Freight transportation route planning and tracking method based on sustainable development

Abstract

The present invention relates to the technical field of path planning and tracking processing, and specifically discloses a method for planning and tracking a cargo transportation path based on sustainable development. The method firstly processes multidimensional data of historical driving information of each cargo transportation vehicle, road condition information of each planned transportation path, and transportation demand information corresponding to an order, comprehensively evaluates the path demand matching degree between each planned transportation path and the cargo transportation vehicle, and screens out the cargo transportation path with the highest path demand matching degree. Then, by real-time tracking of the transportation process of the vehicle in the first cargo transportation path, an accurate and comprehensive data basis is provided for the subsequent matching of a sustainable development transportation feasibility threshold of a second cargo transportation path, and the method is compared with the analyzed sustainable development transportation feasibility of the second cargo transportation path, so as to ensure that through reasonable planning of the cargo transportation path, the pollution emissions generated by the vehicle during the actual transportation process are reduced, thereby ensuring the sustainable development of cargo transportation.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning and tracking processing, and in particular to a method for cargo transportation path planning and tracking based on sustainable development. Background Art

[0002] Nowadays, the use of technologies such as big data, artificial intelligence, and the Internet of Things can better plan and adjust transportation routes to ensure the sustainability of transportation. However, greenhouse gases, pollutants and other emission sources that affect the environment will inevitably be generated during the transportation process. Therefore, it is necessary to optimize the cargo transportation routes to reduce fuel consumption during transportation, thereby reducing greenhouse gas emissions such as carbon dioxide and reducing the impact on the environment.

[0003] For example, the invention patent with publication number CN118171980A discloses a logistics and transportation optimization method and system based on the Internet of Things, which involves the field of logistics and transportation technology, including: using deep learning to analyze historical transportation data of goods; predicting possible delays or problems, collecting feedback information to optimize prediction results; building a multi-objective optimization model, and finding the optimal solution through the Pareto optimization algorithm; based on the optimized prediction results, re-planning the route and optimizing the transportation strategy.

[0004] For example, the invention patent with announcement number CN107844935B announced a vehicle scheduling and path planning method based on environmental protection and cost saving, with the minimum fuel consumption, urea consumption and pollutant emissions as the planning target, and established an integer programming model with a time window. Through the number and model of distribution center vehicles, vehicle load, distribution center and customer location information, time requirements for cargo delivery, supply and demand at each point and cargo delivery priority input by the enterprise, with fuel consumption, urea consumption and pollutant emissions as the target, a reasonable vehicle scheduling plan is designed for the enterprise.

[0005] Combining the above technical solutions, it is found that in the current technical solutions for planning and adjusting transportation routes, multi-dimensional data such as historical data and cargo-related data are usually used to establish a path planning model related to the transportation route, and directly transport goods according to the established model. In this case, during the actual transportation process, the pollution emissions generated will be quite different from the pollution emissions planned in advance, which not only fails to guarantee the sustainable transportation of vehicles, but also further causes environmental pollution. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides a cargo transportation route planning and tracking method based on sustainable development, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a cargo transportation route planning and tracking method based on sustainable development, including: S1. Allocating vehicles and routes: The order service platform receives an order response signal, thereby obtaining the required quantity of goods corresponding to the order, the starting point of the cargo transportation corresponding to the order, and the ending point of the cargo transportation corresponding to the order, and obtains the number of cargo transportation vehicles and each planned transportation route through the allocation mechanism of the order service platform.

[0008] S2. Screening cargo transportation routes: Obtain the historical driving information of each cargo transportation vehicle, the road condition information of each planned transportation route, and the transportation demand information corresponding to the order, comprehensively evaluate the path demand matching degree between each planned transportation route and the cargo transportation vehicle, and screen out a planned transportation route through the arrangement rules, which is recorded as the cargo transportation route.

[0009] S3. Tracking the first route transportation: obtain the first vehicle stop point corresponding to the cargo transportation route, and according to the cargo transportation departure point corresponding to the order, record the cargo transportation route between the cargo transportation departure point and the first vehicle stop point as the first cargo transportation route. The order service platform thus tracks the transportation process of each cargo transportation vehicle in the first cargo transportation route in real time, and finally evaluates the sustainable development transportation value of the cargo transportation vehicle in the first cargo transportation route.

[0010] S4. Predict the second route transportation: obtain the second vehicle stop corresponding to the cargo transportation route, and according to the cargo transportation route corresponding to the first vehicle stop, extract the cargo transportation route between the first vehicle stop and the second vehicle stop as the second cargo transportation route. The order service platform tracks and predicts the transportation process in the second cargo transportation route, and obtains the sustainable development transportation feasibility of the cargo transportation vehicle in the second cargo transportation route.

[0011] S5. Determine the feasibility of vehicle transportation: match the sustainable development transportation value of the freight transport vehicle on the first freight transport path with the sustainable development transportation feasibility thresholds corresponding to each preset sustainable development transportation value interval, obtain and record it as the sustainable development transportation feasibility threshold of the freight transport vehicle on the second freight transport path, compare it with the sustainable development transportation feasibility of the freight transport vehicle on the second freight transport path, obtain the sustainable development transportation comparison result, and finally determine whether each freight transport vehicle directly transports goods through the second freight transport path based on the sustainable development transportation comparison result.

[0012] As a further method, the matching degree between each planned transport route and the path demand of the cargo transport vehicle is analyzed in the following specific process:

[0013] According to the maximum permitted transportation distance of each freight transport vehicle, the transportation distance of each planned transportation route, the estimated total transportation time of each freight transport vehicle on each planned transportation route, and the maximum permitted transportation time corresponding to the order, the path demand matching degree of each freight transport vehicle on each planned transportation route is obtained through comprehensive processing. The specific acquisition method is as follows:

[0014] ;

[0015] In the formula, is the path demand matching degree of the u-th cargo transport vehicle in the n-th planned transport path, is the maximum permitted transport distance of the u-th cargo transport vehicle, is the transportation distance of the nth planned transportation route, The estimated total time for the cargo transportation of the u-th cargo transportation vehicle on the n-th planned transportation route, is the maximum permitted transportation time corresponding to the order, u is the number of each cargo transportation vehicle, , m is the total number of cargo transport vehicles, n is the number of each planned transport route, , w is the total number of planned transport routes; the path demand matching degrees of the freight transport vehicles on the planned transport routes are accumulated, and the result is recorded as the path demand matching degree between the planned transport routes and the freight transport vehicles.

[0016] As a further method, the specific determination process of determining whether each cargo transport vehicle directly transports cargo via the second cargo transport path is as follows:

[0017] If the sustainable development transportation comparison result is the first sustainable development transportation comparison result, each freight transport vehicle directly transports goods through the second freight transport path; if the sustainable development transportation comparison result is the second sustainable development transportation comparison result, each freight transport vehicle cannot directly transport goods through the second freight transport path, and statistics are collected on each alternative freight transport path between the first vehicle stop point and the second vehicle stop point, and the estimated transportation process of each freight transport vehicle in each alternative freight transport path is analyzed.

