Contact type road surface temperature monitoring system

Abstract

The invention provides a contact type road surface temperature monitoring system which comprises a plurality of contact type temperature sensors, each contact type temperature sensor comprises a first temperature monitoring module and a second temperature monitoring module, the first temperature monitoring modules are arranged at different depths of any position of a snowy road section, and the second temperature monitoring modules are arranged at different depths of any position of the snowy road section; the second temperature monitoring module is arranged at a position higher than the surface target distance of the snowy road section and is used for monitoring the environment temperature of the snowy position; the temperature prediction module is used for predicting the temperatures of different depths of a plurality of point locations of the snowy road section according to the internal temperature data, the pressure data and the environment temperature of different depths of a plurality of target positions of the snowy road section; and the predicted temperature display module is used for carrying out image processing on the temperatures of different depths of a plurality of point positions of the snowy road section and displaying a three-dimensional temperature image of the whole snowy road section. According to the invention, comprehensive and effective information can be provided for related personnel, so that potential hidden dangers can be judged in advance.

Description

Technical Field

[0001] The invention belongs to the technical field of temperature monitoring, and in particular relates to a contact type road surface temperature monitoring system. Background Art

[0002] In winter, icy and snowy roads are one of the main causes of frequent traffic accidents. Accurate monitoring of road surface temperature is crucial to preventing and responding to traffic safety risks brought by icy and snowy roads. Traditional road surface temperature monitoring methods are mostly point monitoring, which is difficult to fully reflect the distribution of road surface temperature, especially in sections with complex terrain and different road surface materials, where the temperature difference is large. Point monitoring cannot provide enough information to guide road management and maintenance. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a contact road surface temperature monitoring system to meet the needs of providing comprehensive and effective information to relevant personnel so as to prevent risks in advance.

[0004] In order to achieve the above object, the present invention provides the following technical solutions: The present invention provides a contact road surface temperature monitoring system, comprising: a plurality of contact temperature sensors arranged at a plurality of target positions of a snow-covered road section, each contact temperature sensor comprising a first temperature monitoring module and a second temperature monitoring module, the first temperature monitoring module comprising a first temperature sensing probe and a pressure monitor, the first temperature monitoring module being arranged at different depths at any position of the snow-covered road section, and being used for monitoring internal temperature and pressure data at different depths of the snow-covered road section, the second temperature monitoring module being a second temperature sensing probe, being arranged at a position higher than a target distance from the surface of the snow-covered road section, and being used for monitoring the ambient temperature at the snow-covered position; a temperature prediction module, being used for predicting the temperature at different depths of a plurality of points of the snow-covered road section according to the internal temperature data, pressure data and ambient temperature at different depths of a plurality of target positions of the snow-covered road section; and a predicted temperature display module, being used for performing image processing on the temperature at different depths of a plurality of points of the snow-covered road section, and displaying a three-dimensional temperature image of the entire snow-covered road section.

[0005] Optionally, the temperatures at different depths of multiple points on the snow-covered road section are predicted based on the internal temperature data, pressure data and ambient temperature at different depths of multiple target positions on the snow-covered road section, including: gridding the snow-covered road section, determining the grids where contact temperature sensors are not installed, and taking the center points in the grids as the points to be predicted; predicting the ambient temperatures of the points to be predicted based on a pre-trained first target neural network; determining the geothermal heat of the snow-covered road section based on a geothermal measurement method; determining the relationship between the pressure of the points to be predicted and the snow depth based on the locations of the points to be predicted and historical data; inputting the geothermal heat, ambient temperature and pressures corresponding to different depths into a pre-trained second target neural network to obtain the temperatures corresponding to different snow-covered depths of the road surface represented by the points to be predicted; wherein the pre-trained second target neural network is trained based on the internal temperature data, pressure data and ambient temperature at different depths of multiple target positions on the snow-covered road section.

[0006] Optionally, according to a pre-trained first target neural network, the ambient temperature of the point to be predicted is predicted, including: obtaining weather factors and ambient temperatures obtained by contact temperature sensors at multiple target positions; inputting the ambient temperature, weather factors and their corresponding target positions into the first target neural network; in the first target neural network, using the gradient descent method to adjust the network weights to minimize the loss function until the training is completed to obtain a trained first target neural network; inputting the location information of the point to be predicted and the weather factors into the trained first target neural network to obtain the ambient temperature of the point to be predicted.

[0007] Optionally, the second target neural network training process includes: taking the geothermal, snow temperature, pressure data, and ambient temperature obtained by the same contact temperature sensor as a data group, wherein the depth, ambient temperature, pressure, and geothermal in the data group are used as samples, and the internal temperature is used as a sample label; inputting multiple data groups into the second target neural network, adjusting the network weights using the gradient descent method, minimizing the overall loss function, until the training is completed, and obtaining a trained second target neural network, wherein the second target neural network is a physical information neural network, and its overall loss function is composed of the error terms of the predicted temperature and the actual temperature, the error terms of the constraints on the heat transfer relationship between snow, and the error terms of the constraints on the heat transfer relationship between snow and ground.

