TMS intelligent scheduling method and system based on real-time data analysis

Abstract

The invention discloses a TMS intelligent scheduling method and system based on real-time data analysis, and relates to the technical field of transportation path planning. A TMS intelligent scheduling system based on real-time data analysis comprises a path acquisition module, a driving evaluation module, a path evaluation module, a comprehensive evaluation module and a path updating module. According to the invention, the average traffic flow density of the section of path is calculated according to the width of each section of path and the current traffic flow, so that the actual traffic condition of each section of path can be accurately reflected, and the vehicle density score of the transportation path is obtained through weighted summation by taking the path length as the weight. According to the method, the traffic congestion degree of each section of path and the proportion of the traffic congestion degree in the whole transportation path can be comprehensively considered, so that the traffic condition of the whole path can be reflected more objectively, and the optimal transportation path is selected based on the vehicle density score, so that the transportation efficiency can be improved, and the transportation cost can be reduced; and the safety of vehicle transportation can be improved to a certain extent.

Description

Technical Field

[0001] The present invention relates to the technical field of transport route planning, and in particular to a TMS intelligent scheduling method and system based on real-time data analysis. Background Art

[0002] TMS, or Transportation Management System, is a software solution used to optimize and manage logistics and transportation processes. TMS intelligent scheduling methods and systems cover every key aspect of the transportation process, from order generation and transportation planning to vehicle scheduling, cargo tracking, and transportation cost settlement. By integrating advanced technologies such as real-time data analysis, machine learning, and optimization algorithms, TMS enables comprehensive management and intelligent scheduling of transportation resources, transportation plans, transportation processes, order processing, and cost control. The core of TMS intelligent scheduling methods and systems lies in dynamically adjusting scheduling plans based on real-time data to ensure transportation safety, efficiency, economy, and reliability, thereby improving customer satisfaction and reducing operating costs.

[0003] Existing TMS intelligent scheduling methods and systems typically only select the shortest overall distance or the shortest overall time when planning the transportation routes of goods, thereby reducing transportation costs and improving transportation efficiency. However, when transporting fragile items such as glass products, if only the length and transportation time of the route are considered without considering indicators such as the bumpiness, tortuosity, and undulation of the transportation route, it is easy for fragile items to be repeatedly impacted and vibrated during transportation due to vehicle bumps, shaking, sharp turns, and frequent starts and stops, causing damage, seriously affecting the safety of fragile goods transportation.

[0004] Based on the above situation, the present invention proposes a TMS intelligent scheduling method and system based on real-time data analysis suitable for the transportation of fragile goods. Summary of the Invention

[0005] To overcome the shortcomings of existing TMS intelligent scheduling methods and systems, which typically choose the shortest overall distance or the shortest overall time when planning cargo transportation routes to reduce transportation costs and improve transportation efficiency, but fail to consider indicators such as the bumpiness, tortuosity, and undulation of the transportation route when transporting fragile items such as glass products, resulting in fragile items being easily damaged by repeated impact and vibration due to vehicle bumps, shaking, sharp turns, and frequent starts and stops during transportation, thereby seriously affecting the safety of fragile item transportation, the present invention proposes a TMS intelligent scheduling method and system based on real-time data analysis suitable for the transportation of fragile items.

[0006] A TMS intelligent scheduling method based on real-time data analysis includes the following steps:

[0007] Input the starting and ending points of the cargo transportation into the map service software or GIS software to obtain all the transportation routes from the starting point to the ending point. Obtain the road attribute data and vehicle travel data of all the transportation routes through the map service software and the traffic management department;

[0008] Based on the vehicle driving data of all transportation routes, the vehicle density score and driving danger score of each transportation route are calculated;

[0009] Based on the road attribute data of all transport routes, the bumpiness score, tortuosity score and undulation score of each transport route are calculated;

[0010] Conduct a safety assessment of each transport route based on its vehicle density score, driving hazard score, bumpiness score, tortuosity score, and undulation score. Select the optimal transport route based on the safety assessment results.

