Snow removal method, device, computer equipment and storage medium suitable for bridge cables

Abstract

The present application relates to the field of snow removal on bridges, and discloses a snow removal method, device, computer equipment and storage medium suitable for bridge cables, which are applied to a snow removal monitoring platform, wherein a snow removal device is installed on the bridge cable, and the snow removal device is connected to the snow removal monitoring platform in communication. Among them, firstly, the snow accumulation state on the bridge cable, the traffic flow data of the target bridge where the bridge cable is located, and the weather data of the geographical area to which the target bridge belongs are obtained; the position to be snowed on the bridge cable and the target operating parameters of the snow removal device are determined according to the snow accumulation state; the operating period of the snow removal device is determined according to the traffic flow data, and the start time and / or end time of the snow removal device is adjusted according to the snowing time period corresponding to the weather data to determine the target operating period, and finally the snow removal device is instructed to remove snow from the bridge cable according to the target operating parameters during the target operating period. At this point, a snow removal method is realized that remotely monitors the snow accumulation state of the bridge cable and controls the snow removal device in real time to carry out intelligent and automatic snow removal.

Description

Technical Field

[0001] The present application relates to the field of snow removal for bridges, and in particular to a snow removal method, device, computer equipment and storage medium suitable for bridge cables. Background Art

[0002] Bridges usually cross rivers, lakes or roads and are exposed to changing environmental conditions. When it rains or snows, snow will accumulate on the bridges. The accumulated snow will generate additional loads on the bridge structure, affecting the stability and bearing capacity of the bridge. However, the methods for snow removal on bridge cables in the related art have limitations, and a snow removal method suitable for bridge cables is needed. Summary of the invention

[0003] The present application aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the present application proposes a snow removal method, device, computer equipment and storage medium suitable for bridge cables. The main technical solutions adopted in the present application include:

[0004] In a first aspect, an embodiment of the present application provides a snow removal method suitable for bridge cables, which is applied to a snow removal monitoring platform, wherein a snow removal device is installed on the bridge cable, and the snow removal device is communicatively connected to the snow removal monitoring platform; the method comprises: obtaining the snow accumulation state on the bridge cable, the traffic flow data of the target bridge where the bridge cable is located, and the weather data of the geographical area to which the target bridge belongs; wherein the weather data is used to describe the snowing time period when snowy weather occurs in the geographical area; determining the position to be snowed on the bridge cable according to the snow accumulation state, and the target operating parameters of the snow removal device for snow removal at the position to be snowed; determining the initial operating time period of the snow removal device according to the traffic flow data; wherein the traffic flow data within the initial operating time period does not reach a preset traffic flow threshold; adjusting the start time and / or end time of the initial operating time period using the snowing time period corresponding to the weather data to determine the target operating time period; sending the target operating parameters and the target operating time period to the snow removal device to instruct the snow removal device to remove snow from the bridge cable according to the target operating parameters during the target operating time period.

[0005] Optionally, the snow state includes the thickness of snow; the thickness of snow on the bridge cables is obtained in the following manner: an image of snow on the bridge cables is obtained, and the snow image is segmented and feature projected to obtain a first-stage feature map; the first-stage feature map is downsampled to obtain a first downsampled feature map, and feature extraction and feature fusion are performed based on the first downsampled feature map to obtain a second-stage feature map; the second-stage feature map is downsampled to obtain a second downsampled feature map, and feature extraction and feature fusion are performed based on the second downsampled feature map to obtain a third-stage feature map; the third-stage feature map is downsampled to obtain a third downsampled feature map, and feature extraction and feature fusion are performed based on the third downsampled feature map to obtain a fourth-stage feature map; classification processing is performed based on the fourth-stage feature map to obtain the thickness of snow on the bridge cables.

[0006] Optionally, the target operating parameters include a target speed parameter; the target speed parameter is determined by: determining a current speed parameter corresponding to a current operating state of the snow removal device; judging whether to adjust the current speed parameter based on the thickness of snow on the bridge cable and the location of snow to be removed, so as to determine the target speed parameter.

[0007] Optionally, the snow removal device includes a heating element, and the target operating parameters include a target heating mode of the heating element; the target heating mode is determined by searching in the heating relationship data according to the thickness of the snow, and obtaining the target heating mode corresponding to the thickness of the snow; wherein the heating relationship data is used to describe the correspondence between the thickness of the snow on the bridge cable and the heating operation mode.

[0008] Optionally, the snow accumulation state includes snow accumulation shape; the snow accumulation shape on the bridge cable is obtained by: obtaining air humidity and weather temperature of a geographical area; determining the snow accumulation shape according to the air humidity and weather temperature; wherein the snow accumulation shape is used to describe the hardness and adhesion of the snow on the bridge cable.

[0009] Optionally, the target operating parameters include a target force parameter; the target force parameter is determined by: determining a current force parameter corresponding to a current operating state of the snow removal device; judging whether to adjust the current force parameter based on the hardness and adhesion of the snow on the bridge cable to determine the target force parameter.

[0010] Optionally, there are multiple snow removal devices, and the snow removal monitoring platform is connected to each snow removal device in real-time communication; the method also includes: receiving operation effect data sent by each snow removal device in real time during the snow removal operation; and dynamically adjusting the target operation parameters of each snow removal device in real time according to the operation effect data, so as to coordinate each snow removal device to operate under operation parameters that match the real-time snow accumulation status on the bridge cables.

