An information processing method, device and computer readable storage medium

Abstract

Embodiments of the present application disclose an information processing method and device and a computer readable storage medium. The embodiments of the present application display a target map, acquire traffic information in each detection area, sort each detection area according to the traffic information from high to low, determine a detection area with the highest traffic information after sorting as a target detection area, and determine a driving path of an unmanned device according to a starting point of the unmanned device and the target detection area. In this way, the traffic information in each detection area is sorted, the detection area with the highest activity is determined as the target detection area, and the driving path is planned according to the starting point of the unmanned device and the target detection area, so that the unmanned retail vehicle can automatically drive to the vicinity of the target detection area with the highest activity along the shortest route without manual intervention, and the efficiency of information processing is greatly improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to an information processing method, apparatus, and computer-readable storage medium. Background Technology

[0002] Unmanned retail vehicles are small cars with autonomous driving capabilities that are transformed into mobile vending platforms. These vehicles are loaded with various daily service products such as beverages and snacks, and then appear in specific areas such as parks, squares, and residential communities at specific times for sale.

[0003] In existing technologies, unmanned retail vehicles can be controlled to patrol along fixed routes. When the unmanned vehicle is patrolling, consumers can wave in front of it and the vehicle will stop. They can then open the retail counter by scanning a QR code with their mobile phones to make a purchase. However, since the flow of people changes in real time, and many areas have low foot traffic, continuing to patrol will lead to consumer fatigue and low efficiency in information processing. Summary of the Invention

[0004] This application provides an information processing method, apparatus, and computer-readable storage medium, which can improve the efficiency of information processing.

[0005] To address the aforementioned technical problems, this application provides the following technical solutions: An information processing method, comprising: Display a target map, which includes multiple detection areas; Obtain traffic information for each detection area; Each detection area is sorted from high to low based on the traffic flow information; The detection area with the highest traffic information after sorting is determined as the target detection area; The driving path of the unmanned vehicle is determined based on the starting point of the unmanned vehicle and the target detection area.

[0006] An information processing method, comprising: The server receives the driving path, which is the path determined by the server based on the highest target detection area after sorting the traffic information in each detection area. Obtain the target detection area in the driving path and drive to the target detection area.

[0007] An information processing apparatus, comprising: A display unit is used to display a target map, which includes multiple detection areas and multiple road segments, and the road segments include multiple locations. The first acquisition unit is used to acquire traffic information in each detection area; The sorting unit is used to sort each detection area in descending order of traffic information; The first determining unit is used to determine the detection area with the highest traffic information after sorting as the target detection area; The second determining unit is used to determine the driving path of the unmanned driving device based on the starting point of the unmanned driving device and the target detection area.

[0008] In some embodiments, the target map further includes multiple road segments, each road segment including multiple locations, and the second determining unit includes: The acquisition subunit is used to acquire the target location that is closest to the target center point of the target detection area among multiple road segments; A determination subunit is used to determine the driving path based on the starting point of the unmanned driving device and the target location.

[0009] In some embodiments, the acquisition subunit is configured to: acquire the target center point of the target detection region; Calculate the distances between the target center point and locations on each road segment to obtain a distance set; Sort the distance sets in ascending order; The location with the smallest distance after sorting is determined as the target location.

[0010] In some embodiments, the apparatus further includes a detection unit for: Detect whether the target location is in a restricted area; When the target location is detected to be in a restricted area, the target detection area is hidden, and the system returns to sort each detection area in descending order of traffic flow information. When the target location is detected to be outside the restricted area, the driving route is determined based on the starting point and the target location.

[0011] In some embodiments, the first acquisition unit is configured to: Control the camera device in each detection area to capture target images; Analyze the target image to determine the information about the people in the target image; By analyzing the information of the individuals, traffic flow information is obtained for each detection area.

[0012] In some embodiments, the apparatus further includes: The second acquisition unit is used to acquire the preset location of the center point of each detection area in multiple road segments, wherein the distance between the preset location and the center point of the corresponding detection area is less than the distance between the non-preset location and the center point of the corresponding detection area. The recording unit is used to record the preset mapping relationship between the center point of each detection area and the corresponding preset location.

[0013] In some embodiments, the acquisition subunit is configured to: Based on the target center point of the target detection area, a preset location corresponding to the target center point is determined by matching it in the preset mapping relationship. The preset location corresponding to the target center point is determined as the target location.

[0014] In some embodiments, the determining subunit is configured to: Determine the driving route between the starting point and the target location of the unmanned vehicle; Obtain the distance of each driving route and sort them in ascending order of distance; The route with the shortest distance after sorting is determined as the target route, and the target route does not intersect with the restricted area.

[0015] In some embodiments, the information processing apparatus further includes an assignment unit, configured to: Assign a corresponding weight value to each road segment; Obtain target road segments that intersect with the restricted area, and increase the weight value of the target road segments by a preset value; The determining subunit is further configured to: Determine the driving route between the starting point and the target location of the unmanned vehicle; Obtain the road segments included in each driving route; The sum of the weight values ​​of the road segments included in each driving route is calculated in turn, and the sum of the weight values ​​is determined as the distance of each driving route; Sort the routes in ascending order of distance; Sort the routes in ascending order of distance; The route with the shortest distance after sorting is determined as the target route, and the target route does not intersect with the restricted area.

