A method and system for measuring meteorological parameters applied to a moving vehicle

Abstract

The application provides a meteorological parameter measurement method and system applied to a flow vehicle, relates to the field of meteorological measurement, and comprises the following steps: acquiring the wind speed uncertainty, the business importance and the wind field complexity index of a region to be measured; determining the comprehensive weight of each area in the region to be measured according to the wind speed uncertainty, the business importance, the wind field complexity index and respective weights, and determining the area type of each area based on the comprehensive weight and the area operation accessibility; planning an optimal driving route according to the area type of each area; the optimal driving route covers all priority measurement areas; the optimal driving route is sent to the flow vehicle, real-time meteorological data returned by the flow vehicle is acquired, three-dimensional stereoscopic wind field data and gridded ground meteorological data are generated in combination with the meteorological monitoring data of a ground meteorological observation station. The application continuously improves the data by means of the flow vehicle, builds a numerical mode and continuously optimizes, and constructs the wind field and meteorological element model information of the region to be measured.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of meteorological measurement, in particular to a meteorological parameter measurement method and system applied to a mobile vehicle. BACKGROUND

[0002] The existing unmanned mobile vehicle is only applied to short-distance logistics transportation, and as a mobile and networking platform, it does not fully utilize the existing resources to detect external information. Therefore, how to effectively measure the weather based on the unmanned mobile vehicle is a technical problem that needs to be solved by those skilled in the art. SUMMARY

[0003] The purpose of the present application is to provide a meteorological parameter measurement method, system, computer readable storage medium and electronic device applied to a mobile vehicle, which can measure the weather based on the mobile vehicle.

[0004] To solve the above technical problems, the present application provides a meteorological parameter measurement method applied to a mobile vehicle, wherein the mobile vehicle is provided with a wind laser radar and a meteorological sensor, and the specific technical solution is shown below.

[0005] The wind speed uncertainty, business importance and wind field complexity indexes of the to-be-measured area are obtained; the comprehensive weight of each region in the to-be-measured area is determined according to the wind speed uncertainty, the business importance and the wind field complexity indexes and their respective weights, and the region type of each region is determined based on the comprehensive weight and the region operation accessibility; wherein the region operation accessibility includes passable and prohibited, which is used to indicate the passable region of the mobile vehicle in the to-be-measured area; the region type includes a priority measurement area and a reference measurement area; the optimal driving route is planned according to the region type of each region; the optimal driving route covers all the priority measurement areas; the optimal driving route is sent to the mobile vehicle, and the real-time meteorological data returned by the mobile vehicle is obtained, and the three-dimensional wind field data and the grid ground meteorological data are generated by combining the meteorological monitoring data of the ground meteorological observation station.

[0006] Optionally, the calculation process of the business importance includes: calculating the business importance according to the distance attenuation function, the length density of the business line in the to-be-measured area, the population density of the to-be-measured area and the asset value in the to-be-measured area; wherein the length density of the business line in the to-be-measured area is the ratio of the total length of the business line in the to-be-measured area to the area of the to-be-measured area.

[0007] Optionally, the calculation process of the wind field complexity index includes: calculating the wind field complexity index according to the slope intensity, the terrain undulation, the building coverage rate and the average building height of the to-be-measured area.

[0008] Optionally, determining the comprehensive weight of each slice area in the to-be-measured area according to the wind speed uncertainty, the business importance and the wind field complexity index and respective weights, and determining the slice area type of each slice area based on the comprehensive weight and slice area operation accessibility comprises: normalizing the wind speed uncertainty, the business importance and the wind field complexity index; obtaining a first weight set corresponding to the wind speed uncertainty, a second weight set corresponding to the business importance and a third weight set corresponding to the wind field complexity index; calculating the comprehensive weight of each slice area in the to-be-measured area according to the wind speed uncertainty and the first weight set corresponding thereto, the business importance and the second weight set corresponding thereto, and the wind field complexity index and the third weight set corresponding thereto; regarding a slice area with a comprehensive weight greater than a set threshold as the priority measurement area, and regarding a slice area with a comprehensive weight not greater than the set threshold as the reference measurement area.

[0009] Optionally, if the flow vehicle is a logistics vehicle, planning an optimal driving route according to the slice area type of each slice area comprises: calling a shortest path algorithm to generate a baseline logistics distribution path satisfying a time window constraint corresponding to a distribution task of the logistics vehicle according to the distribution task corresponding to the logistics vehicle and the slice area type of each slice area.

[0010] Optionally, after calling the shortest path algorithm to generate the baseline logistics distribution path satisfying the time window constraint corresponding to the distribution task according to the distribution task corresponding to the logistics vehicle and the slice area type of each slice area, the method further comprises: regarding a distribution address of the distribution task as a discrete point on the baseline logistics distribution path; eliminating an overtime discrete point and a violation discrete point in the discrete point to obtain a standard discrete point, wherein the overtime discrete point is a distribution address that is definitely overtime for distribution, and the violation discrete point is a distribution address that violates a road driving regulation during driving along the baseline logistics distribution path; calculating an additional driving time corresponding to each standard discrete point inserted into the baseline logistics distribution path; and regarding the standard discrete point as a feasible candidate distribution point if the additional driving time is not greater than a distribution time allowance.