[0008] Optionally, the overall loss function includes: ; in, represents the overall loss function, are hyperparameters, representing the error term weight coefficients of the predicted temperature and the actual temperature, the error term weight coefficients of the snow-to-snow heat transfer constraint, and the error term weight coefficients of the snow-to-ground heat transfer constraint. represents the predicted internal temperature, The actual internal temperature, is the snow density, is the specific heat of snow, is the snow layer temperature, is the snow layer conductivity, z is the depth, t is the time, Indicates geothermal heat, The thermal conductivity of the ground.

[0009] Optionally, a contact road surface temperature monitoring system also includes: a laser radar scanning device for scanning the three-dimensional coordinates of snow on a snow-covered road section; performing image processing on the temperatures at different depths at multiple points on the snow-covered road section to display a three-dimensional temperature image of the entire snow-covered road section, including: obtaining the temperatures at different depths at multiple points, and binding the three-dimensional position of any point at a random depth to the temperature to form a data group, and multiple data groups constitute a data set; clustering multiple data groups in the data set to obtain multiple cluster centers; reading the temperature values ​​and distance values ​​of multiple adjacent cluster centers, and calculating the temperature change rate corresponding to the adjacent cluster centers; determining the temperature mark at the cluster center based on the temperature of the cluster center; adjusting the change rate of the temperature mark color based on the temperature mark at the cluster center and the temperature change rate; and color rendering each cluster area in the three-dimensional image of the snow-covered road section based on the position, temperature mark color, and temperature mark color change rate of each cluster center.

[0010] Optionally, the temperature values ​​and distance values ​​of multiple adjacent cluster centers are read, and the temperature change rate corresponding to the adjacent cluster centers is calculated, including: step S1, determining the temperature values ​​of multiple points on the line connecting the adjacent cluster centers according to the temperature difference corresponding to the adjacent cluster centers and the distance value of the adjacent cluster centers; step S2, taking the distance between each point and the corresponding cluster center as the radius to obtain each circular line; step S3, determining the temperature of all points in the first point set corresponding to each circular line and the temperature of all points in the second point set, wherein the first point set is composed of all points on the same circular line, and the second point set is composed of all points on the same circular line. The point set is composed of all points whose distance from the circumference is less than a preset value; step S4, determine whether the temperature difference of all points in the first point set and the second point set does not exceed the preset threshold, if yes, execute step S5, if not, execute step S6; step S5, solve the average value of the temperature of all points as the temperature value of each point on the line connecting adjacent cluster centers, and determine the rate of change of the temperature value according to the temperature value of the point and the distance between the two points; step S6, mark the point exceeding the preset threshold as a new cluster center, perform clustering, and then repeat steps S1-step S4.

[0011] Optionally, based on the position of the point to be predicted and historical data, the relationship between the pressure of the point to be predicted and the snow depth is determined, including: dividing the snow-covered road section into a trampled part and a non-trampled part according to whether it is trampled; for the trampled part, according to the movement trajectory of pedestrians or vehicles in the historical data, the trampled part is divided into trampled areas of different levels, and a plurality of points are randomly selected in each trampled area, and the pressures corresponding to different depths of the plurality of points are sampled, and the pressures corresponding to different depths of the plurality of points are linearly fitted to obtain a pressure-depth fitting curve of the corresponding area; for the non-trampled part, the point pressure value is solved according to the relationship between snow pressure, snow density and snow depth.

[0012] The embodiment of the present invention provides a contact road surface temperature monitoring system, which obtains the ambient temperature at different locations and the temperature and pressure at different snow layer depths at different locations through multiple contact temperature sensors. The temperature prediction module performs comprehensive analysis based on the internal temperature and pressure data of the snow layer and multi-dimensional data such as the ambient temperature, and can more accurately predict the temperature change trend of multiple points at different depths in the snow-covered road section. Then, the visualization method of three-dimensional image display is intuitive and vivid, which allows road managers to clearly understand the temperature distribution of the snow-covered road section, timely discover temperature abnormal areas, and take corresponding measures. Compared with only monitoring the surface temperature of the snow layer, this solution predicts the change of the internal temperature of the snow layer through the data of multiple contact temperature sensors, and can provide comprehensive and effective information to the road section management personnel for them to judge potential safety hazards in advance. For example, when the internal temperature of the snow layer is close to or below the freezing point, the road surface is more likely to freeze, causing vehicles to slip and causing safety problems, so that measures can be taken in advance. In addition, in the snow-covered road section, according to the change of the road surface temperature, the amount and timing of the snow melting agent can be accurately controlled. For another example, during periods of low temperatures and high risk of icing, increase the amount of de-icing agent applied; and when the temperature rises and the ice and snow begin to melt, reduce the use of de-icing agents. This can effectively melt snow and remove ice while avoiding waste of resources. Finally, by monitoring the internal temperature of the snow layer, we can understand the insulation effect of the snow and evaluate its impact on the operating temperature of facilities and equipment. When the temperature inside the snow layer rises, the snow may melt, causing the facilities and equipment to be soaked in water, increasing the risk of equipment damage. By monitoring the internal temperature of the snow layer, we can detect the melting of snow in a timely manner and take corresponding protective measures.

