Human-vehicle separation detection method, device, electronic equipment and storage medium

Abstract

The present application provides a method, device, electronic device and storage medium for detecting separation of people and vehicles, and relates to the field of freight technology. The method makes a risk assessment on freight orders, and then uses different methods for detecting separation of people and vehicles to detect risky orders and non-risky orders respectively, thereby achieving differentiated detection of separation of people and vehicles for freight orders, saving traffic consumption for freight monitoring, and reducing system resource overhead.

Description

Technical Field

[0001] The present application relates to the field of freight technology, and more specifically, to a method, device, electronic device and storage medium for detecting separation of a person from a vehicle. Background Art

[0002] In the field of freight, in order to monitor the risk of cargo dumping and loss during cargo transportation, human-vehicle separation monitoring is performed during cargo transportation. Human-vehicle separation monitoring generally compares the movement trajectories of the driver and the vehicle to determine whether the driver is separated from the vehicle during transportation.

[0003] The existing monitoring system that separates people from vehicles monitors all orders indiscriminately, which will cause excessive traffic consumption and lead to high system resource overhead. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method, device, electronic device and storage medium for detecting separation of a person and a vehicle, so as to improve the problem in the prior art of indiscriminate monitoring of all orders, resulting in excessive traffic consumption and high system resource overhead.

[0005] In a first aspect, an embodiment of the present application provides a method for detecting separation of a person from a vehicle, the method comprising:

[0006] Get freight orders;

[0007] Determine whether the freight order is a risky order and obtain a determination result;

[0008] Determine, according to the judgment result, a person-vehicle separation detection method corresponding to the freight order, wherein the person-vehicle separation detection methods corresponding to risk orders and non-risk orders are different;

[0009] A human-vehicle separation detection is performed on the transportation process of the freight order according to the human-vehicle separation detection method corresponding to the freight order.

[0010] In the above implementation process, by making a risk assessment on the freight orders, and then using different human-vehicle separation detection methods to perform human-vehicle separation detection on risky orders and non-risky orders respectively, differentiated human-vehicle separation detection can be achieved for freight orders, which can save the traffic consumption of freight monitoring and reduce system resource overhead.

[0011] Optionally, determining a person-vehicle separation detection method corresponding to the freight order according to the judgment result includes:

[0012] If the judgment result is that the freight order is a risky order, determining that the person-vehicle separation detection method corresponding to the freight order is a timed detection;

[0013] If the judgment result is that the freight order is a non-risk order, it is determined that the person-vehicle separation detection method corresponding to the freight order is user-triggered detection.

[0014] In the above implementation process, risky orders are subject to regular detection, and non-risky orders are subject to user-triggered detection. This allows the focus to be placed on monitoring risky orders while separating people from vehicles. There is no need to perform regular detection on non-risky orders, which can effectively reduce the traffic consumption of freight monitoring.

[0015] Optionally, the detection frequency of the person-vehicle separation detection method corresponding to the risky order is greater than the detection frequency of the person-vehicle separation detection method corresponding to the non-risky order. In this way, the detection frequency for risky orders is higher, which can achieve differentiated detection and save traffic consumption.

[0016] Optionally, the determining whether the freight order is a risky order and obtaining a determination result includes:

[0017] If the freight order is one of multiple orders undertaken by the same driver within the same time range and is not a carpooling order, the freight order is determined to be a risky order. In this way, repeated freight orders are determined to be risky orders, so that freight orders with high risks can be monitored in a focused manner to improve monitoring accuracy.

[0018] Optionally, the performing a person-vehicle separation detection on the transportation process of the freight order according to the person-vehicle separation detection method corresponding to the freight order includes:

[0019] Acquire the location data of the driver and the location data of the transport vehicle during the transportation process of the freight order;

[0020] Obtaining the degree of overlap between the positioning data of the driver and the positioning data of the transport vehicle;

[0021] Whether a situation of separation between a person and a vehicle occurs is detected according to the overlap degree.

[0022] In the above implementation process, the overlap of the trajectory is obtained by obtaining the positioning data within a preset time period. Compared with the method of obtaining the overlap by obtaining all the positioning data, the amount of calculation is greatly reduced.

