A logistics information supervision and transportation management system based on data sources

Abstract

The application discloses a logistics information supervision and transportation management system based on a data source, and relates to the technical field of logistics transportation. The transportation management system comprises a multi-source sensing acquisition module, a transportation execution control module and a path and loading / unloading optimization engine. The multi-source sensing acquisition module is used for collecting physical transportation data in real time through a vehicle positioning terminal, a cargo state sensor and a transportation environment monitoring device. The transportation execution control module is connected with the multi-source sensing acquisition module and is used for dynamically adjusting vehicle driving parameters and cargo storage environment according to the collected data. The path and loading / unloading optimization engine has the advantages that: the multi-source sensing acquisition module integrates cargo state and vehicle mechanical data, solves the problem of data fragmentation of traditional systems, the transportation execution control module identifies the cargo type based on an RFID label, dynamically adjusts the temperature and humidity of a refrigerated vehicle compartment in combination with a PID algorithm, realizes accurate control of the cargo storage environment, and reduces the loss rate.

Description

Technical Field

[0001] This invention relates to the field of logistics and transportation technology, specifically to a logistics information monitoring and transportation management system based on data sources. Background Technology

[0002] In the current logistics and transportation management field, traditional transportation systems generally suffer from fragmented data collection and insufficient coordination among multiple links. In the entire cargo transportation process, key information such as vehicle driving status, cargo environmental parameters, and loading and unloading operations are often scattered across different systems, leading to difficulties in information traceability and delayed response to anomalies.

[0003] The current practice of monitoring the status of goods in transit relies on manual records or data from a single sensor, which makes it difficult to reflect changes in multiple physical parameters such as temperature, humidity, and vibration in real time. Furthermore, the lack of a dynamic adjustment mechanism during transportation makes it impossible to optimize vehicle driving parameters or the storage environment of goods based on real-time data, which can easily lead to cargo damage or low transportation efficiency. To address this, we propose a logistics information monitoring and transportation management system based on data sources. Summary of the Invention

[0004] The purpose of this invention is to provide a logistics information monitoring and transportation management system based on data sources.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a data source-based logistics information monitoring and transportation management system, the transportation management system comprising:

[0006] The multi-source sensor acquisition module is used to collect physical transportation data in real time through vehicle positioning terminals, cargo status sensors and transportation environment monitoring equipment;

[0007] The transportation execution control module is connected to the multi-source sensor acquisition module and is used to dynamically adjust vehicle driving parameters and cargo storage environment based on the acquired data.

[0008] The route and loading / unloading optimization engine outputs transportation execution instructions through dynamic route planning algorithms and cargo loading / unloading collaborative control algorithms.

[0009] The vehicle-mounted terminal monitoring module is used to display the vehicle's physical status, cargo location, and loading / unloading equipment operating parameters in real time.

[0010] The transportation equipment linkage module is connected to the transportation execution control module and is used to control the temperature control of the refrigerated compartment, the cargo fixing device and the loading and unloading robotic arm.

[0011] The fault diagnosis and early warning module identifies abnormalities in transportation equipment based on sensor data and triggers maintenance commands.

[0012] The data traceability module is used to store physical parameters and equipment operation records throughout the entire transportation process.

[0013] As a further aspect of the present invention: the multi-source sensing acquisition module includes:

[0014] The cargo status sensing unit integrates a vibration sensor (sampling frequency 100Hz), a temperature and humidity sensor (accuracy ±0.5℃ / ±2%RH), and a pressure sensor to collect data on the physical status of the cargo during transit.

[0015] The vehicle's mechanical sensing unit acquires data on engine speed, braking system pressure, and tire pressure via the CAN bus.

