Large material transportation line management and control method and system based on satellite positioning system

Abstract

The embodiment of the invention discloses a large goods transportation line management and control method and system based on a satellite positioning system, relates to the technical field of large goods transportation management, can realize intelligent planning of a large goods transportation line, and ensures rapid and safe arrival of large goods. The method comprises the following steps: acquiring enterprise large goods information such as goods names, large production units or large loading source units, total truck-goods weight, total truck-goods length, total truck-goods width, total truck-goods height and the like; transportation information such as transportation starting time, transportation ending time, transportation starting point, transportation ending point and route information; and according to the enterprise large material information and the transportation information, a transportation route is generated and corrected based on the shortest distance and the optimal driving principle. Therefore, intelligent planning of the large goods transportation line is realized, and rapid and safe arrival of the large goods is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of large material transportation management, and in particular to a large material transportation route management and control method and system based on a satellite positioning system. BACKGROUND

[0002] Current navigation tools and path planning tools are mainly used for household vehicles, and drivers of ordinary household vehicles can already drive according to the prompts of navigation software without much thinking about path planning. However, this mode is difficult to effectively apply in large transportation operations. According to the latest statistical data, the proportion of accidents caused by path planning errors in large transportation operations in China in 2023 reached 34%. Large material transportation (such as wind power equipment, heavy machinery, and chemical devices) has the characteristics of over-limit, over-weight, and non-dissolution, and its transportation process faces multiple challenges such as route complexity, long approval process, high safety risk, and difficult time efficiency guarantee.

[0003] Therefore, current large material transportation route management for enterprises mainly relies on traditional navigation software and manual experience planning, and the management nodes such as transportation route approval, planning, and monitoring are relatively fragmented, which ultimately leads to large errors in path planning results. In actual transportation operations, only some references can be provided to the driver, and more adjustments need to be made by the driver and the co-pilot according to personal experience.

[0004] With the completion of the Beidou-3 global satellite navigation system and its high-precision service, satellite remote sensing, the Internet of Things, and artificial intelligence technologies have made breakthroughs in multi-source perception and intelligent decision-making, which has laid a foundation for the construction of a large material transportation full-link intelligent management system. Therefore, how to further improve the accuracy and effectiveness of large material transportation route intelligent planning to achieve the rapid and safe arrival of large materials at the destination has become a problem that needs to be solved as soon as possible. SUMMARY

[0005] Embodiments of the present application provide a large material transportation route management and control method and system based on a satellite positioning system, which can realize intelligent planning of large material transportation routes and ensure the rapid and safe arrival of large materials.

[0006] To achieve the above-mentioned purpose, embodiments of the present application adopt the following technical solutions:

[0007] In a first aspect, the method provided by the embodiments of the present application, as shown in Figure 3 includes:

[0008] Step 1, collect enterprise large material information and transportation information, wherein the transportation information is divided into material transportation time information and material transportation route information; specifically, the enterprise large material information includes: cargo name, large production unit or large loading source unit, total weight of vehicle and cargo, total length of vehicle and cargo, total width of vehicle and cargo, and total height of vehicle and cargo; the material transportation time information includes: transportation start time, transportation end time; the material transportation route information includes: transportation starting point, transportation ending point and route information.

[0009] Step 2, generate transportation route based on the shortest distance and the best driving principle according to the enterprise large material information and transportation information, and correct it.

[0010] Step 3, send the corrected transportation route to the employee terminal.

[0011] In this embodiment, the large material transportation path is intelligently planned and designed based on the shortest distance and the best driving principle. Optionally, after generating the large material transportation path, it can be used as an initial path, and then manual point selection and path correction are supported. The manual correction operation is performed on the basis of the generated transportation path. For example, in step 2, it includes:

[0012] Based on the Beidou positioning information (latitude and longitude) of the starting point and the ending point of the large material transportation, the coordinate conversion is completed based on the Gauss-Kruger coordinate system in plane projection, and the coordinate conversion is completed based on the DEM digital elevation model in three-dimensional terrain. Further, the Dijkstra-A* hybrid algorithm based on road network topology is used to output the actual transportation distance of the large material in three-dimensional space, and to correct it. According to the transportation information, the transportation distance is calculated: wherein D 路径 is the automatically calculated three-dimensional transportation distance, x i , y i is the node plane projection coordinate, α is the elevation influence coefficient, z i is the node elevation value, and n is the path segment number; according to the enterprise large material information and the calculated transportation distance, the transportation route is generated and the road section in the transportation route is corrected; a multi-objective decision model is constructed based on the shortest distance and the best driving principle to generate the best transportation route.