[0011] Real-time vehicle driving data of all transportation routes is obtained through map service software, and the optimal transportation route is updated based on the real-time vehicle driving data.

[0012] As a preferred aspect of the invention, the road attribute data includes the number of potholes in the transportation route, the total number of curves, the length and chord length of each curve, the total number of ramps, and the length and slope of each ramp; the vehicle driving data includes the width, length, total number of historical accidents, total historical traffic volume and current traffic volume of each section of the transportation route divided by forks in the road.

[0013] As a preferred aspect of the invention, the specific steps of calculating the vehicle density score and driving risk score of each transport path based on the vehicle driving data of all transport paths are as follows:

[0014] For each transport route, the historical accident probability is calculated based on the total number of historical accidents and the total historical traffic volume of each section of the transport route. The historical accident probability is then used as the driving risk score for the transport route. The specific calculation formula used is:

[0015]

[0016] in is the historical probability of an accident occurring, It is The total number of historical accidents for the segment route, It is The total historical traffic volume of the segment route, is the number of path segments contained in this transport path;

[0017] The average traffic density of each route segment is calculated based on the width of the route segment and the current traffic volume. The length of each route segment is used as a weight and the vehicle density score of the transport route is obtained through weighted summation. The calculation formula used is as follows:

[0018]

[0019] in is the vehicle density score, It is The length of the segment path, It is The current traffic volume of the segment, It is The width of the segment path, It is The average traffic density of the route segment.

[0020] As a preferred aspect of the invention, the specific steps of calculating the bumpiness score, the tortuosity score, and the undulation score of each transport path based on the road attribute data of all transport paths are as follows:

[0021] For each transport route, the number of potholes on the transport route is used as the bumpiness score. ;

[0022] Estimate the curvature of each curve in this transport route based on its length and chord length , based on the total number of curves on this transport route and the curvature of each curve The tortuosity of the route is evaluated by the length and the tortuosity score of the transport route is obtained. , tortuosity score The specific calculation formula is:

[0023]

[0024] in It is The curvature of the curve, It is The length of the curve, is the total number of bends;

[0025] The undulation degree of the transport route is evaluated based on the total number of ramps on the transport route and the length and slope of each ramp to obtain the undulation degree score of the transport route. , Fluctuation score The specific calculation formula is:

[0026]

[0027] in is the first segment ramp slope, is the first segment ramp length, is the total number of ramps.

[0028] As a preferred aspect of the application, the curvature of each curve is estimated based on the length and chord length of each curve in the transportation path The specific steps are as follows:

[0029] For each curve, the length and chord length of the curve are brought into the calculation formula of the radius of curvature , and the radius of curvature of the curve is obtained by solving the calculation formula , wherein the calculation formula of the radius of curvature is as follows:

[0030]

[0031] wherein is the chord length of the curve, is the length of the curve;

[0032] The curvature of the curve is calculated according to the radius of curvature , and the calculation formula of the curvature is as follows: .

[0033] As a preferred aspect of the application, the safety of each transportation path is evaluated according to the vehicle density score, the driving risk score, the bumping degree score, the tortuosity score and the undulation degree score of all transportation paths, and the optimal transportation path is selected according to the result of the safety evaluation.

[0034] For each transportation path, the scores of the transportation path are normalized to obtain the normalized scores.

[0035] The normalized scores are weighted and summed to obtain the safety score of the transportation path, and the transportation path with the smallest safety score is selected as the optimal transportation path, wherein the calculation formula of the safety score is as follows:

[0036]

[0037] wherein , , , and ​respectively are the normalized driving risk score, the vehicle density score, the bumpiness score, the tortuosity score and the elevation score, respectively, and 、 、 、 and are the weight coefficients of the corresponding scores, respectively, and .

[0038] As a preferred aspect of the invention, the specific step of updating the optimal transportation path according to real-time vehicle driving data is:

[0039] segmenting the optimal transportation path according to the road junctions, taking the next junction as the updated starting point each time the vehicle passes a road junction, and obtaining all transportation paths from the updated starting point to the end point through the map service software or GIS software;

[0040] obtaining the vehicle driving data at this time through the map service software and calculating the safety scores of all transportation paths from the updated starting point to the end point , and selecting the transportation path with the smallest safety score as the updated optimal transportation path.