[0016] As a further aspect of the present invention: the transportation execution control module includes:

[0017] The dynamic temperature control subunit obtains cargo type data by reading the RFID tag on the cargo packaging through the cargo status sensing unit of the multi-source sensing acquisition module. The dynamic temperature control subunit presets temperature and humidity thresholds based on the obtained cargo type and adjusts the refrigerated compartment compressor power using a PID algorithm. The control formula is as follows:

[0018] ;

[0019] in, For the power adjustment of the refrigerated compartment compressor, The proportionality coefficient for rapid response to temperature and humidity deviations, To eliminate the integral coefficient of accumulated bias, To suppress overshoot fluctuations, the differential coefficient, This represents the deviation between the actual temperature and humidity and the set threshold. This is the integral term of the deviation, used to reflect the cumulative effect of historical deviations. This is the differential term of the deviation, used to reflect the rate of change of the deviation.

[0020] As a further aspect of the present invention: the path and loading / unloading optimization engine also includes:

[0021] The cargo loading and unloading collaborative control submodule is implemented through a cargo loading and unloading collaborative control algorithm.

[0022] The gripping force of the loading and unloading robotic arm is allocated based on the cargo weight sensor data (gripping force = cargo weight × 1.2 safety factor).

[0023] A stacking scheme is generated based on the 3D scanning data of the carriage space, and the stacking stability is verified by a center of gravity offset of ≤5cm.

[0024] As a further aspect of the present invention: the cargo status sensing unit includes:

[0025] The vibration sensor (sampling frequency 5kHz) extracts characteristic frequencies through Fourier transform. ,in, For the first Each characteristic frequency, For frequency component index, The sampling frequency of the vibration spectrum. The number of sampling points is used to trigger a fault warning when the characteristic frequency deviates from the standard value by ±5%.

[0026] As a further aspect of the present invention: the vehicle-mounted terminal monitoring module also includes a genetic algorithm resource scheduling model, which is linked with the control unit in the loading and unloading robotic arm, as detailed below:

[0027] The cargo-robotic arm allocation scheme is encoded as a chromosome, with constraints such as the robotic arm load rate and loading / unloading time window.

[0028] fitness function ,in, The fitness function of the genetic algorithm resource scheduling model is... For loading and unloading efficiency, For the energy consumption of the robotic arm, , The weights for loading / unloading efficiency and robotic arm energy consumption are respectively... The scheduling scheme output after 50 iterations directly controls the order of robotic arm operations.

[0029] As a further aspect of the present invention: the transportation equipment linkage module includes a cargo fixing execution unit; when the vibration sensor detects an amplitude > 0.5g, the cargo fixing execution unit automatically activates the hydraulic fixing device, the response time of the hydraulic fixing device is ≤ 0.3 seconds, and sends a warning signal to the driver's cab.

[0030] As a further aspect of the present invention: the data traceability module adopts a distributed storage architecture, and the recorded physical transportation parameters include: vehicle driving trajectory, timestamp accuracy ≤ 1 second, position accuracy ≤ 5 meters; cargo temperature and humidity curve, sampling interval ≤ 5 minutes, storage duration ≥ 90 days; loading and unloading robotic arm operation log, including gripping force, rotation angle and execution time.

[0031] Compared with the prior art, the beneficial effects of the present invention by adopting the above technical solution are as follows:

[0032] 1. This invention integrates cargo status (vibration, temperature and humidity, pressure) and vehicle mechanical (engine speed, braking pressure, etc.) data through a multi-source sensor acquisition module, solving the data fragmentation problem of traditional systems. The transportation execution control module identifies cargo type based on RFID tags and dynamically adjusts the temperature and humidity of the refrigerated compartment using a PID algorithm, achieving precise control of the cargo storage environment and reducing loss rate.

[0033] 2. The path and loading / unloading optimization engine in this invention uses a dynamic path planning algorithm (to shorten transportation time) and a loading / unloading collaborative control algorithm (to generate stacking schemes based on weight sensors and 3D scanning data) combined with a genetic algorithm resource scheduling model to improve loading / unloading efficiency and reduce robotic arm energy consumption, thereby reducing resource consumption.