[0013] In the process of modifying the road section in the transport line, filtering is performed according to multiple constraint conditions such as total weight of vehicle and goods, total length of vehicle and goods, total height of vehicle and goods, and total width of vehicle and goods, to ensure optimal driving of large material transportation, wherein: for the road section in the transport line, the road section with total weight of vehicle and goods greater than the road load threshold of the road section is determined through the road surface BIM model, and the road section is excluded; and the road section with total height of vehicle and goods plus safety margin greater than the height of the bridge hole in the road section is determined through the elevation positioning of the satellite positioning system, and the road section is excluded; and the road section with total width of vehicle and goods greater than the effective width of the lane of the road section is determined through the image recognition result of the roadside camera, and the road section is excluded; and the road section with the road turning radius of the road section less than the minimum turning radius corresponding to the total length of vehicle and goods is determined, and the road section is excluded.

[0014] Specifically, a multi-objective decision-making model is constructed according to the shortest distance and optimal driving principle, including: constructing a multi-objective decision-making model: wherein, R 风险，k represents the quantitative value corresponding to the kth risk type, β1 and β2 are the first and second weight factors, k represents the risk ordinal number, and m represents the total number of risk types; the risk types include: sharp turn risk, steep slope risk, accident black spot risk, side wind overturning risk, and / or underground pipeline compression risk.

[0015] The risk types include: wherein, k 载 represents the load influence factor, r represents the actual turning radius of the road, r min represents the minimum turning radius of the vehicle, and σ represents the risk sensitivity coefficient. wherein, μ represents the base slope coefficient, s represents the slope percentage, represents the slope nonlinear correction coefficient, γ represents the vehicle speed influence coefficient, v 实 represents the actual vehicle speed.

[0016] wherein, A represents the historical accident frequency, B represents the serious accident weight, w 时 represents the time period risk coefficient, w 气 represents the meteorological risk coefficient, T represents the road improvement period, p 载 represents the load sensitivity coefficient, d represents the distance from the black spot center, and d0 represents the risk attenuation radius.

[0017] wherein, C d represents the wind resistance coefficient, ρ represents the air density, A represents the windward projection area of the transported goods, v 风 represents the wind speed, h 质心 represents the centroid height of the transported goods, L 轮距 represents the wheel track of the transport vehicle, and θ represents the wind direction angle.

[0018] In the preferred scheme, μ = 0.08, γ = 0.01; B = number of casualties * 1.5 + economic loss amount; p 载 = 1 + 0.02 * (total weight of vehicle and goods - 80); d0 = 200 m in a regular road section and d0 = 500 m in a tunnel road section.

[0019] Further, the risk type includes: Wherein, k p represents the pipeline type coefficient, G represents the axle load of the transport vehicle, A represents the tire ground contact area of the transport vehicle, P 允 represents the pipeline safety pressure, d represents the pipeline burial depth, and e is a natural constant.

[0020] In a second aspect, embodiments of the present application provide a system, comprising:

[0021] The large material transportation line project module, the large material transportation line marking module, and the large material transportation line management module;

[0022] The large material transportation line project module is configured to collect enterprise large material information and transportation information, wherein the transportation information includes material transportation time information and material transportation line information.

[0023] The large material transportation line marking module is configured to generate a transportation line based on the shortest distance and the optimal driving principle and correct the transportation line according to the enterprise large material information and the transportation information.

[0024] The large material transportation line management module is configured to send the corrected transportation line to an employee terminal.

