TW202119348A - Smart supervision of cargo transportation system - Google Patents

Abstract

This invention provides a smart supervision of cargo transportation system comprising a driving database for storing each road section and the driving time corresponding to each vehicle type in each road section, a monitoring server providing transportation vehicle types and driving routes, and a geographic information calculation server for calculating the estimated travel time, wherein this invention obtains the travel time corresponding to the vehicle type conforming to the transportation task from the driving database through the geographic information calculation server to calculate the average time of the road section and further generates a reasonable estimated travel time for transporting vehicles on the driving route. In addition, it also includes monitoring equipment installed on the transport vehicle to instantly send back traffic information and location information through the monitoring equipment so as to achieve the purpose of monitoring the transport vehicle in the transportation process.

Description

Smart supervision of cargo transportation system

The present invention is a supervised cargo transportation system, and particularly refers to a smart supervised cargo transportation system that can provide estimated driving spaces for transporting vehicles and real-time supervision of the real-time conditions of transporting vehicles that perform transport tasks.

In the past, the customs usually took into account factors such as road conditions and weather when stipulating the delivery time of trucks, and gave the driving of the trucks a loose delivery time. However, because the given delivery time is often too loose, the goods are in the delivery process. It is extremely vulnerable to smuggling and subcontracting by criminals.

In addition, during the transportation of the truck, the driving conditions of the truck can only be known when the truck enters and exits the gate, after being recorded and transmitted by the operator, that is, if the delivery time is abnormal, the truck can only enter the gate at the gate. , After the outbound time difference is compared with the delivery time stipulated by the customs, the abnormal message will be sent for warning notice when the comparison result does not meet the specifications. Therefore, the current dynamic information of the trucks that the customs knows is after the fact. Information cannot effectively control the real-time situation. When it cannot be managed and dealt with in real time, in other words, the conventional technology cannot track and check the abnormal situation of the truck during the transportation. Taking smuggling cases as an example, it is still a common method to transfer packages while on the way. The method can be roughly divided into three methods: destroying/forging seals, destroying the structure of the container, and replacing the entire container. The computer system can only compare the delivery time, and the delivery path specified by the customs is only for declaring.

Therefore, in the conventional technology, the customs does not have the function of managing the transportation of vehicles and driving in the transportation of trucks. That is, vehicles that transport related controlled goods or containers cannot effectively check whether the vehicle number and driver information are correct. It is also impossible to further assess whether it is possible to effectively control the delivery of high-risk goods by vehicles and drivers that have violated the regulations in the delivery record.

In view of this, how to provide a cargo transportation system that can provide a reasonable estimated time for the route and can perform real-time monitoring of the truck that transports the cargo will become an urgent goal pursued by those skilled in the art.

In order to solve the above-mentioned problems in the prior art, the present invention discloses a smartly supervised cargo transportation system, which includes: a driving database, which stores a plurality of road sections and the driving time of each vehicle type in each of the plurality of road sections; a monitoring server, which provides transportation The vehicle type and driving route of the delivery vehicle of the task; and the map data calculation server, which is based on the driving route and selects the road section that matches the driving route from the plurality of road sections as the selected road section, so as to obtain the selected road section according to the vehicle type. The average travel time of the road segment is calculated to generate the estimated travel time of the transport vehicle.

In one embodiment, the intelligently supervised cargo transportation system includes a monitoring device for storing identification information of the vehicle and providing driving information and location information. The monitoring device is installed on the transportation vehicle and monitors the operation status of the transportation vehicle. To generate the driving information and the location information of the transport vehicle, so as to transmit the identification information of the vehicle, the The driving information and the location information are sent to the monitoring server for the monitoring server to monitor the transport vehicle in real time.

In another embodiment, the identification information of the vehicle, the driving information and the location information are stored in the driving database.

In another embodiment, the vehicle type includes a truck, a bonded truck, or a container truck.

In another embodiment, the monitoring device includes a communication module for providing the telecommunication network service and a positioning module for sensing the location information.

In another embodiment, the monitoring device includes a gravity sensor for the monitoring device to monitor the transport vehicle through the gravity sensor, and the monitoring device generates the driving information through the gravity sensor and the positioning module .