[0034] 3. The fault diagnosis module in this invention is based on vibration sensor characteristic frequency analysis and equipment parameter monitoring to achieve real-time identification of transportation equipment anomalies. The data traceability module adopts a distributed storage architecture to record vehicle trajectories, temperature and humidity curves, and robotic arm operation logs, ensuring that the entire process is auditable and traceable, and improving the emergency response speed and responsibility determination efficiency in scenarios such as hazardous materials. Attached Figure Description

[0035] Figure 1 This is an overall flowchart of the transportation management system in an embodiment of the present invention;

[0036] Figure 2 This is a schematic diagram of the dynamic temperature control subunit in an embodiment of the present invention;

[0037] Figure 3 This is a flowchart of the loading and unloading collaborative control and genetic algorithm scheduling in an embodiment of the present invention. Detailed Implementation

[0038] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0039] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0040] Please see the appendix Figure 1 -Appendix Figure 3 This invention discloses a data source-based logistics information monitoring and transportation management system, the transportation management system comprising:

[0041] The multi-source sensor acquisition module is used to collect physical transportation data in real time through vehicle positioning terminals, cargo status sensors and transportation environment monitoring equipment;

[0042] The transport execution control module is connected to the multi-source sensor acquisition module and is used to dynamically adjust vehicle driving parameters and cargo storage environment based on the acquired data.

[0043] The route and loading / unloading optimization engine outputs transportation execution instructions through dynamic route planning algorithms and cargo loading / unloading collaborative control algorithms.

[0044] The vehicle-mounted terminal monitoring module is used to display the vehicle's physical status, cargo location, and loading / unloading equipment operating parameters in real time.

[0045] The transportation equipment linkage module is connected to the transportation execution control module and is used to control the temperature control of the refrigerated compartment, the cargo fixing device, and the movement of the loading and unloading robotic arm.

[0046] The fault diagnosis and early warning module identifies abnormalities in transportation equipment based on sensor data and triggers maintenance commands.

[0047] The data traceability module is used to store physical parameters and equipment operation records throughout the entire transportation process.

[0048] In one embodiment of the present invention: the multi-source sensing acquisition module includes:

[0049] The cargo status sensing unit integrates a vibration sensor (sampling frequency 100Hz), a temperature and humidity sensor (accuracy ±0.5℃ / ±2%RH), and a pressure sensor to collect data on the physical status of the cargo during transit.

[0050] The vehicle's mechanical sensing unit acquires data on engine speed, braking system pressure, and tire pressure via the CAN bus.

[0051] In one embodiment of the present invention: the transport execution control module includes:

[0052] The dynamic temperature control subunit reads the RFID tag on the cargo packaging from the cargo status sensor unit of the multi-source sensor acquisition module to obtain cargo type data. Based on the obtained cargo type, the dynamic temperature control subunit presets temperature and humidity thresholds and adjusts the refrigerated compartment compressor power using a PID algorithm. The control formula is as follows:

[0053] ;

[0054] in, For the power adjustment of the refrigerated compartment compressor, The proportionality coefficient for rapid response to temperature and humidity deviations, To eliminate the integral coefficient of accumulated bias, To suppress overshoot fluctuations, the differential coefficient, This represents the deviation between the actual temperature and humidity and the set threshold. This is the integral term of the deviation, used to reflect the cumulative effect of historical deviations. This is the differential term of the deviation, used to reflect the rate of change of the deviation.

[0055] In one embodiment of the present invention, the path and loading / unloading optimization engine further includes:

[0056] The cargo loading and unloading collaborative control submodule is implemented through a cargo loading and unloading collaborative control algorithm.

[0057] The gripping force of the loading and unloading robotic arm is allocated based on the cargo weight sensor data (gripping force = cargo weight × 1.2 safety factor).

[0058] A stacking scheme is generated based on the 3D scanning data of the carriage space, and the stacking stability is verified by a center of gravity offset of ≤5cm.