[0018] As a further method, the estimated transportation process of each cargo transportation vehicle in each backup cargo transportation path is analyzed, and the specific analysis process is:

[0019] By analyzing the estimated transportation process of each cargo transport vehicle in each alternative cargo transport path, the estimated transportation data of each cargo transport vehicle in each alternative cargo transport path is obtained, and the data analysis is performed to obtain the estimated transportation feasibility of sustainable development of the cargo transport vehicle in each alternative cargo transport path; the estimated transportation feasibility of sustainable development of the above-mentioned cargo transport vehicles in each alternative cargo transport path is arranged in order from large to small, and the alternative cargo transport path corresponding to the sustainable development estimated transportation feasibility ranked first is extracted and recorded as the second actual cargo transportation path, so that each cargo transport vehicle transports goods through the second actual cargo transportation path.

[0020] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0021] (1) The present invention provides a method for planning and tracking freight transportation routes based on sustainable development. First, the historical driving information of each freight transportation vehicle, the road condition information of each planned transportation route, and the transportation demand information corresponding to the order are obtained. Through data processing of multi-dimensional information, the path demand matching degree between each planned transportation route and the freight transportation vehicle is comprehensively evaluated, and the freight transportation route with the highest path demand matching degree is screened out to obtain the most suitable freight transportation route. By real-time tracking the transportation process of each freight transportation vehicle in the first freight transportation route, the sustainable transportation value of the freight transportation vehicle in the first freight transportation route is finally evaluated, and accurate and comprehensive data basis is provided for the subsequent matching of the sustainable transportation feasibility threshold of the second freight transportation route. Finally, the method is compared with the analyzed sustainable transportation feasibility of the second freight transportation route to determine whether the freight transportation can be directly carried out through the second freight transportation route. The purpose is to reduce the pollution emissions generated in the actual transportation process as much as possible through reasonable planning of the freight transportation route, so as to reduce unnecessary transportation distance and transportation cost, which can not only improve the transportation efficiency but also ensure the sustainable development of freight transportation.

[0022] (2) The present invention obtains the number of cargo transport vehicles and each planned transport route through the allocation mechanism of the order service platform, thereby accurately obtaining the historical driving information of each cargo transport vehicle, the road condition information of each planned transport route and the transport demand information corresponding to the order, so as to obtain the precise path demand matching degree between each planned transport route and the cargo transport vehicle, and screen out the cargo transport routes. It is possible to obtain the cargo transport route that best matches the transport distance and transport time before actual transportation, and at the same time provide a data basis for the actual transportation of subsequent vehicles on this cargo transport route.

[0023] (3) The present invention tracks the transportation process of each cargo transportation vehicle in the first cargo transportation path in real time through the order service platform, and can obtain the sustainable development transportation degree of the vehicle in the first cargo transportation path in real time and accurately. Based on this analysis, the present invention matches the minimum feasible value of the sustainable development transportation degree of the vehicle in the next transportation path, i.e., the second cargo transportation path, to ensure that the vehicle can reduce fuel consumption as much as possible during the actual transportation process of the second transportation path, so as to reduce the negative pollution impact on the environment.

[0024] (4) The present invention obtains the sustainable development transport feasibility of the freight transport vehicle on the second freight transport path by tracking and predicting the transport process on the second freight transport path, and compares it with the sustainable development transport feasibility threshold. It can determine whether the initially set second freight transport path can meet the minimum standard of sustainable development transport. If the minimum standard cannot be met, it is necessary to optimize the second transport path, thereby realizing sustainable management of vehicles in the actual transportation process and ultimately forming a sustainable virtuous cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute the present invention.

[0026] Without any limitation, ordinary technicians in this field can also obtain other drawings based on the following drawings without paying any creative work.

[0027] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] Reference Figure 1 As shown, the present invention provides a cargo transportation route planning and tracking method based on sustainable development, including: S1. Allocating vehicles and routes: The order service platform receives an order response signal, thereby obtaining the required quantity of cargo corresponding to the order, the starting point of the cargo transportation corresponding to the order, and the ending point of the cargo transportation corresponding to the order, and obtains the number of cargo transportation vehicles and each planned transportation route through the allocation mechanism of the order service platform.

[0030] In this example, the order service platform is a comprehensive information system designed to manage the receipt, processing and distribution of orders. It can exchange information with customers, suppliers and transportation companies to ensure the efficient execution of orders. The order service platform usually includes multi-dimensional modules such as order management, inventory management, and transportation scheduling; the above-mentioned order response signal refers to the confirmation or response information received by the order service platform from the customer's order. This signal usually contains basic information about the order, such as the order ID, the quantity of goods required by the customer, the departure point of the goods transportation, and the end point of the goods transportation; the above-mentioned allocation mechanism refers to the rules or algorithms used in the order service platform to determine how to allocate resources (such as vehicles, personnel, etc.) and plan transportation routes. This mechanism is usually based on multiple factors, such as the required quantity of goods corresponding to the order, the departure point of the goods transportation corresponding to the order, and the end point of the goods transportation corresponding to the order, so as to determine how many transportation vehicles are needed to meet the order requirements, and at the same time, multiple transportation routes that the vehicles can travel can be planned.

[0031] It should be explained that the departure point of the cargo transportation corresponding to the above order indicates the location where the vehicle starts to transport the cargo; the termination point of the cargo transportation corresponding to the above order indicates the location where the cargo is delivered to the order required location.

[0032] S2. Screening cargo transportation routes: Obtain the historical driving information of each cargo transportation vehicle, the road condition information of each planned transportation route, and the transportation demand information corresponding to the order, comprehensively evaluate the path demand matching degree between each planned transportation route and the cargo transportation vehicle, and screen out a planned transportation route through the arrangement rules, which is recorded as the cargo transportation route.

[0033] It should be explained that since the order service platform can exchange information with customers, suppliers and transportation companies, the historical driving information of the above-mentioned freight transport vehicles and the transportation demand information corresponding to the order can be extracted through the vehicle transportation management and control platform of the transportation company that interacts with the order service platform and the comprehensive information of the order requirements stated by the customer; the road condition information of each planned transportation route is obtained by querying the specific road conditions of each planned transportation route in the geographic information system (GIS).

[0034] Specifically, a certain planned transportation path is selected through the arrangement rule and recorded as the cargo transportation path. The specific selection process is as follows:

[0035] The arrangement rule is specifically to arrange the path demand matching degrees between each planned transportation path and the cargo transportation vehicle in order from large to small, thereby selecting the planned transportation path corresponding to the path demand matching degree ranked first and recording it as the cargo transportation path.

[0036] It should be explained that the above arrangement rules are specifically a path demand matching ranking algorithm built into the order service platform, which aims to select the best cargo transportation route based on the degree of path demand matching between different planned transportation routes and cargo transportation vehicles.

[0037] Furthermore, the historical driving information of each freight transport vehicle, the road condition information of each planned transport route, and the transportation demand information corresponding to the order, wherein the historical driving information of each freight transport vehicle specifically includes the historical maximum driving speed of each freight transport vehicle, the historical average failure frequency of each freight transport vehicle, and the historical average single maintenance time of each freight transport vehicle, wherein the road condition information of each planned transport route is specifically the transportation distance of each planned transport route, and wherein the transportation demand information corresponding to the order is specifically the maximum permitted transportation time corresponding to the order.

[0038] Specifically, the path demand matching degree between each planned transportation path and the cargo transportation vehicle is evaluated, and the specific data evaluation process is as follows:

[0039] The maximum permitted transportation distance of each freight transport vehicle is obtained by multiplying the historical maximum driving speed of each freight transport vehicle by the maximum permitted transportation time corresponding to the order.

