Snow blocking and collecting regulation control system in wind and snow extreme environment

Abstract

This invention discloses a snow-blocking and snow-collecting regulation and control system for extreme wind-blown snow environments, belonging to the field of environmental management technology. The invention includes an acquisition module for acquiring meteorological and building data of the target area where the transportation track is located, and collecting stress data of the snow-blocking device; an identification module for predicting snow accumulation data and corresponding risk data for each sub-location in the target area based on the meteorological, building, and stress data; and a control module for determining a snow collection scheme based on the snow accumulation data and corresponding risk data for each sub-location. This achieves the technical effect of reducing the resources consumed in snow blocking and snow collection.

Description

Technical Field

[0001] This invention relates to the field of environmental management, and in particular to a snow trapping and snow collection regulation and control system for extreme wind-blown snow environments. Background Technology

[0002] Blowing snow generally refers to the phenomenon of snow particles being carried by air currents near the ground. It can usually be generated with snow accumulation and a certain wind speed. Blowing snow can cause snow redistribution, which can seriously threaten transportation. Currently, snow barriers or avalanche shelters are usually installed on both sides of railway tracks to keep the snow off the tracks. Residual snow is then collected and removed by mechanical snow collection devices. In extreme cases of blowing snow, it is necessary to inspect the snow accumulation on both sides of the tracks frequently, which consumes too many resources.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a snow-blocking and snow-collecting regulation and control system for extreme wind-blown snow environments, aiming to reduce the resources consumed in snow blocking and snow collection. To achieve the above objective, this invention provides a snow-blocking and snow-collecting regulation and control system for extreme wind-blown snow environments, comprising: The acquisition module is used to acquire meteorological and building data of the target area where the traffic track is located, and to collect the force data of the snow-blocking device; The identification module is used to predict the snow accumulation data and corresponding risk data for each sub-location in the target area based on the meteorological data, the building data, and the stress data. The control module is used to determine a snow collection scheme based on the snow accumulation data and corresponding risk data for each sub-location.

[0005] Furthermore, to achieve the above objectives, the present invention also provides a snow-blocking and snow-collecting adjustment and control method for extreme wind-blown snow environments, characterized in that snow-blocking devices are installed on both sides of the traffic track, and the snow-blocking and snow-collecting adjustment and control method for extreme wind-blown snow environments includes the following steps: Acquire meteorological and building data of the target area where the traffic track is located, and collect the force data of the snow-blocking device; Based on the meteorological data, the building data, and the stress data, predict the snow accumulation data and corresponding risk data for each sub-location in the target area; A snow collection scheme is determined based on the snow accumulation data and corresponding risk data for each sub-location.

[0006] Optionally, the meteorological data includes temperature data, and the step of predicting snow accumulation data and corresponding risk data for each sub-location in the target area based on the meteorological data, the building data, and the stress data includes: Extract feature information from each of the meteorological data, the building data, and the stress data, and determine the first feature vector corresponding to the sub-location based on the feature information to obtain multiple first feature vectors; The snow data corresponding to each sub-location is determined based on the first feature vector and the second feature vector corresponding to the historical snow data. The risk data is determined based on the temperature data.

[0007] Optionally, the step of determining the snow data corresponding to each sub-location based on the first feature vector and the second feature vector corresponding to the historical snow data includes: The second feature vectors are classified according to the clustering algorithm to obtain multiple grouped data; Each of the first feature vectors is matched with the plurality of grouped data to determine the matching group corresponding to each of the first feature vectors; The snow cover data corresponding to each sub-location is determined based on the matching grouping.

[0008] Optionally, the step of matching each first feature vector with the plurality of grouped data to determine the matching group corresponding to each first feature vector includes: When a first feature vector of a corresponding matching group exists, any first feature vector is used as the comparison vector; The distance between the alignment vector and the corresponding center vector of each group of data is calculated to obtain multiple alignment distances; Based on the multiple comparison distances, the matching group corresponding to the comparison vector is determined in multiple group data, and the matching group is associated with the first feature vector as the comparison vector. Then, the step of using any first feature vector as the comparison vector when there is a corresponding matching group is returned.

