Transportation management system for carrying out freight transport by using urban public transport

Abstract

The invention, which relates to the technical field of data management, discloses a transportation management system for carrying out freight transport by using urban public transport, comprising a freight transport task management module, an intelligent scheduling module, a cargo storage and fixed management module and an information interaction module. In combination with a public transport operation timetable, passenger flow density and dynamic operation characteristics, a freight route is optimized, and passenger and freight coexistence is ensured without affecting passenger travel; based on spatial layout and vehicle operation characteristics, a cargo storage optimization strategy is designed, the transportation stability is improved, and cargo damage and potential safety hazards are prevented; and meanwhile, the information interaction module monitors the cargo storage state in real time and provides an abnormal alarm, so that the safety, reliability and operation efficiency of cargo transportation are ensured, and intelligent fusion of public transportation and urban freight transportation is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management, and particularly relates to a transport management system for freight transportation by using urban public transportation. BACKGROUND

[0002] With the rapid development of urbanization, the demand for urban logistics is increasing, especially the rise of e-commerce and express industry, which gradually increases the pressure of urban freight. However, the traditional urban freight mode usually relies on special freight vehicles for distribution, which not only leads to the aggravation of traffic congestion, but also increases environmental pollution and logistics cost. Therefore, how to optimize the urban logistics system, improve the transportation efficiency, and reduce the traffic burden has become a problem to be solved.

[0003] In recent years, the rise of the sharing economy model provides a new solution for urban freight. Based on the concept of "passenger and freight integration", researchers propose the concept of using urban public transportation network for freight transportation. Urban public transportation, such as subway and bus, usually has fixed operating lines and high operating frequency, and there is a certain idle capacity during off-peak hours. Through reasonable scheduling and optimization, the public transportation network can be used to carry part of the urban freight demand, realize passenger and freight co-loading, thereby improving the overall transportation efficiency, reducing carbon emissions, and reducing logistics cost.

[0004] The prior art has the following deficiencies: Since the main goal of the public transportation system is to serve passengers, the introduction of freight tasks cannot affect the normal travel of passengers. For example, during peak hours, the space of the bus or subway car is extremely limited, and if the freight transportation conflicts with the passenger travel, it may reduce the comfort of passengers, and even affect the operation efficiency. In addition, the stability of freight transportation is also a key problem. During the operation of public transportation tools, they often experience more starting, braking and turning actions, and if the freight is not properly fixed, it may cause damage or safety accidents. Therefore, how to design a reasonable freight storage and fixing scheme in the dynamic public transportation environment, and optimize the freight boarding and alighting time and location combined with the intelligent scheduling system to ensure the coexistence of passengers and freight, is a big problem in this technical field. SUMMARY

[0005] The purpose of the present application is to provide a transport management system for freight transportation by using urban public transportation to solve the deficiencies in the background art.

[0006] In order to achieve the above purpose, the present application provides the following technical scheme: a transport management system for freight transportation by using urban public transportation, comprising a freight task management module, an intelligent scheduling module, a freight storage and fixing management module, and an information interaction module. The freight task management module is configured to receive, store and optimize freight order information, and dynamically allocate the transportation route and time of the freight based on the public transportation operation timetable, passenger flow data and route planning; The intelligent scheduling module is configured to analyze the real-time running state of the public transportation tool, passenger flow density and freight demand, determine the freight loading site and unloading site, and coordinate the freight task and passenger travel demand; The freight storage and fixation management module is configured to plan the freight storage area based on the spatial layout and dynamic running characteristics of the public transportation tool; The information interaction module is configured to connect the transportation system, the public transportation operation management platform and the freight owner, and provide the stability condition and abnormal alarm of the freight storage area in the running process of the vehicle.

[0007] Preferably, the freight task management module can access real-time data of the public transportation operation, including timetable information, passenger flow data, route planning and traffic state, and dynamically adjust the transportation path based on the freight characteristics, including volume, weight and storage demand.

[0008] Preferably, the intelligent scheduling module models the public transportation system as a reinforcement learning environment by using reinforcement learning combined with adaptive heuristic search algorithm, optimizes the loading site, unloading site and transportation path of the freight through the trained model, so as to maximize the transportation efficiency and minimize the influence on passengers.