[0040] The historical average failure frequency of each freight transport vehicle is multiplied by the transport distance of each planned transport route, and the result is rounded up to be recorded as the estimated number of failures of each freight transport vehicle on each planned transport route. The estimated number of repairs of each freight transport vehicle on each planned transport route is obtained through the fault repair protocol preset in the transport database, and then multiplied by the historical average duration of a single repair of each freight transport vehicle to obtain the estimated total maintenance time of each freight transport vehicle on each planned transport route.

[0041] It should be explained that the above-mentioned historical average failure frequency is expressed in times / km. For example, the historical average failure frequency of a freight transport vehicle is 0.05 times / km, and the transportation distance of a planned transportation route is 110 km. Then the number of failures of the freight transport vehicle on this planned transportation route is 5.5 times, which is rounded to 6 times, that is, the estimated number of failures of the freight transport vehicle on this planned transportation route is 6 times.

[0042] It needs to be further explained that the above-mentioned fault repair agreement stipulates that the number of vehicle repairs is the same as the number of vehicle faults. Therefore, the estimated number of faults of the above-mentioned cargo transport vehicle in this planned transport route is 6 times. According to the fault repair agreement here, the estimated number of repairs of the cargo transport vehicle in this planned transport route is equal to 6 times.

[0043] It also needs to be explained that the unit of the above-mentioned historical average single maintenance time is hours / time. Therefore, if it is set to 2 hours / time, the estimated number of maintenance times of the above-mentioned freight transport vehicle on this planned transport route is equivalent to 6 times, and the estimated total maintenance time of the freight transport vehicle on this planned transport route is 12 hours.

[0044] The transportation distance of each planned transportation route and the historical maximum driving speed of each freight transport vehicle are processed. The data processing here specifically refers to the transportation distance divided by the historical maximum driving speed. The estimated total transportation time of each freight transport vehicle on each planned transportation route is obtained, and the estimated total maintenance time of each freight transport vehicle on each planned transportation route is added. The result is recorded as the estimated total cargo transportation time of each freight transport vehicle on each planned transportation route.

[0045] Furthermore, the path demand matching degree between each planned transportation path and the cargo transportation vehicle is specifically analyzed as follows:

[0046] According to the maximum permitted transportation distance of each freight transport vehicle, the transportation distance of each planned transportation route, the estimated total transportation time of each freight transport vehicle on each planned transportation route, and the maximum permitted transportation time corresponding to the order, the path demand matching degree of each freight transport vehicle on each planned transportation route is obtained through comprehensive processing. The specific acquisition method is as follows:

[0047] ;

[0048] In the formula, is the maximum permitted transport distance of the u-th cargo transport vehicle, indicating the longest transport distance that the vehicle can travel at the maximum speed and the maximum transport time.

[0049] It is the transportation distance of the nth planned transportation path, which means the distance the vehicle travels on each path.

[0050] It is the estimated total time of cargo transportation for the u-th cargo transport vehicle on the n-th planned transport route, which represents the sum of the time the vehicle travels on the route and the time the vehicle is repaired due to a fault on the route.

[0051] The maximum permitted transportation time for the order, indicating the maximum transportation time allowed by the customer for the order.

[0052] u is the number of each cargo transport vehicle, , m is the total number of cargo transport vehicles, n is the number of each planned transport route, , w is the total number of planned transportation paths.

[0053] is the path demand matching degree of the u-th cargo transport vehicle in the n-th planned transport path. In this example, the larger the difference between the above-mentioned maximum permitted transport distance and the transport distance of the planned transport path, the greater the distance traveled by the transport vehicle far exceeds the distance specified by the path. Therefore, it can avoid the situation where emergency acceleration is required due to the long journey, and can effectively reduce the transportation risk caused by time pressure, so the transportation time will increase. At the same time, if the above-mentioned maximum permitted transport time corresponding to the order is significantly different from the total estimated time of cargo transportation, it means that there are fewer failures during transportation, that is, the vehicle will always be in a state of driving, so the probability of acceleration, maintenance, etc. will be reduced, thereby shortening the actual transportation distance of the vehicle. In summary, the comprehensive path demand matching degree can be regarded as the result of the combined effect of the distance difference and the time difference. When both are large (that is, the value of the maximum permitted transport distance minus the transport distance of the planned transport path is large, and the value of the maximum permitted transport time corresponding to the order minus the total estimated time of cargo transportation is large), the vehicle will have higher freedom and lower risk when choosing a transport path, thereby improving the overall path demand matching degree.

[0054] It should be explained that in the above-mentioned method for obtaining the path demand matching degree, the difference between the maximum permitted transportation distance and the transportation distance of the planned transportation route, and the difference between the maximum permitted transportation time corresponding to the order and the estimated total transportation time of the goods are all regarded as positive integers, that is, there is no situation where the transportation distance of the planned transportation route is greater than the maximum permitted transportation distance, or the estimated total transportation time of the goods is greater than the maximum permitted transportation time corresponding to the order; this is because the order service platform has screened out various transportation routes before obtaining the planned transportation route through the allocation mechanism, that is, the transportation distance of the planned transportation route is greater than the maximum permitted transportation distance, or the estimated total transportation time of the goods is greater than the transportation route corresponding to the maximum permitted transportation time of the order has been screened and removed. The purpose is to ensure that even if the vehicle breaks down, the goods can still be delivered on time within the transportation time required by the customer, and at the same time to avoid unsafe situations such as emergency acceleration of the driver due to selecting the wrong mismatched route.

[0055] The path demand matching degrees of the freight transport vehicles on the planned transport paths are accumulated, and the obtained result is recorded as the path demand matching degree between the planned transport paths and the freight transport vehicles.

[0056] It should be explained that the above method for obtaining the path demand matching degree does not directly perform accumulation. The purpose is to intuitively describe the relationship between the transportation time and the transportation distance. By intuitively describing the relationship between the transportation time and the transportation distance, the best adaptation balance between the transportation time and the transportation distance can be accurately obtained, and the planned transportation path corresponding to the path demand matching degree under this balance state can be extracted, that is, the cargo transportation path.

[0057] S3. Tracking the first route transportation: obtain the first vehicle stop point corresponding to the cargo transportation route, and according to the cargo transportation departure point corresponding to the order, record the cargo transportation route between the cargo transportation departure point and the first vehicle stop point as the first cargo transportation route. The order service platform thus tracks the transportation process of each cargo transportation vehicle in the first cargo transportation route in real time, and finally evaluates the sustainable development transportation value of the cargo transportation vehicle in the first cargo transportation route.

[0058] It should be explained that in the order response signal in this example, the order corresponds to the goods, due to factors such as large volume and the large quantity required by the customer, so a single vehicle cannot meet the transportation demand, and multiple transport vehicles must be deployed to ensure that all goods can be transported at one time to avoid time delays and cost increases caused by multiple transportations; at the same time, the transportation distance between the departure point of the order corresponding to the goods and the end point of the order corresponding to the goods is relatively long. Therefore, in order to ensure the timely arrival of the goods, the vehicle needs to enter a fast and stable transportation route type such as a highway. This route can not only reduce transportation time, but also improve transportation efficiency and reduce the risk of delays. Therefore, the above-mentioned first vehicle stop refers to an actual highway service area, that is, a place where the vehicle can be refueled and the driver can take a temporary rest.