[0009] Optionally, the meteorological data includes: wind speed data, wind direction data, snowfall data, and temperature data, and the step of extracting feature information from each of the meteorological data, the building data, and the stress data includes: Determine the building shading data corresponding to each sub-location based on the wind direction data and the building data; Extract the data features corresponding to the building shading data, wind speed data, snowfall data, building data, force data, and temperature data, and use the data features as the feature information.

[0010] Optionally, the snow collection plan includes: the number of snow removal devices and their operating routes, and the step of determining the snow collection plan based on the snow accumulation data and corresponding risk data for each sub-location includes: Calculate the current total snow cover and snow distribution based on the multiple snow cover data; The number of devices is determined based on the total snow accumulation, and the operating route is determined based on the snow distribution and the risk data.

[0011] Optionally, the snow-blocking device is equipped with a pressure sensor. The step of collecting the force data of the snow-blocking device includes: The pressure sensor is controlled to record the force data.

[0012] Furthermore, to achieve the above objectives, the present invention also provides a snow-blocking and snow-collecting adjustment and control device for extreme wind-blown snow environments. The snow-blocking and snow-collecting adjustment and control device for extreme wind-blown snow environments includes: a memory, a processor, and a snow-blocking and snow-collecting adjustment and control program for extreme wind-blown snow environments stored in the memory and executable on the processor. The snow-blocking and snow-collecting adjustment and control program for extreme wind-blown snow environments is configured to implement the steps of the snow-blocking and snow-collecting adjustment and control method for extreme wind-blown snow environments described above.

[0013] In addition, to achieve the above objectives, the present invention also provides a storage medium storing a snow-blocking and snow-collecting adjustment and control program for extreme wind-blown snow environments. When the snow-blocking and snow-collecting adjustment and control program for extreme wind-blown snow environments is executed by a processor, it implements the steps of the snow-blocking and snow-collecting adjustment and control method for extreme wind-blown snow environments described above.

[0014] This invention proposes a snow trapping and snow collection regulation and control method under extreme wind-blown snow conditions. This method extracts feature information from each meteorological data point, building data point, and stress data point, and determines a first feature vector corresponding to each sub-location based on the feature information, resulting in multiple first feature vectors. The method then determines the snow accumulation data corresponding to each sub-location based on the first feature vectors and a second feature vector corresponding to historical snow accumulation data, and determines the risk data based on the temperature data. This improves the amount of data used to identify and determine snow accumulation data, and allows for the assessment of the risk data corresponding to the snow accumulation data based on the temperature data. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the structure of the snow-blocking and snow-collecting adjustment and control device under the extreme wind-blown snow environment involved in the hardware operating environment of the embodiment of the present invention; Figure 2This is a flowchart illustrating the first embodiment of the snow-blocking and snow-collecting regulation and control method for extreme wind-blown snow environments of the present invention. Figure 3 This is a flowchart illustrating the second embodiment of the snow-blocking and snow-collecting regulation and control method for extreme wind-blown snow environments of the present invention.

[0016] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0018] Reference Figure 1 , Figure 1 This is a schematic diagram of the snow-blocking and snow-collecting adjustment and control device under extreme wind and snow conditions in the hardware operating environment involved in the embodiments of the present invention.

[0019] like Figure 1 As shown, the snow-blocking and snow-collecting regulation and control device under extreme wind and snow conditions may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interactive device 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The interactive device 1003 may include a display screen or an input unit such as a keyboard. Optionally, the interactive device 1003 may also be connected to the communication bus via standard wired or wireless interfaces. The network interface 1004 may optionally include standard wired or wireless interfaces (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001. Snow collection and regulation control equipment in extreme wind and snow environments can generate corresponding snow collection plans and adjust the operation of snow collection equipment in the city through these plans, such as shutdown, operating frequency, and operating routes.

[0020] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on snow trapping and snow collection regulation control devices in extreme wind and snow environments. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0021] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a snow-blocking and snow-collecting regulation and control program for extreme wind and snow conditions.

[0022] exist Figure 1 In the snow-blocking and snow-collecting adjustment and control device shown in the extreme wind and snow environment, the network interface 1004 is mainly used for data communication with other devices; the interactive device 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the snow-blocking and snow-collecting adjustment and control device of the present invention can be set in the snow-blocking and snow-collecting adjustment and control device of the extreme wind and snow environment. The snow-blocking and snow-collecting adjustment and control device of the extreme wind and snow environment calls the snow-blocking and snow-collecting adjustment and control program of the extreme wind and snow environment stored in the memory 1005 through the processor 1001, and executes the snow-blocking and snow-collecting adjustment and control method of the extreme wind and snow environment provided in the embodiment of the present invention.