[0011] Optionally, the three-dimensional stereoscopic wind field data and the gridded ground meteorological data are generated by combining the real-time meteorological data returned by the mobile vehicle and the meteorological monitoring data of the ground meteorological observation station, including: for wind speed, using the Kriging interpolation method for each height layer, respectively, interpolating the horizontal wind amount in the real-time meteorological data returned by the mobile vehicle and the meteorological monitoring data of the ground meteorological observation station to obtain the planning gridded wind speed and wind direction data of each height layer; fusing the planning gridded wind speed with the near-ground layer wind speed and wind direction observed by the ground meteorological station to construct a three-dimensional stereoscopic wind field scene including low and high layers; for meteorological elements, taking the real-time meteorological data returned by the mobile vehicle and the meteorological monitoring data of the ground meteorological observation station as input, using the Kriging interpolation method to perform spatial interpolation on the horizontal two-dimensional grid to generate a regular gridded distribution field of each meteorological element; the meteorological elements include temperature, humidity and air pressure; and integrating the regular gridded distribution fields corresponding to all the meteorological elements to obtain the gridded ground meteorological data.

[0012] The application also provides a meteorological parameter measurement system applied to a mobile vehicle, wherein the mobile vehicle is provided with a wind measurement laser radar and a meteorological sensor, and the meteorological parameter measurement system comprises: an index acquisition module, configured to acquire wind speed uncertainty, business importance and wind field complexity indexes of a to-be-measured region; a slice area type determination module, configured to determine a comprehensive weight of each slice area in the to-be-measured region according to the wind speed uncertainty, the business importance and the wind field complexity indexes and respective weights, and determine a slice area type of each slice area based on the comprehensive weight and slice area operation accessibility; wherein the slice area operation accessibility includes passable and prohibited, and is used to indicate passable slice areas of the mobile vehicle in the to-be-measured region; the slice area type includes a priority measurement region and a reference measurement region; a driving route planning module, configured to plan an optimal driving route according to the slice area type of each slice area; the optimal driving route covers all the priority measurement regions; and a meteorological parameter measurement module, configured to send the optimal driving route to the mobile vehicle, acquire real-time meteorological data returned by the mobile vehicle, and generate three-dimensional stereoscopic wind field data and gridded ground meteorological data by combining the real-time meteorological data and the meteorological monitoring data of the ground meteorological observation station.

[0013] The application also provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the meteorological parameter measurement method.

[0014] The application also provides an electronic device comprising a memory and a processor, wherein the memory has a computer program stored therein, and the processor invokes the computer program in the memory to implement the steps of the meteorological parameter measurement method.

[0015] The application provides a meteorological parameter measurement method applied to a mobile vehicle, the mobile vehicle is provided with a wind measurement laser radar and a meteorological sensor, and the meteorological parameter measurement method comprises the following steps: acquiring a wind speed uncertainty, a business importance and a wind field complexity index of a region to be measured; determining a comprehensive weight of each area in the region to be measured according to the wind speed uncertainty, the business importance, the wind field complexity index and respective weights, and determining an area type of each area based on the comprehensive weight and an area operation accessibility; wherein the area operation accessibility comprises passable and prohibited, and is used for indicating a passable area of the mobile vehicle in the region to be measured; the area type comprises a priority measurement area and a reference measurement area; planning an optimal driving route according to the area type of each area; the optimal driving route covers all the priority measurement areas; sending the optimal driving route to the mobile vehicle, and acquiring real-time meteorological data returned by the mobile vehicle, and combining meteorological monitoring data of a ground meteorological observation station to generate three-dimensional wind field data and gridded ground meteorological data.

[0016] The application divides a target region according to three-dimensional indexes of a wind speed uncertainty, a business importance and a wind field complexity before starting a task, introduces an area operation accessibility constraint, and distinguishes a priority measurement area and a reference measurement area in space. An optimal driving route is generated by taking covering all the priority measurement areas as a hard constraint, so that the mobile vehicle always operates along a path with the highest meteorological value and physical passability. Furthermore, the data density of the priority measurement area with a higher data measurement value is actively increased, and a low-value or impassable area is strategically abandoned, so that the meteorological parameter measurement efficiency is improved. The vehicle-mounted wind measurement laser radar continuously scans an air wind field during driving, and the ground meteorological sensor synchronously records elements such as temperature, humidity and pressure, and the real-time data of the two and a fixed ground meteorological station are fused in a unified space-time framework, so that the inherent defects of a single-point station with a blind area in the air are made up, and the short board of asynchronous time of pure mobile observation is overcome, and finally three-dimensional wind field and ground element field with vertical levels and horizontal grid resolution are generated, which provides a high-credibility dynamic basis for traffic scheduling, urban emergency and fine prediction. In addition, the mobile vehicle drives according to the optimal route, avoids the redundant mileage and carbon emission caused by traditional carpeted inspection, and realizes the dual optimization of observation accuracy and operation cost.

[0017] The application also provides a meteorological parameter measurement system applied to a mobile vehicle, a computer readable storage medium and an electronic device, which have the above beneficial effects, and details are not repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on the provided drawings.

[0019] Figure 1 A flow chart of a meteorological parameter measurement method applied to a mobile vehicle provided by an embodiment of the present application.

[0020] Figure 2 A structural schematic diagram of a mobile vehicle provided by an embodiment of the present application.

[0021] Figure 3 A structural schematic diagram of a meteorological parameter measurement system applied to a mobile vehicle provided by an embodiment of the present application.

[0022] Figure 4 A structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0024] Reference Figure 1 , Figure 1 A flow chart of a meteorological parameter measurement method applied to a mobile vehicle provided by an embodiment of the present application.

[0025] S101: Obtain a wind speed uncertainty, a business importance and a wind field complexity index of a to-be-measured region.