[0023] Optionally, the driver's positioning data includes positioning data at multiple time points within the preset time period, and the transportation vehicle's positioning data includes positioning data at multiple time points within the preset time period, and obtaining the degree of overlap between the driver's positioning data and the transportation vehicle's positioning data includes:

[0024] Acquire the distance between the positioning data of the driver and the positioning data of the transport vehicle corresponding to the same time point, and obtain a total of multiple distances, wherein the multiple distances represent the overlap degree;

[0025] The detecting whether a person-vehicle separation situation occurs according to the overlap degree includes:

[0026] If the distances exceeding the preset ratio among the multiple distances are greater than the set distance, it is determined that a person-vehicle separation situation occurs.

[0027] In the above implementation process, the separation of the driver and the transport vehicle is determined by obtaining the distance between the driver and the transport vehicle at multiple time points, which can reduce the complexity of monitoring and improve the detection efficiency.

[0028] Optionally, after determining that a person-vehicle separation occurs, the method further includes:

[0029] The track information including the location data of the transport vehicle is displayed on the display interface to avoid the situation where the location data of the driver and the transport vehicle are combined and displayed to cause track jumps.

[0030] Optionally, after determining that no separation of the person and the vehicle occurs, the method further includes:

[0031] The display interface displays the trajectory information including the location data of the transport vehicle and the location data of the driver, so that the user can view more detailed trajectory information.

[0032] In a second aspect, an embodiment of the present application provides a human-vehicle separation detection device, the device comprising:

[0033] Order acquisition module, used to obtain freight orders;

[0034] A judgment module, used to judge whether the freight order is a risky order and obtain a judgment result;

[0035] A detection method determination module, used to determine a person-vehicle separation detection method corresponding to the freight order according to the judgment result, wherein the person-vehicle separation detection methods corresponding to risk orders and non-risk orders are different;

[0036] The detection module is used to perform a person-vehicle separation detection on the transportation process of the freight order according to the person-vehicle separation detection method corresponding to the freight order.

[0037] Optionally, the detection method determination module is used to determine that the person-vehicle separation detection method corresponding to the freight order is timed detection if the judgment result is that the freight order is a risky order; if the judgment result is that the freight order is a non-risky order, determine that the person-vehicle separation detection method corresponding to the freight order is user-triggered detection.

[0038] Optionally, the detection frequency of the human-vehicle separation detection method corresponding to the risky order is greater than the detection frequency of the human-vehicle separation detection method corresponding to the non-risky order.

[0039] Optionally, the judgment module is used to determine that the freight order is a risky order if the freight order is one of multiple orders undertaken by the same driver within the same time range and is a non-carpooling order.

[0040] Optionally, the detection module is used to obtain the driver's positioning data and the transportation vehicle's positioning data within a preset time period during the transportation of the freight order; obtain the overlap between the driver's positioning data and the transportation vehicle's positioning data; and detect whether a person-vehicle separation occurs based on the overlap.

[0041] Optionally, the driver's positioning data includes positioning data at multiple time points within the preset time period, and the transport vehicle's positioning data includes positioning data at multiple time points within the preset time period. The detection module is used to obtain the distance between the driver's positioning data and the transport vehicle's positioning data corresponding to the same time point, and obtain a total of multiple distances, and the multiple distances represent the overlap; if the distance that exceeds a preset number ratio among the multiple distances is greater than the set distance, it is determined that a person-vehicle separation situation has occurred.

[0042] Optionally, after determining that a person-vehicle separation occurs, the device further includes:

[0043] The display module is used to display the trajectory information including the positioning data of the transport vehicle on the display interface.

[0044] Optionally, after determining that no separation of the person and the vehicle occurs, the device further comprises:

[0045] The display module is used to display the trajectory information including the positioning data of the transport vehicle and the positioning data of the driver on the display interface.

[0046] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method provided in the first aspect are performed.

[0047] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the method provided in the first aspect are performed.

[0048] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 A flow chart of a method for detecting separation of a person from a vehicle provided in an embodiment of the present application;

[0051] Figure 2 A display schematic diagram of a display interface provided in an embodiment of the present application;

[0052] Figure 3 A structural block diagram of a vehicle-person separation detection device provided in an embodiment of the present application;

[0053] Figure 4 A schematic diagram of the structure of an electronic device for executing a method for detecting separation of a person from a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present application.

[0055] It should be noted that the terms "system" and "network" in the embodiments of the present invention can be used interchangeably. "Multiple" means two or more. In view of this, in the embodiments of the present invention, "multiple" can also be understood as "at least two". "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / ", unless otherwise specified, generally indicates that the previously associated objects are in an "or" relationship.

