Intelligent logistics freight platform transportation optimal route planning method and supervision system

Abstract

The application discloses a kind of wisdom logistics freight platform transport optimal line planning method and supervisory system, the method includes: acquisition multidimensional static data and dynamic data;Based on the multidimensional static data and the dynamic data, construct weighted multi-objective optimization model;Using improved intelligent optimization algorithm to solve the weighted multi-objective optimization model, obtain initial optimal transport line;Based on real-time updated dynamic data, judge whether to meet line adaptive update condition, if meet then re-solve weighted multi-objective optimization model, generate updated optimal transport line;Real-time supervision is carried out to transport process, compare actual transport trajectory with the optimal transport line, when deviation exceeds preset threshold, trigger early warning, and generate adjustment suggestion;Its effect is: the present application can improve precision, dynamic adaptability is strong, and trajectory deviation is early warned in real time, and generates adjustment suggestion, to avoid supervision lag.

Description

Technical Field

[0001] This invention relates to the field of digital logistics and route planning technology, specifically to a method and monitoring system for optimal transportation route planning of a smart logistics freight platform. Background Technology

[0002] With the rapid development of digital logistics, the rationality of route planning for transport vehicles directly affects logistics efficiency, cost, and safety. Traditional route planning methods are mostly based on static road data, such as road length and toll costs, and employ single-objective optimization, thus exhibiting the following technical shortcomings:

[0003] 1. Low accuracy: The lack of full integration of multi-dimensional dynamic data leads to a disconnect between planned routes and actual transportation scenarios, which can easily result in problems such as congestion and detours;

[0004] 2. Poor dynamic adaptability: After route planning, it is impossible to make real-time adjustments based on changes in dynamic data, resulting in delays in transportation time and increased costs;

[0005] 3. Lagging supervision: There is a lack of real-time monitoring and early warning mechanisms for deviations from actual transportation trajectories. When vehicles deviate from the planned route or abnormalities occur, timely intervention is not possible, posing safety hazards. Summary of the Invention

[0006] In view of the technical defects mentioned in the background art, the purpose of this invention is to provide a method and monitoring system for optimal transportation routes of a smart logistics freight platform, aiming to at least partially solve one of the technical problems in the related art.

[0007] To achieve the above objectives, in a first aspect, embodiments of the present invention provide a method for optimal route planning in a smart logistics freight platform, the method comprising the following steps:

[0008] Collect multi-dimensional static and dynamic data;

[0009] Based on the multi-dimensional static data and the dynamic data, a weighted multi-objective optimization model is constructed; wherein, the optimization objectives include the shortest transportation time, the lowest transportation cost, the lowest transportation energy consumption, and the highest transportation safety factor, and the weights are dynamically allocated based on the freight task type and user needs;

[0010] An improved intelligent optimization algorithm is used to solve the weighted multi-objective optimization model to obtain the initial optimal transportation route;

[0011] Based on real-time updated dynamic data, it is determined whether the route adaptive update condition is met. If it is met, the weighted multi-objective optimization model is re-solved to generate the updated optimal transportation route.

[0012] The transportation process is monitored in real time, and the actual transportation trajectory is compared with the optimal transportation route. When the deviation exceeds a preset threshold, an early warning is triggered and adjustment suggestions are generated.

[0013] As a specific implementation of this application, the static data includes road basic attribute data, freight task attribute data, and logistics node attribute data; the dynamic data includes real-time traffic condition data, meteorological data, vehicle operation status data, and order change data.

[0014] As a specific implementation of this application, the constraints of the weighted multi-objective optimization model include road weight limit constraints, vehicle range constraints, transportation timeliness constraints, and path connectivity constraints.

[0015] As a specific implementation of this application, the improved intelligent optimization algorithm employs an improved genetic algorithm; wherein, the improvement includes:

[0016] A real-number encoding method is used to represent the sequence of route nodes, and a fitness function is designed based on transportation distance and congestion index.

[0017] An adaptive crossover operator is employed, ensuring that the crossover probability decreases linearly with the number of generations in the population.

[0018] An adaptive mutation operator is adopted to dynamically adjust the mutation probability according to the individual fitness value, and an elite retention mechanism is introduced to retain the best individual in each generation to directly enter the next generation of the population, so as to improve the solution accuracy and convergence speed.