[0026] S102: Determine a comprehensive weight of each area in the to-be-measured region according to the wind speed uncertainty, the business importance and the wind field complexity index and respective weights, and determine an area type of each area based on the comprehensive weight and an area operation accessibility. The area operation accessibility includes passable and prohibited passable, and is used to indicate a passable area of the mobile vehicle in the to-be-measured region. The area type includes a priority measurement region and a reference measurement region.

[0027] S103: Plan an optimal driving route according to the area type of each area. The optimal driving route covers all the priority measurement regions.

[0028] S104: Send the optimal driving route to the mobile vehicle and obtain real-time meteorological data returned by the mobile vehicle, generate three-dimensional wind field data and gridded ground meteorological data in combination with meteorological monitoring data of ground meteorological observation stations.

[0029] The embodiment of the present application is applied to a mobile vehicle, including but not limited to unmanned operation vehicles and manned operation vehicles, and is particularly suitable for mobile vehicles in fixed areas, such as unmanned patrol vehicles, unmanned delivery vehicles, taxis, buses and the like. The mobile vehicle needs to contain a wind measuring laser radar and a meteorological sensor. The measured meteorological parameters are determined based on the activity range of all mobile vehicles.

[0030] The composition of a single measuring unit is as shown in Figure 2 , Figure 2 The structure schematic diagram of the mobile vehicle provided by the embodiment of the present application is shown in the figure. 1 is an unmanned delivery vehicle, 2 is a wind measuring laser radar, and 3 is a multi-element meteorological sensor. The unmanned delivery vehicle provides a working platform for the wind measuring laser radar and the multi-element meteorological sensor to perform mobile observation.

[0031] The satellite inertial device is integrated in the wind measuring laser radar, which can obtain its own attitude and speed information for the unmanned delivery vehicle. The multi-element meteorological sensor information is transmitted to the wind measuring laser radar for integration, and the vertical wind field system and the ground meteorological multi-element (temperature, humidity, air pressure, wind speed and direction, etc.) are processed and transmitted by the wind measuring laser radar system. The wireless router device is integrated in the radar system, which can perform remote control and data integration transmission.

[0032] In the operation of the mobile vehicle, different mobile vehicles usually run different routes. Taking a delivery vehicle as an example, under the premise of not affecting the overall logistics demand, the delivery route network planning can be performed, and important areas or complex areas can be run with encryption. Encryption can be performed in the form of time encryption or route coverage encryption.

[0033] The area segmentation information of the target area is formulated by the overlapping of the delivery route and the target area. The importance and complexity of different segmentation areas are used to perform the area type. For example, A and B partitions can be performed, in which the A area is a key area, the reference measurement area is a general area, and the specific division method is to discretize the target area by grid, and collect the following information for each area.

[0034] The wind speed uncertainty , can be determined according to the standard deviation of the historical wind field of the analysis data .

[0035] The normalized business importance , is determined by the following elements.

[0036] The unmanned vehicle parking lot or distribution center is taken as the key target, the key object is the distribution site, and the distance decay function from the key object The shortest distance from the key object to the grid point Determination: .

[0037] Wherein, is the influence scale, with a unit of km. The influence scale is used to represent the characteristic distance L of obvious influence decay, which determines the range of "influence diffusion". When L is small, the influence range is concentrated, and only the grid points near the target are important. When L is large, the influence range is more extensive, and the grid points far away will also have a certain weight, such as airports, ports, and city CBDs, which are targets that depend on large-scale weather.

[0038] When the grid point is very close to the key target, close to 1, indicating that the grid point is almost completely affected by the key target, which can be used to represent the high business importance of the grid point.

[0039] The length density of the business line in the grid : .

[0040] Normalized to . The population density is represented by . The asset value / customer level is represented by .

[0041] The calculation formula of the business importance is: .

[0042] In the above formula, , , , , respectively, are the parameter coefficients of the distance decay function, the normalized length density, the population density, and the asset value.

[0043] In step S101, the wind field complexity index determined according to the terrain undulation and building height / density . Specifically, the wind field complexity index can be calculated according to the slope intensity, terrain undulation, building coverage rate, and average building height of the to-be-measured area.

[0044] Wherein, the slope intensity is: .

[0045] Wherein, h represents the terrain elevation, and respectively represent the gradients of the elevation in the x and y directions.

[0046] Terrain roughness is: .

[0047] where (p) represents the elevation of grid point p, Max and min represent the highest point and the lowest point in the slice area respectively, represents the set of all grid points contained in the slice area.

[0048] Building coverage (also known as planar density): .

[0049] where Area(b) is the planar projection area of building b, represents the set of all buildings in slice area i, represents the total area of slice area i.

[0050] The average building height and roughness formula is: .

[0051] where is the average building height, |Bi| is the number of buildings in slice area i, is the height of building b, is the roughness of building height in slice area i.

[0052] Wind field complexity index : .

[0053] where , , and are the weights of slope intensity, terrain roughness, building coverage, and average building height respectively, where is the normalized representation of slope intensity , is the normalized representation of terrain roughness , is the normalized representation of building coverage , is the normalized representation of average building height , and the wind field complexity index is the normalized representation of wind field complexity .

[0054] Thereafter, the comprehensive weight of each slice area in the to-be-measured area is determined according to the wind speed uncertainty, the business importance, and the wind field complexity index and the respective weights, and the slice area type of each slice area is determined based on the comprehensive weight and slice area operation accessibility.