[0013] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art may be taught from the practice of the present invention. The objectives and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration: Figure 1 A specific example diagram of a contact-type road surface temperature monitoring system in the present invention; Figure 2 This is a specific example flow chart of calculating the temperature change rate corresponding to adjacent cluster centers in the present invention. DETAILED DESCRIPTION

[0015] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, it can also be the internal connection of two components, it can be a wireless connection, or it can be a wired connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0017] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0018] The embodiment of the present invention provides a contact type road surface temperature monitoring system, such as Figure 1 As shown, including: A plurality of contact temperature sensors 101 are arranged at a plurality of target positions of the snow-covered road section, each of the contact temperature sensors comprises a first temperature monitoring module and a second temperature monitoring module, the first temperature monitoring module comprises a first temperature sensing probe and a pressure monitor, the first temperature monitoring module is arranged at different depths at any position of the snow-covered road section, and is used to monitor the internal temperature and pressure data of the snow-covered road section at different depths, the second temperature monitoring module is a second temperature sensing probe, which is arranged at a position higher than the target distance on the surface of the snow-covered road section, and is used to monitor the ambient temperature of the snow-covered position; The temperature prediction module 102 is used to predict the temperature at different depths of multiple points on the snow-covered road section based on the internal temperature data, pressure data, and ambient temperature at different depths of multiple target locations on the snow-covered road section; The predicted temperature display module 103 is used to perform image processing on the temperatures at different depths at multiple points on the snow-covered road section to display a three-dimensional temperature image of the entire snow-covered road section.

[0019] Exemplarily, a plurality of contact temperature sensors 101 can be arranged at different positions of a snow-covered road section, covering key areas of the entire road section. Since the temperature data inside the snow reflects the heat conduction of the snow layer, the ambient temperature data reflects the influence of the external heat source, and the pressure data is related to the density and thermal conductivity characteristics of the snow layer. Therefore, in this embodiment, each contact temperature sensor includes two modules, a first temperature monitoring module and a second temperature monitoring module. The first temperature monitoring module is used to penetrate into the snow layer to monitor the temperature and pressure at different snow layer depths, and the second temperature monitoring module is used to monitor the ambient temperature at the location of the contact temperature sensor. The second temperature monitoring module is retractably connected to the contact temperature sensor body, and the height of the second temperature monitoring module from the snow surface can be adjusted arbitrarily.

[0020] The temperature prediction module 102 predicts the temperature at different depths at multiple points on the snow-covered road section based on the internal temperature data, pressure data, and ambient temperature at different depths at multiple target locations on the snow-covered road section. Taking into account multi-dimensional data such as internal temperature, pressure, and ambient temperature, the heat conduction process and temperature change law of the snow layer can be simulated more accurately. Compared with the prediction model of a single factor, the method proposed in this embodiment can improve the accuracy and reliability of the prediction. Compared with using all sensors to obtain the internal temperature of the snow-covered road section, in this embodiment, the neural network can be trained to obtain the corresponding neural network model to complete the temperature prediction of different points and depths on the snow-covered road section, thereby reducing the number of hardware devices, improving the degree of intelligence, and reducing the cost of temperature monitoring.

[0021] The predicted temperature display module 103 performs image processing on the temperatures at different depths at multiple points on the snow-covered road section, and displays a three-dimensional temperature image of the entire snow-covered road section, so that the staff can check the overall snow layer temperature of the snow-covered road section.

[0022] The embodiment of the present invention provides a contact road surface temperature monitoring system, which obtains the ambient temperature at different locations and the temperature and pressure at different snow layer depths at different locations through multiple contact temperature sensors. The temperature prediction module performs comprehensive analysis based on the internal temperature and pressure data of the snow layer and multi-dimensional data such as the ambient temperature, and can more accurately predict the temperature change trend of multiple points at different depths in the snow-covered road section. Then, the visualization method of three-dimensional image display is intuitive and vivid, which allows road managers to clearly understand the temperature distribution of the snow-covered road section, timely discover temperature abnormal areas, and take corresponding measures. Compared with only monitoring the surface temperature of the snow layer, this solution predicts the change of the internal temperature of the snow layer through the data of multiple contact temperature sensors, and can provide effective information to the road section management personnel for them to judge potential safety hazards in advance. For example, when the internal temperature of the snow layer is close to or below the freezing point, the road surface is more likely to freeze, causing vehicles to slip and causing safety problems, so that measures can be taken in advance. In addition, in the snow-covered road section, according to the change of the road surface temperature, the amount and timing of the snow melting agent can be accurately controlled. For example, during periods of low temperatures and high risk of icing, increase the amount of de-icing agent applied; and when the temperature rises and the ice and snow begin to melt, reduce the use of de-icing agents. This can effectively melt snow and remove ice while avoiding waste of resources. Finally, by monitoring the internal temperature of the snow layer, we can understand the insulation effect of the snow and evaluate its impact on the operating temperature of facilities and equipment. When the temperature inside the snow layer rises, the snow may melt, causing the facilities and equipment to be soaked in water, increasing the risk of equipment damage. By monitoring the internal temperature of the snow layer, we can detect the melting of snow in time and take corresponding protective measures.