[0009] Preferably, the intelligent scheduling module determines the optimal execution scheme of the freight task based on the real-time running state of the vehicle, passenger flow density and freight demand, and automatically recalculates the path to provide alternative transportation scheme in the case of emergency.

[0010] Preferably, the carriage center of gravity offset rate is generated after analyzing the spatial layout of the public transportation tool, and the method for obtaining the carriage center of gravity offset rate is as follows: Let the carriage length be Lcarriage and the carriage width be Wcarriage, and let the original center of gravity of the carriage be Gorig(xorig, yorig). Let there be N goods, and the parameters of each good i include: mass ; position coordinates (xi, yi); volume limit Vi; each particle represents a freight storage scheme, i.e. the coordinate combination of all goods: Pk={(x1, y1), (x2, y2), …, (xN, yN)}; let the particle swarm size be M, and let the initial position of each particle be randomly distributed in the available area of the carriage, and let the initial speed of each particle be randomly set. For each particle Pk, calculate the new center of gravity of the carriage : ; wherein: and are the horizontal and vertical coordinates of the new center of gravity, and the mass The new center of gravity is affected by the position, and then the car center of gravity deviation rate CGD is calculated, expressed as: .

[0011] Preferably, the dynamic running characteristics during vehicle driving are analyzed to generate a dynamic sway index, and the dynamic sway index is obtained by: During vehicle driving, an inertial measurement unit is installed to collect three-axis acceleration data: front-back direction ax(t), left-right direction ay(t), and up-down direction az(t); the sampling frequency is set to fs, and the Fourier transform is used to convert the time domain signal to the frequency domain to calculate the vibration intensity of the signal at different frequencies. For ax(t), ay(t), and az(t) signals, the fast Fourier transform is applied, expressed as: ; where A(f) is the amplitude at frequency f, N is the number of data points, is the Fourier basis function; calculate the vibration energy spectrum P(f), expressed as: ; reflecting the energy size of each frequency component, extract the main vibration frequency component, and calculate the vibration energy sum of frequency band; define the dynamic sway index DVI as the weighted sum of vibration energy in the frequency range, and the calculation expression is: ; where: = 0.5 Hz, = 20 Hz, is the total vibration energy of the entire frequency spectrum, expressed as: ; where .

[0012] Preferably, the car center of gravity deviation rate and the dynamic sway index are converted into a comprehensive feature vector, the comprehensive feature vector is taken as the input of a machine learning model, the machine learning model takes each set of comprehensive feature vector prediction cargo storage area stability analysis value label as the prediction target, minimizes the sum of prediction errors of all cargo storage area stability analysis value labels as the training target, and trains the machine learning model until the sum of prediction errors converges to stop model training. According to the model output result, the cargo storage area stability analysis value is determined, wherein the machine learning model is a polynomial regression model.

[0013] Preferably, in the information interaction module, the obtained stability analysis value of the cargo storage area is compared with a pre-set stability reference threshold value. If the stability analysis value of the cargo storage area is greater than or equal to the pre-set stability reference threshold value, it indicates that the cargo can be stably stored in the storage area during vehicle driving, and no pre-warning signal is generated and no storage area adjustment is performed. If the stability analysis value of the cargo storage area is less than the pre-set stability reference threshold value, it indicates that the cargo cannot be stably stored in the storage area during vehicle driving, a pre-warning signal is generated, and storage area adjustment is required, including adjusting the cargo storage position, optimizing the fixing method or optimizing the vehicle operation strategy, to improve the transportation safety.

[0014] In the above technical solution, the present application provides technical effects and advantages: 1. The present application provides a transportation management system for cargo transportation using public transportation, effectively solving key problems such as the impact of public transportation on passenger travel and the lack of stability of cargo transportation. First, the system dynamically allocates cargo transportation routes by combining real-time passenger flow data, public transportation operation schedules and route planning through the intelligent scheduling module, avoiding peak hours, ensuring passenger and cargo coexistence, and improving public transportation utilization. At the same time, the system optimizes loading and unloading sites using reinforcement learning and adaptive heuristic search algorithm, automatically recalculates the path in emergency situations (such as route adjustment and passenger flow changes), provides alternative transportation solutions, and ensures smooth execution of cargo transportation tasks. In addition, the system uses a polynomial regression model to accurately evaluate the stability of the cargo storage area by combining the center of gravity deviation rate (CGD) and the dynamic shaking index (DVI), and automatically triggers a warning signal when the stability of the cargo storage area is below the threshold, optimizing the cargo storage solution or adjusting the transportation strategy, thereby improving the safety of cargo transportation.