In another embodiment, the driving information includes rapid acceleration, rapid braking, sharp turn, transit time, speeding, accident, transit risk, or transit distance.

In another embodiment, the intelligently supervised cargo transportation system includes a transportation risk database that stores driving violation records or bad records, and the monitoring server dispatches drivers suitable for the transportation task based on the violation records or bad records.

In another embodiment, the monitoring server includes an input module, a processor module, and an output module. The transportation task and the driving route input by the input module are transmitted to the processor module. The monitoring server enables the processor module to provide the transportation task and the driving route to the control map data calculation server 13 for calculation to generate the estimated driving time.

It can be seen from the above that the supervised cargo transportation system of the present invention obtains the traveling time corresponding to the vehicle type that meets the transportation task through the bicycle database of the map data computing server, so as to calculate the average time of the road section, and then generate a reasonable transportation vehicle on the driving route. Estimated travel time, In addition, the monitoring equipment installed on the transportation vehicle returns driving information and location information, so as to achieve the purpose of real-time monitoring of the transportation vehicle's condition by the monitoring server.

1‧‧‧Smart supervised cargo transportation system

11‧‧‧Driving Database

12‧‧‧Monitoring server

121‧‧‧Input Module

122‧‧‧Processor Module

123‧‧‧Output Module

13‧‧‧Map data computing server

14‧‧‧Monitoring equipment

141‧‧‧Communication Module

142‧‧‧Positioning Module

143‧‧‧Gravity Sensor

15‧‧‧Transportation Risk Database

2‧‧‧Transport vehicle

The embodiments of the present invention will be described with reference to the drawings as an example:

Figure 1 is a system architecture diagram of the present invention for evaluating driving time;

Figure 2 is a schematic diagram of the internal structure of the monitoring server of the present invention;

Figure 3 is a schematic diagram of the structure of the monitoring system of the present invention; and

Figure 4 is a schematic diagram of the internal structure of the monitoring device of the present invention.

The following describes the technical content of the present invention with specific specific embodiments. Those familiar with the art can easily understand the advantages and effects of the present invention from the content disclosed in this specification. However, the present invention can also be implemented or applied by other different specific embodiments.

Figure 1 is a diagram illustrating the system architecture of the present invention for estimating driving time. As shown in the figure, the intelligent supervised cargo transportation system 1 of the present invention includes a driving database 11, a monitoring server 12, and a map data calculation server 13. The invention uses the map data calculation server 13 according to the data in the driving database 11 And monitoring the delivery tasks and driving routes provided by the server 12 to calculate a reasonable estimated travel time in the process of delivering goods, as detailed below.

The driving database 11 stores a plurality of road sections and the driving time of each vehicle type in each plurality of road sections, where the vehicle type can be a bonded truck or a container truck. Since the driving method of container trucks or bonded trucks is different from ordinary cars, the transportation of goods on the same road section is different from that of ordinary cars. The travel time of vehicles and ordinary cars is also different. Therefore, the travel time of vehicles transporting goods on different road sections is collected and stored to establish a travel database 11, that is, the travel database 11 stores various types of vehicles The travel time of each road segment, for example, the travel time of multiple container trucks or multiple bonded trucks traveling on Section A. In detail, the same type of vehicle traveling on the same road segment will also have multiple travel time records for a certain type of vehicle in a certain road segment in the vehicle database 11 due to the difference in driving and travel time. A specific road in the map is formed by integrating multiple corresponding road sections in the traffic database, where the specific route is the designated transportation route of the vehicle (such as container truck or bonded truck) that loads and transports goods. In other words, a specific route is divided into multiple road segments, and each road segment corresponds to a designated vehicle with multiple travel times. According to this, the vehicle travel database 11 of the present invention is constructed and formed by the aforementioned multiple data.