[0055] In the specific implementation process, the wind speed uncertainty, business importance, and wind field complexity indicators can be normalized to obtain the first weight set corresponding to wind speed uncertainty, the second weight set corresponding to business importance, and the third weight set corresponding to wind field complexity. Then, based on the wind speed uncertainty and its corresponding first weight set, business importance and its corresponding second weight set, and wind field complexity and its corresponding third weight set, the comprehensive weight of each area within the area to be measured is calculated. Areas with a comprehensive weight greater than a set threshold are designated as priority measurement areas, while areas with a comprehensive weight not greater than the set threshold are designated as reference measurement areas.

[0056] Operational reachability can be represented as , where 1 represents an area where vehicles can pass and park, and 0 represents an area where communication is not possible or parking is prohibited.

[0057] Normalize the above indicators, that is .

[0058] Define the overall weight as follows: , For business importance The normalized representation is the normalized business importance, where q, s, and t are the normalized business importance values, respectively. Wind speed uncertainty Wind field complexity index The parameters for each can be set by those skilled in the art.

[0059] In one feasible implementation, the wind speed uncertainty, the business importance, and the wind field complexity indices, and their respective weight ranges, can be: .

[0060] according to The top K% (e.g., 15-30%) are selected as the priority measurement area, and the rest are the reference measurement area.

[0061] Unreachable lattice ( It is downgraded to a reference measurement area or used only for remote measurement reference, and is not included in the encrypted insertion point set.

[0062] Based on the above-mentioned measurement areas, and according to the drivable routes, a route covering all priority measurement areas is developed.

[0063] The optimal driving route is sent to the mobile vehicle, and the real-time meteorological data returned by the mobile vehicle is obtained. Combined with the meteorological monitoring data of the ground meteorological observation station, three-dimensional wind field data and gridded ground meteorological data are generated.

[0064] According to the characteristics of the slice area type and the road network structure, a multi-objective path optimization model is constructed, and factors such as road accessibility, traffic flow, meteorological sensitive area coverage rate, and vehicle driving efficiency are comprehensively considered. An intelligent path planning algorithm (such as an improved ant colony algorithm or a genetic algorithm) is used to generate one or more optimal driving routes that cover all priority measurement areas. In the route planning process, congested road sections and construction areas are dynamically avoided to ensure the timeliness and continuity of data collection. In this way, the efficient scheduling of mobile observation vehicles is achieved, maximizing the spatial coverage and temporal density of meteorological data collection, and improving the response speed and operating efficiency of the overall observation system.

[0065] The planned optimal driving route is sent in real time to the vehicle terminal of the mobile meteorological observation vehicle through a wireless communication network (such as 4G / 5G or Beidou short message). The vehicle-mounted system integrates a high-precision satellite navigation module and an electronic map to realize visual display and voice navigation of the route, assisting the driver to drive according to the planned route. At the same time, the system supports dynamic route adjustment function, which can make local path correction according to real-time traffic conditions. The embodiments of the application can ensure that the mobile vehicle operates according to the planned route, reduces human path deviation, and ensures the spatial consistency of meteorological data collection and the normativity of task execution.

[0066] When generating three-dimensional wind field data and gridded ground meteorological data, the mobile vehicle has GPS positioning function to record the latitude and longitude coordinates and collection timestamp of each meteorological data. At the same time, a wireless communication module (such as 4G / 5G) is used to upload the data to the cloud server in real time. In addition, the historical accumulated data and real-time updated data of the ground meteorological observation station in the region are synchronously accessed to form a multi-source heterogeneous meteorological database, so as to generate three-dimensional wind field data and gridded ground meteorological data based on the multi-source heterogeneous meteorological database.

[0067] Here, how to generate three-dimensional wind field data and gridded ground meteorological data is not limited, and in a feasible implementation, for wind speed, the Kriging interpolation method is used for each height layer, and the horizontal wind speed and direction data of each height layer are obtained by interpolating the real-time meteorological data returned by the mobile vehicle and the horizontal wind speed and direction data in the meteorological monitoring data of the ground meteorological observation station.

[0068] The planned gridded wind speed is fused with the near-surface layer wind speed and direction observed by the ground meteorological station to construct a three-dimensional wind field scene including low and high layers.

[0069] And for the meteorological elements, the real-time meteorological data returned by the moving vehicle and the meteorological monitoring data of the ground meteorological observation station are taken as the input, and the Kriging interpolation method is used to perform spatial interpolation on the horizontal two-dimensional grid to generate the regular gridded distribution field of each meteorological element. Finally, the regular gridded distribution field corresponding to all meteorological elements is integrated to obtain the gridded ground meteorological data. The meteorological elements include temperature, humidity, air pressure and the like.

[0070] In addition to collecting the observed wind speed and direction data, GPS positioning data and observation time transmitted by the unmanned flow vehicle, the comprehensive data system also collects temperature, humidity, air pressure, wind speed and direction data observed by the ground meteorological observation station and vertical wind field measurement data of the fixed point wind laser radar. The comprehensive data system uses the Kriging interpolation method to form gridded ground meteorological data and three-dimensional wind field data.

[0071] Suppose the position of observation point i in three-dimensional space is , .

[0072] At each observation point, the three-dimensional wind field component is measured. Now it is necessary to interpolate these irregular observations to a certain grid point position , .

[0073] The principle of Kriging interpolation method is described below taking the U component as an example, The U component of point i is , The U component of point j is .

[0074] For any two observation points and , the spatial distance is: .