[0059] In one embodiment of the present invention: the cargo status sensing unit includes:

[0060] The vibration sensor (sampling frequency 5kHz) extracts characteristic frequencies through Fourier transform. ,in, For the first Each characteristic frequency, For frequency component index, The sampling frequency of the vibration spectrum. The number of sampling points is used to trigger a fault warning when the characteristic frequency deviates from the standard value by ±5%.

[0061] In one embodiment of the present invention: the vehicle-mounted terminal monitoring module further includes a genetic algorithm resource scheduling model, which is linked with the control unit in the loading and unloading robotic arm, as detailed below:

[0062] The cargo-robotic arm allocation scheme is encoded as a chromosome, with constraints such as the robotic arm load rate and loading / unloading time window.

[0063] fitness function ,in, The fitness function of the genetic algorithm resource scheduling model is... For loading and unloading efficiency, For the energy consumption of the robotic arm, , The weights for loading / unloading efficiency and robotic arm energy consumption are respectively... The scheduling scheme output after 50 iterations directly controls the order of robotic arm operations.

[0064] In one embodiment of the present invention: the transportation equipment linkage module includes a cargo fixing execution unit; when the vibration sensor detects an amplitude > 0.5g, the cargo fixing execution unit automatically activates the hydraulic fixing device, the response time of the hydraulic fixing device is ≤ 0.3 seconds, and sends a warning signal to the driver's cab.

[0065] In one embodiment of the present invention: the data traceability module adopts a distributed storage architecture, and the recorded physical transportation parameters include: vehicle driving trajectory, timestamp accuracy ≤ 1 second, position accuracy ≤ 5 meters; cargo temperature and humidity curve, sampling interval ≤ 5 minutes, storage duration ≥ 90 days; loading and unloading robotic arm operation log, including gripping force, rotation angle and execution time.

[0066] Example 1: Application in Cold Chain Fresh Produce Transportation

[0067] This embodiment is applied to the long-distance transportation of frozen meat. The system is deployed on a 4.2-meter refrigerated truck, and the transported goods are frozen pork (storage temperature requirement -18℃±2℃). In the multi-source sensor acquisition module, the cargo status sensing unit integrates vibration sensors, temperature and humidity sensors and pressure sensors, which are installed on the cargo pallet and the inner wall of the truck to collect data in real time. The vehicle mechanical sensing unit obtains engine speed, braking system pressure and tire pressure data through the CAN bus.

[0068] The dynamic temperature control subunit of the transportation execution control module identifies the type of goods by reading the RFID tag on the goods packaging. The preset temperature and humidity threshold is -18℃ / 60%RH. The temperature and humidity sensor collects data every 30 seconds. When the actual temperature rises to -16.5℃, the PID algorithm triggers compressor power adjustment. The control formula is:

[0069] ;

[0070] Where the proportionality coefficient =2.5, integral coefficient ᵢ=0.15, differential coefficient =0.8, by adjusting the compressor power output, the temperature in the carriage can be restored to the target range within 15 minutes;

[0071] The route and loading / unloading optimization engine initiates 3D scanning during the loading stage to generate a cargo compartment space model. Based on the weight of 25kg of frozen pork per box fed by the cargo weight sensor, the loading and unloading collaborative control algorithm calculates the robotic arm's gripping force to be 30kg. The stacking scheme adopts a "well" shaped staggered stacking, and the center of gravity offset is verified by the algorithm to be 3.2cm. During transportation, the dynamic route planning algorithm updates the route every 5 minutes, and combines real-time traffic data to avoid construction sections, shortening the transportation time by 28 minutes.