[0023] This invention provides a snow trapping and snow collection regulation and control method under extreme wind-blown snow conditions, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a snow trapping and snow collection regulation and control method for extreme wind-blown snow environments according to the present invention.

[0024] In this embodiment, snow-blocking devices are installed on both sides of the transportation track, and the snow-blocking and snow-collecting adjustment and control method under extreme wind-blown snow conditions includes: Step S1: Obtain meteorological and building data of the target area where the traffic track is located, and collect the force data of the snow-blocking device; In this embodiment, the snow-blocking device is a snow fence, typically installed along the railway to prevent snow from being blown onto the tracks. The target area refers to the area along the railway line, and meteorological data can be collected from meteorological observation devices within the target area. Building data can be obtained from map data, and specific building data may include: the distance between the building and the snow-blocking device or railway track, the building's height, and the building's area. Furthermore, the snow-blocking device can be equipped with sensors to collect force data on the device. Multiple sensors can be used, typically spaced at preset intervals, to obtain force data for the snow-blocking device at various locations.

[0025] Step S2: Based on the meteorological data, the building data, and the stress data, predict the snow accumulation data and corresponding risk data for each sub-location in the target area; In this embodiment, features are extracted from meteorological data, building data, and stress data. These extracted features are then combined with data features collected before the current moment to predict snow accumulation. The risk corresponding to this snow accumulation is then determined based on the meteorological data. It should be noted that snow accumulation is often related to multiple conditions. Even without snowfall, wind-driven snow accumulation can alter the snow cover in the environment. In this embodiment, by extracting features from meteorological data, building data, and stress data, a multi-dimensional environmental vector describing each sub-location within the target area can be generated. This environmental vector is then used to determine the snow accumulation data from historical data. Since the impact of snow accumulation on the environment varies at different temperatures, it is necessary to determine the risk of the corresponding snow accumulation to the railway or its location.

[0026] Step S3: Determine a snow collection plan based on the snow accumulation data and corresponding risk data for each sub-location.

[0027] To ensure smooth and safe traffic flow, snow needs to be cleared to prevent snow from occupying road space, especially compacted snowdrifts or snow mounds, which can severely affect vehicle traffic capacity and pedestrian safety. Based on the snow accumulation data and corresponding risk data for each sub-location, a snow collection plan is determined. Specifically, the operation of snow collection equipment is planned to achieve the concentration and clearing of snow. Common snow collection equipment includes snowplows, loaders, and transport trucks. In this embodiment, the snow accumulation data and corresponding risk data determine the clearing frequency of the snow collection equipment at each location, and the operating route of the snow collection equipment is planned according to the clearing frequency, thus defining the snow collection plan.

[0028] In this embodiment, by acquiring meteorological and building data of the target area where the traffic track is located, and collecting the force data of the snow-blocking device, the snow accumulation data and corresponding risk data of each sub-location in the target area are predicted based on the meteorological data, the building data, and the force data. A snow collection plan is determined based on the snow accumulation data and corresponding risk data of each sub-location, thereby enabling precise collection and clearing of snow on the traffic line and improving the efficiency of clearing.

[0029] Furthermore, based on the first embodiment, a second embodiment of the snow-blocking and snow-collecting regulation and control method of the present invention under extreme wind-blown snow conditions is proposed. In this embodiment, referring to... Figure 3 The meteorological data includes temperature data. The step of predicting snow accumulation data and corresponding risk data for each sub-location in the target area based on the meteorological data, the building data, and the stress data includes: Step S21: Extract feature information of each meteorological data, building data, and force data, and determine the first feature vector corresponding to the sub-location based on the feature information to obtain multiple first feature vectors; In this embodiment, some meteorological data fluctuates frequently, such as wind direction, wind speed, and snowfall, which typically change within an hour or several minutes. Therefore, features related to these fluctuations can be extracted from this type of meteorological data. Furthermore, features related to the meteorological data and the building data can be calculated, and the change features of the force data can be extracted. These features are then used as the feature information, and a first feature vector corresponding to the sub-location is generated based on this feature information. Specifically, the first feature vector is generated according to a preset feature information arrangement order.