[0075] In order to calculate the average difference at different distances, the distance axis is divided into several intervals. Let the target distance be h, , the number of point pairs is .

[0076] Then .

[0077] The empirical semi-variogram is , which represents the average size of the observation value difference between point pairs with a distance of about h. The larger the distance, is generally larger, indicating that the correlation is weakened.

[0078] It should be noted that .

[0079] Select a theoretical model, for example, the exponential model has . The parameters are solved by least square fitting, which makes the square error between the empirical semi-variation function value and the theoretical model minimum , structural variance and variation range . When the theoretical semi-variation model is determined, the interpolation weight can be obtained by solving the ordinary Kriging equation group.

[0080] The Kriging equation is composed of two parts: , .

[0081] .

[0082] In the above formula, is the Lagrange multiplier, and the solution is , that is, the weight of each observation point. The left side of the first Kriging equation represents the weighted average of the similarity between the first observation point and all other observation points, and the right side is the similarity between the first observation point and the target point to be interpolated. The second equation constrains the weight sum to be 1 to ensure that the interpolation result is unbiased.

[0083] The U component of the grid point position is , which indicates that the predicted value of the target point is a weighted combination of the surrounding observation points.

[0084] By using the Kriging interpolation method for each height layer, the U, V, and W (U represents east-west, V represents north-south, and W represents vertical) orthogonal components of the wind speed obtained by the unmanned vehicle-mounted wind laser radar and the fixed-point laser radar are interpolated. For the point data to be interpolated, the data is equal to all observation point data multiplied by the weight of each point, and then converted into wind speed and direction information, so that the regular gridded wind speed and direction data of each height layer can be obtained. By fusing these layered wind fields with the near-surface wind speed and direction observed by the ground meteorological station, a three-dimensional wind field scene including the low layer and the high layer is constructed, and the spatial continuous description of the entire operation area wind field is realized.

[0085] For temperature, humidity, and pressure, the ground meteorological station and unmanned vehicle ground observation data are used as input, and the same Kriging interpolation method is used for spatial interpolation on the two-dimensional horizontal grid to generate regular gridded distribution fields of each element.

[0086] The present application can simultaneously output three-dimensional wind field and multi-element meteorological field under a unified grid framework, and can also output the uncertainty (prediction variance) information of each grid, providing a data basis for subsequent meteorological analysis, forecast correction, and business decision-making.

[0087] The embodiment of the application integrates the wind measurement laser radar and the multi-source meteorological sensor on the movable carrier, finely divides the target area according to the three-dimensional indexes of wind speed uncertainty, business importance and wind field complexity before starting the task, introduces the slice area operation accessibility constraint, and distinguishes the priority measurement area and the reference measurement area in space. The optimal driving route is generated by taking the coverage of all priority measurement areas as a hard constraint, so as to ensure that the mobile vehicle always operates along the path with the highest meteorological value and physical accessibility. Further, the data density of the priority measurement area with higher data measurement value is actively increased, and the low-value or impassable area is strategically abandoned, so as to improve the measurement efficiency of meteorological parameters. The wind measurement laser radar on the vehicle continuously scans the air wind field during the driving, and the ground meteorological sensor synchronously records the temperature, humidity and pressure elements, and the real-time data of the two and the fixed ground meteorological station are fused in the unified space-time framework, so as to make up for the inherent defects of the single-point station with blind area in the air, and overcome the short board of the time asynchronization of pure mobile observation, and finally generate the three-dimensional wind field and ground element field with vertical level and horizontal grid resolution, so as to provide high reliability dynamic basis for traffic scheduling, urban emergency and fine prediction. In addition, the mobile vehicle drives according to the optimal route, avoids the redundant mileage and carbon emission caused by the traditional carpet type inspection, and realizes the dual optimization of observation accuracy and operation cost.

[0088] In a feasible implementation, the shortest path algorithm can be called according to the distribution task corresponding to the logistics vehicle and the slice type of each slice area to generate a reference logistics distribution path meeting the time window constraint of the distribution task.

[0089] On this basis, the distribution time can be further considered, the distribution address is taken as a discrete point on the reference logistics distribution path, and the distribution address which is inconvenient for distribution or seriously affects the subsequent distribution task is removed. The specific process can be as follows.

[0090] Firstly, the distribution address of the distribution task is taken as a discrete point on the reference logistics distribution path.

[0091] Secondly, the overtime discrete point and the rule violation discrete point in the discrete point are removed to obtain a standard discrete point. The overtime discrete point is a distribution address that must be distributed overtime, and the rule violation discrete point is a distribution address that violates the road driving regulations during driving along the reference logistics distribution path.

[0092] Thirdly, the additional driving time corresponding to the insertion of each standard discrete point into the reference logistics distribution path is calculated.

[0093] Fourthly, if the additional driving time is not greater than the distribution time allowance, the standard discrete point is confirmed as a feasible candidate distribution point.

[0094] All target addresses involved in the delivery tasks to be performed are located on the pre-set reference delivery path (which is usually a regular transportation route covering the main service area, including the starting point, passing nodes, and the end point), forming a series of discrete points with clear spatial coordinates. In the specific implementation process, precise matching can be achieved with the help of geographic information systems (GIS) or map APIs to ensure that each delivery address can be uniquely matched to a specific location on the reference path. If an address cannot be directly matched to the area near the path, it will be associated with the nearest path segment.

[0095] For each discrete point, simulate the vehicle's driving process from the starting point of the path along the reference path until the point, combine real-time traffic data (such as congestion probability, speed limit rules), and vehicle average speed model to calculate the estimated time consumption to reach the point. If the time consumption has exceeded the deadline threshold required by the order, it is determined as a timeout discrete point.

