0052] Preferably, while performing gradient calibration, the area controller will also dynamically evaluate the reliability of the non-calibration anchor point devices and adjust their calibration weights according to the historical stability of the devices. For example, if a certain device has large fluctuations in the past data records, the system will assign a lower weight to its calibration result to reduce the impact of its data on the overall fog area recognition result. Improve the credibility of the data, make the fog area recognition more accurate, and avoid misjudgment caused by a single device failure or abnormal measurement. Finally, all the calibrated data will be integrated to generate the road visibility spatial distribution information.

[0053] The specific steps of A2.3 are as follows: A2.3.1: Based on the position coordinates of the calibration anchor point and the corresponding local visibility data, construct a discretized visibility space gradient field, where the initial calibration value of the visibility of each remaining highway safety driving guidance device is calculated by an exponential decay function according to its Euclidean distance and visibility difference from the calibration anchor point; In this embodiment, the purpose of constructing the visibility spatial gradient field is to achieve high-precision calibration of visibility data, so that the measurement data of all highway safety driving guidance devices can be consistent globally and reduce the overall measurement distortion caused by single-point device errors.

[0054] Specifically, first determine the position coordinates of the calibration anchor points, and establish a basic visibility reference point in combination with the measured visibility data. The selection of the calibration anchor points is determined based on multiple factors such as historical measurement stability, device reliability, and geographical location uniformity, and its measurement data is considered the most reliable reference value.

[0055] Furthermore, it is necessary to reasonably calibrate the initial visibility values of the highway safety driving guidance devices that are not selected as calibration anchor points. Simply using the methods of mean interpolation or direct assignment will result in poor spatial consistency of the data and it is difficult to reflect the fog area distribution in different regions of the road. Therefore, this embodiment uses an exponential decay function to calculate the initial visibility calibration values of each uncalibrated anchor point device. Its basic idea is to give a dynamic correction weight according to the difference between the spatial distance of the device from the calibration anchor point and the measured value.

[0056] The core function of the exponential decay function is to ensure that devices closer to the calibration anchor point can obtain a higher calibration credibility, while the calibration values of devices farther away are gradually decreasingly affected by the calibration anchor point. This can ensure that the calibrated data does not overly rely on a specific anchor point, thereby maintaining the spatial smoothness of the overall data. At the same time, since the fog area distribution is greatly affected by environmental factors, traditional linear interpolation methods may cause excessive errors in the fog area edge area, while the exponential decay method can better adapt to the non-uniform diffusion characteristics of the fog area, making the calibrated visibility distribution more in line with the actual shape of the road fog area. In this way, each highway safety driving guidance device that is not selected as a calibration anchor point can obtain a preliminary visibility calibration value, which is not only affected by the data of adjacent calibration anchor points, but also takes into account the spatial gradient characteristics, making the overall calibration process more in line with the actual distribution law of the road fog area.

[0057] A2.3.2: According to the visibility spatial gradient field, perform multiple iterative gradient calculations on the visibility differences between nodes until the number of iterations is reached, where each iteration calibrates the visibility values of each node according to the gradient direction and distance weight of adjacent nodes; In this embodiment, the gradient calibration process does not stop at the preliminary exponential decay calibration stage, but uses the method of iterative gradient calculation to make the visibility data of each node tend to be reasonable and stable in the process of continuous optimization.

[0058] Specifically, after completing the preliminary exponential decay calibration, the system will perform global calibration on all uncalibrated nodes based on the visibility spatial gradient field. The iterative gradient utilizes the visibility change trend between adjacent nodes and combines their spatial position relationships for dynamic correction to ensure that the finally generated visibility distribution not only conforms to the actual diffusion characteristics of the fog area but also effectively suppresses the impact of the error of a single measurement node on the overall data.

[0059] Furthermore, during each round of gradient calculation, the visibility value of each uncalibrated node will be adjusted according to the gradient differences with multiple adjacent nodes. Among them, the calculation of the gradient direction is based on the visibility differences between adjacent nodes, and the gradient weight is jointly determined by the spatial distance and measurement error of adjacent nodes. Ensure that the calibration value of each node does not solely depend on a certain calibration anchor point but is constrained by the entire spatial data, making the calibrated visibility data show smooth changes in space and avoiding sudden changes or discontinuities. At the same time, the iterative process is set based on the error convergence threshold or the maximum number of iterations. Once the calibration errors of all nodes converge within the set error range or reach the maximum number of iterations, the gradient calibration process terminates and outputs the final visibility calibration value.