[0019] As one specific implementation of this application, the line adaptive update conditions include:

[0020] When the real-time traffic congestion index exceeds the set congestion threshold, when weather data reaches the severe weather level, when vehicle operation status issues a malfunction warning, when order change data triggers a timeliness adjustment, or when the transportation time deviation exceeds the preset time threshold.

[0021] As one specific implementation of this application, the deviation is obtained by weighted calculation of spatial deviation and temporal deviation.

[0022] As one specific implementation of this application, the early warning includes audible and visual early warning, platform push early warning, and SMS early warning; the adjustment suggestion generates a temporary optimal line segment in real time based on the current dynamic data.

[0023] Secondly, embodiments of the present invention also provide a smart logistics freight platform transportation optimal route monitoring system, the system comprising:

[0024] The data acquisition module collects multi-dimensional static and dynamic data;

[0025] The construction module constructs a weighted multi-objective optimization model based on the multi-dimensional static data and the dynamic data; wherein, the optimization objectives include the shortest transportation time, the lowest transportation cost, the lowest transportation energy consumption, and the highest transportation safety factor, and the weights are dynamically allocated based on the freight task type and user needs;

[0026] The route solving module uses an improved intelligent optimization algorithm to solve the weighted multi-objective optimization model to obtain the initial optimal transportation route;

[0027] The adaptive update module, based on real-time updated dynamic data, determines whether the adaptive update conditions for the route are met. If they are met, the weighted multi-objective optimization model is re-solved to generate the updated optimal transportation route.

[0028] The real-time monitoring module monitors the transportation process in real time, compares the actual transportation trajectory with the optimal transportation route, triggers an early warning when the deviation exceeds a preset threshold, and generates adjustment suggestions.

[0029] As a preferred implementation of this application, the real-time monitoring module further includes an anomaly handling unit, which, after receiving the early warning information, automatically associates the driver terminal and the logistics dispatch center terminal, pushes deviation details and adjustment suggestions, and records the anomaly handling process and results.

[0030] As a preferred implementation of this application, the system further includes a data storage and interaction module. The data storage and interaction module uses a distributed database to store structured and unstructured data, and supports data interface docking with third-party logistics platforms, traffic management department platforms, and meteorological service platforms to achieve data sharing and collaboration.

[0031] The technical solutions provided in the embodiments of the present invention have the following beneficial effects:

[0032] 1. Improved accuracy: Integrates multi-dimensional static and dynamic data to build a multi-objective optimization model that considers the multi-dimensional needs of transportation time, cost, energy consumption and safety, and dynamically allocates weights to adapt to different freight scenarios;

[0033] 2. Strong dynamic adaptability: Based on real-time data triggering line adaptive updates, it quickly responds to real-time changes and reduces the risk of time delays;

[0034] 3. Real-time monitoring: Real-time early warning of trajectory deviation is achieved through comprehensive deviation calculation, and adjustment suggestions are generated, which not only intervenes in a timely manner but also improves the safety of transportation. Attached Figure Description

[0035] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0036] Figure 1 This is a flowchart of an optimal route planning method for a smart logistics freight platform provided in an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of the optimal transportation route monitoring system for a smart logistics freight platform provided in an embodiment of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0040] It should be noted that, unless otherwise stated, the technical terms used in this embodiment have the common meaning as understood in the relevant technical field.

[0041] Please refer to Figure 1 This invention provides a method for optimal route planning in a smart logistics freight platform, the method comprising the following steps:

[0042] S101, collect multi-dimensional static and dynamic data; wherein, the static data includes road basic attribute data, freight task attribute data, and logistics node attribute data; the dynamic data includes real-time traffic condition data, weather data, vehicle operation status data, and order change data;

[0043] S102, Based on the multi-dimensional static data and the dynamic data, a weighted multi-objective optimization model is constructed; wherein, the optimization objectives include the shortest transportation time, the lowest transportation cost, the lowest transportation energy consumption, and the highest transportation safety factor, and the weights are dynamically allocated based on the freight task type and user needs;

[0044] S103, The weighted multi-objective optimization model is solved using an improved intelligent optimization algorithm to obtain the initial optimal transportation route;

[0045] S104. Based on real-time updated dynamic data, determine whether the route adaptive update condition is met. If it is met, re-solve the weighted multi-objective optimization model to generate the updated optimal transportation route.