[0016] In some embodiments, the information processing apparatus further includes a patrol unit for: When the unmanned vehicle is detected to have stayed at the target location for a period of time exceeding a preset threshold, a patrol command is generated; The patrol command is sent to the unmanned vehicle so that the unmanned vehicle can drive around the target detection area within a preset time according to the patrol command.

[0017] In some embodiments, the information processing apparatus further includes a hiding unit for: The target detection area is hidden, and the process returns to the step of sorting each detection area in descending order of traffic information.

[0018] An information processing device, used in unmanned driving equipment, includes: The receiving unit is used to receive the driving path sent by the server. The driving path is the path determined by the server based on the highest target detection area after sorting the traffic information in each detection area. The driving unit is used to acquire the target detection area in the driving path and drive to the target detection area.

[0019] In some embodiments, the information processing apparatus further includes a patrol unit for: Receive patrol instructions sent by the server; According to the patrol instructions, the patrol will travel around the target detection area within a preset time.

[0020] A computer-readable storage medium storing a plurality of instructions adapted for loading by a processor to perform the steps in the above-described information processing method.

[0021] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the information processing method described above.

[0022] A computer program product or computer program includes computer instructions stored in a storage medium. A processor of a computer device reads the computer instructions from the storage medium and executes the computer instructions, causing the computer to perform the steps of the aforementioned information processing method.

[0023] This application embodiment displays a target map; acquires traffic information in each detection area; sorts each detection area according to the traffic information from high to low; determines the detection area with the highest traffic information after sorting as the target detection area; and determines the driving path of the autonomous driving device based on the starting point and the target detection area. In this way, the traffic information in each detection area can be sorted, the detection area with the highest activity can be determined as the target detection area, and the driving path can be planned based on the starting point and the target detection area, allowing the unmanned retail vehicle to automatically travel to the vicinity of the most active target detection area along the shortest route without human intervention, greatly improving the efficiency of information processing. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of a scenario for the information processing system provided in an embodiment of this application; Figure 2 This is a flowchart illustrating the information processing method provided in an embodiment of this application; Figure 3 This is another flowchart illustrating the information processing method provided in the embodiments of this application; Figure 4 A schematic diagram of a scenario for the information processing method provided in an embodiment of this application; Figure 5 This is a flowchart illustrating the information processing method provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of the information processing device provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of the information processing device provided in the embodiments of this application; Figure 8 This is a schematic diagram of the server structure provided in an embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] This application provides an information processing method, apparatus, and computer-readable storage medium.

[0028] Please see Figure 1 , Figure 1This is a schematic diagram of a scenario for the information processing system provided in this application embodiment, including: a camera device, an unmanned retail vehicle, and a server (the specific number of camera devices and unmanned retail vehicles is not limited here). The camera device, the unmanned retail vehicle, and the server can be connected via a communication network. This communication network can include wireless networks and wired networks, wherein the wireless network includes one or more combinations of wireless wide area networks, wireless local area networks, wireless metropolitan area networks, and wireless personal networks. The network includes network entities such as routers and gateways, which are not shown in the figure. The camera and the unmanned retail vehicle can interact with the server through the communication network; for example, the camera device can send a target image to the server.

[0029] The information processing system may include an information processing device, which can be integrated into a computer device. This computer device can be a terminal or a server, and the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, in-vehicle terminal, smart TV, etc. Taking the information processing method executed by a server as an example, the server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. Figure 1 As shown, the server can display a target map, which includes multiple detection areas; acquire traffic information in each detection area; sort each detection area according to the traffic information from high to low; determine the detection area with the highest traffic information after sorting as the target detection area; and determine the driving path of the autonomous driving device based on the starting point of the autonomous driving device and the target detection area.

[0030] The camera device can be installed in each detection area. The camera device can be a digital high-definition camera to capture target images in each detection area and send the target images to the server in real time through encoding and compression, so that the server can count the traffic information in each detection area in real time based on the target images.

[0031] This unmanned retail vehicle is an example of an autonomous driving device. It transforms a small car with autonomous driving capabilities into a mobile vending platform. The unmanned retail vehicle carries various daily necessities such as beverages and snacks, and then appears at specific times in designated areas such as parks, squares, and residential communities for sale. This unmanned retail vehicle can only travel on designated roads. It can share its location information with a server in real time or receive driving paths sent by the server, and automatically drive to the target location according to the driving path. This allows it to operate in high-activity areas, increasing sales. The unmanned retail vehicle can receive driving paths sent by the server, which are determined by the server based on the highest target detection area after sorting traffic information in each detection area; it then obtains the target detection area within the driving path and drives to that target detection area.

[0032] It should be noted that, Figure 1 The schematic diagram of the information processing system shown is merely an example. The information processing system and scenario described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of information processing systems and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0033] The following sections will provide detailed explanations.