[0056] An embodiment of the present invention provides a method for detecting separation of a person and a vehicle. The method performs risk assessment on freight orders, and then uses different methods for detecting separation of a person and a vehicle to respectively perform separation of a person and a vehicle on risky orders and non-risky orders, thereby achieving differentiated separation of a person and a vehicle for freight orders, saving traffic consumption for freight monitoring, and reducing system resource overhead.

[0057] Please refer to Figure 1 , Figure 1 A flowchart of a method for detecting separation of a person from a vehicle provided in an embodiment of the present application, the method comprising the following steps:

[0058] Step S110: Obtain a freight order.

[0059] The human-vehicle separation detection method in this application can be executed by a track monitoring system. The track monitoring system can be deployed in a server as a separate system to communicate with a freight platform. Of course, the track monitoring system can also be deployed on a freight platform. The cargo owner can publish freight orders on the freight platform, and the driver can accept orders through the freight platform. When the track monitoring system is deployed on the server, the freight platform can send the order accepted by the driver to the track monitoring system after detecting that the driver has accepted the order, so as to perform human-vehicle separation detection on the freight order through the track monitoring system. When the track monitoring system is deployed on the freight platform, the order system on the freight platform can communicate with the track monitoring system. The order system is used to receive freight orders issued by the cargo owner and detect freight orders accepted by the driver. After the driver accepts the order, the freight order is sent to the track monitoring system for human-vehicle separation detection. For the convenience of description, the deployment of the track monitoring system on the freight platform will be used as an example for explanation.

[0060] Step S120: Determine whether the freight order is a risky order and obtain a determination result.

[0061] A risky order refers to an order that involves a certain risk of separation between the driver and the vehicle. That is to say, the probability of separation between the driver and the vehicle in a non-risky order is lower than the probability of separation between the driver and the vehicle in a risky order.

[0062] After obtaining the freight order, the trajectory monitoring system can determine whether the freight order is a risky order, or the order system in the freight platform can send the freight order to the risk control system in the freight platform. The risk control system can make a risk judgment on the freight order, mark the freight order with a corresponding mark, and then send it to the trajectory monitoring system. For example, when the risk control system determines that the freight order is a risky order, it marks the freight order with a corresponding risk mark and then sends it to the trajectory monitoring system. For non-risk orders, the risk mark is not marked, or a non-risk mark is marked. In this way, after obtaining the freight order, the trajectory monitoring system can determine whether the freight order has a risk mark. If a risk mark is marked, the freight order is determined to be a risky order. If there is no risk mark or it is marked with a non-risk mark, the freight order is determined to be a non-risk order.

[0063] Step S130: Determine the person-vehicle separation detection method corresponding to the freight order according to the judgment result.

[0064] After the trajectory monitoring system determines whether a freight order is a risky order, the corresponding human-vehicle separation detection method can be determined based on the judgment result. In the embodiment of the present application, the human-vehicle separation detection methods corresponding to risky orders and non-risky orders are different. In this way, differentiated detection can be performed for risky orders and non-risky orders, thereby avoiding the problem of excessive traffic consumption and low detection accuracy caused by detecting all freight orders in the same human-vehicle separation detection method.

[0065] It can be understood that the trajectory monitoring system can pre-store the human-vehicle separation detection method corresponding to risky orders and non-risky orders. If the judgment result is that the freight order is a risky order, the human-vehicle separation detection method corresponding to the risky order is obtained. If the judgment result is that the freight order is a non-risky order, the human-vehicle separation detection method corresponding to the non-risky order is obtained.

[0066] Step S140: performing a person-vehicle separation detection on the transportation process of the freight order according to the person-vehicle separation detection method corresponding to the freight order.

[0067] After obtaining the human-vehicle separation detection method corresponding to the freight order, the human-vehicle separation detection can be performed on the transportation process of the freight order according to the human-vehicle separation detection method.

[0068] Among them, human-vehicle separation detection refers to detecting whether the moving trajectories of the driver and the truck are separated during the process of transporting goods, that is, whether the driver is separated from the truck for a long time, so as to monitor whether human-vehicle separation occurs during freight transportation, thereby ensuring the safety of freight transportation and avoiding problems such as reverse loading and loss of goods.

[0069] In the above implementation process, by making a risk assessment on the freight orders, and then using different human-vehicle separation detection methods to perform human-vehicle separation detection on risky orders and non-risky orders respectively, differentiated human-vehicle separation detection can be achieved for freight orders, which can save the traffic consumption of freight monitoring and reduce system resource overhead.

