Network freight logistics verification system based on trajectory tracking

Abstract

The invention discloses a network freight logistics verification system based on trajectory tracking, and relates to the technical field of cargo transportation, the verification system comprises a control module, a data acquisition module, a positioning module, a data processing module, an optimization module and an early warning module, the control module is used for controlling acceleration, stop and turning of a transportation robot, and the data acquisition module is used for acquiring data; the data acquisition module is used for acquiring loading and unloading data of the transportation robot at a freight point and cargo load data of the transportation robot in the transportation process. The system has the advantages that the cargo transportation data are remotely analyzed through the data acquisition module, the positioning module and the data processing module, and then the transportation robot is called to load cargoes; and then the data processing module analyzes the operation paths of the multiple transportation robots and transmits the operation path data to the control module, so that transportation control over the multiple transportation robots is facilitated, and meanwhile goods can be prevented from falling off in the transportation process.

Description

Technical Field

[0001] The present invention relates to the technical field of cargo transportation, and in particular to a network freight logistics verification system based on track tracking. Background Art

[0002] With the rapid development of Internet technology, the online freight industry has flourished. The traditional freight model has gradually exposed many problems in management efficiency, transportation safety and cost control. The online freight platform can improve the resource allocation efficiency of the freight market by integrating cargo and transportation resources. However, in the actual operation process, how to ensure the authenticity, compliance and efficiency of the transportation process has become a key challenge facing the development of the industry.

[0003] However, the existing network freight logistics verification system is unable to make predictions for some locations, resulting in the inability to promptly handle goods after they fall off at special locations, which in turn affects the overall transportation efficiency. To this end, we propose a network freight logistics verification system based on trajectory tracking. Summary of the invention

[0004] The purpose of the present invention is to provide a network freight logistics verification system based on track tracking.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a network freight logistics verification system based on trajectory tracking, the verification system comprising a control module, a data acquisition module, a positioning module, a data processing module, an optimization module and an early warning module; The control module is used to control the acceleration, stopping and turning of the transport robot; The data acquisition module is used to collect the loading and unloading data of the transport robot at the freight point and the cargo load data of the transport robot during the transportation process; The positioning module is provided with a GPS and a sensor, which uses the GPS to obtain the position data of the transport robot in real time and transmits the position data of the transport robot to the data processing module. The sensor is used to be installed on the transport robot; The data processing module includes a virtual model, a real-time monitoring unit and a data analysis unit. The virtual model constructs a transport robot model and a transport trajectory model. The real-time monitoring unit monitors the transport robot in real time after receiving the data transmitted by the positioning module. When the real-time monitoring unit detects that the transport robot data and the cargo load data transmitted by the data acquisition module have changed, the data analysis unit creates abnormal instruction data. At the same time, the data analysis unit obtains the total weight of the cargo through the sensor in the transport robot, and predicts the control instruction data of the transport robot at different positions in the transport trajectory in the virtual model, wherein the control instruction data includes the speed of the transport robot, and transmits the control instruction data to the control module, and then adjusts the moving speed of the transport robot when multiple transport robots intersect, staggers the transport robots, and avoids collisions between the transport robots; When the optimization module receives the abnormal creation instruction data, the optimization module adjusts the transportation trajectory model data analyzed in the virtual model and transmits the adjusted data to the early warning module; After receiving the instruction transmitted by the optimization module, the early warning module sends an early warning signal to the freight point location according to the data transmitted from the optimization module.

[0006] As a further solution of the present invention: the control module includes a braking unit, a motor and a robotic arm, and the braking unit is used to brake the transport robot, and multiple motors are used to accelerate and turn the transport robot, and the robotic arm is used to pick up fallen goods.

[0007] As a further solution of the present invention: the data acquisition module obtains the data of the cargo to be loaded by setting an external port and scanning the code, transmits the loading and unloading speed of the freight point to the data processing module, obtains the loading time data of the transport robot according to the data processing module, and then verifies that the system retrieves the time for the next transport robot to enter the freight point according to the loading time.

[0008] As a further solution of the present invention: an acceleration sensor and a displacement sensor are arranged in the sensor, and the status data of the transport robot is obtained through the acceleration sensor and the displacement sensor, and the status data is transmitted to the data processing module, and the abnormal driving position data of the transport robot is judged through the movement data of the GPS.