[0011] In a second aspect, an embodiment of the present application provides a snow removal device suitable for a bridge cable, which is applied to a snow removal monitoring platform and installed on the bridge cable. The snow removal device is communicatively connected to the snow removal monitoring platform; the device includes:

[0012] A data acquisition module is used to acquire the snow accumulation state on the bridge cable, the traffic flow data of the target bridge where the bridge cable is located, and the weather data of the geographical area to which the target bridge belongs; wherein the weather data is used to describe the snowing time period when snowing occurs in the geographical area;

[0013] A parameter determination module, used to determine the position to be snow-removed on the bridge cable according to the snow accumulation state, and the target operation parameters of the snow removal device for snow-removing the position to be snow-removed;

[0014] An initial operation period determination module is used to determine an initial operation period of the snow removal device according to the vehicle flow data; wherein the vehicle flow data in the initial operation period does not reach a preset vehicle flow threshold;

[0015] A target operation period determination module is used to adjust the start time and / or end time of the initial operation period by using the snowing period corresponding to the weather data to determine the target operation period;

[0016] The snow removal execution module is used to send target operation parameters and target operation time period to the snow removal device to instruct the snow removal device to remove snow from the bridge cables according to the target operation parameters during the target operation period.

[0017] In a third aspect, the present application further provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0018] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when the computer program is executed by a processor.

[0019] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which implements the steps of any of the above methods when executed by a processor.

[0020] In the above embodiment, the snow accumulation state on the bridge cable, the traffic flow data of the target bridge where the bridge cable is located, and the weather data of the geographical area to which the target bridge belongs are first obtained to determine the position on the bridge cable to be snowed off, as well as the target operating parameters of the snow removal device. Secondly, the operating time period of the snow removal device is determined according to the traffic flow data, and it is adjusted based on the weather data. Furthermore, the target operating parameters and target operating time period are sent to the snow removal device to instruct the snow removal device to remove snow from the bridge cable according to the target operating parameters during the target operating time period. Finally, a snow removal method is implemented that remotely monitors the snow accumulation state of the bridge cable with high precision and controls the snow removal device in real time to carry out intelligent and automatic snow removal. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 A flow chart of a snow removal method suitable for bridge cables according to one embodiment of the present application;

[0023] Figure 2a A flowchart of a method for obtaining snow thickness according to an embodiment of the present application;

[0024] Figure 2b A structural block diagram of a network model provided according to an embodiment of the present application;

[0025] Figure 3 A flow chart of a method for determining a speed parameter according to an embodiment of the present application;

[0026] Figure 4 A flowchart of a method for obtaining snow morphology according to an embodiment of the present application;

[0027] Figure 5 A flow chart of a method for determining a force parameter according to an embodiment of the present application;

[0028] Figure 6 A flowchart of real-time adjustment of operation parameters according to an embodiment of the present application;

[0029] Figure 7 A structural block diagram of a snow removal device suitable for bridge cables provided according to an embodiment of the present application;

[0030] Figure 8 The figure is a diagram of the internal structure of a computer device according to one embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0032] Bridges usually cross rivers, lakes or roads and are exposed to changing environmental conditions. When it rains or snows, snow will accumulate on the bridge. The snow will generate additional loads on the bridge structure, affecting the stability and bearing capacity of the bridge. Weather conditions such as temperature, humidity, wind speed and wind direction will affect the formation and distribution of snow, making the snow situation more complex and unpredictable. Due to the complexity and dynamics of the snow accumulation on the bridge, how to accurately monitor the location, thickness and distribution of snow, as well as the changes in the load on the bridge cables, is a key problem that the snow removal device needs to solve. Most of the bridge snow removal methods in the related art rely on manual or simple mechanical equipment, the environment is complex and diverse, the efficiency is low, and there are safety hazards. Specifically, although manual snow removal is flexible, it is inefficient and there are safety hazards; although mechanical snow removal improves efficiency, it is often limited by weather conditions and working environment, and it is difficult to achieve accurate and automated snow removal operations. In the related art related to automated monitoring and snow removal technology, the monitoring technology often only focuses on the situation of the snow itself, but ignores the changes in the load on the bridge cables, which may lead to insufficient assessment of the safety of the bridge. At the same time, the efficiency and automation of snow removal need to be improved. Although some automatic snow removal systems have been applied, they are often unable to cope with complex and dynamic snow accumulation due to their technical limitations and interference from environmental factors. In addition, these systems usually require manual intervention and adjustment and cannot achieve fully automated operation, which limits their application scope in snow removal operations. At the same time, although the bridge snow removal robot based on machine vision has achieved automated snow removal to a certain extent, it still needs to be improved in terms of accurate monitoring, environmental adaptability and safety.

[0033] In summary, existing technologies have made some progress in bridge snow monitoring and automatic snow removal, but there are still many problems and challenges.

[0034] Based on this, according to an embodiment of the present application, an embodiment of a snow removal method suitable for bridge cables is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0035] In this embodiment, a snow removal method suitable for bridge cables is provided. The method is applied to a snow removal monitoring platform. A snow removal device is installed on the bridge cables, and the snow removal device is communicatively connected with the snow removal monitoring platform. Figure 1 is a flow chart of a snow removal method suitable for bridge cables according to an embodiment of the present application, such as Figure 1 As shown, the process includes the following steps:

[0036] S110, obtaining the snow accumulation status on the bridge cables, the traffic flow data of the target bridge where the bridge cables are located, and the weather data of the geographical area to which the target bridge belongs.

[0037] The snow state can be data used to reflect the specific conditions of snow accumulation at various locations on the bridge deck and bridge cables, reflecting parameters such as the thickness, distribution, shape and density of the snow. For example, a video or picture can be used to extract the snow state. Specifically, the snow removal device is equipped with a camera, which can be used to obtain real-time images of the bridge cables and the bridge deck. After obtaining the real-time image data, it is further analyzed using computer vision technology to obtain the snow state on the bridge cables.