[0034] This application provides an information processing method, which can be executed by a terminal or a server, or by both a terminal and a server. This application uses the example of the information processing method being executed by a server to illustrate the method.

[0035] Please see Figure 2 , Figure 2 This is a flowchart illustrating the information processing method provided in an embodiment of this application. The information processing method includes: In step 101, the target map is displayed.

[0036] In related technologies, unmanned retail vehicles use static maps as the basis for vehicle scheduling. These static maps can consist of sensor feature point maps and road network maps. The sensor feature point map, typically using LiDAR point cloud maps or camera visual feature point maps, is the most basic data constituting the map. The road network map identifies specific semantic information contained in the sensor feature point map, including road network data such as drivable road edges and connectivity relationships, as well as traffic sign data such as one-way traffic and speed limits.

[0037] It can be seen that the static map cannot reflect the changes in the actual drivable area of ​​the road and the changes in pedestrian traffic related to the operation. Moreover, in the actual operation process, the rules for driving unmanned retail vehicles are often affected by changes such as road maintenance, rain and snow, and operating hours. The static map cannot reflect these changes, which will cause unmanned retail vehicles to be unable to patrol normally and often require manual remote control for scheduling, resulting in extremely low operational efficiency.

[0038] Therefore, this application embodiment introduces a quasi-dynamic map and a pedestrian flow distribution map in addition to the static map. The quasi-dynamic map can be understood as a restricted area, which may be formed due to changes in traffic rules caused by road maintenance, rain, snow, etc., such as area closures, route adjustments, local congestion caused by performances, promotional activities, or changes in spatiotemporal rules due to operations, such as tidal one-way traffic restrictions set up during park opening and closing times. These can be set by maintenance personnel according to actual conditions. The pedestrian flow distribution map can be a dynamic distribution map of pedestrian flow reflected by target images captured by camera devices, such as a heat map, which can directly reflect the degree of pedestrian flow concentration.

[0039] The system can directly display the target map, which consists of multiple detection areas and multiple road segments. The multiple detection areas are composed of rectangular grids that divide the area into regions, representing the actual park area. Each detection area contains a certain range of pedestrian traffic. The multiple road segments are multiple interconnected line segments, and each road segment includes multiple locations. Multiple locations are connected to form a road segment. These locations are measured using latitude and longitude. Connecting these locations together generates a road segment, which is distributed across multiple detection areas.

[0040] In step 102, the flow information in each detection area is obtained.

[0041] Each detection area represents a physical location within the park. The traffic flow information can be pedestrian flow data; higher traffic flow indicates greater activity in the detection area, while lower traffic flow indicates lower activity. Traffic flow directly impacts the operation of the unmanned retail vehicles. To increase the retail sales of these vehicles, they should be directed to detection areas with higher traffic flow.

[0042] In one embodiment, the number of mobile phone signals in each detection area can be obtained. The larger the number, the more users in the detection area and the greater the traffic information. The smaller the number, the fewer users in the detection area and the smaller the traffic information.

[0043] In some implementations, the step of acquiring traffic information in each detection area may include: (1) Control the camera device in each detection area to capture target images; (2) Analyze the target image to determine the information of the people in the target image; (3) Collect information about the person and obtain traffic information in each detection area.

[0044] Each detection area can be equipped with a camera device, which includes at least one camera and can capture a complete image of the target corresponding to the entire detection area.

[0045] Furthermore, human information in a target image can be identified using Convolutional Neural Networks (CNNs). This human information can be human feature information, that is, features represented by the shape of the human body. This convolutional neural network can be constructed by mimicking the visual perception mechanism of biological organisms, so it can be used to identify human feature information in a target image and can select the human feature information with a rectangular or circular bounding box.

[0046] In this way, the flow of people in each detection area can be obtained based on the amount of this human feature information.

[0047] In step 103, each detection area is sorted according to the flow rate information from high to low.

[0048] In this method, each detection area can be sorted in descending order according to the flow information from high to low. In one embodiment, each detection area can be represented in the form of a heat map, with the detection area having a darker color for higher flow information and a lighter color for lower flow information. In this way, the most active detection area can be found simply and intuitively.

[0049] In step 104, the detection area with the highest traffic information after sorting is determined as the target detection area.

[0050] In order to maximize the retail sales of unmanned retail vehicles, it is necessary to identify the detection area with the highest pedestrian activity, that is, the detection area with the highest traffic information after sorting. This detection area with the highest traffic information after sorting is determined as the target detection area, so that the unmanned retail vehicle can be dispatched to the target detection area for sale in the future.

[0051] In step 105, the driving path of the autonomous driving device is determined based on the starting point and target detection area of ​​the autonomous driving device.

[0052] The target detection area is the detection area with the highest traffic information after sorting, that is, the target detection area has the highest human activity. The autonomous vehicle (i.e., the unmanned retail vehicle) should be driven as close to the target detection area as possible. The best way is to drive into the target detection area. In this way, the driving path of the autonomous vehicle can be determined based on the starting point (i.e., the current parking point of the autonomous vehicle) and the target detection area. For example, a suitable parking point can be found around the target detection area as the destination. Based on the starting point and the parking point, at least one driving path from the starting point to the parking point can be determined. In one embodiment, the shortest driving path can be selected from the at least one driving path, and the autonomous vehicle can be controlled to drive to the target detection area corresponding to the parking point for operation.