[0046] S105, the transportation process is monitored in real time, the actual transportation trajectory is compared with the optimal transportation route, and an early warning is triggered when the deviation exceeds a preset threshold, and adjustment suggestions are generated.

[0047] The basic road attribute data includes road grade, number of lanes, weight limit standard, toll fee, and road length;

[0048] The freight task attribute data includes cargo type, cargo weight, cargo volume, origin, destination, and transportation time requirements;

[0049] The logistics node attribute data includes the location of storage nodes, loading and unloading capacity, and layout of transit nodes.

[0050] The real-time traffic data includes road congestion index, average driving speed, and traffic accident information;

[0051] The meteorological data includes rainfall intensity, visibility, and wind speed.

[0052] The vehicle operating status data includes the vehicle's remaining fuel / battery level, vehicle load, vehicle fault information, and real-time location.

[0053] The order change data includes destination changes, timeliness adjustments, and cargo addition / reduction information;

[0054] The user requirements include priorities such as timeliness and cost; meanwhile, the constraints of the weighted multi-objective optimization model include road weight limits, vehicle range constraints, transportation timeliness constraints, and path connectivity constraints.

[0055] The objective function of the weighted multi-objective optimization model can be expressed as:

[0056] ;

[0057] Where x is the transportation route variable, Let the objective function be transportation time. Let the objective function be transportation cost. Let the objective function be transportation energy consumption. The objective function for the transportation safety factor is... Let each objective have a weight, and satisfy the following conditions: .

[0058] The improved intelligent optimization algorithm employs an improved genetic algorithm; wherein the improvements include:

[0059] A real-number encoding method is used to represent the sequence of route nodes, and a fitness function is designed based on transportation distance and congestion index.

[0060] An adaptive crossover operator is employed, ensuring that the crossover probability decreases linearly with the number of generations in the population.

[0061] An adaptive mutation operator is adopted to dynamically adjust the mutation probability according to the individual fitness value, and an elite retention mechanism is introduced to retain the best individual in each generation to directly enter the next generation of the population, so as to improve the solution accuracy and convergence speed.

[0062] Specifically, the encoding method uses real number encoding, with each chromosome corresponding to one route, and the gene sequence being the coordinates or numbers of the nodes along the route;

[0063] The genetic operations involved include:

[0064] The selection process employs a roulette wheel selection method, combined with elite retention;

[0065] Crossover and linearly decreasing patterns help avoid premature convergence in the later stages.

[0066] Mutations.

[0067] In this embodiment, the line adaptive update conditions include:

[0068] When the real-time traffic congestion index exceeds the set congestion threshold, when weather data reaches the severe weather level, when vehicle operation status issues a malfunction warning, when order change data triggers a timeliness adjustment, or when the transportation time deviation exceeds the preset time threshold.

[0069] That is, an update is triggered when any of the above update conditions are met; the update process involves reloading the current dynamic data, resolving the model, and generating the updated transportation route.

[0070] In application, the deviation is calculated by weighting the spatial deviation and the temporal deviation;

[0071] Deviation ;

[0072] in, The spatial deviation is represented by the Hausdorff distance. The deviation rate between actual travel time and planned travel time is α, and β are weighting coefficients, where α+β=1;

[0073] When D ≥ preset threshold; such as a value range of 5%-20%, an alert will be triggered if configured according to freight type.

[0074] The warnings include audible and visual warnings, platform push warnings, and SMS warnings; the adjustment suggestions are based on real-time generation of temporary optimal route segments using current dynamic data.

[0075] Adjustment suggestion generation: Based on the current real-time traffic conditions and remaining route, quickly solve for the local optimal route segment; if there is congestion 5km ahead, it is recommended to detour via provincial highway, which is expected to save 20 minutes;

[0076] Anomaly Handling: The anomaly handling unit records the warning time, deviation cause, adjustment suggestions, and driver response, forming a closed-loop record to facilitate subsequent data analysis and optimization.