[0038] Traffic flow data can be data used to reflect the number and frequency of vehicles passing through the target bridge at different times. For example, if the video or traffic flow information obtained by the traffic monitoring camera or induction coil on the bridge finds that the target bridge has a traffic peak period from 7 to 9 in the morning and from 5 to 7 in the afternoon, these two periods can be used as traffic flow data for subsequent analysis.

[0039] Weather data may refer to meteorological information data of the geographical area where the target bridge is located. For example, weather data may be data used to describe the snowing period when snowing occurs in the geographical area, such as snowfall, snowfall start and end time, etc.; weather data may also be data related to windy weather or rainy weather, such as wind level, wind direction, and rainfall, etc. Specifically, the snow removal monitoring platform may obtain real-time and predicted weather data from the meteorological station; it may also monitor snowfall conditions and predict snowfall trends through image information; it may also obtain weather data through various weather service providers.

[0040] S120, determining the position on the bridge cable to be cleared of snow according to the snow accumulation state, and the target operation parameters of the snow removal device for clearing snow from the position to be cleared of snow.

[0041] Among them, the position to be snow-removed may refer to a specific area on the bridge cable where snow removal operations are required. For example, it may be a position with thicker snow, or it may be a key position where snow needs to be cleared first to ensure the safety of the bridge and smooth traffic. It is understandable that since the snow accumulation conditions at each position to be snow-removed are not exactly the same, the snow removal device has its corresponding target operating parameters for snow removal for each position to be snow-removed. Among them, the target operating parameters may refer to a series of operating parameters that the snow removal device needs to follow when performing snow removal operations, which may include, for example, the speed, strength, mode and direction of snow removal.

[0042] Specifically, the positions on the bridge cables to be cleared of snow can be determined by analyzing the images captured by the camera on the snow removal device and using computer vision technology to analyze and identify the snow accumulation areas and different snow accumulation conditions in the snow accumulation areas in the images, so as to further determine the positions to be cleared of snow. The data sent back by the sensors on the snow removal device can also be used to further determine the areas with severe snow accumulation and use them as the positions to be cleared of snow.

[0043] Furthermore, for each snow removal location, based on its snow accumulation state, the target operating parameters for snow removal by the snow removal device are determined accordingly. Specifically, the snow accumulation characteristics of the snow removal location can be analyzed to determine the optimal target operating parameters. For example, if at a certain position of the bridge cable, after analyzing the snow accumulation state, it is determined that there is heavy snow accumulation and the snow is thick or hard, then the operating parameters can be greater snow shoveling force, faster snow clearing speed, slower moving speed, etc.

[0044] S130: Determine an initial operation period of the snow removal device according to the traffic flow data.

[0045] Among them, the initial operation period may refer to the time period for snow removal operations that is preliminarily determined in a day. Exemplarily, 5:00 to 7:00 in the morning can be taken as the initial operation period of the snow removal device. Specifically, the initial operation period can be selected based on the traffic flow data, so that snow removal operations are performed when the traffic flow data does not reach the preset traffic flow threshold, thereby reducing the impact on traffic flow. Among them, the preset traffic flow threshold can be a value used to quantitatively describe the traffic flow on the bridge deck. Exemplarily, a lower traffic flow value can be set as the preset traffic flow threshold. That is, if the traffic flow value detected in a certain period of a day does not exceed the preset traffic flow threshold, this period can be used as the initial operation period of the snow removal device.

[0046] S140. Use the snowing time period corresponding to the weather data to adjust the start time and / or end time of the initial operation period to determine the target operation period.

[0047] Among them, the target operation period may refer to the snow removal operation period of the snow removal device that is finally determined after adjustment. It is understandable that the target operation period takes into account the traffic flow data and weather data (especially the snowing period) so as to ensure that the snow removal operation is carried out when the traffic impact is minimal and the snow removal effect is optimal. Specifically, after the initial operation period is determined based on the traffic flow data, the initial operation period is further adjusted based on the acquired weather data to determine the optimal target operation period for the snow removal device operation. Exemplarily, if by analyzing the historical traffic flow data, it is found that the target bridge is the peak traffic flow period from 7 to 9 in the morning and from 5 to 7 in the afternoon, then these two time periods can be used as traffic flow data, and the initial operation period is determined to be from 5 to 6 in the morning in combination with the analysis of the snow accumulation state. Further, if the weather data shows that the snowfall on that day is large, it may be necessary to adjust the start time and / or end time of the initial operation period. Exemplarily, the start time of the initial operation period can be advanced, and the snow removal operation can be started from 4 o'clock, and the target operation period is from 4 to 6 in the morning. Similarly, the end time of the initial operation period can be postponed to 7 o'clock to end the snow removal operation. In this case, the target operation period is from 5 o'clock to 7 o'clock in the morning.

[0048] S150. Sending target operation parameters and target operation period to the snow removal device to instruct the snow removal device to remove snow from the bridge cables according to the target operation parameters during the target operation period.

[0049] Specifically, since the snow removal device is connected to the snow removal monitoring platform through communication, the target operation parameters and target operation time period can be sent to the control system of the snow removal device through wireless communication technology. Furthermore, the control system of the snow removal device will control the snow removal device to carry out snow removal operations according to the received signals of the target operation parameters and target operation time period.