[0025] The large material transportation line statistics module is configured to track and aggregate enterprise personnel large material transportation line management information, wherein the dimensions of the aggregated data include user statistics, priority statistics, and operation time statistics.

[0026] The method and system for controlling the transportation route of large goods based on the satellite positioning system provided by the embodiment of the present application collect enterprise large goods information such as the name of goods, the large goods production unit or large goods loading source unit, the total weight of vehicle and goods, the total length of vehicle and goods, the total width of vehicle and goods, and the total height of vehicle and goods; and transportation information such as the starting time of transportation, the ending time of transportation, the starting point of transportation, the ending point of transportation, and route information; generate the transportation route based on the principle of shortest distance and optimal driving according to the enterprise large goods information and the transportation information, and correct the transportation route; and send the corrected transportation route to the employee terminal. The present scheme is especially suitable for being combined with the satellite positioning system in China at present, and improves the accuracy of large goods transportation path planning, the dynamic supervision efficiency, and the intelligent level of risk prediction. The large goods transportation route can be planned automatically and provided to the personnel, that is, the large goods transportation route can be planned by one key, which realizes the intelligent planning of large goods transportation route and ensures the rapid and safe arrival of large goods. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0028] Figure 1 The structure diagram of the large goods transportation route management system provided by the embodiment of the present application.

[0029] Figure 2 The operation flowchart of the large goods transportation route management system provided by the embodiment of the present application.

[0030] Figure 3 The method flowchart provided by the embodiment of the present application. DETAILED DESCRIPTION

[0031] For those skilled in the art to better understand the technical solutions of the present application, the present application will be described in further detail below in conjunction with the drawings and specific embodiments. In the following, the embodiments of the present application will be described in detail, and the examples of the embodiments are shown in the drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present application, but cannot be interpreted as a limitation on the present application. Those skilled in the art can understand that, unless specifically stated, the singular forms "a", "an" and "the" used herein can also include the plural forms. It should be further understood that the phrase "comprising" used in the specification of the present application means that the features, integers, steps, operations, elements and / or components exist, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we say an element is "connected" or "coupled" to another element, it can be directly connected or coupled to the other element, or there can be an intermediate element. In addition, "connected" or "coupled" used herein can include wireless connection or coupling. The phrase "and / or" used herein includes any one of the associated listed items and all combinations thereof. Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as that generally understood by those skilled in the art to which the present application belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the prior art, and should not be interpreted with idealized or overly formal meanings unless defined as such.

[0032] In practical applications, the present embodiment can utilize a "Beidou+" fusion application framework, integrate Beidou real-time centimeter-level positioning, satellite remote sensing multi-dimensional environmental perception and intelligent algorithm model, and construct a large material transportation line management system.

[0033] As shown in Figure 1 A large material transportation line management system includes a large material transportation line project module, a large material transportation line management module, a large material transportation line labeling module, and a large material transportation line statistics module, each of which is associated with each other.

[0034] The large material transportation line project module is characterized in that it collects and acquires enterprise large material information, including cargo name, large production unit / large loading source unit, total weight of vehicle and cargo, total length of vehicle and cargo, total width of vehicle and cargo, and total height of vehicle and cargo; collects and acquires enterprise large material transportation time information, including transportation start time and transportation end time; and collects and acquires enterprise large material transportation line information, including transportation starting point, transportation ending point, and route information.

[0035] The oversized cargo transportation route marking module is used to intelligently plan and design transportation routes for oversized cargo based on the principles of shortest distance and optimal driving, and supports manual point selection and route correction.

[0036] The large-item material transportation route management module is used to review and optimize intelligently planned routes and locations.

[0037] The large-item material transportation route statistics module is used to track and summarize the management of large-item material transportation routes by company personnel. Statistical dimensions include statistics by user, by priority, and by operation time. Statistics by user refer to summarizing the number of planned and corrected routes based on system login ID; statistics by priority refer to summarizing the number of planned and corrected routes based on the priority of large-item material transportation projects; and statistics by operation time refer to summarizing the number of planned and corrected routes daily. Optionally, after the corrected transportation route is sent to the employee's terminal, the employee can also use the terminal for subsequent review and confirmation, including: checking for point errors in the intelligently planned route; checking for detours in the intelligently planned route; checking for unauthorized U-turns in the intelligently planned route; if none of the above issues exist, the review is approved; if any of the above issues exist, the review is rejected, and the route is corrected; after approval, transportation follows the planned route. It should be noted that these review operations are optional and can be performed manually. For skilled technical personnel, automated scripts can also be set up to complete the above review operations.