[0096] Analyze the operations (such as lane changing, U-turn, reverse driving, etc.) that the moving vehicle needs to perform when driving along the reference path to the discrete point. If such operations violate road traffic safety regulations (such as left turn prohibition, truck restricted period / section), it is marked as a non-compliance discrete point.

[0097] Remove the above two types of abnormal points from the original discrete point set, and the remaining points are the standard discrete points that meet the time efficiency and compliance requirements.

[0098] For each standard discrete point, use the segmented calculation method to evaluate its impact on the reference path.

[0099] Assuming that the vehicle needs to temporarily deviate from the original path to complete the delivery of the point, re-plan the shortest feasible sub-path from the previous node to the point and then return to the original path. Compare the driving time of the original path in this interval with the driving time of the new path, and the difference between the two is the additional driving time caused by inserting the point. During the measurement process, factors such as road grade, intersection waiting time, and special section restrictions need to be considered.

[0100] According to the total time limit of the current transportation task, deduct the actual time consumption of the completed sections and the planned time consumption of the future sections to obtain the time buffer value (i.e., the delivery time margin) that can be used for flexible scheduling.

[0101] Compare the additional driving time of each standard discrete point with the delivery time margin. If the additional time of a point is not greater than the margin, it is included in the list of feasible candidate delivery points. Otherwise, it is temporarily processed. The candidate points that meet the conditions are arranged in ascending order of additional time, and the point that has the least impact on the total time efficiency is preferentially selected into the final delivery plan.

[0102] Through the above operation, the optimal solution under resource constraints can be screened, and it is ensured that the newly added distribution point will not break the time bottom line. Through dynamic matching of time margin and individual time consumption, both the existing transport capacity is fully utilized and the performance risk caused by excessive commitment is avoided, so as to balance between efficiency and service quality.

[0103] In the following, one specific implementation process of the above embodiment is exemplarily described: taking a logistics vehicle as a mobile vehicle, under the premise of not affecting the distribution timeliness, through the adjacent coverage and the greedy insertion strategy, the key areas are observed multiple times, and the spatial resolution of meteorological element measurement is improved. According to the distribution task of the day (distribution point position and time window), the shortest path algorithm is used to generate a benchmark logistics path that meets all the time window constraints, denoted as .

[0104] The work area is divided into a reference measurement area and a priority measurement area by using the method described above. The priority measurement area is an area with significant meteorological changes or high business importance. The road nodes in the key area are marked with a weight > 1, and 1 is the default weight of the reference measurement area.

[0105] Then, from the benchmark path , the key points within a distance of from the path are selected as the candidate set , and the positions that will cause the deadline of the distribution points to be exceeded if visited are excluded, as well as the positions that do not meet the traffic regulations or safety requirements.

[0106] For each candidate point p, the additional driving time after inserting the path is calculated .

[0107] , is the driving time of the benchmark logistics path, is the detour time after inserting the path.

[0108] If (the proportion of the distribution time margin, which can be between 0.1 and 0.3), it is determined that the discrete point is feasible, indicating that the distribution task can be executed.

[0109] Then, the priority of each feasible candidate point is calculated .

[0110] .

[0111] wherein is the road node marking weight, is the additional driving time. The priorities are sorted from high to low.

[0112] If the greedy insertion strategy is executed, starting from the candidate point with the highest priority, the insertion into the appropriate position in the path is attempted in turn, and the insertion condition is that all delivery time windows are still met and the total detour distance of the vehicle does not exceed the set upper limit. After inserting each point, the path and the remaining capacity are updated, and the next candidate point is continued.

[0113] Path execution and dynamic adjustment The final path is uploaded to the unmanned logistics vehicle control system for execution. During execution, if the previous delivery is completed ahead of schedule, the remaining capacity is recalculated, and the number of insertable key points is dynamically increased to achieve encryption coverage in operation.

[0114] In the above, the logistics vehicle is taken as an example of a mobile vehicle. Those skilled in the art can also use other mobile vehicles on the basis of the present application, set different task execution limits for different types of mobile vehicles, or increase the feasible area limit, etc., which all belong to adaptive changes on the basis of the present application.

[0115] Referring to Figure 3 , Figure 3 A weather parameter measurement system structure schematic diagram applied to a mobile vehicle is provided in the embodiments of the present application.

[0116] An index acquisition module is configured to acquire a wind speed uncertainty, a business importance, and a wind field complexity index of a to-be-measured region.

[0117] A slice type determination module is configured to determine a comprehensive weight of each slice in the to-be-measured region according to the wind speed uncertainty, the business importance, and the wind field complexity index and respective weights, and determine a slice type of each slice based on the comprehensive weight and slice operation accessibility. The slice operation accessibility includes passable and prohibited passable, and is used to indicate a passable slice of the mobile vehicle in the to-be-measured region. The slice type includes a priority measurement region and a reference measurement region.

[0118] A driving route planning module is configured to plan an optimal driving route according to the slice type of each slice. The optimal driving route covers all the priority measurement regions.

[0119] A weather parameter measurement module is configured to send the optimal driving route to the mobile vehicle, acquire real-time weather data returned by the mobile vehicle, and generate three-dimensional stereoscopic wind field data and gridded ground weather data in combination with weather monitoring data of a ground weather observation station.