[0050] Optionally, during the snow removal operation, the snow removal device is provided with a real-time adaptive control function, that is, the control system of the snow removal device can automatically adjust the operation parameters according to real-time feedback (such as changes in snow thickness and evaluation of the operation effect, etc.). Furthermore, after the snow removal device completes the snow removal operation, a feedback learning function is provided, that is, the snow state data after the snow removal operation is collected through the sensors and cameras on the snow removal device, and the snow state data before the snow removal operation is analyzed and evaluated to optimize the future snow removal operation parameters and snow removal operation period.

[0051] In the above implementation, the snow accumulation state on the bridge cable, the traffic flow data of the target bridge where the bridge cable is located, and the weather data of the geographical area to which the target bridge belongs are first obtained to determine the position on the bridge cable to be snowed off, as well as the target operating parameters of the snow removal device. Secondly, the operating time period of the snow removal device is determined according to the traffic flow data, and it is adjusted based on the weather data. Furthermore, the target operating parameters and target operating time period are sent to the snow removal device to instruct the snow removal device to remove snow from the bridge cable according to the target operating parameters during the target operating time period. Finally, a snow removal method is implemented that remotely monitors the snow accumulation state of the bridge cable with high precision and controls the snow removal device in real time to carry out intelligent and automatic snow removal.

[0052] In some embodiments, the snow state includes snow thickness. Figure 2a , the thickness of snow on the bridge cables can be obtained by:

[0053] S210, obtaining an image of snow on the bridge cables, segmenting and feature projecting the snow image, and obtaining a first-stage feature map.

[0054] The first stage feature map may refer to a feature map that is slightly higher than the spatial dimension of the primary snow image and contains the basic attributes of the snow state. For example, a network model based on Swin-Transformer may be used for implementation. Figure 2b The Swin-Transformer network model includes an image segmentation layer, a linear embedding layer, a residual window attention layer, a normalization layer, an average pooling layer, and a fully connected layer. Optionally, there can be an image block merging layer (Patch Merging Layer) between the linear embedding layer and the residual window attention layer to merge adjacent image blocks.

[0055] For example, the original snow RGB image is collected in real time by the camera, and the image segmentation layer in the Swin-Transformer network model is used to divide the input original snow RGB image into multiple 4x4 non-overlapping patches. The feature dimension of each patch is 4x4x3=48. Furthermore, the features are projected into any first dimension through the linear embedding layer to obtain the first stage feature map.

[0056] S220, down-sample the first-stage feature map to obtain a first down-sampled feature map, perform feature extraction and feature fusion based on the first down-sampled feature map, and obtain a second-stage feature map.

[0057] Among them, the first down-sampled feature map may refer to a feature map whose spatial dimension is smaller than the feature map of the first stage. Similarly, it can be implemented using a network model based on Swin-Transformer. Exemplarily, there may be an image block merging layer between the linear embedding layer and the residual window attention layer, and downsampling processing can be performed through the image block merging layer in the Swin-Transformer network model, thereby obtaining a first down-sampled feature map with halved width and height and doubled number of channels.

[0058] Furthermore, based on the Residual Swin Transformer Block (RSTB) in the residual window attention layer, the features of the region of interest are extracted from the image block through the self-attention mechanism, and then the features before and after processing are fused through the residual connection, finally obtaining a more accurate second-stage feature map.

[0059] S230, down-sampling the second stage feature map to obtain a second down-sampled feature map, and performing feature extraction and feature fusion based on the second down-sampled feature map to obtain a third stage feature map.

[0060] Specifically, after the residual window attention layer, for the more accurate second-stage feature map, the linear embedding layer can also be used for feature projection, and the image block merging layer included in the Swin-Transformer network model can be downsampled again to obtain a second downsampled feature map with halved width and height and doubled number of channels.

[0061] Furthermore, based on the Residual Swin Transformer Block (RSTB) in the residual window attention layer, the features of the region of interest are extracted from the image block through the self-attention mechanism, and then the features before and after processing are fused through the residual connection to obtain the third-stage feature map.

[0062] S240, down-sample the third stage feature map to obtain a third down-sampled feature map, perform feature extraction and feature fusion based on the third down-sampled feature map, and obtain a fourth stage feature map.

[0063] Similarly, after the residual window attention layer, the linear embedding layer can also be used for feature projection for the third stage feature map, and downsampled again through the image block merging layer included in the Swin-Transformer network model, so as to obtain the third downsampled feature map with halved width and height and doubled number of channels.

[0064] Furthermore, based on the Residual Swin Transformer Block (RSTB) in the residual window attention layer, the features of the region of interest are extracted from the image block through the self-attention mechanism, and then the features before and after processing are fused through the residual connection to obtain the feature map of the fourth stage.

[0065] S250: Classify and process the characteristic graph of the fourth stage to obtain the thickness of snow on the bridge cables.

[0066] The snow thickness may be different thickness categories used to describe the snow state. For example, when the snow thickness exceeds 20 cm, it may be considered that the snow thickness at this time belongs to heavy snow.

[0067] Specifically, based on the fourth stage feature map, a classifier is used to classify the features and finally output the prediction result of snow thickness. Figure 2b , a Swin-Transformer network model can be trained with multiple different snow thickness data in advance. After obtaining the fourth-stage feature map, the normalization layer in the Swin-Transformer network model is used to normalize the features, and the average pooling layer is used to further reduce the spatial dimension of the fourth-stage feature map while keeping the number of channels unchanged. Finally, the category of snow thickness on the bridge cable is output through the fully connected layer in the Swin-Transformer network model.