[0030] Step S22: Determine the snow data corresponding to each sub-location based on the first feature vector and the second feature vector corresponding to the historical snow data; In this embodiment, elements at the same position in the first and second feature vectors have the same type. Optionally, the similarity between the first and second feature vectors is calculated to determine the matching vector, and the historical snow cover data corresponding to the matching vector is used as the snow cover data.

[0031] Step S23: Determine the risk data based on the temperature data.

[0032] Different temperature data correspond to different risk data, and the risk data is negatively correlated with the temperature data. Specifically, when the temperature is 0 degrees Celsius or above 0 degrees Celsius, the risk is low because snow undergoes a natural melting process. When the temperature is below 0 degrees Celsius and above -10 degrees Celsius, snow removal needs to be carried out at intervals when using de-icing agents. When the temperature is below -10 degrees Celsius, residual snow or melted ice will refreeze within minutes, therefore, snow collection and removal need to be carried out frequently.

[0033] In this embodiment, feature information is extracted from each of the meteorological data, building data, and stress data, and a first feature vector corresponding to the sub-location is determined based on the feature information to obtain multiple first feature vectors; the snow data corresponding to each sub-location is determined based on the first feature vector and the second feature vector corresponding to the historical snow data, and the risk data is determined based on the temperature data, thereby increasing the amount of data used to identify and determine the snow data, and judging the risk data corresponding to the snow data based on the temperature data.

[0034] Furthermore, based on the first or second embodiment, a third embodiment of the snow-blocking and snow-collecting regulation and control method for extreme wind-blown snow environments of the present invention is proposed. In this embodiment, the step of determining the snow accumulation data corresponding to each sub-location based on the first feature vector and the second feature vector corresponding to the historical snow accumulation data includes: The second feature vectors are classified according to the clustering algorithm to obtain multiple grouped data; Each of the first feature vectors is matched with the plurality of grouped data to determine the matching group corresponding to each of the first feature vectors; The snow cover data corresponding to each sub-location is determined based on the matching grouping.

[0035] In this embodiment, optionally, the K-means algorithm is used to classify multiple second feature vectors to obtain multiple grouped data. Clustering is an unsupervised learning method that automatically divides data into several groups based solely on the distance between samples, resulting in high similarity within the same group and significant differences between different groups. After obtaining multiple grouped data, the average historical snow cover and standard deviation of each second feature vector in each group are calculated. Optionally, if any group has a standard deviation greater than a preset standard deviation, the number of groups is increased, and the clustering algorithm is used again to classify the multiple second feature vectors to obtain multiple grouped data. When all standard deviations are less than or equal to the preset standard deviation, the obtained multiple grouped data are accurate. Specifically, each first feature vector is matched with the multiple grouped data, and the matching group corresponding to each first feature vector is determined based on the matching criteria. These matching criteria include the distance to the center point of the grouped data. After determining the matching group, the snow cover data is determined based on the average historical snow cover and standard deviation of the matching group.

[0036] In this embodiment, multiple second feature vectors are classified using a clustering algorithm to obtain multiple grouped data; each first feature vector is matched with the multiple grouped data to determine the matching group corresponding to each first feature vector; the snow cover data corresponding to each sub-location is determined based on the matching group, thereby improving the accuracy of predicted snow cover data.

[0037] Furthermore, the step of matching each first feature vector with the plurality of grouped data to determine the matching group corresponding to each first feature vector includes: When a first feature vector of a corresponding matching group exists, any first feature vector is used as the comparison vector; The distance between the alignment vector and the corresponding center vector of each group of data is calculated to obtain multiple alignment distances; Based on the multiple comparison distances, the matching group corresponding to the comparison vector is determined in multiple group data, and the matching group is associated with the first feature vector as the comparison vector. Then, the step of using any first feature vector as the comparison vector when there is a corresponding matching group is returned.

[0038] In this embodiment, it is determined whether the first feature vector has a corresponding or associated matching group. Any first feature vector of an unassociated matching group is selected as the comparison vector. The distance between the comparison vector and the corresponding center vector of each matching group is calculated to obtain multiple comparison distances. The multiple comparison distances are sorted, and the group data corresponding to the smallest comparison distance is selected as the matching group. In some embodiments, the two smallest comparison distances are selected, and the distance difference between the two smallest comparison distances is determined. When the distance difference is less than a preset distance difference, the snow accumulation data is determined based on the average historical snow accumulation data and standard deviation of the group data corresponding to the two smallest comparison distances.