[0023] As an optional implementation, the temperature at different depths of multiple points on the snow-covered road section is predicted based on the internal temperature data, pressure data, and ambient temperature at different depths of multiple target locations on the snow-covered road section, including: Divide the snow-covered road section into a grid, determine the grid where no contact temperature sensor is installed, and take the center point in the grid as the point to be predicted; predict the ambient temperature of the point to be predicted based on the pre-trained first target neural network; determine the geothermal heat of the snow-covered road section based on the geothermal measurement method; determine the relationship between the pressure of the point to be predicted and the snow depth based on the location of the point to be predicted and historical data; input the geothermal heat, ambient temperature and pressure corresponding to different depths into the pre-trained second target neural network to obtain the temperature corresponding to different snow-covered depths of the road surface represented by the point to be predicted; wherein the pre-trained second target neural network is trained based on the internal temperature data, pressure data and ambient temperature of multiple target positions at different depths in the snow-covered road section.

[0024] Exemplarily, the contact temperature sensor may further include a positioning device to determine its longitude and latitude coordinates, and determine the grid in which it is located based on the longitude and latitude coordinates, thereby determining the grid where the contact temperature sensor is not set; in addition, when the contact temperature sensor does not include a positioning device, the grid where the contact temperature sensor is set can be manually marked to determine the grid where the contact temperature sensor is not set.

[0025] In this embodiment, in order to obtain the temperature corresponding to different snow depths of the road surface represented by the predicted point, first of all, the influence of ambient temperature on it is considered. In order to improve the intelligence and accuracy of the temperature prediction of different snow depths, this embodiment predicts the ambient temperature through the first target neural network. On the one hand, compared with directly using weather temperature to predict the temperature of different snow depths, the ambient temperature predicted by this method is the ambient temperature of a specific point. Compared with weather temperature, the mapping relationship between the ambient temperature of a specific point and the temperature of the snow layer is more accurate, so as to speed up the learning speed of the second target neural network and improve the accuracy of the prediction. On the other hand, the scope of snow-covered road sections is generally large. This method collects the ambient temperature of some locations and uses machine learning to predict the temperature of other points where contact temperature sensors are not laid. It can reduce the number of hardware equipment used and save costs. There is no need for staff to lay sensors on a large scale, which improves the intelligence of the overall prediction.

[0026] Specifically, the ambient temperature of the point to be predicted is predicted by training the first target neural network, and then the ambient temperature of the point to be predicted is used as one of the inputs of the second target neural network. Then, the pressure and depth are also considered as the input of the second target neural network to obtain the temperature of the point to be predicted with different snow cover depths on the road surface.

[0027] A specific implementation method for predicting the ambient temperature of a point to be predicted based on a pre-trained first target neural network includes: obtaining weather factors and ambient temperatures obtained by contact temperature sensors at multiple target locations; inputting the ambient temperature, weather factors and their corresponding target locations into the first target neural network; in the first target neural network, using the gradient descent method to adjust network weights and minimize the loss function until training is completed to obtain a trained first target neural network; inputting the location information of the point to be predicted and the weather factors into the trained first target neural network to obtain the ambient temperature of the point to be predicted.

[0028] The first target neural network includes an input layer, a hidden layer, and an output layer. The number of nodes in the input layer is consistent with the dimension of the number of positions, and the number of nodes in the output layer is one, which represents the predicted ambient temperature. Weather factors may include humidity, wind direction, etc., and weather factors may be determined based on weather forecast data. The acquired ambient temperature, weather factors, and their corresponding target positions are used as training data and input into the first target neural network. The weather factors and target positions are used as input features, and the ambient temperature is used as a label. The mean square error can be used as a loss function to calculate the difference between the predicted value and the actual value. During the training process, the gradient descent method can be used to optimize the weights and biases of the first target neural network, calculate the gradient of the loss function with respect to each weight, and then update the weights according to the learning rate, gradually reduce the loss function value, until the loss function converges, and complete the training. At this point, the trained first target neural network can learn the mapping relationship between the ambient temperature of the current location under the influence of different regions and different weather factors.