[0068] In the above implementation, by repeatedly performing the feature processing of projection, downsampling, fusion, and residual self-attention conversion, the different levels of features in the image are gradually extracted and integrated, and finally the categories of different snow thicknesses contained in the image are identified. So far, through the above steps, the purpose of automatically extracting snow features from images and accurately predicting snow thickness is achieved, providing important decision-making data support for snow removal operations.

[0069] In some embodiments, the target operating parameters include target speed parameters. Figure 3 , the target speed parameters are determined by:

[0070] S310: Determine a current speed parameter corresponding to the current working state of the snow removal device.

[0071] Among them, the target speed parameter can be used to refer to the ideal speed that needs to be achieved when performing snow removal operations at different snow removal locations in order to achieve the best snow removal effect and efficiency. It should be understood that since the snow removal device will also monitor the surrounding snow accumulation status in real time during the snow removal operation, the current working state of the snow removal device refers to the operating status of the snow removal device at a certain moment, including its position and operating mode. Specifically, the current working state reflects the real-time performance and operating conditions of the snow removal device. Unlike the target speed parameter under the ideal state, the snow removal device corresponds to a current speed parameter in the current working state. Among them, the current speed parameter can be used to refer to the real-time speed of the actual operation of the snow removal device in the current working state.

[0072] Specifically, the operating speed of the snow removal device can be determined and the current speed parameter can be obtained by real-time monitoring or receiving real-time feedback data transmitted by the snow removal device in the current working state.

[0073] S320: Determine whether to adjust the current speed parameter based on the thickness of snow on the bridge cables and the location where snow is to be removed, so as to determine the target speed parameter.

[0074] Specifically, the image recognition technology is used to analyze and process the real-time snow thickness on the bridge cables based on the real-time snow state on the bridge cables. Further, based on the real-time snow thickness and the specific conditions of different snow removal locations, it is evaluated whether the current speed parameter needs to be adjusted.

[0075] For example, if the snow is thick or hard, the speed may need to be reduced to increase the snow removal force; if the snow is thin, the speed may be increased to improve efficiency. Similarly, if the snow thickness at the previous snow removal location is moderate snow, and the snow thickness at the next snow removal location is heavy snow, then the current speed parameter needs to be adjusted to increase the speed of the snow removal operation.

[0076] Furthermore, the snow removal monitoring platform communicates with the snow removal device through the communication system, sends adjusted target speed parameters to the snow removal device based on real-time data, and guides the snow removal device to perform snow removal operations according to the target speed parameters.

[0077] In the above-mentioned implementation, the snow removal monitoring platform communicates with the snow removal device in real time, and dynamically adjusts the current operation speed according to the real-time snow thickness and the specific conditions of different snow removal locations, thereby ensuring the efficiency and safety of the snow removal operation.

[0078] In some embodiments, the snow removal device includes a heating element, and the target operating parameter includes a target heating mode of the heating element. The target heating mode is determined by:

[0079] According to the snow thickness, the heating relationship data is searched to obtain the target heating method corresponding to the snow thickness.

[0080] Among them, the heating element may refer to a component in a snow removal device that is used to melt or prevent snow from freezing. Exemplarily, the heating element may be a heating wire, a heating plate, or a resistor that can generate heat, which accelerates the melting of snow to achieve the purpose of snow removal by generating heat. The target heating mode may refer to a specific working mode set for the heating element according to different snow thicknesses. It can be understood that the corresponding relationship between the thickness of snow on the bridge cable and the heating operation mode is described by heating relationship data. Exemplarily, when the snow thickness is moderate, the target heating mode at this time may be a medium-temperature heating snow removal mode, which may last for ten minutes; when the snow thickness is heavy, the target heating mode at this time may be a high-temperature heating snow removal mode, which may last for thirty minutes.

[0081] Further, the target heating method suitable for the current snow thickness is determined based on the search results. Finally, according to the determined target heating method, the snow removal device sends different signals to the heating element through the control system, adjusts its working state to match the target heating method, and carries out snow removal operations.

[0082] In the above-mentioned embodiment, the snow removal device can intelligently adjust the working mode of the heating element according to the actual snow accumulation conditions to achieve the best snow removal effect, which not only improves the efficiency and safety of the snow removal operation, but also helps to reduce unnecessary energy consumption and avoid overheating or insufficient heating, thereby reducing the cost of snow removal operations.

[0083] In some embodiments, the snow state includes the snow form. Figure 4 , the snow accumulation on the bridge cable is obtained by:

[0084] S410: Obtain air humidity and weather temperature in a geographical area.

[0085] Specifically, the real-time air humidity and weather temperature of the geographical area where the target bridge is located can be obtained through sensors carried by the snow removal device. The air humidity and weather temperature of the geographical area can also be obtained through various weather service providers.

[0086] S420: Determine the snow accumulation form according to air humidity and weather temperature.

[0087] Among them, the snow form is used to describe the hardness and adhesion of the snow on the bridge cables. Exemplarily, the snow form can include wet snow in a semi-melted state, which is relatively heavy and has strong adhesion; it also includes very dry, light, almost non-adhesive, powder-like powder snow; it also includes hard and slippery ice snow; it can also include compacted snow with increased hardness and adhesion, etc. Specifically, wet snow is usually formed when the temperature is slightly above the freezing point and the air humidity is high; powder snow is formed under conditions of low temperature and dry air; when the temperature is below the freezing point, wet snow freezes or directly falling snow freezes quickly to form ice snow.

[0088] In the above implementation, by monitoring and analyzing the air humidity and weather temperature of the geographical area where the target bridge is located, the occurrence of different snow accumulation forms can be predicted, thereby providing different working parameter guidance for snow removal operations.