[0009] As a further solution of the present invention: the virtual model is used to construct the transportation trajectory model data between two freight points, the data processing module predicts the predicted data of the sensor when the transport robot reaches the turning and bumpy sections in the transportation trajectory model, and transmits the predicted data to the real-time monitoring unit, and then when the transport robot reaches the turning and bumpy sections in the transportation trajectory model, the actual sensor monitoring data is compared with the predicted data to obtain the changes in the cargo data of the transport robot in the turning and bumpy roads. When the actual sensor monitoring data is consistent with the predicted data, it indicates that the cargo data transported by the transport robot has not fallen. When the actual sensor monitoring data is less than the predicted data, it indicates that the transported cargo data has fallen. The data analysis unit records the cargo data at this point and waits for the subsequent data processing module to call other transport robots to pick up the cargo data at this point. When the actual sensor monitoring data is greater than the predicted data, it indicates that there is a problem with the current transport robot sensor. The data processing module creates early warning instruction data and transmits the early warning instruction data to the early warning module.

[0010] As a further solution of the present invention: after the early warning instruction data is created in the data processing module, the route of the transport robot is replanned in the virtual model, and other transport robots are called to receive the goods carried by the current transport robot.

[0011] As a further solution of the present invention: when the real-time monitoring unit detects that goods have fallen from the transport robot and the data acquisition module detects that the corresponding goods have not been delivered to the transport robot, the optimization module analyzes the transport efficiency of the transport robot, and then determines whether the route of the transport robot needs to be adjusted based on the transport efficiency. When the route of the transport robot does not need to be adjusted, the optimization module calls other transport robots to pick up the fallen goods based on the GPS data and the track data of the transport robot, and the delivery positions of the other transport robots are consistent with the delivery position of the transport robot that dropped the goods. When the route of the transport robot needs to be adjusted, the optimization module transmits the data to the virtual model, and then adjusts the picking route of the transport robot according to the virtual model.

[0012] As a further solution of the present invention: the optimization module determines whether the transport robot needs to optimize the route according to the formula. When , the optimization module optimizes the route of the transport robot and generates corresponding control instructions to enable the transport robot to pick up the dropped goods. , the transport robot transports the goods normally, and the optimization module calls other transport robots to pick up the goods. Indicates The transport efficiency of transport robots is different. Indicates The number of goods transported by the transport robot without handling the goods, Indicates The transportation time of a transport robot without handling the goods, Indicates The number of goods transported by the transport robot under cargo handling, Indicates The module calculates the delivery time of each transport robot under cargo handling, and then optimizes the module to determine whether it is necessary to pick up the fallen cargo and control the transport robot to transport it.

[0013] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are: 1. The present invention remotely analyzes cargo transportation data through a data acquisition module, a positioning module, and a data processing module, and then calls a transport robot to load the cargo. Then, the data processing module analyzes the running paths of multiple transport robots, and transmits the running path data to the control module, thereby facilitating the transportation control of multiple transport robots and preventing the cargo from falling during the transportation process. 2. The present invention analyzes the turning and bumpy road section data of the transport robot through the data analysis unit, and then determines the possible drop points of the transport robot transporting goods, so as to facilitate processing according to the drop points of the goods, and facilitate the data processing module and the optimization module to generate control instructions to control the transport robot to process the goods; 3. The present invention optimizes and adjusts the route of the transport robot through the optimization module. When the cargo of the transport robot falls, the system can adjust a different transport robot or the current transport robot to handle the fallen cargo, thereby ensuring the working efficiency of the transport robot in the system.

[0014] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 1 is a flow chart of the verification system in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0017] In addition, 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.

[0018] Embodiment 1:

[0019] Please refer to the attached Figure 1 The network freight logistics verification system based on trajectory tracking of the present invention includes a control module, a data acquisition module, a positioning module, a data processing module, an optimization module and an early warning module; The control module is used to control the acceleration, stopping and turning of the transport robot; The data acquisition module is used to collect the loading and unloading data of the transport robot at the freight point and the cargo load data of the transport robot during the transportation process; The positioning module is provided with GPS and sensors, which use GPS to obtain the position data of the transport robot in real time and transmit the position data of the transport robot to the data processing module. The sensor is used to be installed on the transport robot; The data processing module includes a virtual model, a real-time monitoring unit and a data analysis unit. The virtual model constructs a transport robot model and a transport trajectory model. The real-time monitoring unit monitors the transport robot in real time after receiving the data transmitted by the positioning module. When the real-time monitoring unit detects that the transport robot data and the cargo load data transmitted by the data acquisition module have changed, the data analysis unit creates abnormal instruction data. At the same time, the data analysis unit obtains the total weight of the cargo through the sensor in the transport robot, and predicts the control instruction data of the transport robot at different positions in the transport trajectory in the virtual model, wherein the control instruction data includes the speed of the transport robot, and transmits the control instruction data to the control module, and then adjusts the moving speed of the transport robot when multiple transport robots intersect, staggers the transport robots, and avoids collisions between the transport robots; When the optimization module receives the abnormal creation instruction data, the optimization module adjusts the transportation trajectory model data analyzed in the virtual model and transmits the adjusted data to the early warning module; After receiving the instruction transmitted by the optimization module, the early warning module sends an early warning signal to the location of the freight point according to the data transmitted from the optimization module.