[0029] In order to further improve the accuracy of the prediction of the first target neural network, the first target neural network in this embodiment can also be a transfer learning network. Since the number of target positions and ambient temperatures as samples and sample labels in the current environment is small, the accuracy of the trained first target neural network may be low. Therefore, a neural network model pre-trained on a large-scale meteorological data set can be selected. The model has learned the complex patterns and characteristics of temperature changes in different locations and under different meteorological conditions. When the pre-trained model is applied to the current task, most of the parameters of the model can be frozen, especially the parameters of the early layers, because the features learned by these layers are universal in different tasks. Use a small amount of temperature data group model under the current task for fine-tuning. During the fine-tuning process, update some trainable parameters in the pre-trained model. Through fine-tuning, the model can better adapt to the data distribution and characteristics of the new task. The location information and weather factors of the point to be predicted are input into the trained first target neural network to obtain the ambient temperature of the point to be predicted.

[0030] Before predicting the temperature corresponding to different snow depths on the road surface represented by the predicted point, it is also necessary to determine the relationship between the pressure of the predicted point and the snow depth and the geothermal heat according to the location of the predicted point and historical data. Specifically, the snow-covered road section can be divided into a trampled part and a non-trampled part. For the trampled part, according to the movement trajectory of pedestrians or vehicles, the trampled part is divided into trampled areas of different levels, and multiple points are randomly selected in each area, and the pressures corresponding to different depths of multiple points are sampled. The pressures corresponding to different depths of multiple points are linearly fitted to obtain the pressure-depth fitting curve of the area. For the non-trampled part, the point pressure value is solved according to the relationship between snow pressure, snow density and snow depth. When it is necessary to determine the relationship between the pressure of the predicted point and the snow depth, first, according to the location of the predicted point, the pressure-depth relationship corresponding to the point is determined. For example, when the point belongs to a trampled area, the pre-fitted pressure-depth fitting curve of the trampled area is retrieved to obtain the pressures corresponding to different depths.

[0031] The geothermal determination method can be to select a representative deep well or geothermal well (hole) in the snow to measure the temperature in the hole (well). After obtaining the pressure value, ambient temperature value, and geothermal, the temperature corresponding to different snow depths of the road surface represented by the predicted point can be predicted. Specifically, the geothermal, ambient temperature, and pressure corresponding to different depths are input into the pre-trained second target neural network to obtain the temperature corresponding to different snow depths of the road surface represented by the predicted point.

[0032] The second target neural network training process includes: taking the geothermal, snow temperature, pressure data, and ambient temperature obtained by the same contact temperature sensor as a data group, wherein the depth, ambient temperature, pressure, and geothermal in the data group are taken as samples, and the internal temperature is taken as a sample label; inputting multiple data groups into the second target neural network, adjusting the network weights using the gradient descent method, minimizing the overall loss function, until the training is completed, and obtaining a trained second target neural network, wherein the second target neural network is a physical information neural network, and its overall loss function is composed of the error term of the predicted temperature and the actual temperature, the error term of the constraint of the heat transfer relationship between snow and the heat transfer relationship between snow and ground.

[0033] The overall loss function includes: ; in, represents the overall loss function, are hyperparameters, representing the error term weight coefficients of the predicted temperature and the actual temperature, the error term weight coefficients of the snow-to-snow heat transfer constraint, and the error term weight coefficients of the snow-to-ground heat transfer constraint. represents the predicted internal temperature, The actual internal temperature, is the snow density, is the specific heat of snow, is the snow layer temperature, is the snow layer conductivity, z is the depth, t is the time, Indicates geothermal heat, The thermal conductivity of the ground.

[0034] The embodiment of the present invention provides a contact road surface temperature monitoring system, which not only considers the main factors such as ambient temperature and geothermal heat in the prediction process, but also combines other influencing factors such as the relationship between the pressure of the predicted point and the snow depth. This multi-factor comprehensive prediction method can more comprehensively reflect the actual situation of the temperature change of the snow-covered road section and improve the accuracy and reliability of the prediction results.

[0035] As an optional implementation, a contact road surface temperature monitoring system further includes: a laser radar scanning device for scanning the three-dimensional coordinates of snow on the snow-covered road section; The temperatures at different depths at multiple points on a snow-covered road section are processed by image processing to display a three-dimensional temperature image of the entire snow-covered road section, including: obtaining the temperatures at different depths at multiple points, and binding the three-dimensional position of any point at a random depth with the temperature to form a data group, and multiple data groups constitute a data set; clustering the multiple data groups in the data set to obtain multiple cluster centers; reading the temperature values ​​and distance values ​​of multiple adjacent cluster centers, and calculating the temperature change rate corresponding to the adjacent cluster centers; determining the temperature mark at the cluster center according to the temperature of the cluster center; adjusting the change rate of the temperature mark color according to the temperature mark at the cluster center and the temperature change rate; and color rendering each cluster area in the three-dimensional image of the snow-covered road section according to the position, temperature mark color and temperature mark color change rate of each cluster center.