[0038] Subsequent processing also includes: tracking and summarizing the management of large-item material transportation routes by enterprise personnel. The statistical dimensions include statistics by user, statistics by priority, and statistics by operation time. Statistics by user refers to summarizing the number of route plans and corrections based on the system login ID; statistics by priority refers to summarizing the number of route plans and corrections based on the priority of large-item material transportation projects; and statistics by operation time refers to summarizing the number of route plans and corrections daily on a daily basis.

[0039] like Figure 2 As shown, a large-item material transportation route management system includes the following steps in its operation:

[0040] S1: Collect and obtain initial data on large-item material information and transportation information;

[0041] S1.1: Collect and obtain information on large items of goods from enterprises, including the name of the goods, the production unit of the large items / the source unit of the large items loading, the total weight of the vehicle and the goods, the total length of the vehicle and the goods, the total width of the vehicle and the goods, and the total height of the vehicle and the goods;

[0042] S1.2: Collect and obtain transportation time information for large items of goods from enterprises, including transportation start time, transportation end time, etc.

[0043] S1.3: Collecting the information of the transportation route of the large material of the enterprise, including the transportation starting point and the transportation ending point, etc.

[0044] S2: Automatically designing the transportation route by combining the transportation starting and ending points with the platform;

[0045] S2.1: Automatically calculating the transportation distance based on the starting point and the ending point of the large material, taking the Beidou positioning information (latitude and longitude) of the starting point and the ending point of the large material as the basis, completing the coordinate conversion based on the Gauss-Kruger coordinate system in the plane projection, completing the coordinate conversion based on the DEM digital elevation model in the three-dimensional terrain, further, using the Dijkstra-A* hybrid algorithm based on the road network topology, outputting the actual transportation distance of the large material in the three-dimensional space, and correcting it, the specific calculation method is as follows:

[0046]

[0047] wherein, D 路径 is the output three-dimensional transportation distance, x i , y i is the node plane projection coordinate, and a is the elevation influence coefficient, z i is the node elevation value, and n is the path segmentation;

[0048] S2.2: Designing the transportation road according to the total weight, total length, total height and total width of the vehicle and the goods, filtering through multiple constraint conditions such as load capacity, height limit, width limit and turning, and ensuring the optimal driving of the large material transportation.

[0049] In terms of load capacity, based on the road surface BIM model, if the total weight of the vehicle and the goods is greater than the road load threshold, the road section is excluded;

[0050] In terms of height limit, based on the Beidou elevation positioning, if the total height of the vehicle and the goods + 0.5m safety margin is greater than the bridge hole height, the road section is excluded;

[0051] In terms of width limit, based on the roadside camera AI recognition, if the total width of the vehicle and the goods is greater than the effective width of the lane, the road section is excluded;

[0052] In terms of turning, based on high-precision road network curvature analysis, if the road turning radius is less than the minimum turning radius of the vehicle, the road section is excluded.

[0053] S2.3: Considering the shortest distance and the optimal driving to generate the best transportation route, based on the shortest distance and the optimal driving principle, a multi-objective decision model is constructed to generate the best transportation route, and the specific selection algorithm is as follows:

[0054]

[0055] wherein, D 路径R is the three-dimensional transportation distance output by S1 风险 R is the road risk quantitative value, β1 and β2 are weight factors (the default values are 0.6 and 0.4), k is the risk ordinal number, and m is the total number of risks.