[0039] In this embodiment, the distance between the alignment vector and the corresponding center vector of each group of data is calculated to obtain multiple alignment distances; the matching group corresponding to the alignment vector is determined in the multiple group of data based on the multiple alignment distances, and the matching group is associated with the first feature vector as the alignment vector. Then, the step of taking any first feature vector as the alignment vector when there is a corresponding matching group is returned to be executed, so that the matching group corresponding to each first feature vector can be determined iteratively.

[0040] Furthermore, based on any of the above embodiments, a fourth embodiment of the snow-trapping and snow-collecting regulation and control method for extreme wind-blown snow environments of the present invention is proposed. In this embodiment, the meteorological data includes: wind speed data, wind direction data, snowfall data, and temperature data. The step of extracting feature information of each of the meteorological data, the building data, and the stress data includes: Determine the building shading data corresponding to each sub-location based on the wind direction data and the building data; Extract the data features corresponding to the building shading data, wind speed data, snowfall data, building data, force data, and temperature data, and use the data features as the feature information.

[0041] The wind direction data here corresponds to wind directions that can form different angles with the snow-blocking device. Specifically, in this embodiment, buildings within the target range are projected onto the snow-blocking device according to the wind direction. It should be noted that the wind direction here does not consider the component perpendicular to the ground. For example, if the wind direction is blowing obliquely towards the snow-blocking device, in this embodiment, the wind direction is decomposed into a first wind direction in the horizontal direction and a second wind direction in the vertical direction. The buildings within the target range are projected onto the snow-blocking device according to the first wind direction. Each specific sub-location can be a snow-blocking device of a preset length. After calculating the projection, the shading ratio of the obstructed area and the unobstructed area is calculated, and the shading ratio is used as the building shading data. It should be noted that the target range can be preset, generally set to be 10 meters or 20 meters away from the snow-blocking device. In some embodiments, the value of the target range is not specifically limited, but rather the preset number of buildings closest to the snow-blocking device is used as the target range. Furthermore, a correction coefficient is set according to the distance between the building and the snow-blocking device to adjust the shading ratio. The greater the distance between the building and the snow-blocking device, the smaller the corrected shading ratio. The wind speed data here can include features such as average wind speed, maximum wind speed, and wind speed fluctuation frequency. The snowfall data can be snowfall amount; generally, within the same time period, all sub-locations correspond to one snowfall amount. Furthermore, the building data includes: the size and location of each building, and the extracted features can be building density, the average height of buildings corresponding to each sub-region, etc.

[0042] In this embodiment, building shading data corresponding to each sub-location is determined by the wind direction data and the building data, constructing a composite feature. Data features corresponding to the building shading data, wind speed data, snowfall data, building data, force data, and temperature data are extracted and used as feature information, thereby effectively improving the information content of the data features and thus improving the accuracy of the clustering method in subdividing data groups.

[0043] Furthermore, based on any of the above embodiments, a fifth embodiment of the snow collection and regulation control method for snow interception and harvesting under extreme wind-blown snow conditions of the present invention is proposed. In this embodiment, the snow collection scheme includes: the number of snow removal devices and their operating routes. The step of determining the snow collection scheme based on the snow accumulation data and corresponding risk data at each sub-location includes: Calculate the current total snow cover and snow distribution based on the multiple snow cover data; The number of devices is determined based on the total snow accumulation, and the operating route is determined based on the snow distribution and the risk data.

[0044] In this embodiment, the total snow accumulation is obtained by summing all the snow data. This snow data is then mapped onto a map to determine the distribution of snow intercepted by snow-blocking devices on both sides of the traffic track. The number of snowplows is determined based on the different total snow accumulations. The snow collection frequency for each sub-location is set based on the snow distribution and risk data. The operating route of the snowplows is determined based on the snow collection frequency. It should be noted that the snowplows' routes, in addition to collecting and removing snow from the snow-blocking devices on both sides of the traffic track, can also clear snow blown by wind from other areas. The specific operating route can be determined using a route planning algorithm.

[0045] Furthermore, the snow-blocking device is equipped with a pressure sensor. The step of collecting the force data of the snow-blocking device includes: The pressure sensor is controlled to record the force data.