[0036] Exemplarily, the working principle of the laser radar scanning device is as follows: When the laser pulse encounters snow, it will be reflected back, and the laser radar receiver will capture these reflected signals. By measuring the time difference between the emission and reception of the laser pulse, combined with the propagation speed of the laser, the distance between the laser radar and the reflection point is calculated. At the same time, the angle of the laser emission is recorded. Based on the distance, angle, and the position and posture information of the laser radar device, the three-dimensional coordinates of each reflection point are calculated. After obtaining the three-dimensional coordinates of each reflection point, the digital model of the snow on the snow-covered road section is reconstructed based on the three-dimensional coordinates.

[0037] On the basis of the digital model of snow accumulation on the snow-covered road section being reconstructed, the temperature of multiple points on the snow-covered road section is randomly predicted, and the predicted point temperatures are filled and rendered on the digital model according to the display colors corresponding to the temperatures, thereby obtaining a three-dimensional temperature image of the entire snow-covered road section. Specifically, in order to avoid the problem of large amount of calculation and waste of computing resources caused by temperature prediction for each point on the snow-covered road section, this embodiment adopts the method of randomly selecting multiple points, predicting the temperature of the points, obtaining the temperature at different depths, and then randomly selecting any depth to determine its temperature, and binding the temperature to the three-dimensional coordinates of the randomly selected depth of the point to form a data set. , Indicates the three-dimensional position of the point, T represents the temperature, and then cluster analysis is performed based on the data group. The digital model is rendered according to the cluster analysis results. In this scheme, only some points need to be randomly selected for prediction to complete the rendering of the entire digital model.

[0038] The specific clustering method is as follows: After forming data groups, multiple data groups are clustered. The specific clustering method can be Clustering, before clustering, it is necessary to determine the number of clusters (i.e., the number of clusters). This can be achieved through the elbow rule, that is, calculating the total square error under different numbers of clusters, and selecting the point where the error begins to drop significantly as the number of clusters. Randomly select K points as the initial cluster centers, and measure the similarity between data groups in accordance with the distance metric. In this embodiment, the Euclidean distance can be used as the similarity measurement calculation formula. Specifically, the Euclidean distance formula can be: ; in, The weights of the distance difference and temperature difference between two data points on the similarity calculation are respectively represented and can be adjusted as needed. In this embodiment, , , and They represent different data groups respectively.

[0039] Each data group is assigned to the nearest cluster center to form K clusters, the centroid of each cluster is calculated, and the cluster center is updated to the centroid. The above assignment and update process is repeated until the cluster center no longer changes or the preset number of iterations is reached. According to the clustering results, the characteristics and distribution of different clusters are analyzed, and it can be found that data groups with similar temperatures and close distances are clustered together to form different temperature areas. Each temperature area has a cluster center. The temperature values ​​and distance values ​​of multiple adjacent cluster centers are read. The temperature change rate corresponding to the adjacent cluster centers is calculated by comparing the temperature difference with the distance value. At the same time, according to the temperature of the cluster center, the temperature and temperature identification color comparison table is searched to determine the temperature identification at the cluster center. In order to avoid single coloring of a single point each time, resulting in low coloring efficiency, this embodiment adjusts the change rate of the temperature identification color according to the temperature identification and temperature change rate at the cluster center, and then fills the temperature identification color at the cluster center position, and in the direction from the current cluster center to each other adjacent cluster center, the cluster area is filled and rendered according to the temperature identification color change rate. It can be understood that the cluster area is a closed space connected by the outermost data point positions. By connecting the cluster centers and solving the temperature change rate between the cluster centers, and filling the color according to the change rate between the cluster centers, the problem of sudden color changes and inability to connect between different cluster areas can be effectively avoided.

[0040] It should be explained in detail that in the direction extending from the current cluster center to each other adjacent cluster center, the specific process of color filling and rendering the cluster area according to the temperature identification color change rate is as follows: when there are multiple other adjacent cluster centers of the current cluster center, any two-dimensional slice of the cluster area is divided into multiple sub-areas by the line connecting the current cluster center and each adjacent cluster center, and a line is made in each sub-area, which divides the angle between the current cluster center and the two adjacent cluster centers equally, and the average value of the temperature identification color change rate corresponding to the line connecting the current cluster center and the two adjacent cluster centers is solved as the average value of the temperature identification color change rate of the line, and the entire sub-area is filled with color according to the mean value, and the above steps are repeated to obtain the coloring of multiple two-dimensional slices of the cluster area. When the number of two-dimensional slices filled with color meets the preset number, the remaining three-dimensional gaps in the cluster area are filled with color according to the color and distance of the corresponding positions of the two two-dimensional slices.