[0041] A TMS intelligent scheduling system based on real-time data analysis, comprising:

[0042] a path obtaining module for obtaining all transportation paths from the starting point to the end point of the goods transportation through the map service software or GIS software, and obtaining the road attribute data and vehicle driving data of all transportation paths through the map service software and the traffic management department;

[0043] a driving evaluation module for obtaining the vehicle density score and the driving risk score of each transportation path based on the vehicle driving data of all transportation paths and through calculation;

[0044] a path evaluation module for obtaining the bumpiness score, the tortuosity score and the elevation score of each transportation path based on the road attribute data of all transportation paths and through calculation;

[0045] a comprehensive evaluation module for performing safety evaluation on each transportation path according to the vehicle density score, the driving risk score, the bumpiness score, the tortuosity score and the elevation score of all transportation paths, and selecting the optimal transportation path according to the result of the safety evaluation;

[0046] a path updating module for obtaining the real-time vehicle driving data of all transportation paths through the map service software, and updating the optimal transportation path according to the real-time vehicle driving data.

[0047] The present application has the following advantages:

[0048] 1、The present application can accurately reflect the actual traffic conditions of each path by calculating the average vehicle density of each path according to the width of the path and the current traffic volume, and can comprehensively consider the traffic congestion degree of each path and its proportion in the whole transportation path by weighting the path length and obtaining the vehicle density score of the transportation path through weighted summation, so as to more objectively reflect the traffic conditions of the whole path, avoid misjudgment of the congestion degree of the whole path due to some short-distance and high-density sections, and improve the rationality and scientificity of the TMS intelligent scheduling method and system when planning the transportation path of fragile goods.

[0049] 2、The present application can comprehensively consider the influence of various factors on the transportation of fragile goods by safety evaluation of each transportation path according to the vehicle density score, driving risk score, jolt degree score, tortuosity degree score and undulation degree score of all transportation paths, so as to help the driver to select a transportation path with higher safety, so as to not only ensure the safety and integrity of fragile goods during transportation, but also ensure the smoothness and reliability of the transportation process, and improve the rationality and scientificity of the TMS intelligent scheduling method and system when planning the transportation path of fragile goods.

[0050] 3、The present application can update the optimal transportation path in real time according to real-time vehicle driving data, which can analyze the road conditions in real time and dynamically update the optimal transportation path to quickly avoid congestion and unexpected situations, so as to not only reduce the transportation time and jolt and reduce the breakage rate of fragile goods, but also adjust the transportation path in real time according to the safety and flatness of the subsequent road, so as to ensure transportation safety and improve the efficiency and reliability of goods transportation, thereby realizing efficient and safe transportation of fragile goods, and improving the rationality and scientificity of the TMS intelligent scheduling method and system when planning the transportation path of fragile goods. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 A flowchart of a TMS intelligent scheduling method based on real-time data analysis used by an embodiment of the present application.

[0052] Figure 2 A structure diagram of a TMS intelligent scheduling system based on real-time data analysis used by an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order for those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

[0054] Embodiment 1, a TMS intelligent scheduling method based on real-time data analysis, as shown in the figure, comprises the following steps: Figure 1

[0055] The starting point and the ending point of the cargo transportation are input into the map service software or the GIS software to obtain all transportation paths from the starting point to the ending point, and the road attribute data and the vehicle driving data of all transportation paths are obtained through the map service software and the traffic management department;

[0056] The vehicle density score and the driving risk score of each transportation path are obtained through calculation based on the vehicle driving data of all transportation paths;

[0057] The bumping degree score, the tortuosity degree score and the undulation degree score of each transportation path are obtained through calculation based on the road attribute data of all transportation paths;

[0058] The safety of each transportation path is evaluated according to the vehicle density score, the driving risk score, the bumping degree score, the tortuosity degree score and the undulation degree score of all transportation paths, and the optimal transportation path is selected according to the result of the safety evaluation;

[0059] The real-time vehicle driving data of all transportation paths are obtained through the map service software, and the optimal transportation path is updated according to the real-time vehicle driving data.