[0120] Based on the above embodiments, as a preferred embodiment, if the mobile vehicle is a logistics vehicle, the driving route planning module is a module configured to generate a benchmark logistics delivery path meeting a time window constraint of a delivery task corresponding to the logistics vehicle according to the delivery task and the slice type of each slice by calling a shortest path algorithm.

[0121] Based on the above-mentioned embodiments, as a preferred embodiment, the distribution point candidate module is further configured to take the distribution address of the distribution task as a discrete point on the reference logistics distribution path. The discrete points that exceed the time limit and the discrete points that violate the road traffic regulations are removed to obtain standard discrete points. The standard discrete points are distribution addresses that do not exceed the time limit and do not violate the road traffic regulations. The additional driving time corresponding to the insertion of each standard discrete point into the reference logistics distribution path is calculated. If the additional driving time is not greater than the distribution time allowance, the standard discrete point is determined as a feasible candidate distribution point.

[0122] Based on the above-mentioned embodiments, as a preferred embodiment, the meteorological parameter measurement module is configured to perform the following steps: for wind speed, the horizontal wind speed in the real-time meteorological data returned by the flow vehicle and the meteorological monitoring data of the ground meteorological observation station is respectively interpolated and calculated using the Kriging interpolation method for each height layer to obtain the planning gridded wind speed and wind direction data of each height layer. The planning gridded wind speed is fused with the near-surface layer wind speed and wind direction observed by the ground meteorological station to construct a three-dimensional wind field scene including low layers and high layers. For meteorological elements, the real-time meteorological data returned by the flow vehicle and the meteorological monitoring data of the ground meteorological observation station are taken as inputs, and the Kriging interpolation method is used for spatial interpolation on the horizontal two-dimensional grid to generate a regular gridded distribution field of each meteorological element. The meteorological elements include temperature, humidity, and air pressure. The regular gridded distribution fields corresponding to all the meteorological elements are integrated to obtain gridded ground meteorological data.

[0123] The present application also provides a computer-readable storage medium corresponding embodiment. The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method described in the above method embodiment.

[0124] It can be understood that if the method in the above-mentioned embodiments is implemented in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and executes all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0125] The computer readable storage medium provided by the embodiment includes the method mentioned above, and the effects are the same as above.

[0126] The application also provides an electronic device, referring to Figure 4 The structural diagram of the electronic device provided by the embodiment of the application, as shown in Figure 4 may include a processor 1410 and a memory 1420.

[0127] The processor 1410 can include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1410 can be implemented in at least one hardware form of a DSP (Digital Signal Processing), a FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 1410 can also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit). The coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 1410 can be integrated with a GPU (Graphics Processing Unit) for rendering and drawing the content to be displayed by the display screen. In some embodiments, the processor 1410 can also include an AI (Artificial Intelligence) processor for processing machine learning-related computing operations.

[0128] The memory 1420 can include one or more computer readable storage media, which can be non-transitory. The memory 1420 can also include a high-speed random access memory and a non-volatile memory such as one or more disk storage devices, flash storage devices. In the embodiment, the memory 1420 is at least used to store the following computer program 1421, wherein the computer program is loaded and executed by the processor 1410, and can realize the related steps in the method executed by the electronic device side disclosed in any of the preceding embodiments. In addition, the resources stored by the memory 1420 can also include an operating system 1422 and data 1423, etc., and the storage mode can be temporary storage or permanent storage. The operating system 1422 can include Windows, Linux, Android, etc.

[0129] In some embodiments, the electronic device can further include a display screen 1430, an input / output interface 1440, a communication interface 1450, a sensor 1460, a power supply 1470, and a communication bus 1480.

[0130] Of course, Figure 4 The structure of the electronic device shown does not constitute a limitation on the electronic device in the embodiments of the present application. In actual applications, the electronic device can include more or fewer components than those shown, or combine some components. Figure 4

[0131] The various embodiments described in the specification are progressive in nature, and each embodiment highlights the differences from other embodiments. The same or similar parts between various embodiments can be mutually referred to. For the system provided by the embodiments, since it corresponds to the method provided by the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0132] The principles and implementation manners of the present application are described by using specific examples in the present application. The above description of the embodiments is only used to help understand the method of the present application and its core idea. It should be pointed out that, for those skilled in the art, without departing from the principles of the present application, some improvements and modifications can be made to the present application, and these improvements and modifications also fall within the protection scope of the present application.

[0133] It should also be noted that in the specification, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or device including the element.​

Claims

1. A method of measuring meteorological parameters applied to a moving vehicle, characterized in that, The flow vehicle is provided with a wind measuring laser radar and a meteorological sensor, and the meteorological parameter measurement method comprises: Obtaining the wind speed uncertainty, the business importance and the wind field complexity index of the to-be-measured region; the wind speed uncertainty is determined by the standard deviation of the historical wind field; According to the wind speed uncertainty, the business importance and the wind field complexity index and respective weights, determining the comprehensive weight of each area in the to-be-measured region, and determining the area type of each area based on the comprehensive weight and the area operation accessibility; wherein the area operation accessibility comprises passable and prohibited, and is used for indicating the passable area of the flow vehicle in the to-be-measured region; the area type comprises a priority measurement area and a reference measurement area; Planning an optimal driving route according to the area type of each area; the optimal driving route covers all the priority measurement areas; Sending the optimal driving route to the flow vehicle, and obtaining the real-time meteorological data returned by the flow vehicle, combining the meteorological monitoring data of the ground meteorological observation station to generate three-dimensional wind field data and grid ground meteorological data; The calculation process of the business importance comprises: According to the distance attenuation function, the length density of the business line in the to-be-measured region, the population density of the to-be-measured region and the asset value in the to-be-measured region, the business importance is calculated; wherein the length density of the business line in the to-be-measured region is the ratio of the total length of the business line in the to-be-measured region to the area of the to-be-measured region; The calculation process of the wind field complexity index comprises: According to the slope intensity, the terrain undulation, the building coverage rate and the average building height of the to-be-measured region, the wind field complexity index is calculated.