[0056] The road risk quantitative value R 风险 characterized in that it includes sharp turn risk, steep slope risk, accident black spot risk, crosswind overturning risk, underground pipeline compression risk and the like, and the specific calculation method is as follows:

[0057]

[0058] wherein k 载 is the load influence factor r is the actual turning radius of the road, r min is the minimum turning radius of the vehicle (dynamically called according to the vehicle type and total length of the vehicle and goods), and σ is the risk sensitivity coefficient (the default value is 0.5).

[0059]

[0060] wherein μ is the base slope coefficient (the default value is 0.08), s is the slope percentage, is the slope nonlinear correction coefficient (the default value of the heavy truck correction coefficient is 0.005), γ is the vehicle speed influence coefficient (the default value is 0.01), v 实 is the actual vehicle speed;

[0061]

[0062] wherein A is the historical accident frequency (taken from the traffic department black spot database), B is the serious accident weight (B = number of casualties * 1.5 + economic loss amount), w 时 is the time period risk coefficient (taken from the Beidou timing module), w 气 is the meteorological risk coefficient (taken from the meteorological department database), T is the road improvement period (1 if not improved), p 载 is the load sensitivity coefficient (p 载 = 1 + 0.02 * (total vehicle load - 80)), d is the distance from the black spot center (taken from the Beidou RTK positioning), and d0 is the risk attenuation radius (conventional 200m, tunnel 500m).

[0063]

[0064] wherein C d is the wind resistance coefficient (1.8 for blades, 0.5 for cylindrical tanks), ρ is the air density (taken from the Beidou temperature and pressure sensor), A is the windward projection area of the transported goods, v 风is the wind speed (taken from Beidou meteorological data), h 质心 is the centroid height of the transported goods, L 轮距 is the wheelbase of the transport vehicle, and θ is the wind direction angle (taken from Beidou meteorological data);

[0065]

[0066] wherein k p is the pipeline type coefficient (1.5 for gas pipeline, 1.3 for oil pipeline, and 1.1 for drainage pipeline), G is the axle load of the transport vehicle, A is the tire ground contact area of the transport vehicle, and P 允 is the pipeline safety pressure (taken from the national standard pipeline safety pressure data), and d is the pipeline burial depth (taken from the GIS database);

[0067] S3: correcting the intelligent planning route and submitting the automatic program script for review (which can also be manually reviewed by technical personnel, but the efficiency is not as good as the automatic script), and the specific review items include:

[0068] S3.1: reviewing whether the intelligent planning route has point position errors;

[0069] S3.2: reviewing whether the intelligent planning route has detouring;

[0070] S3.3: reviewing whether the intelligent planning route has unnecessary U-turns;

[0071] S3.4: if there are no problems above, the review is passed; if there are problems above, the review is not passed, and the route is corrected;

[0072] After the review is passed, the transport executes the planning route; and the enterprise personnel can continuously track and summarize the management of the large material transport route.

[0073] Each of the embodiments in the specification is described in a progressive manner, and the same and similar parts of each of the embodiments can be mutually referred to. Each of the embodiments mainly describes the differences from other embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, they are described more simply, and the relevant parts can be referred to the part of the description of the method embodiments. The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or replacements within the technical range disclosed by the present application can be easily thought of by those skilled in the art, and should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for controlling the transportation routes of large-item goods based on a satellite positioning system, characterized in that, include: Step 1: Collect and obtain information on large-item materials and transportation information of enterprises. The transportation information is divided into material transportation time information and material transportation route information. Step 2: Based on the enterprise's large-item material information and transportation information, generate and modify transportation routes according to the principles of shortest distance and optimal driving. Step 3: Send the revised transportation route to the employee terminal.

2. The method according to claim 1, characterized in that, The information on large items of goods of the enterprise includes: name of goods, production unit of large items or source unit of large items loading, total weight of vehicle and goods, total length of vehicle and goods, total width of vehicle and goods and total height of vehicle and goods; The material transportation time information includes: transportation start time and transportation end time; the material transportation route information includes: transportation origin, transportation destination and route information.