[0077] The above solution has the following beneficial effects:

[0078] 1. Improved accuracy: Integrates multi-dimensional static and dynamic data to build a multi-objective optimization model that considers the multi-dimensional needs of transportation time, cost, energy consumption and safety, and dynamically allocates weights to adapt to different freight scenarios;

[0079] 2. Strong dynamic adaptability: Based on real-time data triggering line adaptive updates, it quickly responds to real-time changes and reduces the risk of time delays;

[0080] 3. Real-time monitoring: Real-time early warning of trajectory deviation is achieved through comprehensive deviation calculation, and adjustment suggestions are generated, which not only intervenes in a timely manner but also improves the safety of transportation.

[0081] Reference Figure 2 Based on the same inventive concept, embodiments of the present invention also provide a smart logistics freight platform transportation optimal route monitoring system, the system comprising:

[0082] The data acquisition module collects multi-dimensional static and dynamic data;

[0083] The construction module constructs a weighted multi-objective optimization model based on the multi-dimensional static data and the dynamic data; wherein, the optimization objectives include the shortest transportation time, the lowest transportation cost, the lowest transportation energy consumption, and the highest transportation safety factor, and the weights are dynamically allocated based on the freight task type and user needs;

[0084] The route solving module uses an improved intelligent optimization algorithm to solve the weighted multi-objective optimization model to obtain the initial optimal transportation route;

[0085] The adaptive update module, based on real-time updated dynamic data, determines whether the adaptive update conditions for the route are met. If they are met, the weighted multi-objective optimization model is re-solved to generate the updated optimal transportation route.

[0086] The real-time monitoring module monitors the transportation process in real time, compares the actual transportation trajectory with the optimal transportation route, triggers an early warning when the deviation exceeds a preset threshold, and generates adjustment suggestions.

[0087] The improved intelligent optimization algorithm employs an improved genetic algorithm; wherein the improvement includes:

[0088] A real-number encoding method is used to represent the sequence of route nodes, and a fitness function is designed based on transportation distance and congestion index.

[0089] An adaptive crossover operator is employed, ensuring that the crossover probability decreases linearly with the number of generations in the population.

[0090] An adaptive mutation operator is adopted to dynamically adjust the mutation probability according to the individual fitness value, and an elite retention mechanism is introduced to retain the best individual in each generation to directly enter the next generation of the population, so as to improve the solution accuracy and convergence speed.

[0091] The adaptive update conditions for the line include:

[0092] When the real-time traffic congestion index exceeds the set congestion threshold, when weather data reaches the severe weather level, when vehicle operation status issues a malfunction warning, when order change data triggers a timeliness adjustment, or when the transportation time deviation exceeds the preset time threshold.

[0093] Furthermore, the real-time monitoring module also includes an anomaly handling unit, which, after receiving the early warning information, automatically associates the driver terminal and the logistics dispatch center terminal, pushes deviation details and adjustment suggestions, and records the anomaly handling process and results;

[0094] This enables: real-time linkage in anomaly handling, shortening response time to prevent anomalies from escalating; generating targeted adjustment suggestions based on current dynamic data, reducing the difficulty of driver decision-making; establishing full-process traceability to provide a basis for subsequent data analysis, route optimization, and liability determination; and strengthening the regulatory closed-loop capability.

[0095] In another embodiment, based on the above technical solution, the system further includes a data storage and interaction module. The data storage and interaction module uses a distributed database to store structured and unstructured data, and supports data interface docking with third-party logistics platforms, traffic management department platforms and meteorological service platforms to achieve data sharing and collaboration.

[0096] While improving compatibility, the long-term stored data can also be used for model parameter optimization, freight scenario profiling, and system performance iteration, thereby improving the applicability and intelligence level.

[0097] It should be noted that for a more specific application description of the system implementation, please refer to the aforementioned method implementation section, which will not be repeated here.

[0098] This technical solution improves the dynamic adaptability and accuracy of route planning through data acquisition, model building, route solving, adaptive updating, real-time monitoring, and data storage and interaction. It achieves closed-loop monitoring of the entire transportation process, effectively improving the safety and timeliness of transportation, and is applicable to various transportation scenarios.

[0099] In the embodiments provided in this application, it should be understood that the disclosed methods and systems can also be implemented in other ways. The embodiments described above are merely illustrative. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or actions, or using a combination of dedicated hardware and computer instructions.