2. The method of measuring a meteorological parameter according to claim 1, characterized by, According to the wind speed uncertainty, the business importance and the wind field complexity index and respective weights, determining the comprehensive weight of each area in the to-be-measured region, and determining the area type of each area based on the comprehensive weight and the area operation accessibility comprises: Normalizing the wind speed uncertainty, the business importance and the wind field complexity index; Obtaining a first weight set corresponding to the wind speed uncertainty, a second weight set corresponding to the business importance and a third weight set corresponding to the wind field complexity index; According to the wind speed uncertainty and the first weight set corresponding thereto, the business importance and the second weight set corresponding thereto, and the wind field complexity index and the third weight set corresponding thereto, calculating the comprehensive weight of each area in the to-be-measured region; Taking the area with a comprehensive weight greater than a set threshold as the priority measurement area, and taking the area with a comprehensive weight not greater than the set threshold as the reference measurement area.

3. The method of claim 1, wherein If the flow vehicle is a logistics vehicle, planning an optimal driving route according to the area type of each area comprises: According to the delivery task corresponding to the logistics vehicle and the area type of each area, calling a shortest path algorithm to generate a benchmark logistics delivery path meeting the time window constraint of the delivery task.

4. The method of claim 3, wherein After the shortest path algorithm is called to generate a benchmark logistics distribution path meeting time window constraints of the distribution task corresponding to the logistics vehicle according to a distribution task corresponding to the logistics vehicle and a district type of each district, the method further includes: taking the distribution addresses of the distribution task as discrete points on the benchmark logistics distribution path; eliminating overtime discrete points and rule violation discrete points in the discrete points to obtain standard discrete points; the overtime discrete points are distribution addresses that must be distributed overtime, and the rule violation discrete points are distribution addresses that violate road driving regulations during driving along the benchmark logistics distribution path; calculating extra driving time corresponding to insertion of each standard discrete point into the benchmark logistics distribution path; if the extra driving time is not greater than a distribution time allowance, confirming that the standard discrete point is a feasible candidate distribution point.

5. The method of measuring a meteorological parameter according to claim 4, characterized by, The real-time meteorological data returned by the mobile vehicle is acquired, and three-dimensional wind field data and grid-based ground meteorological data are generated by combining meteorological monitoring data of ground meteorological observation stations, including: For wind speed, the Kriging interpolation method is used for each height layer to interpolate and calculate horizontal wind data in the real-time meteorological data returned by the mobile vehicle and the meteorological monitoring data of the ground meteorological observation stations, to obtain planning grid-based wind speed and direction data of each height layer; The planning grid-based wind speed is fused with near-ground layer wind speed and direction observed by the ground meteorological station to construct a three-dimensional wind field scene including low and high layers; For meteorological elements, the real-time meteorological data returned by the mobile vehicle and the meteorological monitoring data of the ground meteorological observation stations are taken as inputs, and the Kriging interpolation method is used for spatial interpolation on a horizontal two-dimensional grid to generate regular grid-based distribution fields of each meteorological element; the meteorological elements include temperature, humidity, and air pressure; The regular grid-based distribution fields corresponding to all the meteorological elements are integrated to obtain grid-based ground meteorological data.

6. A meteorological parameter measuring system for a moving vehicle, characterized by The mobile vehicle is provided with a wind measurement laser radar and a meteorological sensor, and the meteorological parameter measurement system includes: an index acquisition module configured to acquire wind speed uncertainty, business importance, and wind field complexity indexes of a to-be-measured region; the wind speed uncertainty is determined by a standard deviation of a historical wind field; a district type determination module configured to determine a comprehensive weight of each district in the to-be-measured region according to the wind speed uncertainty, the business importance, the wind field complexity index, and respective weights, and determine a district type of each district based on the comprehensive weight and district operation accessibility; the district operation accessibility includes passable and prohibited passable, and is used to indicate passable districts of the mobile vehicle in the to-be-measured region; the district type includes a priority measurement region and a reference measurement region; a driving route planning module configured to plan an optimal driving route according to the district type of each district; the optimal driving route covers all the priority measurement regions; a meteorological parameter measurement module configured to send the optimal driving route to the mobile vehicle, acquire real-time meteorological data returned by the mobile vehicle, and generate three-dimensional wind field data and grid-based ground meteorological data by combining meteorological monitoring data of ground meteorological observation stations. The calculation process of the service importance degree comprises: The service importance degree is calculated according to a distance attenuation function, a length density of the service line in the to-be-measured area, a population density of the to-be-measured area and an asset value in the to-be-measured area; wherein the length density of the service line in the to-be-measured area is a ratio of a total length of the service line in the to-be-measured area to an area of the to-be-measured area; The calculation process of the wind field complexity index comprises: The wind field complexity index is calculated according to a slope intensity, a terrain undulation, a building coverage rate and an average building height of the to-be-measured area.

7. An electronic device, comprising: The computer program comprises: a memory for storing a computer program; a processor for executing the computer program to implement the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer program is stored on the computer readable storage medium and is executed to implement the steps of the method according to any one of claims 1 to 5.

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