[0059] Specifically, the real-time tracking of the transportation process of each cargo transport vehicle in the first cargo transport route is specifically to track and obtain the transportation process data of each cargo transport vehicle and the transportation environment data of the area to which the first cargo transport route belongs, wherein the transportation process data of each cargo transport vehicle specifically includes the number of emergency braking of each cargo transport vehicle during the transportation process, the additional fuel consumption corresponding to each emergency braking of each cargo transport vehicle during the transportation process, the average fuel consumption rate of each cargo transport vehicle during the transportation process, and the electric energy consumption of each cargo transport vehicle during the transportation process, wherein the transportation environment data of the area to which the first cargo transport route belongs is specifically the transportation time corresponding to the bad weather in the area to which the first cargo transport route belongs.

[0060] It should be explained that the above-mentioned tracking and acquisition of the transportation process data of each cargo transport vehicle and the transportation environment data of the area to which the first cargo transport route belongs are still obtained by querying the order service platform; specifically, each cargo transport vehicle itself is equipped with on-board GPS, sensors and OBD (on-board diagnostic system) and other equipment, and multiple devices are connected to the order service platform through the Internet of Things. Therefore, the required data can be obtained through the query interface provided by the order service platform, namely the above-mentioned emergency braking number, additional fuel consumption, average fuel consumption rate, power consumption and transportation time corresponding to bad weather.

[0061] Furthermore, the sustainable development transportation value of the cargo transportation vehicle on the first cargo transportation path is specifically analyzed as follows:

[0062] The number of emergency braking times of each cargo transport vehicle during the transportation process is multiplied by the additional fuel consumption corresponding to each emergency braking time of each cargo transport vehicle during the transportation process to obtain the corresponding fuel consumption of emergency braking times of each cargo transport vehicle during the transportation process; the emergency braking in this example refers to the behavior of cargo transport vehicles to quickly apply brakes due to emergencies during driving, which is usually to avoid collisions, reduce accident damage or respond to sudden obstacles; emergencies include but are not limited to changes in severe weather such as heavy rain, hail, and snowstorms, sudden potholes, cracks and other road damage, sudden mechanical failures such as brake failure and tire blowouts, and the appearance of emergency vehicles (ambulances, fire trucks, etc.).

[0063] The above-mentioned on-board diagnostic system can read the vehicle's fuel consumption, speed, acceleration, braking status and other data in real time, and define the vehicle's normal driving and emergency braking standards based on the vehicle's historical driving data in the transportation management and control platform of the transportation company. For example, normal driving means acceleration within the range of 0 to 3m / s², while emergency braking means acceleration less than -5m / s². Therefore, through the vehicle's driving process, the on-board diagnostic system continuously monitors and records the vehicle's driving speed, acceleration, fuel consumption and braking status (whether the brakes are applied), and organizes the collected data in time series (i.e., divides the transportation process into multiple time periods), marking each time period as "normal driving". Or "emergency braking", here the marking can use acceleration data as the main basis. If the acceleration is within the normal range, it is marked as "normal driving". If the acceleration is lower than the set emergency braking threshold (according to the above vehicle historical driving data, the emergency braking threshold here is -5m / s²), it is marked as "emergency braking". Through the above process, the actual number of emergency braking times of the vehicle during transportation can be known, and by multiplying the average fuel consumption rate of the vehicle with the time period corresponding to each emergency braking, the fuel consumption corresponding to each emergency braking of the vehicle can be finally obtained, which is the additional fuel consumption corresponding to each emergency braking during transportation mentioned above.

[0064] The transportation time of the cargo transport vehicle on the first cargo transport route is obtained, which is recorded as the first cargo transport time. The specific acquisition method is the same as the acquisition method of the above-mentioned emergency braking times, additional fuel consumption, average fuel consumption rate, electric energy consumption and transportation time corresponding to bad weather.

[0065] The transportation time corresponding to the bad weather in the area to which the first cargo transportation route belongs is ratioed with the first cargo transportation time, where the transportation time corresponding to the bad weather is divided by the first cargo transportation time; the ratio of the time corresponding to bad weather is obtained and recorded as the ratio of the time corresponding to bad weather for the cargo transportation vehicle on the first cargo transportation route.

[0066] Obtain the path demand matching degree between the cargo transportation path and the cargo transportation vehicle. Since the path demand matching degree between each planned transportation path and the cargo transportation vehicle has been analyzed above, and the planned transportation path corresponding to the path demand matching degree ranked first is recorded as the cargo transportation path, the path demand matching degree between the cargo transportation path and the cargo transportation vehicle can be obtained.

[0067] The sustainable development transportation value of the freight transport vehicles on the first freight transport route is finally evaluated by comprehensively considering the corresponding fuel consumption of emergency braking of each freight transport vehicle during transportation, the average fuel consumption rate of each freight transport vehicle during transportation, the power consumption of each freight transport vehicle during transportation, and the corresponding proportion of bad weather time of the freight transport vehicles on the first freight transport route. The specific acquisition method is as follows:

[0068] ;

[0069] In the formula, It is the path demand matching degree between the cargo transportation route and the cargo transportation vehicle, which is obtained by comprehensively processing the maximum permitted transportation distance, the transportation distance of the cargo transportation route, the estimated total duration of the cargo transportation, and the maximum permitted transportation duration corresponding to the order.

[0070] The weight factor of the path demand matching degree preset in the transportation database represents the numerical value of the influence degree of the path demand matching degree on the sustainable development transportation value. When used, the weight factor of the path demand matching degree can be directly obtained from the transportation database, and the corresponding relationship can be a preset mapping relationship. For example, the path demand matching degree and the weight factor of the path demand matching degree preset in the transportation database form a mapping set, and the real-time path demand matching degree is input into the mapping set to obtain the weight factor of the path demand matching degree. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0071] is the corresponding fuel consumption of emergency braking of the u-th cargo transport vehicle during transportation, indicating the additional fuel consumption caused by emergency braking of the cargo transport vehicle during transportation.

[0072] The influencing factor of the unit value of the emergency braking corresponding to the fuel consumption preset in the transportation database represents the numerical value of the influence of the unit value of the emergency braking corresponding to the fuel consumption on the sustainable development transportation value. When used, the influencing factor of the unit value of the emergency braking corresponding to the fuel consumption can be directly obtained from the transportation database. The corresponding relationship can be a preset mapping relationship. For example, the emergency braking corresponding to the fuel consumption and the influencing factor of the unit value of the emergency braking corresponding to the fuel consumption preset in the transportation database form a mapping set. The real-time emergency braking corresponding to the fuel consumption is input into the mapping set to obtain the influencing factor of the unit value of the emergency braking corresponding to the fuel consumption. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0073] is the average fuel consumption rate of the u-th cargo transport vehicle during the transportation process, indicating the actual fuel consumption rate of the cargo transport vehicle during the transportation process.

[0074] is the electric energy consumption of the u-th cargo transport vehicle during the transportation process, indicating the actual amount of electricity consumed by the cargo transport vehicle during the transportation process.

[0075] It is the proportion of the time corresponding to bad weather for the cargo transport vehicle on the first cargo transport route, indicating the proportion of the length of bad weather time experienced by the vehicle during the transportation time period of the first cargo transport route to the total transportation time length of the vehicle on the first cargo transport route.