[0015] 2. The information interaction module of the present application connects the transportation system, the public transportation operation management platform and the consignor through wireless communication, provides real-time monitoring of cargo transportation status, data sharing and abnormal alarm, and ensures that all parties can timely grasp the cargo transportation status. When the cargo storage is unstable (such as loose, dumping or violent shaking), the system automatically triggers an alarm to notify the driver, dispatch center or consignor, ensuring the safety and efficiency of cargo transportation. In addition, the present application calculates the dynamic shaking index based on deep sensor fusion (IMU+FFT) and intelligent algorithm, accurately evaluates the stability of the cargo during driving, and ensures safe storage and transportation of goods in different public transportation environments (bus, subway, tram). Ultimately, the present application can maximize the use of idle capacity of public transportation, reduce urban freight costs, reduce carbon emissions, improve the utilization rate of public transportation resources, and provide a more intelligent, efficient and safe transportation solution for urban logistics. BRIEF DESCRIPTION OF DRAWINGS

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 This is a system module diagram of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 embodiments of the present invention, not all embodiments. 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.

[0019] For examples, please refer to Figure 1 As shown in the figure, the transportation management system for freight transportation using urban public transportation described in this embodiment includes a freight task management module, an intelligent scheduling module, a cargo storage and fixed management module, and an information interaction module. The freight task management module is used to receive, store, and optimize freight order information, and dynamically allocate freight transportation routes and times based on public transportation timetables, passenger flow data, and route planning. The intelligent dispatch module is used to analyze the real-time operating status, passenger flow density and freight demand of public transportation vehicles, determine cargo loading and unloading stations, and coordinate freight tasks with passenger travel needs. The cargo storage and fixed management module is used to plan cargo storage areas based on the spatial layout and dynamic operation characteristics of public transportation vehicles. The information interaction module is used to connect the transportation system, public transportation operation management platform and cargo owners, and to provide information on the stability of the cargo storage area during vehicle operation and abnormal alarms.

[0020] The freight task management module uses public transportation timetables, passenger flow data, and route planning to dynamically allocate freight transportation routes and times.

[0021] The freight task management module receives freight orders from cargo owners, logistics companies, or public transportation operators, including: cargo information (type, volume, weight, special storage requirements), pick-up and delivery locations, expected delivery time, and acceptable transportation time range. The order information is stored in the database for subsequent dynamic allocation.

[0022] Access real-time public transportation data, including: timetable information: running time, departure interval of each line; passenger flow data: bus, subway passenger load rate in each time period; line planning: available bus / subway lines, transfer options; traffic status: whether there are emergencies, line adjustments, etc.

[0023] According to the characteristics of the goods (volume, weight, storage requirements), combined with passenger flow data, assess whether it is safe to transport goods on the target line and time period, avoiding peak hours affecting passenger travel. If the passenger flow is too high in the target time period, adjust to a low passenger flow period or transfer to other lines. If there is no available transport capacity on the target line, adjust the transportation path or arrange for delayed delivery.

[0024] Calculate the optimal transportation path, following the following optimization objectives: avoid peak hours to ensure that goods transportation does not affect passenger comfort; shortest transportation time, reduce transfers, improve transportation efficiency; minimize additional operating costs, use existing public transportation capacity as much as possible.

[0025] Path planning scheme example: Scheme A (preferred): bus line A (off-peak) → subway line B → delivery site. Scheme B (alternative): bus line C → transfer line D (night low passenger flow) → delivery site. Determine the goods loading and unloading sites, prefer sites with less passenger flow and storage space.

[0026] Send transportation task instructions to the intelligent scheduling module, notify relevant sites and drivers to load and unload goods. Send transportation arrangements to the consignor through the information exchange module, including estimated delivery time and real-time freight status.