[0053] In some implementations, the step of determining the driving path of the autonomous vehicle based on its starting point and the target detection area may include: (1) Obtain the target location that is closest to the target center point of the target detection area among multiple road segments; (2) Determine the driving path based on the starting point of the unmanned driving equipment and the target location.

[0054] Since the autonomous vehicle can only travel on road sections, the target center point in the target detection area can be used as a reference point to find the target location among multiple road sections that are closest to the target center point. The distance between the target location and the target center point is less than the distance between other locations on the road section and the target center point. That is, the target location can be used as the destination of the autonomous retail vehicle. When the autonomous vehicle stops at the target location, the maximum operating efficiency can be achieved. Thus, the most suitable driving route can be determined based on the starting point of the autonomous vehicle and the target location, and the autonomous vehicle can be driven to the target location for operation.

[0055] In some implementations, after obtaining the target location closest to the target center point of the target detection area among multiple road segments, the method further includes: (1) Check whether the target location is in a restricted area; (2) When the target location is detected to be in a restricted area, the target detection area is hidden, and the detection areas are sorted in descending order of traffic flow information. (3) When the target location is detected to be outside the restricted area, the driving route is determined based on the starting point and the target location.

[0056] Since this embodiment introduces a restricted area, if the target location is located in a restricted area, the unmanned retail vehicle will be unable to reach the target location. Therefore, it is necessary to detect whether the target location is in a restricted area. When the target location is detected to be in a restricted area, it means that the unmanned retail vehicle cannot reach the target location. The target detection area can be hidden, and the system can return to sorting each detection area in descending order of traffic information. The detection area with the second highest traffic information is then re-determined, and so on, until the target location is detected to be not in a restricted area. At this point, the step of determining the driving path based on the starting point and the target location is executed.

[0057] As described above, this embodiment of the application displays a target map; acquires traffic information in each detection area; sorts each detection area according to the traffic information from high to low; determines the detection area with the highest traffic information after sorting as the target detection area; and determines the driving path of the autonomous driving device based on the starting point and the target detection area. In this way, the traffic information in each detection area can be sorted, the detection area with the highest activity can be determined as the target detection area, and the driving path can be planned based on the starting point and the target detection area, allowing the unmanned retail vehicle to automatically travel to the vicinity of the most active target detection area along the shortest route without human intervention, greatly improving the efficiency of information processing.

[0058] Based on the methods described in the above embodiments, the following examples will provide further detailed explanations.

[0059] In this embodiment, the information processing device will be specifically integrated into the server and the unmanned retail vehicle will be used as examples for explanation. Please refer to the following description for details.

[0060] Please see Figure 3 , Figure 3 Another schematic flowchart illustrating the information processing method provided in this application embodiment. The method flow may include: In step 201, the server displays the target map.

[0061] For a better understanding of the embodiments of this application, please refer to the following: Figure 4 As shown, Figure 4This is an application scenario diagram of the information processing method provided in the embodiments of this application. The server can display a target map 10, which includes multiple rectangular detection areas, such as A1 (i.e., rectangular area 11), A2, A3, A4 and A5, etc., and multiple road segments, such as S1, S2, S3, S4, S5 and S6, etc. Assuming that the target map covers a park scenic area, each detection area actually represents a piece of land within the scenic area, and the road segment is a drivable road segment within the scenic area. The road segment contains multiple locations, and each location is measured using latitude and longitude. By connecting these locations, multiple road segments can be generated. In step 202, the server controls the camera device in each detection area to capture target images, analyzes the target images, determines the information of people in the target images, counts the information of people, and obtains the traffic information in each detection area.

[0062] Each detection area can be equipped with a camera device, which can be a webcam. The camera device can capture the target image corresponding to the entire detection area in real time and completely. The target image can be a two-dimensional image of the entire detection area, and the target image can contain people, actions and scenery.

[0063] Furthermore, the server can use CNN to identify human information in the image, that is, human-specific human features, and count human information to obtain the number of people in each detection area, that is, traffic information.

[0064] In step 203, the server sorts each detection area in descending order of traffic information.

[0065] The server can sort each detection area according to the traffic information from high to low, and filter out the detection areas with high activity. For example, in the target image 10, the traffic information of detection area A1 is greater than that of detection area A2, the traffic information of detection area A2 is greater than that of detection area A3, the traffic information of detection area A3 is greater than that of detection area A4, and the traffic information of detection area A4 is greater than that of detection area A5.

[0066] In step 204, the server determines the detection area with the highest sorted traffic information as the target detection area.

[0067] The server can determine the detection area A1 with the highest traffic information after sorting as the target detection area. The target detection area A1 is the area with the highest traffic activity.

[0068] In step 205, the server obtains the preset location of the center point of each detection area in multiple road segments.