[0072] The onboard terminal monitoring module displays vehicle status and cargo information in different areas. The left side updates engine speed (2200 rpm), brake pressure (0.8 MPa), and tire pressure in real time. The right side displays a heat map showing the temperature distribution inside the cargo compartment, with red warning zones marking locations deviating from the threshold by more than 1°C. A genetic algorithm resource scheduling model, constrained by the robotic arm load rate and a 30-minute loading / unloading time window, allocates 12 boxes of cargo to two robotic arms. The fitness function includes a weight for loading / unloading efficiency. =0.6, energy consumption weight =0.4, the scheduling scheme output after 50 iterations reduces the idle time of the robotic arm to 8 minutes;

[0073] When the vehicle travels on a bumpy road, the vibration sensor detects an amplitude of 0.6g. Within 0.25 seconds, the cargo securing unit activates the hydraulic securing device, and an alarm sounds from the driver's cab. The fault diagnosis module detects a sudden increase in compressor current to 11A, determines the condenser is clogged, triggers a level-three warning, and sends a repair work order. The data traceability module stores the driving trajectory (timestamp accuracy 0.5 seconds, position error 3 meters), temperature and humidity curves (4-minute intervals), and robotic arm operation logs. All data is retained for 120 days and supports third-party auditing and traceability.

[0074] Example 2: Scenario for Safety Supervision of Dangerous Goods Transportation

[0075] I. System Configuration

[0076] This embodiment is designed for the transportation of flammable and explosive hazardous materials (such as liquefied natural gas tank trucks), and the system enhances safety monitoring and emergency control functions:

[0077] Multi-source sensor acquisition module: Added gas concentration sensor (detection range 0-1000ppm, response time ≤10s) and flame detector (infrared wavelength 4.3μm), integrated into the top and chassis of the vehicle body;

[0078] Transportation equipment linkage module: equipped with an emergency shut-off valve (action time ≤ 1s), a dry powder fire extinguishing device (spray coverage radius 3m) and an electrostatic grounding monitor (grounding resistance ≤ 5Ω).

[0079] Route and loading / unloading optimization engine: Equipped with dynamic route planning algorithm, and access to real-time traffic control data (updated every 5 minutes) and electronic map of hazardous materials restricted areas.

[0080] II. Work Process

[0081] Path planning stage

[0082] The logistics center inputs the origin (refinery), destination (chemical plant), and hazardous material type (liquefied natural gas, UN number 1972) into the route and loading / unloading optimization engine. The system automatically avoids sensitive areas such as schools and hospitals, generates three alternative routes, and the algorithm dynamically adjusts the weights based on real-time traffic data (such as highway congestion index). Finally, it selects the route "National Highway G30 → Provincial Highway S22", with a total distance of 280km and an estimated travel time of 4.5 hours. Twelve emergency parking points are marked along the way (one every 25km).

[0083] On-the-go safety monitoring

[0084] Gas Leakage Warning: The gas concentration sensor samples every 2 seconds. When the methane concentration reaches 500 ppm (10% of the lower explosive limit), the fault diagnosis module immediately triggers a three-level warning: ① Audible and visual alarm in the driver's cab (yellow indicator light + voice prompt "Please check the leak source"); ② The transportation equipment linkage module closes the emergency shut-off valve (shutting off the liquid phase pipeline of the tank truck); ③ Automatically sends GPS coordinates and leakage concentration curves to the logistics monitoring center.

[0085] Vehicle mechanical condition monitoring: The vehicle mechanical sensing unit monitors the braking system pressure via the CAN bus. When the pressure is collected three times in a row below 0.6MPa (reference value 0.8MPa), it is determined that there is a leak in the brake line. The system automatically reduces the engine output torque (limits the maximum speed to 2000r / min) and displays "Recommended to decelerate to 60km / h" on the vehicle terminal.

[0086] Emergency response mechanism

[0087] If the flame detector detects an open flame (response time 0.5 seconds), the system will initiate a Level 3 emergency response:

[0088] The transportation equipment linkage module activates the dry powder fire extinguishing device, with a spray delay of ≤0.3 seconds and a continuous spraying time of 30 seconds;

[0089] The cargo securing unit activates the hydraulic locking device to rigidly fix the tanker to the chassis (displacement ≤2mm).