[0060] The specific steps of A3 are as follows: A3.1: According to the road visibility spatial distribution information, filter out target nodes with visibility less than the visibility threshold according to a preset visibility threshold; A3.2: Connect the target nodes through a connectivity algorithm to generate a low visibility area; A3.3: Fit the boundary of each low visibility area and generate fog area information according to the area information within the boundary.

[0061] The specific steps of A4 are as follows: A4.1: Divide the fog area into different danger levels according to the fog area information; A4.2: According to the fog area danger level, combined with road type, vehicle density, and historical accident information, select the corresponding induction information in a preset strategy library.

[0062] Embodiment 3

[0063] Please refer to Figure 4 , the present invention provides an embodiment: an integrated fog-measuring type highway safe driving induction device, the highway safe driving induction device includes: A fog measurement module configured to generate a multi-level power signal pulse train through a discrete time-sharing coding control rule, cyclically drive an infrared emitting tube, excite the infrared signal in the target area and receive the scattered signal in real time to calculate the local visibility data; A wireless communication terminal, communicatively connected to a regional controller, for uploading local visibility data, impact detection data, and receiving guidance information; An induction execution module, including an LED array with adjustable brightness and a direction indicator light, for generating a dynamic light band or direction guidance according to the induction information sent by the regional controller; An impact detection module, configured with a vibration sensor and an embedded processor, for collecting the vibration signal of the device body, judging whether it is an effective impact event through a time-frequency domain feature extraction algorithm, and generating event data including the collision position, time, and impact intensity when it is determined to be an effective impact; In this embodiment, the total average power corresponding to the road safety driving induction device per unit time length is less than 0.009W. Therefore, the road safety driving induction device of the present application is also configured with a solar power generation device, which can fully meet the power supply process of the corresponding device.

[0064] Embodiment 4

[0065] Please refer to Figure 4 , the present invention provides an embodiment: a regional controller, the regional controller includes: A data receiving module, for receiving local visibility data, temperature and humidity parameters, and impact event data uploaded by multiple road safety driving induction devices; A processor, configured to perform the following operations: Screen and calibrate anchor points according to historical visibility volatility and device reliability, construct a spatial gradient field based on the visibility data and temperature and humidity parameters of the anchor points, calibrate the visibility data of non-calibrated anchor point devices through a non-linear attenuation model and a weight fusion algorithm, and trigger secondary infrared signal verification for visibility abnormal areas; Perform connectivity clustering analysis on the calibrated visibility data, fit the dynamic fog area boundary in combination with the real-time wind speed vector and road topology structure, and generate fog area information including the fog area range and visibility level; Calculate the risk coefficient according to the fog area visibility level, diffusion speed, and road type, match the LED brightness, flashing frequency, and direction guidance rules from a multi-modal strategy library, and synchronize them to an external traffic management system through a collaborative control interface; A collaborative control interface, for synchronizing fog area information and induction strategies to an external traffic management system, and receiving meteorological prediction data to optimize calibration parameters.

[0066] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. An integrated fog-measuring type highway safe driving induction method, which is applied to a highway safe driving induction device. The highway safe driving induction device is communicatively connected to a regional controller, and is characterized in that, The method includes: Obtaining a plurality of local visibility data through a plurality of highway safe driving induction devices, and uploading the local visibility data to the area controller through corresponding wireless communication terminals, so that the area controller generates induction information according to the local visibility data. Each highway safe driving induction device is configured with a fog detection module, and the fog detection module generates a multi-level power signal pulse train by using a discrete time-sharing coding control rule, cyclically drives an infrared emitting tube, excites the infrared signal in the target area and receives the scattered signal in real time to obtain local visibility data; Displaying the induction information transmitted by the area controller.

2. The integrated fog-measuring type highway safe driving induction method according to claim 1, characterized in that, The highway safe driving induction device is further configured with an impact detection sensor, and the method further includes: Monitoring the collision state of the highway safe driving induction device through the impact detection sensor, and reporting the collision event to the area controller through the wireless communication terminal.

3. The integrated fog-measuring type highway safe driving induction method according to claim 2, characterized in that, The monitoring of the collision state of the highway safe driving induction device includes: Collecting the vibration signal of the device body; Performing feature extraction on the vibration signal to determine whether it is an effective impact event; When it is determined to be an effective impact event, the highway safe driving induction device generates event data including the collision position, time, impact intensity, and the operating state of the induction device, and uploads it to the area controller through the wireless communication terminal.