[0069] In order to speed up the subsequent destination calculation time, the server can pre-obtain the preset location that is closest to the center point of each detection area among multiple road segments. The distance between the preset location and the center point of the corresponding detection area is less than the distance between the non-preset location and the center point of the corresponding detection area.

[0070] In step 206, the server records the preset mapping relationship between the center point of each detection area and the corresponding preset location.

[0071] The server can record the preset mapping relationship between the center point of each detection area and the corresponding preset location, such as the mapping relationship between the center point 111 of detection area 11 (A1) and the preset location B1.

[0072] In step 207, the server matches the target center point in the preset mapping relationship according to the target detection area, determines the preset location corresponding to the target center point, and determines the preset location corresponding to the target center point as the target location.

[0073] In this embodiment of the application, the server can match the target center point 111 of the target detection area A1 in a preset mapping relationship, directly determine the preset location B1 corresponding to the target center point 111, and directly determine the preset location B1 corresponding to the target center point as the target location closest to the target center point of the target detection area among multiple road segments. B1 is located on road segment S4.

[0074] In step 208, the server detects whether the target location is in a restricted area.

[0075] Please continue reading for more details. Figure 5 As shown, a restricted area 12 is also introduced in the target map. This restricted area can be a polygon, such as a rectangle in this embodiment. The unmanned retail vehicle is not allowed to pass through this restricted area. Therefore, in order to avoid the unmanned retail vehicle being unable to reach the target location because the target location is in a restricted area, the server needs to detect whether the target location is in a restricted area. When the target location is detected to be in a restricted area, step 209 is executed. When the target location is detected not to be in a restricted area, step 210 is executed.

[0076] In step 209, the server hides the target detection area.

[0077] When the target location is detected to be in a restricted area, it means that the unmanned retail vehicle cannot reach the target location near the detection area and sell there. The detection area needs to be hidden, and the process returns to step 203. Each detection area is sorted in descending order of traffic information. Since the detection area with the highest traffic information after the previous sorting was hidden, the detection area with the second highest traffic information after sorting will be re-identified as the target detection area to obtain a new target location. This process continues until the target location is no longer in a restricted area.

[0078] In step 210, the server assigns a corresponding weight value to each road segment, obtains target road segments that intersect with the restricted area, increases the weight value of the target road segments by a preset value, and determines the driving route between the starting point and the target location.

[0079] The server can assign a corresponding weight value to each road segment, and the weight value can be the actual length of the road segment.

[0080] To prevent unmanned retail vehicles from stopping due to passing through restricted areas, the target road segment S3 that intersects with restricted area 12 can be identified. Figure 4 The bolded section 13 on S3 is the part that intersects with the restricted area 12. In this embodiment, the preset value is a setting value that distinguishes between normal road sections and target road sections that intersect with the restricted area, such as 9999 meters. Thus, the weight of the target road section S3 can be increased by the preset value of 9999 meters, and the current location B0 of the unmanned retail vehicle can be obtained as the starting point and the target location B1. For subsequent calculations, road section S6 can be divided into sub-road sections S61 and S62 through B0, and road section S4 can be divided into sub-road sections S41 and S43 through B1.

[0081] Furthermore, a first driving route between the starting point and the destination is determined, which consists of road segments S62, S3 and S41, and a second driving route, which consists of road segments S62, S2, S5 and S4.

[0082] In step 211, the server obtains the road segments contained in each driving route, calculates the sum of the weight values ​​of the road segments contained in each driving route in turn, determines the sum of the weight values ​​as the distance of each driving route, and sorts them in ascending order of the distance of the driving routes.

[0083] The server can obtain the road segments S62, S3 and S41 included in the first driving route and the road segments S62, S2, S5 and S4 included in the second driving route. It can then calculate the sum of the weight values ​​of the road segments included in the first driving route and the weight values ​​of the road segments included in the second driving route, and use the sum of these weight values ​​as the distance of each driving route.

[0084] In a real-world scenario, the distance of the first driving route is significantly shorter than that of the second driving route. However, since S3 in the first driving route intersects with the restricted area, the sum of the weight values ​​of the first driving route will be greater than the sum of the weight values ​​of the second driving route by adjusting the weight values. This can prevent the unmanned retail vehicle from traveling along the first driving route and failing to reach the target location B1.

[0085] In step 212, the server determines the route with the shortest distance after sorting as the target route.

[0086] The server will select the second route with the shortest distance after sorting, which consists of road segments S62, S2, S5, and S4, as the target route. Since the unmanned retail vehicle is equipped with components such as cameras and lidar to perceive the surrounding environment, it can be driven to the target location B1 for operation based on the target route. Because the target location B1 is closest to the target detection area with the highest activity, the efficiency of operation and information processing can be greatly improved.

[0087] In some implementations, it also includes: (1) When the unmanned vehicle is detected to have stayed at the target location for a period of time longer than a preset threshold, a patrol instruction is generated; (2) Send the patrol instruction to the unmanned vehicle so that the unmanned vehicle can drive around the target detection area within a preset time according to the patrol instruction.