[0090] The data traceability module uploads emergency logs (including the time of fire occurrence, sequence of response actions, and sensor data change curves) to the monitoring platform.

[0091] Example 3: Multimodal Transport Loading and Unloading Collaboration Scenario

[0092] I. System Configuration

[0093] This embodiment is designed for port container multimodal transport scenarios, integrating rail, road, and waterway transport connection functions:

[0094] Path and loading / unloading optimization engine: Deploy a 3D laser scanner (scanning accuracy ±2mm, scanning speed 500,000 points / second) for carriage space modeling;

[0095] Vehicle-mounted terminal monitoring module: Equipped with a 10.1-inch touch screen displaying the three-dimensional coordinates of the robotic arm (X / Y / Z axis accuracy ±1mm) and the work progress bar;

[0096] Transportation equipment linkage module: equipped with a 6-axis hydraulic robotic arm (load capacity 5t, maximum working radius 8m) and an automated guided vehicle (AGV) scheduling interface;

[0097] II. Work Process

[0098] Container loading and unloading planning

[0099] When a container truck enters the port's loading and unloading area, a 3D laser scanner performs a 180° scan of the truck bed (scanning time 15 seconds), generating spatial point cloud data (including truck bed dimensions and existing cargo occupancy information). Based on the scan results and combined with the parameters of the cargo to be loaded (20-foot container, weight 3.5t, dimensions 6.1m×2.4m×2.6m), the cargo loading and unloading collaborative control submodule of the path and loading and unloading optimization engine generates a stacking scheme: two containers are placed horizontally at the bottom (spaced 30cm apart), and one container is placed vertically at the top (center of gravity offset 3cm≤5cm threshold).

[0100] Robotic arm operation scheduling

[0101] The vehicle-mounted terminal monitoring module initiates the genetic algorithm resource scheduling model, encoding the allocation scheme of 3 robotic arms (numbered A / B / C) and 5 containers into chromosomes (e.g., [A1,B3,C2] represents robotic arm A being responsible for container number 1), and sets the fitness function. =0.7 (loading and unloading efficiency) =0.3 (energy consumption), of which loading and unloading efficiency = (Total cargo weight / Total operation time), Energy consumption =∑(robot arm motor power × operation time), after 50 iterations, the output scheduling order is: robot arm A prioritizes loading and unloading boxes 1 and 4 (loaded boxes), robot arm B is responsible for boxes 2 and 5 (empty boxes), and robot arm C is responsible for box 3 (dangerous goods box). The operation order is executed according to "loaded boxes are unloaded first, and empty boxes are loaded last".

[0102] Multimodal transport connections

[0103] When containers are transported by road to the railway freight station, the system connects with the railway dispatching system through the data traceability module and uploads the following data:

[0104] Container RFID tag information (container number, cargo type, weight);

[0105] Temperature and humidity curves for highway transport (if the goods are cold chain);

[0106] Robotic arm loading and unloading log (grabbing force 3.5t × 1.2 = 4.2t, rotation angle 90°, single operation time 45 seconds);

[0107] The railway dispatching system allocates freight train space based on the above data and provides a shipping schedule, achieving seamless connection between "road-rail-waterway".

[0108] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Any variations and modifications can be made by those skilled in the art without departing from the spirit and scope of the invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention, without departing from the scope of the invention, fall within the protection scope defined by the claims of the present invention.