[0027] Real-time monitoring of public transportation operation status, if line adjustment, passenger flow surge, etc. unexpected situation, automatically recalculate the path and adjust the delivery time. If delayed due to unexpected situations, notify the consignor and relevant operating personnel through the information exchange module, and provide backup transportation options (such as transfer or delay).

[0028] The intelligent scheduling module needs to use reinforcement learning combined with adaptive heuristic search to dynamically optimize freight loading and unloading sites and coordinate freight tasks and passenger travel needs.

[0029] This system uses deep reinforcement learning combined with heuristic search for decision optimization. Its main process is as follows: The intelligent scheduling module models the public transportation system as a reinforcement learning environment, defined as follows: State S: Includes real-time public transportation status (vehicle location, line schedule, capacity); passenger density (peak / off-peak hours, station congestion); freight demand (order quantity, cargo volume, time constraints); available space at stations (cargo storage areas, station facilities). Action A: Dispatching decisions that the system can execute, including selecting appropriate loading stations (where to load cargo) and unloading stations (where to unload cargo), and adjusting transportation routes (choosing different lines, transfer options).

[0030] Reward function R: Aimed at maximizing transportation efficiency and minimizing impact on passengers: positive rewards: successful delivery of cargo (+10); choosing low passenger flow stations for loading / unloading (+5); choosing the shortest path and reducing transfers (+3); negative penalties: impact on passenger travel (-10); delayed cargo transportation (-8); station overload and inability to store cargo (-5).

[0031] Based on a reinforcement learning framework (such as Deep Q-Networks, DQN or Proximal Policy Optimization, PPO), the intelligent dispatching module performs the following operations: Collect real-time public transportation data (vehicle status, passenger density, freight demand).

[0032] Calculate the optimal dispatching scheme based on the current environmental state S.

[0033] Use deep Q-networks (DQN) to calculate the Q-value (transportation efficiency score) of each available station and choose the station with the highest Q-value for loading / unloading decisions.

[0034] In the exploration phase, introduce adaptive heuristic search (AHS) to avoid local optima and ensure that the optimal solution is found in different scenarios.

[0035] Execute decisions: The intelligent dispatching module sends dispatching instructions (determines the loading station, unloading station, and path for freight tasks) to the transportation system.

[0036] Based on the completion of freight tasks (such as transportation time and impact on passengers), the reinforcement learning model updates the Q-value to optimize future decisions.

[0037] To ensure that freight tasks do not affect passenger travel, the intelligent dispatching module coordinates transportation arrangements through the following strategies: Peak-avoiding scheduling: Predict peak passenger flow time periods (e.g., morning and evening rush hours) and adjust freight delivery times. Prioritize low-passenger flow stations: Choose stations near commercial and office areas for loading to avoid heavy loading and unloading at core transfer stations. Intelligent dynamic adjustment: If passenger flow suddenly increases, the intelligent scheduling module can automatically adjust the unloading station to a subsequent low-passenger flow station for freight unloading.

[0038] Cargo storage and fixation management module for planning cargo storage areas based on the spatial layout and dynamic operating characteristics of public transportation vehicles and providing adaptive cargo fixation devices.

[0039] According to the spatial characteristics of different public transportation vehicles (buses, subways, trams, etc.), the cargo storage area is reasonably planned: Bus (limited space, need to avoid affecting passengers): rear seat area (partially foldable seats); standing area near the door (set up foldable shelves); subway (larger space, can use specific carriages): set up dedicated freight carriages (such as some low-passenger flow lines); corners near the ends of the carriage (use areas that do not affect passenger traffic); tram (some cities have dedicated freight modes): set up hidden freight space under the carriage; expand small cargo storage under seats or luggage rack area.

[0040] After analyzing the spatial layout of public transportation vehicles, the carriage center of gravity deviation rate is generated, and the method for obtaining the carriage center of gravity deviation rate is as follows: Let the carriage length be Lcarriage and the width be Wcarriage. Let the original center of gravity of the carriage (without cargo) be Gorig(xorig, yorig). Let there be N goods, and each good i has the following parameters: mass ; position coordinates (xi, yi); volume limit Vi; each particle represents a cargo storage scheme, i.e., the coordinate combination of all goods: Pk={(x1, y1), (x2, y2), …, (xN, yN)}; let the particle swarm size be M (i.e., optimize M different cargo storage schemes simultaneously). Let the initial position of each particle be randomly distributed within the available area of the carriage. Let the initial speed of each particle be randomly set to ensure that the search space is adequately covered.