[0070] In some embodiments, the human-vehicle separation detection method may include detection conditions, detection frequency, and detection methods, etc., and for risky orders and non-risky orders, at least one of the detection conditions, detection frequencies, and detection methods of the two is different.

[0071] For example, if the freight order is judged to be a risky order, the human-vehicle separation detection method corresponding to the freight order is determined to be a timed detection; if the freight order is judged to be a non-risky order, the human-vehicle separation detection method corresponding to the freight order is determined to be a user-triggered detection.

[0072] Among them, the scheduled detection can be understood as performing the human-vehicle separation detection at regular intervals from the time when the track monitoring system receives the freight order to the time when the freight order is executed. Multiple human-vehicle separation detections can be performed during this period. During each detection, the human-vehicle separation detection can be performed on the track from the last detection time to the current time, without the need to re-detect the entire track, which can save detection traffic.

[0073] User-triggered detection can be understood as detection only being performed under the condition that the user triggers the detection. For example, the user can check the transportation status of the goods on the freight platform. The freight platform can provide a button to trigger the human-vehicle separation detection. The user can click the button on the freight platform to trigger the human-vehicle separation detection request. After receiving the human-vehicle separation detection request, the trajectory monitoring system will perform human-vehicle separation detection on the non-risk order. At this time, the detection can be performed on the trajectory within the time period from the order transaction time to the current time, which can ensure the detection accuracy. Of course, if the user has triggered the human-vehicle separation detection for the order before, the human-vehicle separation detection can also be performed on the trajectory within the time period from the last detection time to the current time to avoid the problem of traffic consumption caused by repeated detection.

[0074] Of course, if the user does not trigger the human-vehicle separation detection for the freight order, and the freight order is a non-risk order, the human-vehicle separation detection will not be performed on the non-risk order.

[0075] In the above implementation process, risky orders are subject to regular detection, and non-risky orders are subject to user-triggered detection. This allows the focus to be placed on monitoring risky orders while separating people from vehicles. There is no need to perform regular detection on non-risky orders, which can effectively reduce the traffic consumption of freight monitoring.

[0076] In some implementations, the detection frequencies of risky orders and non-risky orders may also be different, such as the detection frequency of the human-vehicle separation detection method corresponding to risky orders is greater than the detection frequency of the human-vehicle separation detection method corresponding to non-risky orders.

[0077] For example, both risky orders and non-risky orders can be checked at regular intervals, but the detection frequency for risky orders can be set to once every hour, while the detection frequency for non-risky orders can be set to once every five hours. In this way, risky orders can be monitored intensively, so that the main resources of the trajectory monitoring system are used for the detection of risky orders, which can save traffic consumption.

[0078] In some embodiments, the detection methods for risky orders and non-risky orders may also be different. For example, for risky orders, a more accurate and complex detection method may be used for detection, while for non-risky orders, a less accurate and simpler detection method may be used for detection. The detection methods may include the longest common sub-sequence (LCSS), dynamic time warping (DTW), Frechet distance, one way distance, locality in-between polylines (LIP), Euclidean distance, trajectory point clustering and other methods.

[0079] For risky orders, complex detection methods such as LCSS and DTW can be used. For non-risky orders, simpler detection methods such as Euclidean distance and one-way distance can be used. This allows differentiated detection of orders of both categories, saving resource overhead of the trajectory monitoring system and reducing traffic consumption.

[0080] Based on the above embodiment, the judgment of risky orders and non-risky orders can be made in the following way: if the freight order is one of multiple orders undertaken by the same driver within the same time range, and it is a non-carpooling order, then the judgment result is that the freight order is a risky order.

[0081] After detecting that a driver has accepted a freight order, the freight platform will check all the orders undertaken by the driver. If the loading time of all the orders undertaken by the driver is within the same time range, such as on the same day, and these orders are not carpooling orders, then the freight order is determined to be a risky order. Otherwise, it is a non-risky order.

[0082] It can be seen that before the human-vehicle separation detection, a risk order inspection link is added. By judging the risk of freight orders, the intensity of subsequent human-vehicle separation monitoring can be reduced, thereby reducing traffic consumption.

[0083] However, in the freight field, there is still a "team captain" model, that is, a driver (team captain) account can receive multiple orders, and then assign these orders to other drivers (team members). Therefore, it is inevitable that risky orders will be misjudged. Therefore, in order to avoid misjudgment, a whitelist inspection link can be added. If a driver accepts multiple non-carpooling orders within the same time range, check whether the driver is on the whitelist. If so, it means that the driver is the team captain, and the driver is allowed to accept multiple non-carpooling orders with loading time within the same time range. These orders are not risky orders, that is, non-risk orders.