[0020] Specific work flow: The control module directly controls the acceleration, stopping and turning of the transport robot. The data processing module predicts the control instruction data of different positions of the transport robot in the transport trajectory through the virtual model, and transmits these data to the control module. This collaborative working mode enables the control module to adjust the robot's motion state more intelligently according to the real-time position and predicted situation of the transport robot. The data acquisition module is responsible for collecting the loading and unloading data of the transport robot at the freight point and the cargo load data during transportation. In the logistics transportation scenario, loading and unloading data is crucial for recording the in and out of the warehouse of goods. The GPS in the positioning module obtains the position data of the transport robot in real time and transmits it to the data processing module. The position data is the key information of this system. Just like in a large logistics park, through the positioning module, the system can accurately know the position of each transport robot. When the real-time monitoring unit detects changes in the transport robot data and cargo load data, the data analysis unit creates abnormal instruction data. This abnormal monitoring mechanism is an important part of this system to ensure transportation safety and normal progress. When the optimization module receives abnormal instruction data, it adjusts the transportation trajectory model data in the virtual model and transmits the adjustment data to the early warning module.

[0021] Furthermore, the cargo transportation data is remotely analyzed through the data acquisition module, the positioning module and the data processing module, and the transport robot is called to load the cargo. Then, the operation paths of multiple transport robots are analyzed through the data processing module, and the operation path data is transmitted to the control module, thereby facilitating the transportation control of multiple transport robots and preventing the cargo from falling during the transportation process.

[0022] Embodiment 2:

[0023] Based on the first embodiment, please refer to the attached Figure 1As shown, the data acquisition module obtains the cargo data to be loaded by setting an external port and scanning the code, transmits the loading and unloading speed of the freight point to the data processing module, obtains the cargo loading time data of the transport robot according to the data processing module, and then verifies that the system retrieves the time for the next transport robot to enter the freight point according to the cargo loading time, an acceleration sensor and a displacement sensor are set in the sensor, the status data of the transport robot is obtained by the acceleration sensor and the displacement sensor, and the status data is transmitted to the data processing module, the abnormal driving position data of the transport robot is judged by the movement data of the GPS, the virtual model is used to construct the transport trajectory model data between the two freight points, the data processing module predicts the predicted data of the sensor when the transport robot reaches the turning and bumpy sections in the transport trajectory model, and transmits the predicted data to the real-time monitoring unit, and then when the transport robot reaches the turning and bumpy sections in the transport trajectory model, the actual sensor monitored data is compared with the predicted data to obtain the transport robot's turning and bumpy sections. The cargo data changes during the bumpy road. When the actual sensor monitoring data is consistent with the predicted data, it means that the cargo data transported by the transport robot has not fallen. When the actual sensor monitoring data is less than the predicted data, it means that the cargo data transported has fallen. The data analysis unit records the cargo data at this point and waits for the subsequent data processing module to call other transport robots to pick up the cargo data at this point. When the actual sensor monitoring data is greater than the predicted data, it means that there is a problem with the current transport robot sensor. The data processing module creates early warning instruction data and transmits the early warning instruction data to the early warning module. After the early warning instruction data is created in the data processing module, the route of the transport robot is re-planned in the virtual model, and other transport robots are called to receive the cargo carried by the current transport robot. The control module includes a braking unit, a motor and a robotic arm, and then the braking unit is used to brake the transport robot, and multiple motors are used to accelerate and turn the transport robot, and the dropped cargo is picked up by the robotic arm.