[0041] For each particle Pk (i.e., a cargo storage scheme), calculate the new center of gravity of the carriage ; where: and are the horizontal and vertical coordinates of the new center of gravity, and the mass of the goods affects the position of the new center of gravity, and then calculate the carriage center of gravity deviation rate CGD, expressed as: .

[0042] ​The center of gravity deviation rate of the vehicle compartment is used to measure the change of the center of gravity of the vehicle after the goods are stored. Excessive deviation of the center of gravity will affect the driving stability of the vehicle, causing the vehicle compartment to shake more severely and increasing the risk of goods falling.

[0043] The greater the center of gravity deviation rate (CGD) of the vehicle compartment, the lower the stability of the goods storage area. When the CGD is too high, it means that the distribution of the mass of the goods is seriously deviated from the original center of gravity of the vehicle compartment, which may cause the center of gravity to be unbalanced during driving, especially during acceleration, braking or turning, and may generate a large inertial force, causing the goods to slide, fall or shake more severely. In addition, excessive CGD may affect the handling stability of the vehicle, increase the uneven stress on the internal structure of the vehicle compartment, and cause the goods fixing devices in some areas to bear additional pressure, thereby reducing the reliability of the fixing devices and causing the safety of the goods storage to decrease. In extreme cases, serious deviation of the center of gravity may affect the driving safety of the vehicle, causing the vehicle compartment to shake more severely and even affecting the overall stability of the vehicle, increasing the risk of safety accidents.

[0044] The smaller the center of gravity deviation rate (CGD) of the vehicle compartment, the higher the stability of the goods storage area. When the CGD is within a reasonable range, it means that the mass of the goods is evenly distributed in the vehicle compartment, allowing the vehicle to maintain balance and stability during driving and avoiding severe shaking or tilting caused by excessive deviation of the center of gravity. Lower CGD allows the inertial force on the goods during acceleration, braking and turning to be evenly distributed, reducing the likelihood of displacement, falling or loosening of the goods. In addition, reasonable distribution of the center of gravity helps to optimize the goods fixing strategy, reduce the additional stress on the fixing devices, improve the safety of the goods storage area, and make freight transportation more stable and efficient. Therefore, during the storage of goods, the CGD should be reasonably optimized to be within a safe range to ensure the stability of freight transportation and the smoothness of vehicle driving.

[0045] The dynamic shaking index is generated by analyzing the dynamic running characteristics of the vehicle during driving. The method for obtaining the dynamic shaking index is as follows: During driving of the vehicle, an inertial measurement unit is installed to collect three-axis acceleration data: front-rear direction (X-axis): ax(t) (acceleration and braking influence), left-right direction (Y-axis): ay(t) (turning lateral shaking influence), and up-down direction (Z-axis): az(t) (road bumping and track vibration influence).

[0046] The sampling frequency fs needs to meet the Nyquist sampling theorem (at least 2 times the highest vibration frequency).

[0047] Typical value fs=100~500fHz (common vibration frequency range of buses, subways and rail vehicles is 0.5Hz-20Hz).

[0048] The Fourier transform is used to convert the time domain signal to the frequency domain, calculating the vibration intensity of the signal at different frequencies. For the ax(t), ay(t), az(t) signals, the fast Fourier transform is applied, expressed as: ; where A(f) is the amplitude at frequency f, N is the number of data points, is the Fourier basis function. The vibration energy spectrum P(f) is calculated, expressed as: ; reflecting the energy size of each frequency component. The main vibration frequency component is extracted, and the vibration energy sum of the frequency band (i.e. the main vehicle shaking frequency range) is calculated. A frequency threshold is set to ignore weak vibrations (such as vibration components below a certain energy value).

[0049] The dynamic shaking index DVI is defined as the weighted sum of vibration energy in the frequency range, calculated as: ; where: = 0.5 Hz, = 20 Hz (main vehicle shaking frequency band). is the total vibration energy of the entire frequency spectrum, expressed as: ; where (Nyquist frequency). The larger the DVI, the stronger the dynamic shaking of the vehicle, and the less stable the cargo storage area. The DVI tends to 0, indicating that the vibration energy is concentrated in the low frequency part, and the goods are relatively stable.