[0060] It should be noted that when transporting the conventional cargo to multiple locations, the cargo transportation priority of each location needs to be determined according to the dynamically changing transportation demand (such as the urgency of cargo transportation and the scheduled arrival time, etc.), then the optimal order of cargo transportation between multiple locations is determined according to the cargo transportation priority of each location, and when the vehicle needs to transport the cargo from one location to another location according to the optimal order, the genetic algorithm, the ant colony algorithm or the simulated annealing method can be used to plan the optimal path with the shortest distance, the shortest driving time or the lowest transportation cost.

[0061] The road attribute data includes the number of pothole defects in the transportation path, the total number of curves, the length and chord length of each curve, the total number of slopes and the slope length and slope of each slope; the vehicle driving data includes the width, length, total number of historical accidents, total amount of historical traffic flow and current traffic flow of each path segmented by the intersection in the transportation path.

[0062] ​The specific steps of calculating the vehicle density score and driving risk score of each transport path based on the vehicle driving data of all transport paths are as follows:

[0063] For each transport route, the historical accident probability is calculated based on the total number of historical accidents and the total historical traffic volume of each section of the transport route. The historical accident probability is then used as the driving risk score for the transport route. The specific calculation formula used is:

[0064]

[0065] in is the historical probability of an accident occurring, It is The total number of historical accidents on the segment route, It is The total historical traffic volume of the segment route, is the number of path segments contained in this transport path;

[0066] The average traffic density of each route segment is calculated based on the width of the route segment and the current traffic volume. The length of each route segment is used as a weight and the vehicle density score of the transport route is obtained through weighted summation. The calculation formula used is as follows:

[0067]

[0068] in is the vehicle density score, It is The length of the segment path, It is The current traffic volume of the segment, It is The width of the segment path, It is The average traffic density of the route segment.

[0069] The above steps calculate the average traffic density of each path segment based on the width of each path segment and the current traffic volume, which can accurately reflect the actual traffic conditions of each path segment. Using the path length as the weight and obtaining the vehicle density score of this transport path through weighted summation can comprehensively consider the traffic congestion level of each path segment and its proportion in the entire transport path, thereby more objectively reflecting the traffic conditions of the entire path, avoiding misjudgment of the congestion level of the entire path due to certain short-distance and high-density sections. Moreover, selecting the optimal transport path based on the vehicle density score can not only improve transportation efficiency and reduce transportation costs, but also improve the safety of vehicle transportation to a certain extent, thereby improving the rationality and scientificity of this TMS intelligent scheduling method and system when planning the transportation path of fragile goods.

[0070] The specific steps of calculating the bumpiness score, the tortuosity score, and the undulation score of each transport path based on the road attribute data of all transport paths are as follows:

[0071] For each transport route, the number of potholes on the transport route is used as the bumpiness score. ;

[0072] Estimate the curvature of each curve in this transport route based on its length and chord length , based on the total number of curves on this transport route and the curvature of each curve The tortuosity of the route is evaluated by the length and the tortuosity score of the transport route is obtained. , tortuosity score The specific calculation formula is:

[0073]

[0074] in It is The curvature of the curve, It is The length of the curve, is the total number of bends;

[0075] The undulation degree of the transport route is evaluated based on the total number of ramps on the transport route and the length and slope of each ramp to obtain the undulation degree score of the transport route. , Fluctuation score The specific calculation formula is:

[0076]

[0077] in It is The slope of the ramp, It is The length of the slope, is the total number of ramps.

[0078] The curvature of each curve is estimated based on the length and chord length of each curve in this transport path. The specific steps are:

[0079] For each curve, substitute the length of the curve and the chord length for the radius of curvature. The calculation relationship is solved to obtain the curvature radius of the curve. , where the radius of curvature The calculation relationship is as follows:

[0080]

[0081] wherein is the chord length of the curved path, is the length of the curved path;

[0082] According to the radius of curvature The curvature of the curved path is calculated , the calculation formula of the curvature The calculation formula of the curvature .