[0088] The preset threshold can be set by the user or preset by the system, such as 5 minutes or 10 minutes, and there is no specific restriction here. After the unmanned retail vehicle travels to the target location according to the target route, it can stay for the preset threshold time to operate. When it is detected that the unmanned retail vehicle stays at the target location for a longer time than the preset threshold, in order to increase the efficiency of operation, a patrol command can be generated and sent to the unmanned retail vehicle. The unmanned retail vehicle can then patrol the target detection area within a preset time, such as 10 minutes, according to the patrol command, thereby further improving the efficiency of operation.

[0089] In some implementations, after sending the patrol command to the unmanned vehicle, the method further includes: hiding the target detection area and returning a step of sorting each detection area in descending order of traffic flow information.

[0090] In this process, after the server sends the patrol instruction to the unmanned retail vehicle, in order to take into account the operation of other hot spot detection areas, the target detection area can be hidden after a preset time, and the process returns to step 203. The detection areas are then reordered according to the traffic information from high to low. Since the detection area with the highest traffic information after the current sort is hidden, the detection area with the next highest traffic information after the current sort will be used as the new target detection area. The new target location is obtained, the target driving route is planned, and the unmanned retail vehicle is controlled to drive to the next hot spot detection area, and so on, which further improves the operational efficiency.

[0091] As described above, this embodiment of the application displays a target map; acquires traffic information in each detection area; sorts each detection area according to the traffic information from high to low; determines the detection area with the highest traffic information after sorting as the target detection area; and determines the driving path of the autonomous driving device based on the starting point and the target detection area. In this way, the traffic information in each detection area can be sorted, the detection area with the highest activity can be determined as the target detection area, and the driving path can be planned based on the starting point and the target detection area, allowing the unmanned retail vehicle to automatically travel to the vicinity of the most active target detection area along the shortest route without human intervention, greatly improving the efficiency of information processing.

[0092] Furthermore, the server can avoid restricted areas when designing the target driving route, enabling unmanned retail vehicles to reach the target location accurately in the shortest time and further improve the efficiency of information processing.

[0093] This application also provides an information processing method that can be executed by an unmanned driving device.

[0094] Please see Figure 5 , Figure 5 This is a flowchart illustrating the information processing method provided in an embodiment of this application. The information processing method includes: In step 301, the driving path sent by the server is received.

[0095] The unmanned driving device can be an unmanned retail vehicle. The unmanned driving device can receive the driving path sent by the server. The driving path is the path determined by the server based on the highest target detection area after sorting the traffic information in each detection area. Please refer to the above embodiment for details.

[0096] In step 302, the target detection area in the driving path is obtained, and the vehicle is driven to the target detection area.

[0097] Among them, the unmanned driving equipment can use the target detection area in the target detection area as the destination and drive to the target detection area for operation through the unmanned driving function, thereby improving the efficiency of operation.

[0098] In some implementations, after sending the patrol command to the unmanned device, the method further includes: (1) Receive patrol instructions sent by the server; (2) Drive around the target detection area within a preset time according to the patrol instructions.

[0099] Among them, the unmanned vehicle can receive patrol instructions sent by the server, and then patrol around the target detection area within a preset time, such as 10 minutes, according to the instructions, thereby improving operational efficiency.

[0100] To facilitate better implementation of the information processing method provided in the embodiments of this application, the embodiments of this application also provide an apparatus based on the above-described information processing method. The meanings of the terms used are the same as in the above-described information processing method, and specific implementation details can be found in the descriptions in the method embodiments.

[0101] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of an information processing device provided in an embodiment of this application. The information processing device may include a display unit 401, a first acquisition unit 402, a sorting unit 403, a first determination unit 404, and a second determination unit 405. The information processing device is applied to a terminal or a server.

[0102] Display unit 401 is used to display a target map, which includes multiple detection areas and multiple road segments, and the road segments include multiple locations.

[0103] The first acquisition unit 402 is used to acquire traffic information in each detection area.

[0104] In some embodiments, the first acquisition unit 402 is configured to: Control the camera device in each detection area to capture target images; Analyze the target image to determine the information about the people in the target image; By analyzing the information of the individuals, traffic flow information is obtained for each detection area.

[0105] The sorting unit 403 is used to sort each detection area according to the traffic information from high to low.

[0106] The first determining unit 404 is used to determine the detection area with the highest traffic information after sorting as the target detection area.

[0107] The second determining unit 405 is used to determine the driving path of the unmanned driving device based on the starting point of the unmanned driving device and the target detection area.

[0108] In some embodiments, the target map further includes multiple road segments, each road segment including multiple locations, and the second determining unit 405 includes: The acquisition subunit is used to acquire the target location that is closest to the target center point of the target detection area among multiple road segments; A determination subunit is used to determine the driving path based on the starting point of the unmanned driving device and the target location.

[0109] In some embodiments, the acquisition subunit is configured to: Obtain the target center point of the target detection area; Calculate the distances between the target center point and locations on each road segment to obtain a distance set; Sort the distance sets in ascending order; The location with the smallest distance after sorting is determined as the target location.