The monitoring server 12 can provide the vehicle type and driving route of the delivery vehicle for the transportation task. It can store the desired driving route by the monitoring personnel, such as the driving route announced by the customs for transporting goods, or may be planned for other purposes. Route, and the monitoring personnel can input the transportation task into the monitoring server 12 to further provide the map data calculation server 13 for calculation. Specifically, as shown in Figure 2, it is a schematic diagram of the internal structure of the monitoring server of the present invention. Please also refer to Figure 1. The monitoring server 12 of the present invention includes an input module 121, a processor module 122, and The output module 123. The monitoring server 12 receives the input transportation task and driving route through the input module 121 and transmits it to the processor module 122. The control server 12 then makes the processor module 122 provide the received transportation task and The driving route is sent to the map data calculation server 13 to control the map data calculation server 13 to calculate the estimated travel time, and then the output module 123 outputs the calculation result.

The map data computing server 13 forms multiple road segments according to the driving route in the monitoring server 12, and then finds the corresponding multiple road segments in the bicycle database 11, and finds that the driving route matches the driving route. The plural road sections are selected road sections, and then according to the vehicle type specified by the monitoring server 12, the vehicle type corresponding to the vehicle type is obtained in the driving database 11 for the driving time corresponding to the selected road section, and then the driving time is calculated to obtain the average driving time Time, and then further integrate and calculate the average time to generate the estimated travel time of the delivery vehicle, that is, when the customs wants to assign a delivery task, the designated driving route and the delivery task including the vehicle type of the delivery vehicle are input to the monitoring server 12 , The map data calculation server 13 obtains multiple road sections corresponding to the route from the driving database 11 according to the input driving route, and the travel time of the vehicle type traveling on the obtained road sections, and then obtains it through calculation The travel time is used to generate a reasonable estimated travel time for the transportation vehicle to perform the transportation task on the designated driving route. According to this, the drawbacks caused by the customs to give an excessively lenient transportation time can be avoided.

From this, it can be seen that the present invention uses the estimated time of arrival (ETA) of the map data system of the map data computing server 13 in combination with the big data platform of the driving database 11 to perform analysis technology, which can provide different types of vehicles. Driving behavior other than driving, reasonably calculate the cargo delivery time. Since the general map data is based on the ETA provided by the integrated driving conditions and distances of the ordinary passenger car driving mode, compared with the application of the present invention to medium and large vehicles such as containers and trucks, there are significant differences due to different driving modes. Therefore, Accurately estimate and meet the required travel time of large vehicles. The present invention uses the big data platform to collect the time required for various types of vehicles to travel on various road sections, and provide them on the same road section. The actual average time of various types of cargo vehicles provides appropriate parameter weights. The Map and Information Institute integrates the ETA estimated time of driving road conditions and distances, which can avoid the problem of standardly estimating the loose delivery time, so as to achieve the purpose of providing a more objective and reasonable delivery time estimate.

Figure 3 is a schematic diagram of the structure of the monitoring system of the present invention. As shown in the figure, the present invention includes a monitoring device 14 for storing identification information of the vehicle and providing driving information and location information. The monitoring device 14 is installed on the transport vehicle 2 and generates driving information by monitoring the operating status of the transport vehicle 2 And the location information of the transport vehicle 2, so that the identification information, driving information and location information of the vehicle are sent to the monitoring server 12 through the telecommunication network service for the monitoring server 12 to monitor the transport vehicle 2 in real time, that is, at the start and end points The gate post of, can return the identification signal of the vehicle transporting related controlled goods or containers to effectively check whether the vehicle number and driver information are correct, and further monitor the real-time monitoring information of the transport vehicle 2 at any time during the transportation process. For example, location information or driving information.

Figure 4 is a schematic diagram of the internal structure of the monitoring device of the present invention. As shown in the figure, please also refer to Figure 3. In a specific embodiment, the monitoring device 14 includes a communication module 141 for providing telecommunication network services, a positioning module 142 for sensing location information, and a The gravity sensor 143 of the transportation vehicle is monitored. The monitoring device 14 generates driving information through the gravity sensor 143 and the positioning module 142. Accordingly, during the transportation of the transportation vehicle 2, the positioning module 142 of the monitoring device 14 is used for positioning to generate position information, and the positioning module 142 and the gravity sensor 143 are used for sensing to generate sudden acceleration, sudden braking, For driving information such as sharp turns, transit time, speeding, accident, transportation risk, or transportation distance, the monitoring device 14 continuously records the location information in the monitoring device. Furthermore, the monitoring device 14 can be set to be real-time or within a period of time (for example, 3 minutes). Or 10 minutes, etc.), the position information and driving information together with the vehicle identification information will be returned to the monitoring server 12 through the communication module, or in the event of sudden acceleration, sudden braking, sharp turns, speeding, accidents or exceeding estimates When the driving information of the driving time, the monitoring device 14 immediately informs the monitoring server 12 through the communication module 141, so as to achieve the purpose of real-time monitoring.