[0089] In some embodiments, the target operating parameters include target strength parameters. Figure 5 , the target force parameters are determined by:

[0090] S510: Determine a current force parameter corresponding to the current working state of the snow removal device.

[0091] Among them, the target force parameter can be used to refer to the ideal force that needs to be achieved when performing snow removal operations at different snow removal locations in order to achieve the best snow removal effect and efficiency. Specifically, the current working state reflects the real-time performance and working conditions of the snow removal device. Unlike the target force parameter under the ideal state, the snow removal device has a current force parameter in the current working state. Among them, the current force parameter can be used to refer to the real-time force of the actual operation of the snow removal device in the current working state.

[0092] Specifically, the operating force of the snow removal device can be determined and the current force parameters can be obtained by real-time monitoring or receiving real-time feedback data transmitted by the snow removal device in the current working state.

[0093] S520: Determine whether to adjust the current force parameter according to the hardness and adhesion of the snow on the bridge cable to determine the target force parameter.

[0094] It is understandable that, based on the hardness and adhesion of different snow accumulations on the bridge cables, it is evaluated whether the current force parameters need to be adjusted. If the snow accumulation state is determined to be wet snow or slush, that is, the snow accumulation is hard or has a high degree of adhesion, the snow removal device may need to be adjusted to increase the force to ensure that the snow is effectively removed; if the snow accumulation state is determined to be powder snow, that is, the snow accumulation is soft and has a low degree of adhesion, the snow removal device may need to reduce the force to avoid excessive wear of the equipment.

[0095] In the above-mentioned embodiment, the snow removal device can dynamically adjust the operating force according to the actual snow accumulation state to ensure the efficiency and safety of the snow removal operation while reducing potential damage to the bridge structure.

[0096] In some embodiments, there are multiple snow removal devices, and the snow removal monitoring platform is connected to each snow removal device in real-time communication. Figure 6 , the method further comprises:

[0097] S610, receiving the operation effect data sent in real time by each snow removal device during the snow removal operation.

[0098] Among them, the operation effect data may refer to a series of data collected in real time by the snow removal device and sent back to the snow removal monitoring platform during the snow removal operation.

[0099] Specifically, the snow removal monitoring platform establishes a real-time communication connection with each snow removal device through wireless communication technology, receives the operation effect data sent in real time by each snow removal device during the snow removal operation, and further analyzes and processes it.

[0100] S620. Dynamically adjust the target operating parameters of each snow removal device in real time based on the operating effect data, so that each snow removal device can operate under operating parameters that match the real-time snow accumulation status on the bridge cables.

[0101] Specifically, after receiving the different operating effect data of each snow removal device, the snow removal monitoring platform uses the data processing and analysis system to evaluate its operating effect data to determine whether the operating effect of each snow removal device meets expectations. Furthermore, the snow removal monitoring platform will generate the adjusted operating parameters of each snow removal device based on the different operating effect data of each snow removal device, so as to dynamically adjust the target operating parameters of each snow removal device in real time, so that each snow removal device can operate under operating parameters that match the real-time snow accumulation status on the bridge cable. Finally, the adjusted operating parameters are sent back to the corresponding snow removal device in real time to guide it to adjust its operating strategy to adapt to the current snow accumulation status.

[0102] In the above implementation, by installing multiple snow removal devices and centrally monitoring and intelligently managing them, the management efficiency and the response speed of snow removal work are improved. At the same time, based on the operation effect data, the operation parameters are adjusted in real time so that the snow removal device can be optimized according to the actual snow accumulation state and operation effect, thereby improving the snow removal efficiency.

[0103] The embodiment of this specification also provides a snow removal method suitable for bridge cables, which is applied to a snow removal monitoring platform. A plurality of snow removal devices are installed on the bridge cables, and each snow removal device is connected to the snow removal monitoring platform in communication. The snow removal device includes a heating element. The method includes the following steps:

[0104] S702: Obtain an image of snow on the bridge cables, segment and perform feature projection on the snow image, and obtain a first-stage feature map.

[0105] S704: downsample the first-stage feature map to obtain a first downsampled feature map, perform feature extraction and feature fusion based on the first downsampled feature map to obtain a second-stage feature map.

[0106] S706: Downsample the second stage feature map to obtain a second downsampled feature map, perform feature extraction and feature fusion based on the second downsampled feature map to obtain a third stage feature map.

[0107] S708. Downsample the third stage feature map to obtain a third downsampled feature map, and perform feature extraction and feature fusion based on the third downsampled feature map to obtain a fourth stage feature map.

[0108] S710: Perform classification processing based on the fourth stage feature map to obtain the thickness of snow on the bridge cables.

[0109] S712: Obtain air humidity and weather temperature in the geographical area.

[0110] S714. Determine the snow accumulation form based on air humidity and weather temperature.

[0111] Among them, snow morphology is used to describe the hardness and adhesion of snow on bridge cables.

[0112] S716. Obtain the traffic flow data of the target bridge where the bridge cable is located and the weather data of the geographical area to which the target bridge belongs.

[0113] Among them, weather data is used to describe the snowy time period when snowy weather occurs in a geographical area.

[0114] S718. Determine the position on the bridge cable where snow is to be removed based on the thickness and shape of the accumulated snow.

[0115] S720: Determine a current speed parameter and a current force parameter corresponding to the current working state of the snow removal device.

[0116] S722. Determine whether to adjust the current speed parameter based on the thickness of snow on the bridge cables and the location where snow is to be removed, so as to determine the target speed parameter.