Claims

1. A logistics information monitoring and transportation management system based on a data source, characterized in that, The transportation management system includes: The multi-source sensor acquisition module is used to collect physical transportation data in real time through vehicle positioning terminals, cargo status sensors and transportation environment monitoring equipment; The transportation execution control module is connected to the multi-source sensor acquisition module and is used to dynamically adjust vehicle driving parameters and cargo storage environment based on the acquired data. The route and loading / unloading optimization engine outputs transportation execution instructions through dynamic route planning algorithms and cargo loading / unloading collaborative control algorithms. The vehicle-mounted terminal monitoring module is used to display the vehicle's physical status, cargo location, and loading / unloading equipment operating parameters in real time. The transportation equipment linkage module is connected to the transportation execution control module and is used to control the temperature control of the refrigerated compartment, the cargo fixing device and the loading and unloading robotic arm. The fault diagnosis and early warning module identifies abnormalities in transportation equipment based on sensor data and triggers maintenance commands. The data traceability module is used to store physical parameters and equipment operation records throughout the entire transportation process.

2. The logistics information monitoring and transportation management system based on a data source according to claim 1, characterized in that, The multi-source sensing acquisition module includes: The cargo status sensing unit integrates vibration sensors, temperature and humidity sensors, and pressure sensors to collect data on the physical status of cargo during transit. The vehicle's mechanical sensing unit acquires data on engine speed, braking system pressure, and tire pressure via the CAN bus.

3. The logistics information monitoring and transportation management system based on a data source according to claim 2, characterized in that, The transportation execution control module includes: The dynamic temperature control subunit obtains cargo type data by reading the RFID tag on the cargo packaging through the cargo status sensing unit of the multi-source sensing acquisition module. The dynamic temperature control subunit presets temperature and humidity thresholds based on the obtained cargo type and adjusts the refrigerated compartment compressor power using a PID algorithm. The control formula is as follows: ； in, For the power adjustment of the refrigerated compartment compressor, The proportionality coefficient for rapid response to temperature and humidity deviations, To eliminate the integral coefficient of accumulated bias, To suppress overshoot fluctuations, the differential coefficient, This represents the deviation between the actual temperature and humidity and the set threshold. This is the integral term of the deviation, used to reflect the cumulative effect of historical deviations. This is the differential term of the deviation, used to reflect the rate of change of the deviation.

4. The logistics information monitoring and transportation management system based on a data source according to claim 1, characterized in that, The path and loading / unloading optimization engine also includes: The cargo loading and unloading collaborative control submodule is implemented through a cargo loading and unloading collaborative control algorithm. The gripping force of the loading and unloading robotic arm is allocated based on cargo weight sensor data; A stacking scheme is generated based on the 3D scanning data of the carriage space, and the stacking stability is verified by the center of gravity offset.

5. A logistics information monitoring and transportation management system based on a data source according to claim 3, characterized in that, The cargo status sensing unit includes: Vibration sensors extract characteristic frequencies through Fourier transform. ,in, For the first Each characteristic frequency, For frequency component index, The sampling frequency of the vibration spectrum. The number of sampling points is used to trigger a fault warning when the characteristic frequency deviates from the standard value by 5%.

6. The logistics information monitoring and transportation management system based on a data source according to claim 1, characterized in that: The vehicle-mounted terminal monitoring module also includes a genetic algorithm resource scheduling model, which is linked with the control unit in the loading and unloading robotic arm, as detailed below: The cargo-robotic arm allocation scheme is encoded as a chromosome, with constraints such as the robotic arm load rate and loading / unloading time window. fitness function ,in, The fitness function of the genetic algorithm resource scheduling model is... For loading and unloading efficiency, For the energy consumption of the robotic arm, , The weights for loading / unloading efficiency and robotic arm energy consumption are respectively... The scheduling scheme output after 50 iterations directly controls the order of robotic arm operations.

7. The logistics information monitoring and transportation management system based on a data source according to claim 1, characterized in that: The transportation equipment linkage module includes a cargo fixing execution unit, which automatically activates the hydraulic fixing device and sends a warning signal to the driver's cab.

8. The logistics information monitoring and transportation management system based on a data source according to claim 1, characterized in that: The data traceability module adopts a distributed storage architecture and records physical transportation parameters including: vehicle driving trajectory, cargo temperature and humidity curve, and loading and unloading robotic arm operation log, including gripping force, rotation angle and execution time.

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