[0041] As an optional implementation, based on the above coloring process, in order to further improve the accuracy of coloring, this embodiment reads the temperature values ​​and distance values ​​of multiple adjacent cluster centers, and calculates the temperature change rate corresponding to the adjacent cluster centers, such as Figure 2 As shown, including: Step S1, determining the temperature values ​​of multiple points on the line connecting the adjacent cluster centers according to the temperature difference corresponding to the adjacent cluster centers and the distance value between the adjacent cluster centers; Step S2, taking the distance between each point and the corresponding cluster center as the radius to obtain each circular line; Step S3, determining the temperatures of all points in the first point set and the temperatures of all points in the second point set corresponding to each circumferential line, wherein the first point set is composed of all points on the same circumferential line, and the second point set is composed of all points whose distance from the circumferential line is less than a preset value; Step S4, determining whether the temperature differences of all points in the first point set and the second point set do not exceed a preset threshold value, if yes, executing step S5, if no, executing step S6; Step S5, solving the average value of the temperature of all points as the temperature value of each point on the line connecting the adjacent cluster centers, and determining the rate of change of the temperature value according to the temperature value of the point and the distance between two points; Step S6, marking the point exceeding the preset threshold as a new cluster center, performing clustering, and then repeating steps S1 to S4.

[0042] Exemplarily, based on the above coloring method, in order to further improve the accuracy and efficiency of coloring, this embodiment determines the temperature values ​​of multiple points on the line connecting adjacent cluster centers according to the temperature difference corresponding to the adjacent cluster centers and the distance value of the adjacent cluster centers, wherein the multiple points can be determined by dividing them with a preset fixed length or a fixed number of points, and the distance between the points is determined according to the ratio of the distance value to the number of points, thereby determining the position of each point, which is not limited in this embodiment; the distance between each point and the corresponding cluster center is used as the radius to obtain each circular line, and then, due to a cluster center There may be multiple adjacent cluster centers, and there may be multiple lines between the cluster center and each adjacent cluster center. Therefore, for any point on the line between the cluster center and any adjacent cluster center, there may be other points on the circumferential line corresponding to the circumferential line, and there may be other points near the circumferential line. Therefore, this embodiment obtains the temperature values ​​of all points on the same circumferential line and all points whose distance from the circumferential line is less than a preset value, and solves the average value as the temperature value of the point on the line connecting the cluster center and adjacent cluster centers, where the preset value may be 3 cm, and the size of the preset value may be set as needed, which is not limited in this embodiment.

[0043] According to the temperature value of the point and the distance between the two points, the rate of change of the temperature value is determined. When the temperature difference of any point in the first point set and the second point set exceeds the preset threshold, the point exceeding the preset threshold is marked as a new cluster center, clustering is performed, and then the above steps are repeated until the temperature change rate corresponding to all adjacent cluster centers is obtained.

[0044] An embodiment of the present invention provides a contact road surface temperature monitoring system, which determines the temperature values ​​of multiple points on the line connecting adjacent cluster centers by calculating the temperature difference corresponding to adjacent cluster centers and the distance value of adjacent cluster centers, and solves the average value of the points on the same circular line and the nearby points as the temperature value of the point on the line, and uses the difference between the temperature value determined at the point and the adjacent temperature value as an element for solving the temperature change rate. Compared with directly determining the temperature change rate based on the temperature difference between two cluster centers, this solution can further visualize the subtle changes in temperature between cluster centers, so that the displayed three-dimensional image of the snow-covered road section is more accurate.

[0045] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A contact road surface temperature monitoring system, characterized in that: include: A plurality of contact temperature sensors are arranged at a plurality of target positions of a snow-covered road section, each of the contact temperature sensors comprises a first temperature monitoring module and a second temperature monitoring module, the first temperature monitoring module comprises a first temperature sensing probe and a pressure monitor, the first temperature monitoring module is arranged at different depths at any position of the snow-covered road section, and is used to monitor the internal temperature and pressure data of different depths of the snow-covered road section, the second temperature monitoring module is a second temperature sensing probe, which is arranged at a position higher than the target distance on the surface of the snow-covered road section, and is used to monitor the ambient temperature of the snow-covered position; A temperature prediction module is used to predict the temperature at different depths of multiple points on the snow-covered road section based on the internal temperature data, pressure data, and ambient temperature at different depths of multiple target locations on the snow-covered road section; The predicted temperature display module is used to perform image processing on the temperatures at different depths at multiple points on the snow-covered road section and display a three-dimensional temperature image of the entire snow-covered road section.

2. A contact-type road surface temperature monitoring system according to claim 1, characterized in that: Based on the internal temperature data, pressure data, and ambient temperature at different depths of multiple target locations on the snow-covered road section, the temperature at different depths of multiple points on the snow-covered road section is predicted, including: Divide the snow-covered road section into grids, determine the grids where no contact temperature sensors are installed, and take the center point in the grid as the point to be predicted; Predicting the ambient temperature of the point to be predicted based on the pre-trained first target neural network; Determine the geothermal heat of the snow-covered road section based on geothermal measurement methods; According to the location of the point to be predicted and historical data, the relationship between the pressure of the point to be predicted and the snow depth is determined; The geothermal heat, ambient temperature and pressures corresponding to different depths are input into a pre-trained second target neural network to obtain the temperatures corresponding to different snow depths of the road surface represented by the predicted point; wherein the pre-trained second target neural network is trained based on the internal temperature data, pressure data and ambient temperature at different depths of multiple target positions on the snow-covered road section.