[0083] It should be noted that the calculation formula of the radius of curvature The calculation formula can be solved by Newton iteration method and the like.

[0084] The specific steps of the safety evaluation of each transportation path according to the vehicle density score, the driving risk score, the jolt degree score, the tortuosity score and the undulation degree score of all transportation paths, and the selection of the optimal transportation path according to the result of the safety evaluation are as follows:

[0085] For each transportation path, the scores of the transportation path are normalized to obtain the normalized scores.

[0086] The normalized scores are weighted and summed to obtain the safety score of the transportation path, and the transportation path with the minimum safety score is selected as the optimal transportation path, wherein the calculation formula of the safety score .

[0087]

[0088] wherein , , , and are the normalized driving risk score, the vehicle density score, the jolt degree score, the tortuosity score and the undulation degree score, respectively, and , , , and are the weight coefficients of the corresponding scores, and .

[0089] It should be noted that the calculation formula of the normalization of the scores of the transportation path is as follows:

[0090]

[0091] wherein is the jth the normalized score of the i-th transportation path, and respectively the maximum and minimum of the score among all transportation paths, and is the score of the i-th transportation path.

[0092] It should be noted that the higher the driving risk score is, the higher the probability of accidents on the transportation path is, and thus the higher the risk of damage to fragile goods on the transportation path is, so the transportation path with a lower driving risk score should be selected as much as possible; the vehicle density score is used to evaluate the traffic density of the transportation path, and a higher traffic density means more mutual interference between vehicles, and more frequent situations such as queuing and sudden braking, which makes the goods in the vehicle body more likely to shake and collide frequently and cause damage to the fragile goods, so the transportation path with a lower vehicle density score should be selected as much as possible; the jolt degree score is used to evaluate the flatness of the road, and a poor road flatness will cause the jolt of the vehicle to increase during driving, and the fragile goods are more likely to be impacted and vibrated repeatedly, thereby increasing the risk of damage to the fragile goods, so the transportation path with a lower jolt degree score should be selected as much as possible; the tortuosity score is used to evaluate the tortuosity of the road, and the greater the curvature of the bend means the greater the turning amplitude, so the vehicle body is more likely to tilt greatly during turning, thereby making the goods more likely to collide with the vehicle body under the action of inertia and cause damage, and too many bends mean that the vehicle needs to turn frequently, thereby increasing the opportunities and risks of goods shaking and colliding, so the transportation path with a lower tortuosity score should be selected as much as possible; the undulation score is used to evaluate the undulation of the road, and a larger slope angle will cause the speed of the vehicle to be difficult to control when going uphill or downhill, such as insufficient power of the vehicle when going uphill, which may cause the vehicle to roll, and long-time braking when going downhill, which is easy to cause brake failure, thereby easily causing the vehicle to jolt and shake more and cause damage to the fragile goods, so the transportation path with a lower undulation score should be selected as much as possible.

[0093] The above steps can comprehensively consider the influence of various factors on the transportation of fragile goods by evaluating the safety of each transportation path according to the vehicle density score, the driving risk score, the jolt degree score, the tortuosity score and the undulation score of all transportation paths, and selecting the optimal transportation path according to the result of the safety evaluation, thereby helping the driver to select a transportation path with higher safety, which not only ensures the safety and integrity of the fragile goods during transportation, but also ensures the smoothness and reliability of the transportation process, and improves the rationality and scientificity of the TMS intelligent scheduling method and system when planning the transportation path of the fragile goods.

[0094] The specific steps of updating the optimal transportation path according to the real-time vehicle driving data are as follows: ​

[0095] According to the branch of the road, the optimal transportation path is segmented, and the vehicle passes through a branch of the road every time. The next branch is taken as the updated starting point, and the map service software or GIS software is used to obtain all transportation paths from the updated starting point to the terminal point;

[0096] The map service software is used to obtain the vehicle driving data at this time and score the safety of all transportation paths from the updated starting point to the terminal point The calculation is performed, and the transportation path with the smallest safety score is selected as the updated optimal transportation path.