[0076] The influencing factor of the unit value of the bad weather corresponding time ratio preset in the transportation database represents the numerical value of the influence of the unit value of the bad weather corresponding time ratio on the sustainable development transportation value. When used, the influencing factor of the unit value of the bad weather corresponding time ratio can be directly obtained from the transportation database, and the corresponding relationship can be a preset mapping relationship. For example, the bad weather corresponding time ratio and the influencing factor of the unit value of the bad weather corresponding time ratio preset in the transportation database form a mapping set, and the real-time bad weather corresponding time ratio is input into the mapping set to obtain the influencing factor of the unit value of the bad weather corresponding time ratio. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0077] The influence factor of the unit value corresponding to the average fuel consumption rate preset in the transportation database represents the influence degree of the unit value corresponding to the average fuel consumption rate on the sustainable development transportation value. When used, the influence factor of the unit value corresponding to the average fuel consumption rate can be directly obtained from the transportation database. The corresponding relationship can be a preset mapping relationship. For example, the average fuel consumption rate and the influence factor of the unit value corresponding to the average fuel consumption rate preset in the transportation database form a mapping set. The real-time average fuel consumption rate is input into the mapping set to obtain the influence factor of the unit value corresponding to the average fuel consumption rate. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0078] The influencing factor of the unit value of the electric energy consumption preset in the transport database represents the numerical value of the influence of the unit value of the electric energy consumption on the sustainable development transport value. When used, the influencing factor of the unit value of the electric energy consumption can be directly obtained from the transport database, and the corresponding relationship can be a preset mapping relationship. For example, the electric energy consumption and the influencing factor of the unit value of the electric energy consumption preset in the transport database form a mapping set, and the real-time electric energy consumption is input into the mapping set to obtain the influencing factor of the unit value of the electric energy consumption. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0079] e is a natural constant, u is the number of each cargo transport vehicle, , m is the total number of cargo transport vehicles.

[0080] is the sustainable development transportation value of the cargo transport vehicle on the first cargo transport path. Even though the path demand matching degree between the cargo transport path and the cargo transport vehicle has been analyzed in detail above, and the path demand matching degree between the cargo transport path and the cargo transport vehicle with the highest value has been obtained, the vehicle will be negatively affected by environmental factors during the actual transportation process, that is, under severe weather conditions, the vehicle may need to perform more emergency braking, resulting in increased fuel consumption and fuel consumption rate; at the same time, when the cargo transport vehicle has emergency braking during transportation, the fuel consumption will increase, resulting in an increase in the fuel consumption rate; high fuel consumption may mean that the vehicle needs more electrical energy to maintain the same transportation efficiency, especially in hybrid or electric vehicles; and more electrical energy consumption means higher environmental impact, which reduces the sustainability of transportation, that is, the final evaluated sustainable development transportation value is reduced; therefore, in summary, in order to be able to understand the sustainable development of the vehicle in the transportation path in real time, it is necessary to track the vehicle's transportation process in real time, that is, to obtain fuel consumption, fuel consumption rate, electrical energy consumption and the corresponding duration ratio of severe weather, and finally obtain accurate and comprehensive sustainable development transportation values ​​of the cargo transport vehicle on the first cargo transport path, so as to improve the sustainable development ability of the vehicle.

[0081] S4. Predict the second route transportation: obtain the second vehicle stop corresponding to the cargo transportation route, and according to the cargo transportation route corresponding to the first vehicle stop, extract the cargo transportation route between the first vehicle stop and the second vehicle stop as the second cargo transportation route. The order service platform tracks and predicts the transportation process in the second cargo transportation route, and obtains the sustainable development transportation feasibility of the cargo transportation vehicle in the second cargo transportation route.

[0082] It should be explained that the above-mentioned second vehicle stop has the same meaning as the first vehicle stop. Due to the long transportation distance, there may be more than one vehicle stop, so they need to be analyzed one by one.

[0083] Specifically, the sustainable development transport feasibility of the cargo transport vehicle on the second cargo transport path is analyzed in the following process:

[0084] By tracking and predicting the transportation process in the second cargo transportation route based on the order service platform, the transportation prediction data in the second cargo transportation route can be obtained, specifically including the historical maximum traffic density of the second cargo transportation route during the prediction period and the historical number of occurrences of severe weather in the area to which the second cargo transportation route belongs during the prediction period.

[0085] It should be explained that the above-mentioned transportation forecast data is obtained by querying the traffic volume in the geographic information system and the weather conditions in the weather radar map.

[0086] The above-mentioned predicted time period represents the time period that the freight transport vehicle needs to go through on the second freight transport route, and the method for obtaining the predicted time period can be obtained through the order service platform.

[0087] Therefore, the sustainable development transport feasibility of the freight transport vehicle on the second freight transport route is comprehensively obtained by the historical maximum traffic density of the second freight transport route during the forecast period and the number of historical occurrences of severe weather in the area to which the second freight transport route belongs during the forecast period. The acquisition method is as follows:

[0088] ;

[0089] In the formula, The impact factor of the unit value corresponding to the historical maximum traffic density preset in the transportation database represents the numerical value of the impact of the unit value corresponding to the historical maximum traffic density on the feasibility of sustainable development transportation. When used, the impact factor of the unit value corresponding to the historical maximum traffic density can be directly obtained from the transportation database. The corresponding relationship can be a preset mapping relationship. For example, the historical maximum traffic density and the impact factor of the unit value corresponding to the historical maximum traffic density preset in the transportation database form a mapping set. The real-time historical maximum traffic density is input into the mapping set to obtain the impact factor of the unit value corresponding to the historical maximum traffic density. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is (0, 1).

[0090] It is the historical maximum traffic density of the second cargo transportation path in the prediction period, indicating the maximum traffic volume experienced by the second cargo transportation path in the current period. The greater the traffic density, the greater the possibility of congestion on the second cargo transportation path.

[0091] The impact factor of the unit value corresponding to the historical number of severe weather occurrences preset in the transport database represents the numerical value of the impact of the unit value corresponding to the historical number of severe weather occurrences on the feasibility of sustainable development transport. When used, the impact factor of the unit value corresponding to the historical number of severe weather occurrences can be directly obtained from the transport database, and the corresponding relationship can be a preset mapping relationship. For example, the historical number of severe weather occurrences and the impact factor of the unit value corresponding to the historical number of severe weather occurrences preset in the transport database form a mapping set, and the real-time historical number of severe weather occurrences is input into the mapping set to obtain the impact factor of the unit value corresponding to the historical number of severe weather occurrences. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is (0, 1).

[0092] It is the number of historical occurrences of severe weather in the area to which the second cargo transportation route belongs during the forecast period. It is the number of severe weather occurrences experienced by the second cargo transportation route during the current period. Severe weather includes but is not limited to extreme weather such as heavy snow and rain. If the number of severe weather occurrences is greater, it means that the transportation difficulty of the vehicle on the second cargo transportation route is higher.

[0093] It is the sustainable transportation feasibility of the cargo transport vehicle on the second cargo transport route. In this example, if you want to maintain the sustainable development capability of the vehicle, you need not only to track the transportation status of the vehicle in real time, but also to predict whether there are influencing factors that will reduce the sustainability of the vehicle in the next route. Therefore, through the prediction of the next route (i.e., the second cargo transport route), it can be known that if the number of bad weather events in the second cargo transport route is higher, it means that it is difficult for the vehicle to maintain the original driving speed and it is necessary to slow down. As a result, there will be vehicle congestion, which increases the traffic density. In addition, the vehicle will increase the stop-go-stop behavior during driving, which further causes the fuel consumption of the vehicle and is not conducive to the sustainable development of the vehicle. In summary, in order to ensure the sustainable development of the vehicle in the next route, it is necessary to make corresponding predictions for the next route.