[0050] The dynamic shaking index is used to measure the impact of dynamic factors such as acceleration, braking, turning, etc. on the stability of the cargo storage area during actual operation of the vehicle. The dynamic shaking index takes into account factors such as vehicle speed changes, track / pavement vibrations, and vehicle acceleration, and is used to predict whether there is a risk of large displacement or dumping of goods during transportation.

[0051] The center of gravity offset rate and the dynamic shaking index are converted into a comprehensive feature vector, and the comprehensive feature vector is used as the input of a machine learning model. The machine learning model takes each set of comprehensive feature vectors as the prediction target to predict the cargo storage area stability analysis value label, and minimizes the sum of prediction errors for all cargo storage area stability analysis value labels as the training target. The machine learning model is trained until the sum of prediction errors converges, and the model training is stopped. The cargo storage area stability analysis value is determined according to the model output result, wherein the machine learning model is a polynomial regression model.

[0052] The method for obtaining the cargo storage area stability analysis value is: from the comprehensive feature vector training data of the trained machine learning model, the corresponding function expression is obtained: ; in the formula, is the output function of the model, CGD is the center of gravity deviation rate, DVI is the dynamic sway index, CGD is the center of gravity deviation rate, DVI is the dynamic sway index,

[0053] The information interaction module is used to connect the transportation system, the public transportation operation management platform and the consignor, to realize real-time monitoring of freight status, data sharing and abnormal alarm. This module collects vehicle driving data, cargo storage stability state (CGD, DVI), fixed device state and other key information through sensors, and uses wireless communication (API / WebSocket) to push real-time updates to relevant parties. When detecting cargo storage abnormalities (such as loosening, dumping, high vibration), the system automatically triggers an alarm mechanism to notify the operator, driver or consignor to take appropriate measures to ensure the safe transportation of goods on public transportation vehicles and optimize the stability and operational efficiency of the passenger and freight mixed loading mode.

[0054] The stability analysis value of the cargo storage area obtained is compared with the pre-set stability reference threshold value. If the stability analysis value of the cargo storage area is greater than or equal to the pre-set stability reference threshold value, it means that the goods can be stably stored in the storage area during vehicle driving. At this time, no warning signal is generated and no storage area adjustment is made. If the stability analysis value of the cargo storage area is less than the pre-set stability reference threshold value, it means that the goods cannot be stably stored in the storage area during vehicle driving. At this time, a warning signal is generated and storage area adjustment is needed, including adjusting the cargo storage position, optimizing the fixing method or optimizing the vehicle operation strategy, to improve transportation safety.

[0055] It should be noted that the stability reference threshold value is a key indicator for evaluating whether the goods are safe to store on public transportation vehicles. It is the minimum stability standard set according to historical data, experimental analysis and machine learning models, used to judge the stability state of the goods during driving.

[0056] The above formulas are dimensionless numerical calculations. The formula is obtained by software simulation of a large amount of data to reflect the current real situation. The pre-set parameters in the formula are set by a person skilled in the art according to the actual situation.

[0057] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center through wired (for example, infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0058] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A transportation management system for freight transport utilizing urban public transportation, characterized in that: It includes a freight task management module, an intelligent scheduling module, a cargo storage and fixed management module, and an information interaction module; The freight task management module is used to receive, store, and optimize freight order information, and dynamically allocate freight transportation routes and times based on public transportation timetables, passenger flow data, and route planning. The intelligent dispatch module is used to analyze the real-time operating status, passenger flow density and freight demand of public transportation vehicles, determine cargo loading and unloading stations, and coordinate freight tasks with passenger travel needs. The cargo storage and fixed management module is used to plan cargo storage areas based on the spatial layout and dynamic operation characteristics of public transportation vehicles. The information interaction module is used to connect the transportation system, public transportation operation management platform and cargo owners, and to provide information on the stability of the cargo storage area during vehicle operation and abnormal alarms.

2. A transportation management system for freight transport using urban public transportation according to claim 1, characterized in that: The freight task management module can access real-time data on public transportation operations, including timetable information, passenger flow data, route planning and traffic status, and dynamically adjust transportation routes based on cargo characteristics, including volume, weight and storage requirements.