3. The method according to claim 1 or 2, characterized in that, Step 2 includes: Calculate the transportation distance based on the aforementioned transportation information: Among them, D 路径 For automatically calculated three-dimensional transportation distance, x i y i These are the nodal plane projection coordinates, α is the elevation influence coefficient, and z i Here, n represents the node elevation value, and n is the number of path segments. Based on the enterprise's large-item material information and the calculated transportation distance, a transportation route is generated and the road segments in the transportation route are corrected. A multi-objective decision-making model is constructed based on the principles of shortest distance and optimal driving to generate the best transportation route.

4. The method according to claim 3, characterized in that, The modified transport route includes the following sections: For road segments in the transportation route, road segments with a total vehicle and cargo weight exceeding the road load threshold are identified using the road surface BIM model, and these segments are excluded. And, by using the elevation positioning of the satellite positioning system, identify road sections where the sum of the total height of the vehicle and cargo and the safety margin is greater than the height of the bridge opening in the road section, and exclude such road sections; And, using the image recognition results from roadside cameras, identify road segments where the total width of vehicles and cargo is greater than the effective width of the lane, and exclude those road segments; And, identify road segments whose turning radius is less than the minimum turning radius corresponding to the total length of the vehicle and cargo, and exclude such road segments.

5. The method according to claim 3, characterized in that, The multi-objective decision-making model constructed based on the principles of shortest distance and optimal driving includes: Constructing a multi-objective decision-making model: Among them, R 风险，k This represents the quantitative value corresponding to the k-th risk type, where β1 and β2 are the first and second weighting factors, k represents the risk ordinal number, and m represents the total number of risk types. The risk types include: sharp bend risk, steep slope risk, accident black spot risk, crosswind overturning risk, and / or underground pipeline compression risk.

6. The method according to claim 5, characterized in that, The types of risk include: Where, k 载 This represents the load-bearing influence factor, where r represents the actual turning radius of the road. min σ represents the minimum turning radius of the vehicle, and σ represents the risk sensitivity coefficient. Where μ represents the base slope coefficient, s represents the slope percentage, θ represents the slope nonlinearity correction coefficient, γ represents the vehicle speed influence coefficient, and v 实 Indicates the actual vehicle speed; Where A represents the frequency of historical accidents, B represents the weight of serious accidents, and w 时 w represents the risk coefficient for a given period. 气 The weather risk coefficient is represented by T, the road repair period is represented by p. 载 d represents the load sensitivity coefficient, d represents the distance from the center of the black point, and d0 represents the risk attenuation radius. Among them, C d The drag coefficient is represented by ρ, the air density by A, and the windward projected area of ​​the transported goods by v. 风 Indicates wind speed, h 质心 L indicates the centroid height of the transported cargo. 轮距 The wheelbase of the transport vehicle is represented by θ, and the wind angle is represented by θ.

7. The method according to claim 6, characterized in that, μ=0.08, γ=0.01; B = Number of casualties * 1.5 + Amount of economic loss; p 载 = 1 + 0.02 * (total weight of vehicle and cargo - 80); In regular road sections, d0 = 200m, and in tunnel sections, d0 = 500m.

8. The method according to claim 5, characterized in that, The types of risk include: Where, k p G represents the pipeline type coefficient, A represents the axle load of the transport vehicle, and P represents the tire contact area of ​​the transport vehicle. 允 d represents the pipeline safety pressure, e represents the pipeline burial depth, and e is a natural constant.

9. A large-item material transportation route control system based on a satellite positioning system, characterized in that, include: The module includes a project module for transporting large items, a module for marking large items transport routes, and a module for managing large items transport routes. The large-item material transportation route project module is used to collect and obtain enterprise large-item material information and transportation information. The transportation information is divided into material transportation time information and material transportation route information. The large-item material transportation route marking module is used to generate and correct transportation routes based on the enterprise's large-item material information and transportation information, according to the principles of shortest distance and optimal driving. The large-item material transportation route management module is used to send the corrected transportation routes to employee terminals.

10. The system according to claim 9, characterized in that, It also includes a large-item material transportation route statistics module, which is used to track and summarize the management of large-item material transportation routes by enterprise personnel; among which, the dimensions of the statistical summary data include: statistics by user, statistics by priority, and statistics by operation time.

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