[0094] S5. Determine the feasibility of vehicle transportation: match the sustainable development transportation value of the freight transport vehicle on the first freight transport path with the sustainable development transportation feasibility thresholds corresponding to each sustainable development transportation value interval preset in the transportation database, obtain and record it as the sustainable development transportation feasibility threshold of the freight transport vehicle on the second freight transport path, compare it with the sustainable development transportation feasibility of the freight transport vehicle on the second freight transport path, obtain a sustainable development transportation comparison result, and finally determine whether each freight transport vehicle directly transports goods through the second freight transport path based on the sustainable development transportation comparison result.

[0095] It should be explained that the sustainable development transport feasibility threshold of the above-mentioned freight transport vehicle on the second freight transport route is specifically matched as follows:

[0096] The above-mentioned sustainable development transport value intervals correspond to sustainable development transport feasibility thresholds. The corresponding values ​​and corresponding rules are formulated by vehicle transportation research experts, which comprehensively consider the road conditions of the cargo transportation route, the driving conditions of the cargo transportation vehicles and other aspects, and make detailed divisions of each sustainable development transport value. For each sustainable development transportation value interval, the corresponding sustainable development transport feasibility threshold is designed according to the vehicle transportation research experts' requirements for the sustainable development of vehicles.

[0097] The sustainable development transport feasibility threshold represents the minimum value for the freight transport vehicle to ensure sustainable development transport in the second freight transport route.

[0098] It should be clarified that the sustainable development transportation value of the freight transport vehicle on the first freight transport path is matched with the sustainable development transportation feasibility threshold of the freight transport vehicle on the second freight transport path. The purpose is to provide a specific data basis through the actual sustainability of the first freight transport path, provide an accurate reference for vehicle transportation on the second freight transport path, and make the allocation of resources (such as fuel, electricity, etc.) more reasonable; at the same time, through the analysis of the first freight transport path, it is possible to identify which factors lead to higher energy consumption, and then make adjustments in the second freight transport path to optimize efficiency; and potential risk points can be identified, such as weather conditions on the road section that may affect the sustainability of the second freight transport path, so as to maintain the sustainability of vehicle transportation.

[0099] Specifically, the sustainable development transport feasibility threshold of the freight transport vehicle on the second freight transport path is compared with the sustainable development transport feasibility of the freight transport vehicle on the second freight transport path to obtain a sustainable development transport comparison result. The specific comparison process is:

[0100] The sustainable development transportation comparison result is specifically a first sustainable development transportation comparison result or a second sustainable development transportation comparison result.

[0101] If the sustainable development transport feasibility of the freight transport vehicle on the second freight transport path is greater than the sustainable development transport feasibility threshold, it means that the sustainable development transport degree of the freight transport vehicle on the second freight transport path is good, that is, the freight transport vehicle can be directly transported on the second freight transport path, and the sustainable development transport comparison result is defined as the first sustainable development transport comparison result.

[0102] If the sustainable development transport feasibility of the freight transport vehicle on the second freight transport path is less than or equal to the sustainable development transport feasibility threshold, it means that the sustainable development transport degree of the freight transport vehicle on the second freight transport path is low and lower than the prescribed minimum standard. In order to ensure sustainable development, the freight transport vehicle cannot be transported on the second freight transport path, and the path needs to be adjusted. The sustainable development transport comparison result is defined as the second sustainable development transport comparison result.

[0103] Furthermore, the specific determination process of determining whether each cargo transport vehicle directly transports cargo via the second cargo transport path is as follows:

[0104] If the sustainable development transport comparison result is the first sustainable development transport comparison result, each cargo transport vehicle directly transports cargo via the second cargo transport route.

[0105] If the sustainable development transportation comparison result is the second sustainable development transportation comparison result, then each freight transport vehicle cannot directly transport goods through the second freight transport path, and the various alternative freight transport paths between the first vehicle stop and the second vehicle stop are counted, wherein the statistical method is to directly query through the order service platform, and the various alternative freight transport paths do not include the above-mentioned second freight transport path; analyze the estimated transportation process of each freight transport vehicle in each alternative freight transport path.

[0106] Specifically, the estimated transportation process of each cargo transportation vehicle in each backup cargo transportation route is analyzed, and the specific analysis process is as follows:

[0107] By analyzing the estimated transportation process of each freight transport vehicle in each alternative freight transport route, the estimated transportation data of each freight transport vehicle in each alternative freight transport route is obtained, and data analysis is performed to obtain the estimated transportation feasibility of the sustainable development of the freight transport vehicle in each alternative freight transport route.

[0108] It needs to be explained that the estimated transportation data of the above-mentioned freight transport vehicles in each alternative freight transport route specifically include the historical maximum fuel consumption corresponding to emergency braking, the historical highest fuel consumption rate and the historical highest energy consumption rate of each freight transport vehicle, as well as the transportation distance of each alternative freight transport route.

[0109] The maximum fuel consumption, the highest fuel consumption rate and the highest energy consumption rate corresponding to the historical emergency braking of the above-mentioned freight transport vehicles are obtained by extracting them from the vehicle transport management and control platform of the transport company that interacts with the corresponding information on the order service platform; the transport distance of each alternative freight transport route is obtained by querying the specific road conditions of each alternative freight transport route in the geographic information system (GIS).

[0110] The historical maximum fuel consumption rate of each freight transport vehicle is multiplied by the transport distance of each alternative freight transport route, and the resulting value is recorded as the predicted fuel consumption of each freight transport vehicle on each alternative freight transport route.

[0111] The historical maximum energy consumption rate of each freight transport vehicle is multiplied by the transport distance of each alternative freight transport route, and the resulting value is recorded as the predicted electric energy consumption of each freight transport vehicle on each alternative freight transport route.

[0112] The estimated transport feasibility of sustainable development of freight transport vehicles on each alternative freight transport route is comprehensively analyzed through the historical maximum fuel consumption corresponding to emergency braking of each freight transport vehicle, the predicted fuel consumption of each freight transport vehicle on each alternative freight transport route, and the predicted power consumption of each freight transport vehicle on each alternative freight transport route. The analysis method is as follows:

[0113] ;

[0114] In the formula, The influencing factor of the unit value of the maximum fuel consumption corresponding to historical emergency braking is preset in the transportation database, which represents the numerical value of the influence of the unit value of the maximum fuel consumption corresponding to historical emergency braking on the transport feasibility of sustainable development estimation. When used, the influencing factor of the unit value of the maximum fuel consumption corresponding to historical emergency braking can be directly obtained from the transportation database, and the corresponding relationship can be a preset mapping relationship. For example, the maximum fuel consumption corresponding to historical emergency braking and the influencing factor of the unit value of the maximum fuel consumption corresponding to historical emergency braking preset in the transportation database form a mapping set, and the real-time maximum fuel consumption corresponding to historical emergency braking is input into the mapping set to obtain the influencing factor of the unit value of the maximum fuel consumption corresponding to historical emergency braking. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is (0, 1).

[0115] It is the maximum fuel consumption corresponding to the historical emergency braking of the u-th cargo transport vehicle, indicating the maximum amount of fuel consumed by the vehicle in all previous emergency braking situations.