In addition, the monitoring server 12 can store the identification information, driving information, and location information of the vehicle, and store the information in the driving database 11, where the driving information includes the vehicle type of the transporting vehicle 2 and the driving information. According to the travel time of each section of the route, the map data calculation server 13 can also consider the travel information of the transport vehicle 2 when calculating the estimated travel time next time to perform calculations, so that the intelligent supervision of goods in the present invention The transportation system 1 can be closer to the actual driving situation in terms of estimated travel time, so as to more reasonably estimate the estimated travel time. Furthermore, as shown in Figure 3, the present invention may include a transportation risk database 15 for storing violation records or bad records of driving. The monitoring server 12 dispatches drivers suitable for the transportation task based on the violation records or the bad records. Therefore, by establishing a transportation risk database 15 for transportation vehicles 2 or driving, it is possible to control the transportation of high-risk goods by vehicles or drivers with major bad records, strengthen transportation control and improve risk prevention management. Based on the evaluation records of the driving behavior of vehicles and drivers, a transportation risk database 15 is established, and high and low coefficients are given according to the occurrences to provide supervisors with a basis for evaluating transportation risks when dispatching vehicles.

In the present invention, as shown in Figures 3 and 4, the monitoring server 12 of the present invention can evaluate the transportation safety of the transportation vehicle based on the driving information. For example, the monitoring server 12 can control the transportation record through the recorded driving information. There have been illegal delivery vehicles and driving to deliver high-risk goods. In other words, the present invention uses the positioning module 142 and the gravity sensor 143 of the monitoring device 14 to generate driving behavior factors of driving information such as rapid acceleration, rapid braking, sharp turns, transit time, overspeed, accident, transit risk, or transit distance. , And then send back to the monitoring server 12 through the communication module 141, and the monitoring server 12 can evaluate the driving behavior based on the driving information.

For example, in the present invention, the rapid acceleration (A) is preset to the positioning module 142 and the gravity sensor 143 to determine the instantaneous speed up to 15km/h, and the sudden braking (B) is the positioning module 142 and gravity. The sensor 143 judges the instantaneous speed up to -15km/h, the sharp turn (C) is the positioning module 142 judges that the driving angle is greater than 50 degrees and the vehicle speed is less than the road speed limit data in the map 5Km/H, and the transportation time (D ) Is based on the arrival time between the starting and ending stations in the location information of the positioning module 142 is less than or equal to the scheduled delivery time, overspeed (E) is based on the speed data converted from the location data returned by each positioning module 142 is greater than the map data Road speed limit data, accident (F) is the positioning module 142 and gravity sensor 143 to determine the instantaneous speed of -23km/h or -25km/h, etc., and the stay time exceeds 10 minutes, and the transportation risk (G) is based on the transportation risk The high and low coefficients and the transportation distance (H) given by the actual major violation records in the database are the distances between the starting and ending stations of the route calculated based on the location data. Further, the driving behavior factors of the aforementioned driving information are calculated through the driving behavior evaluation formula "100 -[Σ(aA, bB, cC, dD, eE, fF)/Max(1,H/100)-G]" to calculate the driving behavior score, where a, b, c, d, e, f Is the weight of the factor.