[0097] The above steps can analyze the road conditions in real time and dynamically update the optimal transportation path by updating the optimal transportation path according to the real-time vehicle driving data, so as to quickly avoid congestion and sudden conditions, thereby not only reducing the transportation time and jolt and reducing the breakage rate of fragile goods, but also adjusting the transportation path in real time according to the safety and flatness of the subsequent road, thereby ensuring transportation safety and improving the efficiency and reliability of goods transportation, and thus realizing efficient and safe transportation of fragile goods, and improving the rationality and scientificity of the TMS intelligent scheduling method and system when planning the transportation path of fragile goods.

[0098] Embodiment 2, a TMS intelligent scheduling system based on real-time data analysis, as shown in Figure 2 , comprising:

[0099] A path acquisition module is used to obtain all transportation paths from the starting point to the terminal point of the goods transportation by the map service software or GIS software, and obtain the road attribute data and vehicle driving data of all transportation paths by the map service software and the traffic management department;

[0100] A driving evaluation module is used to obtain the vehicle density score and driving risk score of each transportation path based on the vehicle driving data of all transportation paths and by calculation;

[0101] A path evaluation module is used to obtain the jolt degree score, the tortuosity degree score and the undulation degree score of each transportation path based on the road attribute data of all transportation paths and by calculation;

[0102] A comprehensive evaluation module is used to evaluate the safety of each transportation path according to the vehicle density score, the driving risk score, the jolt degree score, the tortuosity degree score and the undulation degree score of all transportation paths, and select the optimal transportation path according to the result of the safety evaluation;

[0103] A path updating module is used to obtain the real-time vehicle driving data of all transportation paths by the map service software, and update the optimal transportation path according to the real-time vehicle driving data.

[0104] It should be understood that various changes and modifications to the embodiments described herein will be apparent to those skilled in the art, and it is intended that the scope of the present application can be given by the appended claims. Aspects of the present application not specifically covered by the claims are not to be considered as being outside the scope of the present application.

Claims

1. A TMS intelligent scheduling method based on real-time data analysis, characterized in that: The following steps are involved: Input the starting and ending points of the cargo transportation into the map service software or GIS software to obtain all the transportation routes from the starting point to the ending point. Obtain the road attribute data and vehicle travel data of all the transportation routes through the map service software and the traffic management department; Based on the vehicle driving data of all transportation routes, the vehicle density score and driving danger score of each transportation route are calculated; Based on the road attribute data of all transport routes, the bumpiness score, tortuosity score and undulation score of each transport route are calculated; Conduct a safety assessment of each transport route based on its vehicle density score, driving hazard score, bumpiness score, tortuosity score, and undulation score. Select the optimal transport route based on the safety assessment results. Real-time vehicle driving data of all transportation routes is obtained through map service software, and the optimal transportation route is updated based on the real-time vehicle driving data.

2. A TMS intelligent scheduling method based on real-time data analysis according to claim 1, characterized in that: The road attribute data includes the number of potholes in the transportation route, the total number of curves, the length and chord length of each curve, the total number of ramps, and the length and slope of each ramp; the vehicle driving data includes the width, length, total number of historical accidents, total historical traffic volume and current traffic volume of each section of the transportation route divided by forks.

3. The TMS intelligent scheduling method based on real-time data analysis according to claim 2, characterized in that: The specific steps of calculating the vehicle density score and driving risk score of each transport path based on the vehicle driving data of all transport paths are as follows: For each transport route, the historical accident probability is calculated based on the total number of historical accidents and the total historical traffic volume of each section of the transport route. The historical accident probability is then used as the driving risk score for the transport route. The specific calculation formula used is: ； in is the historical probability of an accident occurring, It is The total number of historical accidents on the segment route, It is The total historical traffic volume of the segment route, is the number of path segments contained in this transport path; The average traffic density of each route segment is calculated based on the width of the route segment and the current traffic volume. The length of each route segment is used as a weight and the vehicle density score of the transport route is obtained through weighted summation. The calculation formula used is as follows: ； in is the vehicle density score, It is The length of the segment path, It is The current traffic volume of the segment, It is The width of the segment path, It is The average traffic density of the route segment.