[0116] The influencing factor of the unit value of the predicted fuel consumption preset in the transportation database represents the numerical value of the unit value of the predicted fuel consumption on the estimated transport feasibility of sustainable development. When used, the influencing factor of the unit value of the predicted fuel consumption can be directly obtained from the transportation database, and the corresponding relationship can be a preset mapping relationship. For example, the predicted fuel consumption and the influencing factor of the unit value of the predicted fuel consumption preset in the transportation database form a mapping set, and the real-time predicted fuel consumption is input into the mapping set to obtain the influencing factor of the unit value of the predicted fuel consumption. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is (0, 1).

[0117] is the predicted fuel consumption of the u-th cargo transport vehicle on the d-th alternative cargo transport route, indicating the maximum total fuel consumption of the vehicle under the transport distance of the alternative cargo transport route.

[0118] The influencing factor of the unit value of the predicted electric energy consumption preset in the transport database represents the numerical value of the influence of the unit value of the predicted electric energy consumption on the estimated transport feasibility of sustainable development. When used, the influencing factor of the unit value of the predicted electric energy consumption can be directly obtained from the transport database, and the corresponding relationship can be a preset mapping relationship. For example, the predicted electric energy consumption and the influencing factor of the unit value of the predicted electric energy consumption preset in the transport database form a mapping set, and the real-time predicted electric energy consumption is input into the mapping set to obtain the influencing factor of the unit value of the predicted electric energy consumption. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is (0, 1).

[0119] is the predicted power consumption of the u-th cargo transport vehicle on the d-th alternative cargo transport path, indicating the maximum total power consumption of the vehicle traveling within the transport distance of the alternative cargo transport path.

[0120] u is the number of each cargo transport vehicle, , m is the total number of cargo transport vehicles, d is the number of each backup cargo transport route, , p is the total number of alternative cargo transportation routes.

[0121] The transport feasibility of the cargo transport vehicle on the dth alternative cargo transport route is estimated for sustainable development. In this example, it can be seen from the above description that if you want to ensure the sustainability of the vehicle during transportation, you need to evaluate and determine through the vehicle's fuel consumption, energy consumption and other information. That is, if the vehicle consumes less fuel and electricity under the transportation distance of a certain alternative cargo transport route (that is, the above predicted fuel consumption and predicted electricity consumption are lower), it means that the vehicle has stronger sustainability in the alternative cargo transport route. At the same time, due to the possibility of emergencies in the road conditions of the alternative cargo transport route, if the maximum fuel consumption corresponding to the emergency braking of the vehicle in this case is less, it will further improve the sustainability of the vehicle, that is, reduce fuel consumption and electricity consumption, reduce unnecessary transportation costs, and reduce the negative impact of pollution on the environment.

[0122] The sustainable development estimated transport feasibility of the above-mentioned freight transport vehicles on each alternative freight transport path is arranged in descending order, and the alternative freight transport path corresponding to the sustainable development estimated transport feasibility ranked first is extracted and recorded as the second freight transport actual path. Thus, each freight transport vehicle transports freight through the second freight transport actual path.

[0123] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A cargo transportation route planning and tracking method based on sustainable development, characterized in that: include: S1. Allocate vehicles and routes: The order service platform receives the order response signal, thereby obtaining the required quantity of goods corresponding to the order, the departure point of the goods transportation corresponding to the order, and the end point of the goods transportation corresponding to the order, and obtains the number of goods transportation vehicles and each planned transportation route through the allocation mechanism of the order service platform; S2. Filtering cargo transportation routes: Obtain the historical driving information of each cargo transportation vehicle, the road condition information of each planned transportation route, and the transportation demand information corresponding to the order, comprehensively evaluate the path demand matching degree between each planned transportation route and the cargo transportation vehicle, and filter out a certain planned transportation route through the arrangement rules, which is recorded as the cargo transportation route; S3. Tracking the first route transportation: obtaining the first vehicle stop point corresponding to the cargo transportation route, and according to the cargo transportation departure point corresponding to the order, recording the cargo transportation route between the cargo transportation departure point and the first vehicle stop point as the first cargo transportation route, thereby the order service platform tracks the transportation process of each cargo transportation vehicle in the first cargo transportation route in real time, and finally evaluates the sustainable development transportation value of the cargo transportation vehicle in the first cargo transportation route; S4. Predicting the second transport path: obtaining the second vehicle stop corresponding to the cargo transport path, and extracting the cargo transport path between the first vehicle stop and the second vehicle stop according to the cargo transport path corresponding to the first vehicle stop, recording it as the second cargo transport path, thereby the order service platform tracks and predicts the transport process in the second cargo transport path, and obtains the sustainable development transport feasibility of the cargo transport vehicle in the second cargo transport path; S5. Determine the feasibility of vehicle transportation: match the sustainable transportation value of the freight transport vehicle on the first freight transport path with the sustainable transportation feasibility thresholds corresponding to each preset sustainable transportation value interval, obtain and record the sustainable transportation feasibility threshold of the freight transport vehicle on the second freight transport path, compare it with the sustainable transportation feasibility of the freight transport vehicle on the second freight transport path, obtain a sustainable transportation comparison result, and finally determine whether each freight transport vehicle directly transports goods through the second freight transport path based on the sustainable transportation comparison result; The sustainable development transport value of the cargo transport vehicle on the first cargo transport path is specifically obtained in the following manner: ； In the formula, is the path demand matching degree between the cargo transportation path and the cargo transportation vehicle, The weight factor of the path demand matching degree preset for the transportation database, is the fuel consumption corresponding to the emergency braking of the u-th cargo transport vehicle during transportation, The influencing factor of the unit value of fuel consumption corresponding to emergency braking preset in the transportation database, is the average fuel consumption rate of the u-th cargo transport vehicle during transportation, is the power consumption of the u-th cargo transport vehicle during transportation, is the proportion of time that the freight transport vehicle experiences bad weather on the first freight transport route, The impact factor of the unit value of the bad weather duration ratio preset for the transportation database, The influence factor of the average fuel consumption rate corresponding to the unit value preset for the transportation database, is the impact factor of the corresponding unit value of the power consumption preset in the transportation database, e is a natural constant, u is the number of each cargo transportation vehicle, , m is the total number of cargo transport vehicles, Sustainable transport value for freight transport vehicles on the first freight transport route; The sustainable development transport feasibility of the cargo transport vehicle on the second cargo transport path is obtained in the following method: ； In the formula, The impact factor of the unit value corresponding to the historical maximum traffic density preset for the transportation database, is the historical maximum traffic density of the second cargo transportation route during the forecast period, The impact factor of the unit value corresponding to the historical occurrence of severe weather preset in the transportation database, is the number of bad weather events that occurred in the area of ​​the second cargo transportation route during the forecast period. The feasibility of sustainable development of freight transport vehicles on the second freight transport route.

2. The method for cargo transportation route planning and tracking based on sustainable development according to claim 1 is characterized in that: The historical driving information of each freight transport vehicle, the road condition information of each planned transport route, and the transportation demand information corresponding to the order, wherein the historical driving information of each freight transport vehicle specifically includes the historical maximum driving speed of each freight transport vehicle, the historical average failure frequency of each freight transport vehicle, and the historical average single maintenance time of each freight transport vehicle, wherein the road condition information of each planned transport route is specifically the transportation distance of each planned transport route, and the transportation demand information corresponding to the order is specifically the maximum permitted transportation time corresponding to the order.