[0084] In addition, there are other ways to judge risky orders, such as matching the cargo information in the freight order with the vehicle information. The cargo information includes cargo weight, volume, packaging method, type, temperature requirements, etc. The vehicle information includes vehicle length, load, vehicle model, cargo box volume, etc. If the cargo information does not match the vehicle information (if any information does not match, the corresponding matching conditions can be pre-configured), the freight order is determined to be a risky order and needs to be monitored. On the contrary, when the cargo information matches the vehicle information, the freight order can be considered a non-risky order.

[0085] On the basis of the above embodiment, when performing human-vehicle separation detection on a freight order, the detection method may be: obtaining the driver's positioning data and the transport vehicle's positioning data within a preset time period during the transportation of the freight order, obtaining the degree of overlap between the driver's positioning data and the transport vehicle's positioning data, and detecting whether human-vehicle separation occurs based on the degree of overlap.

[0086] The preset time period can be understood as the time period from the last detection time to the current time, or the time period from the order transaction time to the click of unloading time, the estimated unloading time, or the order completion time. The detection frequency for risky orders and non-risky orders is different, so the corresponding preset time period may also be different. The detection frequency can be flexibly configured according to actual needs.

[0087] The positioning data of the driver is the GPS data of the driver's mobile terminal, and the Beidou positioning device is installed on the transport vehicle, so the positioning data of the transport vehicle is Beidou data. After obtaining GPS data and Beidou data, the detection method introduced in the above embodiment can be used to obtain the overlap between the two tracks, such as calculating the Fréchet distance between the GPS track formed by the GPS data and the Beidou track formed by the Beidou data, and the Fréchet distance is the overlap between the positioning data of the driver and the positioning data of the transport vehicle, so the separation detection of people and vehicles can be carried out according to the overlap. If the overlap is greater than the set threshold (if the Fréchet distance is less than the set threshold, it means that the overlap is greater than the set threshold), it is considered that there is no separation of people and vehicles, otherwise, there is separation of people and vehicles.

[0088] Of course, other detection methods can also be used to obtain the overlap between the trajectories of the driver and the transport vehicle, which will not be introduced here one by one.

[0089] In the above implementation process, the overlap of the trajectory is obtained by obtaining the positioning data within a preset time period. Compared with the method of obtaining the overlap by obtaining all the positioning data, the amount of calculation is greatly reduced.

[0090] In the above implementation process, in order to achieve rapid detection and improve detection efficiency, the trajectory overlap of the positioning data at some time points within a preset time period can be judged. For example, the driver's positioning data includes positioning data at multiple time points within the preset time period, and the positioning data of the transport vehicle includes positioning data at multiple time points within the preset time period. Then, the distance between the driver's positioning data and the positioning data of the transport vehicle corresponding to the same time point can be obtained, and a total of multiple distances are obtained. The multiple distances represent the trajectory overlap. Then, if the distance that exceeds the preset number ratio among the multiple distances is greater than the set distance, it is determined that the person and the vehicle are separated.

[0091] For example, the positioning data of 10 time points within a preset time period are randomly taken, and the distance between the driver and the transport vehicle at the same time point is calculated respectively, and a total of 10 distances are obtained. Then, it is determined whether there are more than a preset number of distances greater than the set distance among these 10 distances. The preset number ratio can be flexibly set according to actual needs. For example, if it is set to 80%, it is determined whether more than 8 distances among the 10 distances are greater than the set distance. If so, it is determined that a person-vehicle separation situation occurs. Otherwise, a person-vehicle separation situation does not occur.

[0092] In addition, if the time accuracy is 1 minute and the preset time period is 30 minutes, the positioning data of 30 time points can be obtained. At this time, the positioning data of 10 time points (the number of time points can be flexibly set according to needs) can be randomly selected for comparison. If there is no positioning data for 10 time points in the preset time period, the time accuracy is gradually increased until the positioning data of 10 time points are obtained.

[0093] If the time accuracy is adjusted to a maximum of 10 minutes, and the number of time points in the preset time period is less than 10, the positioning data of all time points can be obtained for comparison. If the time points are less than or equal to 3, it can be considered that the positioning data is insufficient and no detection is performed.