4. The TMS intelligent scheduling method based on real-time data analysis according to claim 3 is characterized in that: The specific steps of calculating the bumpiness score, the tortuosity score, and the undulation score of each transport path based on the road attribute data of all transport paths are as follows: For each transport route, the number of potholes on the transport route is used as the bumpiness score. ; Estimate the curvature of each curve in this transport route based on its length and chord length , based on the total number of curves on this transport route and the curvature of each curve The tortuosity of the route is evaluated by the length and the tortuosity score of the transport route is obtained. , tortuosity score The specific calculation formula is: ； in It is The curvature of the curve, It is The length of the curve, is the total number of bends; The undulation degree of the transport route is evaluated based on the total number of ramps on the transport route and the length and slope of each ramp to obtain the undulation degree score of the transport route. , Fluctuation score The specific calculation formula is: ； in It is The slope of the ramp, It is The length of the slope, is the total number of ramps.

5. The TMS intelligent scheduling method based on real-time data analysis according to claim 4 is characterized in that: The curvature of each curve is estimated based on the length and chord length of each curve in this transport path. The specific steps are: For each curve, substitute the length of the curve and the chord length for the radius of curvature. The calculation relationship is solved to obtain the curvature radius of the curve. , where the radius of curvature The calculation relationship is as follows: ； in is the chord length of the curve, is the length of the curve; According to the curvature radius Calculate the curvature of the curve , curvature The calculation formula is as follows: .

6. The TMS intelligent scheduling method based on real-time data analysis according to claim 5, characterized in that: The specific steps of performing a safety assessment on each transport route based on the vehicle density score, driving hazard score, bumpiness score, tortuosity score, and undulation score of all transport routes and selecting the optimal transport route based on the safety assessment results are as follows: For each transport route, normalize the scores of the transport route to obtain normalized scores; The normalized scores are weighted and summed to obtain the safety score of the transport route. , select the safety score The transport path with the smallest score is taken as the optimal transport path, where the safety score The calculation formula is as follows: ； in 、 、 、 and are the normalized driving risk score, vehicle density score, bumpiness score, tortuosity score and undulation score, respectively. 、 、 、 and are the weight coefficients of the corresponding scores, and .

7. The TMS intelligent scheduling method based on real-time data analysis according to claim 6, characterized in that: The specific steps of updating the optimal transportation route according to the real-time vehicle driving data are as follows: The optimal transport route is segmented according to the road forks. Every time the vehicle passes a road fork, the next fork is used as the updated starting point. All transport routes from the updated starting point to the end point are obtained through map service software or GIS software. Obtain the vehicle driving data at this time through the map service software and score the safety of all transportation routes from the updated starting point to the end point Perform calculations and select a safety score The transport path with the smallest score is taken as the updated optimal transport path.

8. A TMS intelligent scheduling system based on real-time data analysis, applied to a TMS intelligent scheduling method based on real-time data analysis according to any one of claims 1 to 7, characterized in that: Includes: The route acquisition module is used to obtain all transportation routes from the starting point to the end point of cargo transportation through map service software or GIS software, and obtain road attribute data and vehicle travel data of all transportation routes through map service software and traffic management departments; A driving assessment module is used to calculate the vehicle density score and driving hazard score of each transportation route based on the vehicle driving data of all transportation routes; A route evaluation module is used to calculate the bumpiness score, tortuosity score and undulation score of each transport route based on the road attribute data of all transport routes; A comprehensive assessment module is used to evaluate the safety of each transport route based on the vehicle density score, driving hazard score, bumpiness score, tortuosity score, and undulation score of all transport routes, and select the optimal transport route based on the results of the safety assessment; The route update module is used to obtain real-time vehicle driving data of all transportation routes through map service software, and update the optimal transportation route based on the real-time vehicle driving data.

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