[0024] Specific workflow: The sensor continuously acquires the status data of the transport robot, including acceleration, displacement change and other information, and transmits it to the data processing module. The GPS positioning module is used to acquire the movement data of the transport robot. By analyzing the position data, it is determined whether the transport robot has an abnormal driving position, such as deviating from the planned transport route, staying in an unplanned area, etc. The virtual model in the data processing module constructs the transport trajectory model data between the two freight points, and based on the model, predicts the data that the sensor should have when the transport robot reaches the turning and bumpy sections in the transport trajectory. The predicted data is transmitted to the real-time monitoring unit. When the transport robot reaches the turning and bumpy sections, real-time monitoring is performed. The unit compares the actual sensor monitoring data with the previous predicted data. If the actual sensor monitoring data is consistent with the predicted data, it means that the cargo data transported by the transport robot has not fallen and the transportation is proceeding normally. If the actual sensor monitoring data is less than the predicted data, it indicates that the cargo data transported has fallen. The data analysis unit records the point data and waits for other transport robots to pick up the fallen cargo data to ensure that the cargo is not missed and can continue to be transported. If the actual sensor monitoring data is greater than the predicted data, it is judged that there may be a problem with the sensor of the current transport robot. The data processing module creates the first-level warning instruction data and transmits it to the warning module.

[0025] Furthermore, the data analysis unit analyzes the turning and bumpy road data of the transport robot to determine the possible drop points of the goods transported by the transport robot, thereby facilitating processing at the drop points of the goods, and facilitating the data processing module and the optimization module to generate control instructions to control the transport robot to process the goods.

[0026] Embodiment three:

[0027] Based on the second embodiment, please refer to the attached Figure 1 As shown, when the real-time monitoring unit detects that the transport robot has dropped goods and the data acquisition module detects that the corresponding goods have not been delivered to the transport robot, the optimization module analyzes the transport efficiency of the transport robot, and then determines whether the route of the transport robot needs to be adjusted based on the transport efficiency. When the route of the transport robot does not need to be adjusted, the optimization module calls other transport robots to pick up the dropped goods based on the GPS data and the track data of the transport robot, and the delivery positions of the other transport robots are consistent with the delivery position of the transport robot that dropped the goods. When the route of the transport robot needs to be adjusted, the optimization module transmits the data to the virtual model, and then adjusts the picking route of the transport robot according to the virtual model. The optimization module determines whether the transport robot needs to optimize the route based on the formula. When , the optimization module optimizes the route of the transport robot and generates corresponding control instructions to enable the transport robot to pick up the dropped goods. , the transport robot transports the goods normally, and the optimization module calls other transport robots to pick up the goods. Indicates The transport efficiency of transport robots is different. Indicates The number of goods transported by the transport robot without handling the goods, Indicates The transportation time of a transport robot without handling the goods, Indicates The number of goods transported by the transport robot under cargo handling, Indicates The module calculates the delivery time of each transport robot under cargo handling, and then optimizes the module to determine whether it is necessary to pick up the fallen cargo and control the transport robot to transport it.

[0028] Specific workflow: Through the formula Get the transport efficiency of the transport robot in the state of unprocessed goods and The transport efficiency of the transport robot when handling goods is obtained, so that the optimization module can determine whether the transport status of the transport robot needs to be adjusted, thereby ensuring the working efficiency of the transport robot.

[0029] Furthermore, the route of the transport robot is optimized and adjusted through the optimization module, and when the cargo of the transport robot falls, the system can adjust a different transport robot or the current transport robot to handle the fallen cargo, thereby ensuring the working efficiency of the transport robot in the system.

[0030] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A network freight logistics verification system based on track tracking, characterized by: The verification system includes a control module, a data acquisition module, a positioning module, a data processing module, an optimization module and an early warning module; The control module is used to control the acceleration, stopping and turning of the transport robot; The data acquisition module is used to collect the loading and unloading data of the transport robot at the freight point and the cargo load data of the transport robot during the transportation process; The positioning module is provided with a GPS and a sensor, which uses the GPS to obtain the position data of the transport robot in real time and transmits the position data of the transport robot to the data processing module. The sensor is used to be installed on the transport robot; The data processing module includes a virtual model, a real-time monitoring unit and a data analysis unit. The virtual model constructs a transport robot model and a transport trajectory model. The real-time monitoring unit monitors the transport robot in real time after receiving the data transmitted by the positioning module. When the real-time monitoring unit detects that the transport robot data and the cargo load data transmitted by the data acquisition module have changed, the data analysis unit creates abnormal instruction data. At the same time, the data analysis unit obtains the total weight of the cargo through the sensor in the transport robot, and predicts the control instruction data of the transport robot at different positions in the transport trajectory in the virtual model, wherein the control instruction data includes the speed of the transport robot, and transmits the control instruction data to the control module, and then adjusts the moving speed of the transport robot when multiple transport robots intersect, staggers the transport robots, and avoids collisions between the transport robots; When the optimization module receives the abnormal creation instruction data, the optimization module adjusts the transportation trajectory model data analyzed in the virtual model and transmits the adjusted data to the early warning module; After receiving the instruction transmitted by the optimization module, the early warning module sends an early warning signal to the freight point location according to the data transmitted from the optimization module.