[0094] The above-mentioned set distance can be flexibly set according to actual needs. For example, if the set distance is 10km / min*time accuracy, if the time accuracy is 2min (indicating that the time difference between two positioning data is 2min), the set distance is 20km.

[0095] In the above implementation process, positioning data at multiple time points within a preset time period are selected to perform human-vehicle separation detection, without the need to obtain all trajectory data for comparison and calculation, which greatly reduces the amount of calculation.

[0096] On the basis of the above embodiment, when performing human-vehicle separation detection, the detection time period can be set according to actual needs. For example, when performing the first detection, it can be detected whether human-vehicle separation occurs within the time period from the order transaction time to the current time. In subsequent detections, detection can be performed according to incremental trajectory data, that is, human-vehicle separation detection is performed on the trajectory within the time period from the last detection time to the current time.

[0097] After determining that a person-vehicle separation occurs in a freight order, the person-vehicle separation detection may not be continued for the freight order. Instead, the freight order may be marked with a person-vehicle separation mark and then sent to the freight platform, or a corresponding alarm message may be generated and sent to manual customer service or the consignee.

[0098] After receiving the notification information of separation of people and vehicles, the cargo owner can open the freight platform to check the track. When displaying the freight orders with separation of people and vehicles, if the driver's positioning data and the transport vehicle's positioning data are directly combined and displayed, there will be track jumps and display abnormalities. Therefore, in order to avoid this problem, the track information including the transport vehicle's positioning data can be displayed on the display interface.

[0099] For example, if a freight order has a situation where a person and a vehicle are separated, a person and a vehicle separation mark can be displayed when it is displayed on the freight platform, such as Figure 2 The "suspected person-vehicle separation mark" is displayed in the track details page. Users can view all track information on the track details page. At this time, the track information only includes the location data of the transport vehicle, but not the driver's location data, that is, the mobile phone location is hidden by default. Of course, all locations can also be displayed. For example, if the user clicks "Show all locations", the driver's location data and the transport vehicle's location data can be displayed at the same time. If the user clicks "Hide mobile phone location" again to hide the driver's location data, only the transport vehicle's location data will be displayed.

[0100] When there is no separation between people and vehicles, the trajectory information including the positioning data of the transport vehicle and the positioning data of the driver can be displayed on the display interface. At this time, the positioning data of the driver and the positioning data of the transport vehicle are combined and displayed, which is conducive to the cargo owner to view the trajectory information.

[0101] In addition, the consignor can also enter the freight platform at any time to view the track information. If the freight order has not yet experienced separation of the person and the vehicle, the display interface can display the driver's positioning data and the transport vehicle's positioning data when the consignor views the track information. If the freight order is marked with a person and the vehicle is separated when the consignor views the freight order next time, only the track information including the transport vehicle's positioning data can be displayed when the consignor views the track information.

[0102] If the human-vehicle separation mark is not displayed on the display interface, the consignor can also automatically trigger the human-vehicle separation detection for the freight order (such as the consignor clicks "Precise Positioning"). At this time, the trajectory monitoring system can perform human-vehicle separation detection on the freight order. If the detection shows that the human-vehicle separation occurs, the display interface will display the trajectory information including the positioning data of the transport vehicle and the human-vehicle separation mark. If the detection shows that the human-vehicle separation does not occur, the display interface will display the trajectory information including the positioning data of the transport vehicle and the positioning data of the driver, and return the detection result that the human-vehicle separation does not occur.

[0103] In addition, a timeout mechanism can be configured for the human-vehicle separation detection. If the detection result of whether the human-vehicle separation occurs is not returned after the detection time expires, the trajectory information will be displayed on the display interface as if there is no human-vehicle separation. After the detection result of human-vehicle separation occurs is returned, the page can be refreshed or the trajectory information can be displayed as if there is human-vehicle separation the next time the page is entered. At this time, the driver's positioning data, base station data, etc. can be hidden by default on the trajectory details page, and "suspected human-vehicle separation" will be displayed. Of course, users can also actively view all trajectory information.

[0104] When displaying trajectory information, the display interface can also add a positioning information channel identifier to distinguish the positioning data (GPS+WIFI+base station) of the mobile terminal and the Beidou data of the transport vehicle.

[0105] It can be seen that after comparing and verifying the driver's positioning data and the transport vehicle's positioning data, freight orders with separation of people and vehicles are identified and marked. By optimizing the track display rules, the full Beidou data is displayed for freight orders with separation of people and vehicles, and the GPS data / base station is hidden by default, thus avoiding the problem of track jumping and reducing traffic consumption.