[0110] In some embodiments, the apparatus further includes: The second acquisition unit is used to acquire the preset location of the center point of each detection area in multiple road segments, wherein the distance between the preset location and the center point of the corresponding detection area is less than the distance between the non-preset location and the center point of the corresponding detection area. The recording unit is used to record the preset mapping relationship between the center point of each detection area and the corresponding preset location.

[0111] In some embodiments, the acquisition subunit is configured to: Based on the target center point of the target detection area, a preset location corresponding to the target center point is determined by matching it in the preset mapping relationship. The preset location corresponding to the target center point is determined as the target location.

[0112] In some embodiments, the determining subunit is configured to: Determine the driving route between the starting point and the target location of the unmanned vehicle; Obtain the distance of each driving route and sort them in ascending order of distance; The route with the shortest distance after sorting is determined as the target route, and the target route does not intersect with the restricted area.

[0113] In some embodiments, the information processing apparatus further includes an assignment unit, configured to: Assign a corresponding weight value to each road segment; Obtain target road segments that intersect with the restricted area, and increase the weight value of the target road segments by a preset value; The determining subunit is further configured to: Determine the driving route between the starting point and the target location of the unmanned vehicle; Obtain the road segments included in each driving route; The sum of the weight values ​​of the road segments included in each driving route is calculated in turn, and the sum of the weight values ​​is determined as the distance of each driving route; Sort the routes in ascending order of distance; Sort the routes in ascending order of distance; The route with the shortest distance after sorting is determined as the target route, and the target route does not intersect with the restricted area.

[0114] In some embodiments, the information processing apparatus further includes a patrol unit for: When the unmanned vehicle is detected to have stayed at the target location for a period of time exceeding a preset threshold, a patrol command is generated; The patrol command is sent to the unmanned vehicle so that the unmanned vehicle can drive around the target detection area within a preset time according to the patrol command.

[0115] In some embodiments, the information processing apparatus further includes a hiding unit for: The target detection area is hidden, and the process returns to the step of sorting each detection area in descending order of traffic information.

[0116] The specific implementation of each of the above units can be found in the previous embodiments, and will not be repeated here.

[0117] As described above, this embodiment displays a target map via a display unit 401; a first acquisition unit 402 acquires traffic information in each detection area; a sorting unit 403 sorts each detection area according to the traffic information from high to low; a first determination unit 404 determines the detection area with the highest traffic information after sorting as the target detection area; and a second determination unit 405 determines the driving path of the unmanned vehicle based on the starting point of the unmanned vehicle and the target detection area. In this way, the traffic information in each detection area can be sorted, the detection area with the highest activity can be determined as the target detection area, and the driving path can be planned based on the starting point of the unmanned vehicle and the target detection area. This allows the unmanned retail vehicle to automatically drive to the vicinity of the most active target detection area along the shortest route without human intervention, greatly improving the efficiency of information processing.

[0118] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of an information processing device provided in an embodiment of this application. The information processing device may include a receiving unit 501 and a driving unit 502. The information processing device is applied to an unmanned driving device.

[0119] The receiving unit 501 is used to receive the driving path sent by the server, wherein the driving path is the path determined by the server based on the highest target detection area after sorting the traffic information in each detection area. The driving unit 502 is used to acquire the target detection area in the driving path and drive to the target detection area.

[0120] In some embodiments, the information processing apparatus further includes a patrol unit for: Receive patrol instructions sent by the server; According to the patrol instructions, the patrol will travel around the target detection area within a preset time.

[0121] This application also provides a computer device, which can be a server or a terminal, such as... Figure 8 As shown, it illustrates a schematic diagram of the server structure involved in an embodiment of this application. Specifically: The computer device may include components such as a processor 601 with one or more processing cores, a memory 602 with one or more computer-readable storage media, a power supply 603, and an input unit 604. Those skilled in the art will understand that... Figure 8 The computer device structure shown does not constitute a limitation on the computer device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 601 is the control center of the computer device. It connects various parts of the computer device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 602, and by calling data stored in the memory 602, it performs various functions of the computer device and processes data, thereby performing overall detection of the computer device. Optionally, the processor 601 may include one or more processing cores; optionally, the processor 601 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the aforementioned modem processor may also not be integrated into the processor 601.

[0122] The memory 602 can be used to store software programs and modules. The processor 601 executes various functional applications and data processing by running the software programs and modules stored in the memory 602. The memory 602 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the server, etc. In addition, the memory 602 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 602 may also include a memory controller to provide the processor 601 with access to the memory 602.

[0123] The computer device also includes a power supply 603 that supplies power to the various components. Optionally, the power supply 603 can be logically connected to the processor 601 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 603 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0124] The computer device may also include an input unit 604, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0125] Although not shown, the computer device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 601 in the computer device loads the executable files corresponding to the processes of one or more applications into the memory 602 according to the following instructions, and the processor 601 runs the applications stored in the memory 602, thereby implementing the various method steps provided in the foregoing embodiments, as follows: Display a target map, which includes multiple detection areas; acquire traffic information in each detection area; sort each detection area according to the traffic information from high to low; determine the detection area with the highest traffic information after sorting as the target detection area; determine the driving path of the autonomous driving device based on the starting point of the autonomous driving device and the target detection area.