[0100] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0101] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0102] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimal route planning in a smart logistics freight platform, characterized in that, The method includes the following steps: Collect multi-dimensional static and dynamic data; Based on the multi-dimensional static data and the dynamic data, a weighted multi-objective optimization model is constructed; wherein, the optimization objectives include the shortest transportation time, the lowest transportation cost, the lowest transportation energy consumption, and the highest transportation safety factor, and the weights are dynamically allocated based on the freight task type and user needs; An improved intelligent optimization algorithm is used to solve the weighted multi-objective optimization model to obtain the initial optimal transportation route; Based on real-time updated dynamic data, it is determined whether the route adaptive update condition is met. If it is met, the weighted multi-objective optimization model is re-solved to generate the updated optimal transportation route. The transportation process is monitored in real time, and the actual transportation trajectory is compared with the optimal transportation route. When the deviation exceeds a preset threshold, an early warning is triggered and adjustment suggestions are generated.

2. The method for optimal route planning in a smart logistics freight platform as described in claim 1, characterized in that, The static data includes basic road attribute data, freight task attribute data, and logistics node attribute data; the dynamic data includes real-time traffic condition data, weather data, vehicle operation status data, and order change data.

3. The method for optimal route planning in a smart logistics freight platform as described in claim 2, characterized in that, The constraints of the weighted multi-objective optimization model include road weight limits, vehicle range constraints, transportation timeliness constraints, and path connectivity constraints.

4. The method for optimal route planning in a smart logistics freight platform as described in claim 3, characterized in that, The improved intelligent optimization algorithm employs an improved genetic algorithm; wherein the improvements include: A real-number encoding method is used to represent the sequence of route nodes, and a fitness function is designed based on transportation distance and congestion index. An adaptive crossover operator is employed, ensuring that the crossover probability decreases linearly with the number of generations in the population. An adaptive mutation operator is adopted to dynamically adjust the mutation probability according to the individual fitness value, and an elite retention mechanism is introduced to retain the best individual in each generation to directly enter the next generation of the population, so as to improve the solution accuracy and convergence speed.

5. The method for optimal route planning in a smart logistics freight platform as described in claim 4, characterized in that, The adaptive update conditions for the line include: When the real-time traffic congestion index exceeds the set congestion threshold, when weather data reaches the severe weather level, when vehicle operation status issues a malfunction warning, when order change data triggers a timeliness adjustment, or when the transportation time deviation exceeds the preset time threshold.

6. The method for optimal route planning in a smart logistics freight platform as described in any one of claims 1 to 5, characterized in that, The deviation is calculated by weighting the spatial deviation and the temporal deviation.

7. The method for optimal route planning in a smart logistics freight platform as described in claim 6, characterized in that, The warnings include audible and visual warnings, platform push warnings, and SMS warnings; the adjustment suggestions are based on real-time generation of temporary optimal route segments using current dynamic data.

8. A smart logistics freight platform optimal route monitoring system, characterized in that, The system includes: The data acquisition module collects multi-dimensional static and dynamic data; The construction module constructs a weighted multi-objective optimization model based on the multi-dimensional static data and the dynamic data; wherein, the optimization objectives include the shortest transportation time, the lowest transportation cost, the lowest transportation energy consumption, and the highest transportation safety factor, and the weights are dynamically allocated based on the freight task type and user needs; The route solving module uses an improved intelligent optimization algorithm to solve the weighted multi-objective optimization model to obtain the initial optimal transportation route; The adaptive update module, based on real-time updated dynamic data, determines whether the adaptive update conditions for the route are met. If they are met, the weighted multi-objective optimization model is re-solved to generate the updated optimal transportation route. The real-time monitoring module monitors the transportation process in real time, compares the actual transportation trajectory with the optimal transportation route, triggers an early warning when the deviation exceeds a preset threshold, and generates adjustment suggestions.

9. The system as described in claim 8, characterized in that, The real-time monitoring module also includes an anomaly handling unit, which, after receiving early warning information, automatically associates the driver terminal and the logistics dispatch center terminal, pushes deviation details and adjustment suggestions, and records the anomaly handling process and results.

10. The system as described in claim 9, characterized in that, It also includes a data storage and interaction module, which uses a distributed database to store structured and unstructured data and supports data interface docking with third-party logistics platforms, traffic management department platforms and meteorological service platforms to achieve data sharing and collaboration.

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