3. A transportation management system for freight transport using urban public transportation according to claim 1, characterized in that: The intelligent scheduling module uses reinforcement learning combined with an adaptive heuristic search algorithm to model the public transportation system as a reinforcement learning environment. By training the model, it optimizes the loading and unloading stations and transportation routes of goods to maximize transportation efficiency and minimize the impact on passengers.

4. A transportation management system for freight transport using urban public transportation according to claim 3, characterized in that: The intelligent scheduling module determines the optimal execution plan for freight tasks based on real-time vehicle operating status, passenger flow density, and freight demand, and automatically recalculates routes in case of emergencies, providing alternative transportation solutions.

5. A transportation management system for freight transport using urban public transportation according to claim 4, characterized in that: After analyzing the spatial layout of public transportation vehicles, the center of gravity offset rate of the carriages is generated. The method for obtaining the center of gravity offset rate of the carriages is as follows: Let the length of the car be Lcarriage and the width be Wcarriage. Let the original center of gravity of the car be Gorig(xorig,yorig). There are N goods, and the parameters of each goods i include: mass. Position coordinates (xi, yi); Volume constraint Vi; Each particle represents a cargo storage scheme, i.e., the coordinate combination of all cargo: Pk={(x1,y1),(x2,y2),…,(xN,yN)}; Let the particle swarm size be M, let the initial position of each particle be randomly distributed within the available area of ​​the carriage, and let the initial velocity of each particle be randomly set. For each particle Pk, calculate the new center of gravity of the carriage. : ;in: and These are the x and y coordinates of the new center of gravity, and the mass of the cargo. The location of the new center of gravity is affected, and then the car's center of gravity offset rate CGD is calculated, expressed as: .

6. A transportation management system for freight transport using urban public transportation according to claim 5, characterized in that: The dynamic sway index is generated after analyzing the dynamic operating characteristics of the vehicle during operation. The method for obtaining the dynamic sway index is as follows: During vehicle operation, an inertial measurement unit is installed to collect triaxial acceleration data: forward and backward ax(t), left and right ay(t), and up and down az(t). The sampling frequency is set to fs. The Fourier transform is used to convert a time-domain signal to the frequency domain, calculating the vibration intensity of the signal at different frequencies. For the signals ax(t), ay(t), and az(t), the Fast Fourier Transform is applied, and the expression is: Where A(f) is the amplitude at frequency f, and N is the number of data points. These are Fourier basis functions; The vibrational energy spectrum P(f) is calculated using the following expression: ; Reflecting the energy magnitude of each frequency component, extracting the principal vibration frequency components, and calculating... The sum of vibrational energy across the frequency band; the dynamic sway index (DVI) is defined as... The weighted sum of vibrational energy within the frequency range is calculated using the following expression: ;in: =0.5Hz, =20Hz, It is the total vibrational energy of the entire spectrum, expressed as: ;in .

7. A transportation management system for freight transport using urban public transportation according to claim 6, characterized in that: The car's center of gravity offset rate and dynamic sway index are converted into a comprehensive feature vector. This comprehensive feature vector is then used as input to a machine learning model. The machine learning model uses the prediction of the cargo storage area stability analysis value label for each set of comprehensive feature vectors as its prediction objective. The training objective is to minimize the sum of prediction errors for all cargo storage area stability analysis value labels. The machine learning model is trained until the sum of prediction errors converges, at which point the model training stops. The cargo storage area stability analysis value is determined based on the model output. The machine learning model is a multinomial regression model.

8. A transportation management system for freight transport using urban public transportation according to claim 7, characterized in that: In the information interaction module, the obtained stability analysis value of the cargo storage area is compared with a preset stability reference threshold. If the stability analysis value of the cargo storage area is greater than or equal to the preset stability reference threshold, it means that the cargo can be stably stored in the storage area during vehicle travel, and no warning signal is generated or the storage area is adjusted. If the stability analysis value of the cargo storage area is less than the preset stability reference threshold, it means that the cargo cannot be stably stored in the storage area during vehicle travel, and a warning signal is generated, requiring adjustment of the storage area, including adjusting the cargo storage location, optimizing the fixing method, or optimizing the vehicle operation strategy to improve transportation safety.

Images