3. The method for cargo transportation route planning and tracking based on sustainable development according to claim 2 is characterized in that: The path demand matching degree between each planned transportation path and the cargo transportation vehicle is evaluated, and the specific data evaluation process is as follows: Multiply the historical maximum speed of each freight transport vehicle by the maximum permitted transport time corresponding to the order to obtain the maximum permitted transport distance of each freight transport vehicle; The historical average failure frequency of each freight transport vehicle is multiplied by the transportation distance of each planned transportation route, and the result is rounded up to be recorded as the estimated number of failures of each freight transport vehicle on each planned transportation route. The estimated number of repairs of each freight transport vehicle on each planned transportation route is obtained through a preset fault repair protocol, and then multiplied by the historical average single repair duration of each freight transport vehicle to obtain the estimated total repair duration of each freight transport vehicle on each planned transportation route; The transportation distance of each planned transportation route and the historical maximum driving speed of each freight transport vehicle are processed to obtain the estimated total transportation time of each freight transport vehicle on each planned transportation route, and the result is added to the estimated total maintenance time of each freight transport vehicle on each planned transportation route. The result is recorded as the estimated total freight transportation time of each freight transport vehicle on each planned transportation route.

4. The method for cargo transportation route planning and tracking based on sustainable development according to claim 3 is characterized by: The specific analysis process of the matching degree between each planned transportation route and the path demand of the cargo transportation vehicle is as follows: According to the maximum permitted transportation distance of each freight transport vehicle, the transportation distance of each planned transportation route, the estimated total transportation time of each freight transport vehicle on each planned transportation route, and the maximum permitted transportation time corresponding to the order, the path demand matching degree of each freight transport vehicle on each planned transportation route is obtained through comprehensive processing. The specific acquisition method is as follows: ； In the formula, is the path demand matching degree of the u-th cargo transport vehicle in the n-th planned transport path, is the maximum permitted transport distance of the u-th cargo transport vehicle, is the transportation distance of the nth planned transportation route, The estimated total time for the cargo transportation of the u-th cargo transportation vehicle on the n-th planned transportation route, is the maximum permitted transportation time corresponding to the order, u is the number of each cargo transportation vehicle, , m is the total number of cargo transport vehicles, n is the number of each planned transport route, , w is the total number of planned transportation routes; The path demand matching degrees of the freight transport vehicles on the planned transport paths are accumulated, and the obtained result is recorded as the path demand matching degree between the planned transport paths and the freight transport vehicles.

5. The method for cargo transportation route planning and tracking based on sustainable development according to claim 1 is characterized in that: The planned transportation path is screened out through the arrangement rules and recorded as the cargo transportation path. The specific screening process is as follows: The arrangement rule is specifically to arrange the path demand matching degrees between each planned transportation path and the cargo transportation vehicle in order from large to small, thereby selecting the planned transportation path corresponding to the path demand matching degree ranked first and recording it as the cargo transportation path.

6. The method for cargo transportation route planning and tracking based on sustainable development according to claim 1 is characterized by: The real-time tracking of the transportation process of each cargo transport vehicle in the first cargo transport path specifically tracks and obtains the transportation process data of each cargo transport vehicle and the transportation environment data of the area to which the first cargo transport path belongs, wherein the transportation process data of each cargo transport vehicle specifically includes the number of emergency braking of each cargo transport vehicle during the transportation process, the additional fuel consumption corresponding to each emergency braking of each cargo transport vehicle during the transportation process, the average fuel consumption rate of each cargo transport vehicle during the transportation process, and the electric energy consumption of each cargo transport vehicle during the transportation process, wherein the transportation environment data of the area to which the first cargo transport path belongs specifically is the transportation time corresponding to the bad weather in the area to which the first cargo transport path belongs.

7. The method for cargo transportation route planning and tracking based on sustainable development according to claim 6 is characterized by: The sustainable development transportation value of the cargo transportation vehicle on the first cargo transportation path is specifically analyzed as follows: The number of emergency braking times of each cargo transport vehicle during the transportation process is multiplied by the additional fuel consumption corresponding to each emergency braking time of each cargo transport vehicle during the transportation process to obtain the corresponding fuel consumption of emergency braking times of each cargo transport vehicle during the transportation process; Obtaining the transportation time of the cargo transportation vehicle on the first cargo transportation route, recorded as the first cargo transportation time; The bad weather corresponding transportation time of the area to which the first cargo transportation route belongs is processed by ratio with the first cargo transportation time to obtain the bad weather corresponding time ratio, and recorded as the bad weather corresponding time ratio of the cargo transportation vehicle on the first cargo transportation route; The path demand matching degree between the cargo transportation route and the cargo transportation vehicles is obtained, and the corresponding fuel consumption of emergency braking of each cargo transportation vehicle during transportation, the average fuel consumption rate of each cargo transportation vehicle during transportation, the electric energy consumption of each cargo transportation vehicle during transportation, and the corresponding proportion of bad weather time of the cargo transportation vehicles on the first cargo transportation route are comprehensively considered to finally evaluate the sustainable development transportation value of the cargo transportation vehicles on the first cargo transportation route.

8. The method for cargo transportation route planning and tracking based on sustainable development according to claim 1 is characterized by: The sustainable development transport feasibility threshold of the freight transport vehicle on the second freight transport path is compared with the sustainable development transport feasibility of the freight transport vehicle on the second freight transport path to obtain a sustainable development transport comparison result. The specific comparison process is: The sustainable development transport comparison result is specifically a first sustainable development transport comparison result or a second sustainable development transport comparison result; If the sustainable development transport feasibility of the freight transport vehicle on the second freight transport route is greater than the sustainable development transport feasibility threshold, the sustainable development transport comparison result is defined as the first sustainable development transport comparison result; If the sustainable development transport feasibility of the freight transport vehicle on the second freight transport path is less than or equal to the sustainable development transport feasibility threshold, the sustainable development transport comparison result is defined as the second sustainable development transport comparison result.

9. The method for cargo transportation route planning and tracking based on sustainable development according to claim 1 is characterized by: The specific determination process of determining whether each cargo transport vehicle directly transports cargo via the second cargo transport path is as follows: If the sustainable development transport comparison result is the first sustainable development transport comparison result, each cargo transport vehicle directly transports cargo via the second cargo transport route; If the sustainable development transportation comparison result is the second sustainable development transportation comparison result, each freight transport vehicle cannot directly transport goods through the second freight transport path, and the various alternative freight transport paths between the first vehicle stop point and the second vehicle stop point are counted, and the estimated transportation process of each freight transport vehicle in each alternative freight transport path is analyzed.

10. The method for cargo transportation route planning and tracking based on sustainable development according to claim 9 is characterized in that: The estimated transportation process of each cargo transportation vehicle in each backup cargo transportation route is analyzed, and the specific analysis process is as follows: By analyzing the estimated transportation process of each cargo transport vehicle in each alternative cargo transport route, the estimated transportation data of each cargo transport vehicle in each alternative cargo transport route is obtained, and the data analysis is performed to obtain the estimated transportation feasibility of the sustainable development of the cargo transport vehicle in each alternative cargo transport route; The sustainable development estimated transport feasibility of the above-mentioned freight transport vehicles on each alternative freight transport path is arranged in descending order, and the alternative freight transport path corresponding to the sustainable development estimated transport feasibility ranked first is extracted and recorded as the second freight transport actual path. Thus, each freight transport vehicle transports freight through the second freight transport actual path.

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