For example, based on the driving safety of large vehicles, the supervisory unit pays special attention to advocating that various traffic regulations should not be violated. The weight of the driving behavior factor is set as: a, b, c, d=1; e, f=5, a certain container The vehicle carried out the transportation mission for about 120 kilometers. During this journey, there were 5 sudden accelerations and 5 sudden braking incidents, 3 sharp turns, and 1 speeding. There were no accidents and no record of major violations in the past. Therefore, the evaluation score of this shipping behavior is 100-[Σ(1*5, 1*5, 1*3, 5*1, 5*0, 5*0)/Max(1,120/100)-0]=85, Accordingly, the driving behavior score can be used as a reference and application for the management and supervision unit to evaluate the basis for the driver to manage safe driving, correct bad driving behavior, the supervision unit to determine abnormalities (accidents), and UBI (Usage-Based Insurance) car insurance. In this way, the present invention can provide safety values for individual vehicles/drivings in terms of transportation safety through big data collection and analysis. This safety value calculation is based on the scores of various driving behaviors of the vehicle driving in the past transportation process (detailed driving behavior evaluation) , Dispatch the selected car in the supervisory unit When transporting vehicles, it can provide a reference for transport safety values to avoid high-risk goods and high-risk transport vehicles from becoming loopholes for illegal personnel operations. It can also reasonably evaluate vehicle travel time and transport safety.

In summary, the present invention can generate a reasonable estimated travel time through big data analysis, and can use the monitoring server to receive the information returned by the monitoring equipment at any time, so as to achieve the purpose of monitoring the transportation vehicles.

The above-mentioned embodiments are merely illustrative and are not used to limit the present invention. Anyone who is familiar with this technique can modify and change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the rights of the present invention is defined by the scope of the patent application attached to the present invention. As long as it does not affect the effect and implementation purpose of the present invention, it should be covered in the technical content of this disclosure.

1‧‧‧Smart supervised cargo transportation system

11‧‧‧Driving Database

12‧‧‧Monitoring server

13‧‧‧Map data computing server

Claims (10)

A smart supervised cargo transportation system, which includes: The driving database, which stores plural road sections and the driving time of each vehicle type in each of the plural road sections; The monitoring server is the vehicle type and driving route of the delivery vehicle that provides the delivery task; and The map data computing server selects the road segment that matches the driving route from the plurality of road segments according to the driving route as the selected road segment, and calculates the average travel time based on the travel time of the selected road segment that matches the vehicle type of the vehicle type, and generates Estimated travel time of the transport vehicle. For example, the intelligently supervised cargo transportation system described in item 1 of the scope of patent application includes monitoring equipment for storing identification information of the vehicle and providing driving information and location information. The monitoring equipment is installed on the transport vehicle and monitors the vehicle. The operating state of the transport vehicle to generate the driving information and the position information of the transport vehicle, so as to transmit the identification information of the vehicle, the driving information and the position information to the monitoring server through the telecommunication network service for the monitoring server The device monitors the delivery vehicle in real time. For example, the intelligent supervised cargo transportation system described in item 2 of the scope of patent application, wherein the identification information of the transport vehicle, the driving information and the location information are stored in the driving database. For example, the intelligent supervised cargo transportation system described in item 2 of the scope of patent application, wherein the type of the transportation vehicle includes trucks, bonded trucks or container trucks. For example, in the intelligent supervised cargo transportation system described in item 2 of the scope of patent application, the monitoring equipment includes a communication module for providing the telecommunication network service and a positioning module for sensing the location information. For example, the intelligent supervised cargo transportation system described in item 5 of the scope of patent application, wherein the monitoring equipment includes a gravity sensor for the monitoring equipment to monitor the transport vehicle through the gravity sensor, and the monitoring equipment through the gravity sensor The detector and the positioning module generate the driving information. For example, the intelligently supervised cargo transportation system described in item 6 of the scope of patent application, wherein the driving information includes rapid acceleration, rapid braking, sharp turns, transit time, speeding, accident, transportation risk or transportation distance. For example, in the intelligently supervised cargo transportation system described in item 7 of the scope of patent application, the monitoring server evaluates the transportation safety of the transportation vehicle based on the driving information. For example, the intelligently supervised cargo transportation system described in item 1 of the scope of patent application includes a transportation risk database that stores driving violation records or bad records, and the monitoring server dispatches suitable transportation tasks based on the violation records or bad records Of driving. For example, the intelligent supervised cargo transportation system described in item 1 of the scope of patent application, wherein the monitoring server includes an input module, a processor module, and an output module, and the transportation task and the driving vehicle are input through the input module The route is transmitted to the processor module, and the monitoring server enables the processor module to provide the transportation task and the driving route to the control map data calculation server for calculation to generate the estimated driving time.

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