[0117] S724. Determine whether to adjust the current force parameter based on the hardness and adhesion of the snow on the bridge cable to determine the target force parameter.

[0118] S726. Search the heating relationship data according to the snow thickness to obtain a target heating method corresponding to the snow thickness.

[0119] Among them, the heating relationship data is used to describe the corresponding relationship between the thickness of snow on the bridge cables and the heating operation mode.

[0120] S728. Determine target operating parameters for the snow removal device to remove snow from the location to be removed based on the target speed parameter, the target force parameter, and the target heating method.

[0121] S730: Determine an initial operation period of the snow removal device according to the traffic flow data.

[0122] Among them, the traffic flow data during the initial operation period did not reach the preset traffic flow threshold.

[0123] S732. Use the snowing time period corresponding to the weather data to adjust the start time and / or end time of the initial operation period to determine the target operation period.

[0124] S734. Send the target operation parameters and the target operation period to the snow removal device to instruct the snow removal device to remove snow from the bridge cables according to the target operation parameters during the target operation period.

[0125] S736. The snow removal monitoring platform receives the operation effect data sent in real time by each snow removal device during the snow removal operation.

[0126] S738. Perform real-time dynamic adjustments to the target operating parameters of each snow removal device based on the operating effect data, so that each snow removal device can operate under operating parameters that match the real-time snow accumulation status on the bridge cables.

[0127] It should be understood that, although the various steps in the above flowchart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the above flowchart may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0128] The embodiment of this specification also provides a snow removal device 700 suitable for bridge cables, which is applied to a snow removal monitoring platform and installed on the bridge cables. The snow removal device is connected to the snow removal monitoring platform for communication. Figure 7 As shown, the device includes: a data acquisition module 710, a parameter determination module 720, an initial operation period determination module 730, a target operation period determination module 740 and a snow removal execution module 750, wherein:

[0129] The data acquisition module 710 is used to acquire the snow accumulation state on the bridge cable, the traffic flow data of the target bridge where the bridge cable is located, and the weather data of the geographical area to which the target bridge belongs. The weather data is used to describe the snowing time period when snowing occurs in the geographical area.

[0130] The parameter determination module 720 is used to determine the position to be cleared of snow on the bridge cable according to the snow accumulation state, and the target operation parameters of the snow removal device for clearing snow from the position to be cleared of snow.

[0131] The initial operation period determination module 730 is used to determine the initial operation period of the snow removal device according to the vehicle flow data, wherein the vehicle flow data in the initial operation period does not reach a preset vehicle flow threshold.

[0132] The target operation period determination module 740 is used to adjust the start time and / or end time of the initial operation period by using the snowing period corresponding to the weather data to determine the target operation period.

[0133] The snow removal execution module 750 is used to send target operation parameters and target operation period to the snow removal device to instruct the snow removal device to remove snow from the bridge cables according to the target operation parameters during the target operation period.

[0134] In some embodiments, the snow state includes snow thickness. The parameter determination module 720 is further used to obtain a snow image on the bridge cable, segment and feature project the snow image to obtain a first-stage feature map; downsample the first-stage feature map to obtain a first downsampled feature map, perform feature extraction and feature fusion based on the first downsampled feature map to obtain a second-stage feature map; downsample the second-stage feature map to obtain a second downsampled feature map, perform feature extraction and feature fusion based on the second downsampled feature map to obtain a third-stage feature map; downsample the third-stage feature map to obtain a third downsampled feature map, perform feature extraction and feature fusion based on the third downsampled feature map to obtain a fourth-stage feature map; perform classification processing based on the fourth-stage feature map to obtain the thickness of snow on the bridge cable.

[0135] In some embodiments, the target operating parameter includes a target speed parameter. The parameter determination module 720 is also used to determine the current speed parameter corresponding to the current working state of the snow removal device. It is determined whether to adjust the current speed parameter according to the thickness of snow on the bridge cable and the position of snow to be removed to determine the target speed parameter.

[0136] In some embodiments, the snow removal device includes a heating element, and the target operation parameter includes a target heating mode of the heating element. The parameter determination module 720 is further used to search the heating relationship data according to the snow thickness to obtain the target heating mode corresponding to the snow thickness. The heating relationship data is used to describe the corresponding relationship between the snow thickness on the bridge cable and the heating operation mode.

[0137] In some embodiments, the snow state includes snow morphology. The parameter determination module 720 is also used to obtain air humidity and weather temperature in the geographical area. The snow morphology is determined according to the air humidity and weather temperature. The snow morphology is used to describe the hardness and adhesion of the snow on the bridge cable.

[0138] In some embodiments, the target operating parameter includes a target force parameter. The parameter determination module 720 is further used to determine the current force parameter corresponding to the current working state of the snow removal device. It is determined whether to adjust the current force parameter according to the hardness and adhesion of the snow on the bridge cable to determine the target force parameter.

[0139] In some embodiments, there are multiple snow removal devices, and the snow removal monitoring platform is connected to each snow removal device in real-time communication. The snow removal execution module 750 is also used to receive the operation effect data sent in real time by each snow removal device during the snow removal operation. According to the operation effect data, the target operation parameters of each snow removal device are adjusted in real time and dynamically, so as to coordinate each snow removal device to operate under the operation parameters matching the real-time snow accumulation state on the bridge cable.

[0140] For the specific definition of a snow removal device suitable for bridge cables, please refer to the definition of a snow removal device method suitable for bridge cables above, which will not be repeated here. Each module in the above-mentioned snow removal device suitable for bridge cables can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0141] In this embodiment, a snow removal device suitable for bridge cables is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0142] The embodiment of the present application also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 8As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a snow removal method suitable for bridge cables is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0143] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0144] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0145] The embodiment of the present application provides a computer program product, which includes computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method of any embodiment of the present application.