[0106] Please refer to Figure 3 , Figure 3 This is a structural block diagram of a vehicle-human separation detection device 200 provided in an embodiment of the present application. The device 200 may be a module, program segment or code on an electronic device. It should be understood that the device 200 is similar to the above-mentioned Figure 1 The method embodiment corresponds to and can be executed Figure 1 The various steps involved in the method embodiment and the specific functions of the device 200 can be found in the above description. To avoid repetition, the detailed description is appropriately omitted here.

[0107] Optionally, the device 200 includes:

[0108] An order acquisition module 210 is used to acquire a freight order;

[0109] A judgment module 220 is used to judge whether the freight order is a risky order and obtain a judgment result;

[0110] A detection method determination module 230 is used to determine a person-vehicle separation detection method corresponding to the freight order according to the judgment result, wherein the person-vehicle separation detection methods corresponding to risk orders and non-risk orders are different;

[0111] The detection module 240 is used to perform a human-vehicle separation detection on the transportation process of the freight order according to the human-vehicle separation detection method corresponding to the freight order.

[0112] Optionally, the detection method determination module 230 is used to determine that the person-vehicle separation detection method corresponding to the freight order is a timed detection if the judgment result is that the freight order is a risky order; if the judgment result is that the freight order is a non-risky order, determine that the person-vehicle separation detection method corresponding to the freight order is a user-triggered detection.

[0113] Optionally, the detection frequency of the human-vehicle separation detection method corresponding to the risky order is greater than the detection frequency of the human-vehicle separation detection method corresponding to the non-risky order.

[0114] Optionally, the judgment module 220 is used to determine that the freight order is a risky order if the freight order is one of multiple orders undertaken by the same driver within the same time range and is a non-carpooling order.

[0115] Optionally, the detection module 240 is used to obtain the positioning data of the driver and the positioning data of the transport vehicle within a preset time period during the transportation of the freight order; obtain the overlap between the positioning data of the driver and the positioning data of the transport vehicle; and detect whether a person-vehicle separation occurs based on the overlap.

[0116] Optionally, the driver's positioning data includes positioning data at multiple time points within the preset time period, and the transport vehicle's positioning data includes positioning data at multiple time points within the preset time period. The detection module 240 is used to obtain the distance between the driver's positioning data and the transport vehicle's positioning data corresponding to the same time point, and obtain a total of multiple distances, and the multiple distances represent the overlap; if the distance that exceeds a preset number ratio among the multiple distances is greater than the set distance, it is determined that a person-vehicle separation situation has occurred.

[0117] Optionally, after determining that a person-vehicle separation occurs, the device 200 further includes:

[0118] The display module is used to display the trajectory information including the positioning data of the transport vehicle on the display interface.

[0119] Optionally, after determining that there is no separation between the person and the vehicle, the device 200 further includes:

[0120] The display module is used to display the trajectory information including the positioning data of the transport vehicle and the positioning data of the driver on the display interface.

[0121] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0122] Please refer to Figure 4 , Figure 4 A schematic diagram of the structure of an electronic device for executing a human-vehicle separation detection method provided in an embodiment of the present application, the electronic device may be a terminal device, and the above-mentioned freight platform is deployed in the electronic device, and the electronic device may include: at least one processor 310, such as a CPU, at least one communication interface 320, at least one memory 330 and at least one communication bus 340. Among them, the communication bus 340 is used to realize direct connection and communication between these components. Among them, the communication interface 320 of the device in the embodiment of the present application is used to communicate signaling or data with other node devices. The memory 330 can be a high-speed RAM memory, or it can be a non-volatile memory (non-volatile memory), such as at least one disk storage. The memory 330 can optionally also be at least one storage device located away from the aforementioned processor. Computer-readable instructions are stored in the memory 330. When the computer-readable instructions are executed by the processor 310, the electronic device executes the above-mentioned Figure 1 The method process is shown.

[0123] Understandably, Figure 4 The structure shown is for illustration only, and the electronic device may also include Figure 4 More or fewer components as shown, or with Figure 4 Different configurations are shown. Figure 4 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0124] The present application embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the following is performed: Figure 1 The method process in the method embodiment shown is executed by the electronic device.