[0046] In this embodiment, by controlling the pressure sensor to record the force data, the accuracy of the force data can be improved.

[0047] Furthermore, this invention also proposes a snow-blocking and snow-collecting adjustment and control device for extreme wind-blown snow environments. The snow-blocking and snow-collecting adjustment and control device for extreme wind-blown snow environments includes: a memory, a processor, and a snow-blocking and snow-collecting adjustment and control program for extreme wind-blown snow environments stored in the memory and executable on the processor. The snow-blocking and snow-collecting adjustment and control program for extreme wind-blown snow environments is configured to implement the steps of the snow-blocking and snow-collecting adjustment and control method for extreme wind-blown snow environments described above.

[0048] Furthermore, this invention also proposes a snow-trapping and snow-collecting regulation and control system for extreme wind-blown snow environments, the system comprising: The acquisition module is used to acquire meteorological and building data of the target area where the traffic track is located, and to collect the force data of the snow-blocking device; The identification module is used to predict the snow accumulation data and corresponding risk data for each sub-location in the target area based on the meteorological data, the building data, and the stress data. The control module is used to determine a snow collection scheme based on the snow accumulation data and corresponding risk data for each sub-location. The steps in any of the above embodiments can be implemented in the snow collection and regulation control system under the extreme wind-blown snow environment.

[0049] Furthermore, this embodiment of the invention also proposes a storage medium storing a snow-blocking and snow-collecting adjustment and control program for extreme wind-blown snow environments. When the snow-blocking and snow-collecting adjustment and control program for extreme wind-blown snow environments is executed by a processor, it implements the steps of the snow-blocking and snow-collecting adjustment and control method for extreme wind-blown snow environments described above.

[0050] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0051] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0052] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0053] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A snow trapping and snow collection regulation and control system for extreme wind-blown snow environments, characterized in that, The snow-blocking and snow-collecting regulation and control system under extreme wind-blown snow conditions includes: The acquisition module is used to acquire meteorological and building data of the target area where the traffic track is located, and to collect the force data of the snow-blocking device; The identification module is used to predict the snow accumulation data and corresponding risk data for each sub-location in the target area based on the meteorological data, the building data, and the stress data. The control module is used to determine a snow collection plan based on the snow accumulation data and corresponding risk data for each sub-location; The meteorological data includes wind speed data, wind direction data, snowfall data, and temperature data. The step of predicting the snow accumulation data and corresponding risk data for each sub-location in the target area based on the meteorological data, the building data, and the stress data includes: Extract feature information from each of the meteorological data, the building data, and the stress data, and determine the first feature vector corresponding to the sub-location based on the feature information to obtain multiple first feature vectors; The snow data corresponding to each sub-location is determined based on the first feature vector and the second feature vector corresponding to the historical snow data. The risk data is determined based on the temperature data; The step of extracting feature information for each of the meteorological data, the building data, and the stress data includes: Determine the building shading data corresponding to each sub-location based on the wind direction data and the building data; Extract the data features corresponding to the building shading data, wind speed data, snowfall data, building data, force data, and temperature data, and use the data features as the feature information; The step of determining the building shading data corresponding to each sub-location based on the wind direction data and the building data includes: decomposing the wind direction data into a first wind direction in the horizontal direction and a second wind direction in the vertical direction; projecting the buildings within the target range onto the snow-blocking device according to the first wind direction; calculating the shading area and the shading ratio of the non-shading area corresponding to each sub-location, and using the shading ratio as the building shading data.