2. The network freight logistics verification system based on track tracking according to claim 1 is characterized by: The control module includes a braking unit, a motor and a robotic arm, and the braking unit is used to brake the transport robot, multiple motors are used to accelerate and turn the transport robot, and the robotic arm is used to pick up fallen goods.

3. The network freight logistics verification system based on track tracking according to claim 1 is characterized by: The data acquisition module obtains the cargo data to be loaded by setting an external port and scanning the code, transmits the loading and unloading speed of the freight point to the data processing module, obtains the cargo loading time data of the transport robot according to the data processing module, and then verifies that the system retrieves the time for the next transport robot to enter the freight point according to the cargo loading time.

4. The network freight logistics verification system based on track tracking according to claim 1 is characterized by: The sensor is provided with an acceleration sensor and a displacement sensor, through which the status data of the transport robot is acquired, and the status data is transmitted to a data processing module, and the abnormal driving position data of the transport robot is determined through the movement data of the GPS.

5. The network freight logistics verification system based on track tracking according to claim 4 is characterized by: The virtual model is used to construct the transportation trajectory model data between two freight points. The data processing module predicts the predicted data of the sensor when the transport robot reaches the turning and bumpy sections in the transportation trajectory model, and transmits the predicted data to the real-time monitoring unit. Then, when the transport robot reaches the turning and bumpy sections in the transportation trajectory model, the actual sensor monitoring data is compared with the predicted data to obtain the changes in the cargo data of the transport robot during the turning and bumpy roads. When the actual sensor monitoring data is consistent with the predicted data, it indicates that the cargo data transported by the transport robot has not fallen. When the actual sensor monitoring data is less than the predicted data, it indicates that the transported cargo data has fallen. The data analysis unit records the cargo data at this point and waits for the subsequent data processing module to call other transport robots to pick up the cargo data at this point. When the actual sensor monitoring data is greater than the predicted data, it indicates that there is a problem with the current transport robot sensor. The data processing module creates early warning instruction data and transmits the early warning instruction data to the early warning module.

6. The network freight logistics verification system based on track tracking according to claim 5 is characterized by: After the early warning instruction data is created in the data processing module, the route of the transport robot is replanned in the virtual model, and other transport robots are called to receive the goods carried by the current transport robot.

7. The network freight logistics verification system based on track tracking according to claim 5 is characterized by: When the real-time monitoring unit detects that goods have fallen from the transport robot and the data acquisition module detects that the corresponding goods have not been delivered to the transport robot, the optimization module analyzes the transport efficiency of the transport robot, and then determines whether the route of the transport robot needs to be adjusted based on the transport efficiency. When the route of the transport robot does not need to be adjusted, the optimization module calls other transport robots to pick up the fallen goods based on the GPS data and the track data of the transport robot, and the delivery positions of other transport robots are consistent with the delivery position of the transport robot that dropped the goods. When the route of the transport robot needs to be adjusted, the optimization module transmits the data to the virtual model, and then adjusts the picking route of the transport robot according to the virtual model.

8. The network freight logistics verification system based on track tracking according to claim 7 is characterized by: The optimization module determines whether the transport robot needs to optimize the route according to the formula. When , the optimization module optimizes the route of the transport robot and generates corresponding control instructions to enable the transport robot to pick up the dropped goods. , the transport robot transports the goods normally, and the optimization module calls other transport robots to pick up the goods. Indicates The transport efficiency of transport robots is different. Indicates The number of goods transported by the transport robot without handling the goods, Indicates The transportation time of a transport robot without handling the goods, Indicates The number of goods transported by the transport robot under cargo handling, Indicates The module calculates the delivery time of each transport robot under cargo handling, and then optimizes the module to determine whether it is necessary to pick up the fallen cargo and control the transport robot to transport it.

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