[0125] The present embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments, for example, including: obtaining a freight order; judging whether the freight order is a risky order and obtaining a judgment result; determining a person-vehicle separation detection method corresponding to the freight order according to the judgment result, wherein the person-vehicle separation detection methods corresponding to risky orders and non-risky orders are different; and performing a person-vehicle separation detection on the transportation process of the freight order according to the person-vehicle separation detection method corresponding to the freight order.

[0126] To summarize, the embodiments of the present application provide a method, device, electronic device and storage medium for detecting human-vehicle separation, which performs risk assessment on freight orders and then uses different human-vehicle separation detection methods to perform human-vehicle separation detection on risky orders and non-risky orders respectively, thereby achieving differentiated human-vehicle separation detection for freight orders, saving traffic consumption of freight monitoring and reducing system resource overhead.

[0127] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0128] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0129] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0130] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0131] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for detecting separation of a person and a vehicle, characterized in that: The method comprises: Get freight orders; Determine whether the freight order is a risky order and obtain a determination result; Determine, according to the judgment result, a person-vehicle separation detection method corresponding to the freight order, wherein the person-vehicle separation detection methods corresponding to risk orders and non-risk orders are different; Performing a person-vehicle separation detection on the transportation process of the freight order according to the person-vehicle separation detection method corresponding to the freight order, wherein the person-vehicle separation detection refers to detecting whether the movement tracks of the driver and the truck are separated during the transportation of the goods; Wherein, the determining whether the freight order is a risky order and obtaining a determination result includes: If the freight order is one of multiple orders undertaken by the same driver within the same time range and is a non-carpooling order, it is determined that the freight order is a risky order.

2. The method according to claim 1, characterized in that The determining, according to the judgment result, a person-vehicle separation detection method corresponding to the freight order includes: If the judgment result is that the freight order is a risky order, determining that the person-vehicle separation detection method corresponding to the freight order is a timed detection; If the judgment result is that the freight order is a non-risk order, it is determined that the person-vehicle separation detection method corresponding to the freight order is user-triggered detection.

3. The method according to claim 1, characterized in that The detection frequency of the human-vehicle separation detection method corresponding to the risky order is greater than the detection frequency of the human-vehicle separation detection method corresponding to the non-risky order.

4. The method according to claim 1, characterized in that: The performing a person-vehicle separation detection on the transportation process of the freight order according to the person-vehicle separation detection method corresponding to the freight order includes: Acquire the location data of the driver and the location data of the transport vehicle during the transportation process of the freight order; Obtaining the degree of overlap between the positioning data of the driver and the positioning data of the transport vehicle; Whether a situation of separation between a person and a vehicle occurs is detected according to the overlap degree.

5. The method according to claim 4, characterized in that The positioning data of the driver includes positioning data at multiple time points within the preset time period, the positioning data of the transport vehicle includes positioning data at multiple time points within the preset time period, and obtaining the degree of overlap between the positioning data of the driver and the positioning data of the transport vehicle includes: Acquire the distance between the positioning data of the driver and the positioning data of the transport vehicle corresponding to the same time point, and obtain a total of multiple distances, wherein the multiple distances represent the overlap degree; The detecting whether a person-vehicle separation situation occurs according to the overlap degree includes: If the distances exceeding the preset ratio among the multiple distances are greater than the set distance, it is determined that a person-vehicle separation situation occurs.

6. The method according to any one of claims 1 to 5, characterized in that: After determining that a person-vehicle separation situation occurs, the method further includes: The display interface displays trajectory information including the positioning data of the transport vehicle.

7. The method according to any one of claims 1 to 5, characterized in that: After determining that there is no separation between the person and the vehicle, the method further includes: The display interface displays trajectory information including the positioning data of the transport vehicle and the positioning data of the driver.

8. A vehicle-passenger separation detection device, characterized in that: The device comprises: Order acquisition module, used to obtain freight orders; A judgment module, used to judge whether the freight order is a risky order and obtain a judgment result; A detection method determination module, used to determine a person-vehicle separation detection method corresponding to the freight order according to the judgment result, wherein the person-vehicle separation detection methods corresponding to risk orders and non-risk orders are different; a detection module, configured to perform a person-vehicle separation detection on the transportation process of the freight order according to the person-vehicle separation detection method corresponding to the freight order, wherein the person-vehicle separation detection refers to detecting whether the movement tracks of the driver and the truck are separated during the transportation of the goods; Among them, the judgment module is specifically used to determine that the freight order is a risky order if the freight order is one of multiple orders undertaken by the same driver within the same time range and is a non-carpooling order.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is executed.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is performed.

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