2. A method for regulating and controlling snow trapping and snow collection under extreme wind-blown snow conditions, characterized in that, Snow-blocking devices are installed on both sides of the transportation track. The snow-blocking and snow-collection adjustment and control method under extreme wind-blown snow conditions includes the following steps: Acquire meteorological and building data of the target area where the traffic track is located, and collect the force data of the snow-blocking device; Based on the meteorological data, the building data, and the stress data, predict the snow accumulation data and corresponding risk data for each sub-location in the target area; A snow collection plan is determined based on the snow accumulation data and corresponding risk data for each sub-location; The meteorological data includes wind speed data, wind direction data, snowfall data, and temperature data. The step of predicting the snow accumulation data and corresponding risk data for each sub-location in the target area based on the meteorological data, the building data, and the stress data includes: Extract feature information from each of the meteorological data, the building data, and the stress data, and determine the first feature vector corresponding to the sub-location based on the feature information to obtain multiple first feature vectors; The snow data corresponding to each sub-location is determined based on the first feature vector and the second feature vector corresponding to the historical snow data. The risk data is determined based on the temperature data; The step of extracting feature information for each of the meteorological data, the building data, and the stress data includes: Determine the building shading data corresponding to each sub-location based on the wind direction data and the building data; Extract the data features corresponding to the building shading data, wind speed data, snowfall data, building data, force data, and temperature data, and use the data features as the feature information; The step of determining the building shading data corresponding to each sub-location based on the wind direction data and the building data includes: decomposing the wind direction data into a first wind direction in the horizontal direction and a second wind direction in the vertical direction; projecting the buildings within the target range onto the snow-blocking device according to the first wind direction; calculating the shading area and the shading ratio of the non-shading area corresponding to each sub-location, and using the shading ratio as the building shading data.

3. The snow trapping and snow collection regulation and control method under extreme wind-blown snow conditions as described in claim 2, characterized in that, The step of determining the snow data corresponding to each sub-location based on the first feature vector and the second feature vector corresponding to the historical snow data includes: The second feature vectors are classified according to the clustering algorithm to obtain multiple grouped data; Each of the first feature vectors is matched with the plurality of grouped data to determine the matching group corresponding to each of the first feature vectors; The snow cover data corresponding to each sub-location is determined based on the matching grouping.

4. The snow trapping and snow collection regulation and control method under extreme wind-blown snow conditions as described in claim 3, characterized in that, The step of matching each first feature vector with the plurality of grouped data to determine the matching group corresponding to each first feature vector includes: When a first feature vector of a corresponding matching group exists, any first feature vector is used as the comparison vector; The distance between the alignment vector and the corresponding center vector of each group of data is calculated to obtain multiple alignment distances; Based on the multiple comparison distances, the matching group corresponding to the comparison vector is determined in multiple group data, and the matching group is associated with the first feature vector as the comparison vector. Then, the step of using any first feature vector as the comparison vector when there is a corresponding matching group is returned.

5. The snow trapping and snow collection regulation and control method under extreme wind-blown snow conditions as described in claim 2, characterized in that, The meteorological data includes: wind speed data, wind direction data, snowfall data, and temperature data. The step of extracting feature information from each of the meteorological data, the building data, and the stress data includes: Determine the building shading data corresponding to each sub-location based on the wind direction data and the building data; Extract the data features corresponding to the building shading data, wind speed data, snowfall data, building data, force data, and temperature data, and use the data features as the feature information.

6. The snow trapping and snow collection regulation and control method under extreme wind-blown snow conditions as described in claim 2, characterized in that, The snow collection plan includes: the number of snow removal equipment and their operating routes. The step of determining the snow collection plan based on the snow accumulation data and corresponding risk data for each sub-location includes: Calculate the current total snow cover and snow distribution based on the multiple snow cover data; The number of devices is determined based on the total snow accumulation, and the operating route is determined based on the snow distribution and the risk data.

7. The snow trapping and snow collection regulation and control method under extreme wind-blown snow conditions as described in any one of claims 2 to 6, characterized in that, The snow-blocking device is equipped with a pressure sensor. The steps for collecting the force data of the snow-blocking device include: The pressure sensor is controlled to record the force data.

8. A snow-trapping and snow-collecting regulation and control device for extreme wind-blown snow environments, characterized in that, The snow-blocking and snow-collecting adjustment and control device under extreme wind and snow conditions includes: a memory, a processor, and a snow-blocking and snow-collecting adjustment and control program under extreme wind and snow conditions stored in the memory and executable on the processor. The snow-blocking and snow-collecting adjustment and control program under extreme wind and snow conditions is configured to implement the steps of the snow-blocking and snow-collecting adjustment and control method under extreme wind and snow conditions as described in any one of claims 2 to 7.

9. A storage medium, characterized in that, The storage medium stores a snow-blocking and snow-collecting adjustment and control program for extreme wind-blown snow environments. When the processor executes the snow-blocking and snow-collecting adjustment and control program for extreme wind-blown snow environments, it implements the steps of the snow-blocking and snow-collecting adjustment and control method for extreme wind-blown snow environments as described in any one of claims 2 to 7.

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