4. An integrated fog-measuring type highway safe driving induction method, which is applied to a regional controller. The method includes: Receiving the local visibility data uploaded by a plurality of highway safe driving induction devices. Each highway safe driving induction device is configured with a fog detection module, and the fog detection module generates a multi-level power signal pulse train by using a discrete time-sharing coding control rule, cyclically drives an infrared emitting tube, excites the infrared signal in the target area and receives the scattered signal in real time to obtain local visibility data; Calibrating the local visibility data to generate road visibility spatial distribution information; Generating fog area information according to the road visibility spatial distribution information, where the fog area information includes the fog area range and the fog area visibility; Generating induction information according to the fog area information.

5. The integrated fog-measuring type highway safe driving induction method according to claim 4, characterized in that, The calibrating of the local visibility data includes: Screening calibration anchor points from a plurality of highway safe driving induction devices based on historical visibility and device reliability; Sending a calibration activation instruction to the calibration anchor points to generate an associated data packet, where the associated data packet includes local visibility data and temperature and humidity parameters; Calibrating the local visibility data of the remaining highway safe driving induction devices by using a gradient calculation method according to the associated data packet to generate locally visibility data corrected by gradient calibration.

6. The integrated fog-measuring type highway safe driving induction method according to claim 5, characterized in that, The calibrating of the local visibility data of the remaining highway safe driving induction devices by using a gradient calculation method includes: Based on the position coordinates of the calibration anchor points and the corresponding local visibility data, constructing a discretized visibility spatial gradient field, where the initial calibration value of the visibility of each remaining highway safe driving induction device is calculated by an exponential decay function according to its Euclidean distance from the calibration anchor points and the visibility difference; Based on the visibility spatial gradient field, perform multiple iterative gradient calculations on the visibility differences between nodes until the number of iterations is reached, where in each iteration, calibrate the visibility values of each node according to the gradient direction and distance weight of adjacent nodes.

7. The integrated fog-measuring type highway safe driving induction method according to claim 4, characterized in that, The generation of fog area information includes: According to the road visibility spatial distribution information, screen out target nodes with visibility less than the visibility threshold according to a preset visibility threshold; Connect the target nodes through a connectivity algorithm to generate a low visibility area; Perform boundary fitting on each low visibility area, and generate fog area information according to the area information within the boundary.

8. The integrated fog-measuring type highway safe driving induction method according to claim 4, characterized in that, The generation of induction information includes: According to the fog area information, divide the fog area into different risk levels; According to the fog area risk level, combine the road type, vehicle density and historical accident information, and select the corresponding induction information from a preset strategy library.

9. An integrated fog-measuring type highway safe driving induction device, which is used to implement an integrated fog-measuring type highway safe driving induction method as described in any one of claims 1-3, and is characterized in that, The highway safety driving induction device includes: A fog detection module configured to generate a multi-level power signal pulse train through a discrete time-sharing coding control rule, cyclically drive an infrared emitting tube, excite the infrared signal in the target area and receive the scattered signal in real time to calculate the local visibility data; A wireless communication terminal communicatively connected to the area controller for uploading local visibility data and receiving induction information; An induction execution module including an LED array with adjustable brightness and a direction indicator light for generating a dynamic light band or direction guidance according to the induction information sent by the area controller; An impact detection module configured with a vibration sensor and an embedded processor for collecting the vibration signal of the device body, judging whether it is an effective impact event through a time-frequency domain feature extraction algorithm, and generating event data including the collision position, time and impact intensity when it is determined to be an effective impact.

10. A regional controller, which is used to implement an integrated fog-measuring type highway safe driving induction method as described in any one of claims 4-8, and is characterized in that, The area controller includes: A data receiving module for receiving the local visibility data, temperature and humidity parameters, and impact event data uploaded by multiple highway safety driving induction devices; A processor configured to perform the following operations: Screen and calibrate anchor points according to historical visibility volatility and device reliability, construct a spatial gradient field based on the visibility data and temperature and humidity parameters of the anchor points, calibrate the visibility data of non-calibrated anchor point devices through a non-linear attenuation model and a weight fusion algorithm, and trigger a secondary infrared signal verification for the visibility abnormal area; Perform connectivity clustering analysis on the calibrated visibility data, fit the dynamic fog area boundary in combination with the real-time wind speed vector and road topology structure, and generate fog area information including the fog area range and visibility level; Calculate the risk coefficient according to the fog area visibility level, diffusion speed and road type, match the LED brightness, flashing frequency and direction guidance rules from a multi-modal strategy library, and synchronize them to the external traffic management system through a collaborative control interface.

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