3. A contact type road surface temperature monitoring system according to claim 2, characterized in that: According to the pre-trained first target neural network, the ambient temperature of the point to be predicted is predicted, including: Obtain weather factors and ambient temperature obtained by contact temperature sensors at multiple target locations; Inputting ambient temperature, weather factors and their corresponding target positions into a first target neural network; In the first target neural network, the gradient descent method is used to adjust the network weights and minimize the loss function until the training is completed to obtain the trained first target neural network; The location information and weather factors of the point to be predicted are input into the trained first target neural network to obtain the ambient temperature of the point to be predicted.

4. A contact type road surface temperature monitoring system according to claim 2, characterized in that: The second objective neural network training process includes: The ground heat, snow temperature, pressure data, and ambient temperature obtained by the same contact temperature sensor are taken as a data group, wherein the depth, ambient temperature, pressure, and ground heat in the data group are taken as samples, and the internal temperature is taken as a sample label; Multiple data groups are input into the second target neural network, and the network weights are adjusted using the gradient descent method to minimize the overall loss function until the training is completed, thereby obtaining a trained second target neural network. The second target neural network is a physical information neural network, and its overall loss function consists of the error terms of the predicted temperature and the actual temperature, the error terms of the constraints on the heat transfer relationship between snow, and the error terms of the constraints on the heat transfer relationship between snow and ground.

5. A contact type road surface temperature monitoring system according to claim 4, characterized in that: The overall loss function includes: ; in, represents the overall loss function, are hyperparameters, representing the error term weight coefficients of the predicted temperature and the actual temperature, the error term weight coefficients of the snow-to-snow heat transfer constraint, and the error term weight coefficients of the snow-to-ground heat transfer constraint. represents the predicted internal temperature, The actual internal temperature, is the snow density, is the specific heat of snow, is the snow layer temperature, is the snow layer conductivity, z is the depth, t is the time, Indicates geothermal heat, The thermal conductivity of the ground.

6. A contact-type road surface temperature monitoring system according to any one of claims 1 to 5, characterized in that: Also includes: LiDAR scanning equipment, used to scan the three-dimensional coordinates of snow on snow-covered road sections; Perform image processing on the temperature at different depths of multiple points on the snow-covered road section to display the three-dimensional temperature image of the entire snow-covered road section, including: Obtain the temperature of multiple points at different depths, and bind the three-dimensional position of any point at random depth to the temperature to form a data group. Multiple data groups form a data set. Cluster multiple data groups in the data set to obtain multiple cluster centers; Read the temperature values ​​and distance values ​​of multiple adjacent cluster centers, and calculate the temperature change rates corresponding to the adjacent cluster centers; According to the temperature of the cluster center, determine the temperature mark at the cluster center; According to the temperature mark at the cluster center and the temperature change rate, the change rate of the temperature mark color is adjusted; According to the position of each cluster center, the temperature identification color and the temperature identification color change rate, each cluster area in the three-dimensional image of the snow-covered road section is rendered in color.

7. A contact-type road surface temperature monitoring system according to claim 6, characterized in that: Read the temperature values ​​and distance values ​​of multiple adjacent cluster centers, and calculate the temperature change rate corresponding to the adjacent cluster centers, including: Step S1, determining the temperature values ​​of multiple points on the line connecting the adjacent cluster centers according to the temperature difference corresponding to the adjacent cluster centers and the distance value between the adjacent cluster centers; Step S2, taking the distance between each point and the corresponding cluster center as the radius to obtain each circular line; Step S3, determining the temperatures of all points in the first point set and the temperatures of all points in the second point set corresponding to each circumferential line, wherein the first point set is composed of all points on the same circumferential line, and the second point set is composed of all points whose distance from the circumferential line is less than a preset value; Step S4, determining whether the temperature differences of all points in the first point set and the second point set do not exceed a preset threshold value, if yes, executing step S5, if no, executing step S6; Step S5, solving the average value of the temperature of all points as the temperature value of each point on the line connecting the adjacent cluster centers, and determining the rate of change of the temperature value according to the temperature value of the point and the distance between two points; Step S6, marking the point exceeding the preset threshold as a new cluster center, performing clustering, and then repeating steps S1 to S4.

8. A contact type road surface temperature monitoring system according to claim 2, characterized in that: According to the location of the predicted point and historical data, the relationship between the pressure of the predicted point and the snow depth is determined, including: Divide the snow-covered road section into a trampled part and a non-trampled part according to whether it is trampled or not; For the trampled part, according to the movement trajectory of pedestrians or vehicles in the historical data, the trampled part is divided into trampled areas of different levels, and multiple points are randomly selected in each trampled area, and the pressures corresponding to different depths of the multiple points are sampled. The pressures corresponding to different depths of the multiple points are linearly fitted to obtain the pressure-depth fitting curve of the corresponding area; For the non-trampled part, the point pressure value is solved based on the relationship between snow pressure, snow density and snow depth.

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