[0146] The above embodiments illustrate a snow removal method, device, computer device and storage medium suitable for bridge cables, which can be implemented by a computer chip or entity, or by a product with a certain function. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0147] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0148] Those skilled in the art should understand that the embodiments of the present application can be provided as a snow removal method, device, computer equipment and storage medium suitable for bridge cables. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0149] The present application is described with reference to a flowchart and / or block diagram of a snow removal method, device, computer device, and storage medium suitable for bridge cables according to an embodiment of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as a combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0150] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0151] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0152] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0153] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0154] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0155] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. Since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0156] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0157] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A snow removal method suitable for bridge cables, characterized in that: Applied to a snow removal monitoring platform, a snow removal device is installed on the bridge cable, and the snow removal device is communicatively connected with the snow removal monitoring platform; the method comprises: Acquire the snow accumulation state on the bridge cable, the traffic flow data of the target bridge where the bridge cable is located, and the weather data of the geographical area to which the target bridge belongs; wherein the weather data is used to describe the snowing time period when snowing occurs in the geographical area; Determine the position to be snow-removed on the bridge cable according to the snow accumulation state, and the target operation parameters of the snow removal device for removing snow from the position to be snow-removed; Determining an initial operation period of the snow removal device according to the vehicle flow data; wherein the vehicle flow data in the initial operation period does not reach a preset vehicle flow threshold; Using the snowing time period corresponding to the weather data to adjust the start time and / or end time of the initial operation period, to determine the target operation period; The target operation parameters and the target operation time period are sent to the snow removal device to instruct the snow removal device to remove snow from the bridge cable according to the target operation parameters during the target operation time period.

2. The method according to claim 1, characterized in that The snow state includes snow thickness; the snow thickness on the bridge cable is obtained by: Acquire a snow image on the bridge cable, perform segmentation and feature projection on the snow image, and obtain a first-stage feature map; Downsampling the first-stage feature map to obtain a first downsampled feature map, and performing feature extraction and feature fusion based on the first downsampled feature map to obtain a second-stage feature map; Downsampling the second-stage feature map to obtain a second downsampled feature map, and performing feature extraction and feature fusion based on the second downsampled feature map to obtain a third-stage feature map; Downsampling the third stage feature map to obtain a third downsampled feature map, and performing feature extraction and feature fusion based on the third downsampled feature map to obtain a fourth stage feature map; Classification processing is performed based on the fourth stage feature map to obtain the thickness of snow on the bridge cable.

3. The method according to claim 2, characterized in that The target operating parameters include target speed parameters; The target speed parameter is determined by: Determine a current speed parameter corresponding to a current working state of the snow removal device; Whether to adjust the current speed parameter is determined according to the thickness of snow on the bridge cable and the position of snow to be removed to determine the target speed parameter.

4. The method according to claim 2, characterized in that: The snow removal device includes a heating element, and the target operating parameter includes a target heating mode of the heating element; the target heating mode is determined by: The target heating mode corresponding to the snow thickness is obtained by searching in the heating relationship data according to the snow thickness; wherein the heating relationship data is used to describe the corresponding relationship between the snow thickness on the bridge cable and the heating operation mode.

5. The method according to claim 1, characterized in that The snow accumulation state includes the snow accumulation form; the snow accumulation form on the bridge cable is obtained by: Obtaining air humidity and weather temperature of the geographical area; Determining the snow accumulation form according to the air humidity and the weather temperature; The snow morphology is used to describe the hardness and adhesion of the snow on the bridge cables.

6. The method according to claim 5, characterized in that The target operation parameters include target strength parameters; The target force parameter is determined by: Determine a current force parameter corresponding to the current working state of the snow removal device; Whether to adjust the current force parameter is determined based on the hardness and adhesion of the snow on the bridge cable to determine the target force parameter.

7. The method according to any one of claims 1 to 6, characterized in that: There are multiple snow removal devices, and the snow removal monitoring platform is connected to each snow removal device in real-time communication; the method further includes: Receiving operation effect data sent in real time by each snow removal device during the snow removal operation; The target operating parameters of each snow removal device are dynamically adjusted in real time according to the operating effect data, so as to coordinate each snow removal device to operate under operating parameters that match the real-time snow accumulation status on the bridge cable.

8. A snow removal device suitable for bridge cables, characterized in that: Applied to a snow removal monitoring platform, installed on the bridge cable, the snow removal device is communicatively connected with the snow removal monitoring platform; the device comprises: A data acquisition module, used to acquire the snow accumulation state on the bridge cable, the traffic flow data of the target bridge where the bridge cable is located, and the weather data of the geographical area to which the target bridge belongs; wherein the weather data is used to describe the snowing time period when snowing occurs in the geographical area; A parameter determination module, used to determine the position to be snow-removed on the bridge cable according to the snow accumulation state, and the target operation parameters of the snow removal device for removing snow from the position to be snow-removed; An initial operation period determination module, used to determine the initial operation period of the snow removal device according to the vehicle flow data; wherein the vehicle flow data in the initial operation period does not reach a preset vehicle flow threshold; A target operation period determination module, used to adjust the start time and / or end time of the initial operation period by using the snowing period corresponding to the weather data to determine the target operation period; The snow removal execution module is used to send the target operation parameters and the target operation period to the snow removal device to instruct the snow removal device to remove snow from the bridge cable according to the target operation parameters during the target operation period.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

Images