[0126] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the detailed description of the information processing method above, which will not be repeated here.

[0127] As described above, the computer device in this embodiment can display a target map; acquire traffic information in each detection area; sort each detection area according to the traffic information from high to low; determine the detection area with the highest traffic information after sorting as the target detection area; and determine the driving path of the unmanned vehicle based on the starting point of the unmanned vehicle and the target detection area. In this way, the traffic information in each detection area can be sorted, the detection area with the highest activity can be determined as the target detection area, and the driving path can be planned based on the starting point of the unmanned vehicle and the target detection area. This allows the unmanned retail vehicle to automatically travel to the vicinity of the most active target detection area along the shortest route without human intervention, greatly improving the efficiency of information processing.

[0128] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0129] Therefore, embodiments of this application provide a computer-readable storage medium storing a plurality of instructions that can be loaded by a processor to execute steps in any of the information processing methods provided in embodiments of this application. For example, the instructions can execute the following steps: Display a target map, which includes multiple detection areas; acquire traffic information in each detection area; sort each detection area according to the traffic information from high to low; determine the detection area with the highest traffic information after sorting as the target detection area; determine the driving path of the autonomous driving device based on the starting point of the autonomous driving device and the target detection area.

[0130] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations of the above embodiments.

[0131] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0132] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0133] Since the instructions stored in the computer-readable storage medium can execute the steps of any of the information processing methods provided in the embodiments of this application, the beneficial effects that any of the information processing methods provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0134] The above provides a detailed description of an information processing method, apparatus, and computer-readable storage medium provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An information processing method, characterized in that, include: Display a target map, which includes multiple detection areas and multiple road segments; The target map includes a static map, a quasi-dynamic map, and a pedestrian distribution map. The quasi-dynamic map represents restricted areas, and the pedestrian distribution map is a heat map used to reflect the degree of pedestrian concentration. Obtain traffic information for each detection area; Each detection area is sorted from high to low based on the traffic flow information; The detection area with the highest traffic information after sorting is determined as the target detection area; Obtain the target location that is closest to the target center point of the target detection area among multiple road segments; Detect whether the target location is in a restricted area; When the target location is detected to be in a restricted area, the target detection area is hidden, and the process returns to the step of sorting each detection area in descending order of traffic information. When the target location is detected to be outside the restricted area, the driving path of the autonomous driving device is determined based on the starting point and the target location. This includes: assigning a corresponding weight value to each road segment; obtaining target road segments that intersect with the restricted area; increasing the weight value of the target road segments by a preset value; and determining the driving route between the starting point and the target location. The driving route is then determined by: obtaining the road segments included in each driving route; calculating the sum of the weight values ​​of the road segments included in each driving route; determining the sum of the weight values ​​as the distance of each driving route; and sorting the driving routes according to their distances from smallest to largest. The driving route with the smallest distance after sorting is then determined as the target driving route. When the unmanned vehicle is detected to have stayed at the target location for a period of time exceeding a preset threshold, a patrol instruction is generated; the patrol instruction is sent to the unmanned vehicle so that the unmanned vehicle can drive around the target detection area within a preset time according to the patrol instruction.

2. The information processing method according to claim 1, characterized in that, The step of obtaining the target location that is closest to the target center point of the target detection area among multiple road segments includes: Obtain the target center point of the target detection area; Calculate the distances between the target center point and locations on each road segment to obtain a distance set; Sort the distance sets in ascending order; The location with the smallest distance after sorting is determined as the target location.

3. The information processing method according to claim 1, characterized in that, The acquisition of traffic information in each detection area includes: Control the camera device in each detection area to capture target images; Analyze the target image to determine the information about the people in the target image; By analyzing the information of the individuals, traffic flow information is obtained for each detection area.

4. The information processing method according to claim 1, characterized in that, The method further includes: The center point of each detection area is obtained at a preset location in multiple road segments, and the distance between the preset location and the center point of the corresponding detection area is less than the distance between a non-preset location and the center point of the corresponding detection area. Record the preset mapping relationship between the center point of each detection area and the corresponding preset location.

5. The information processing method according to claim 4, characterized in that, The step of obtaining the target location that is closest to the target center point of the target detection area among multiple road segments includes: Based on the target center point of the target detection area, a preset location corresponding to the target center point is determined by matching it in the preset mapping relationship. The preset location corresponding to the target center point is determined as the target location.

6. The information processing method according to claim 1, characterized in that, After sending the patrol command to the unmanned vehicle, the process further includes: The target detection area is hidden, and the process returns to the step of sorting each detection area in descending order of traffic information.

7. An information processing method employing the method described in any one of claims 1 to 6, applied to an unmanned driving device, characterized in that, include: The server receives the driving path, which is the path determined by the server based on the highest target detection area after sorting the traffic information in each detection area. Obtain the target detection area in the driving path and drive to the target detection area.

8. The information processing method according to claim 7, characterized in that, The method further includes: Receive patrol instructions sent by the server; According to the patrol instructions, the patrol will travel around the target detection area within a preset time.

9. An information processing device, characterized in that, include: The display unit is used to display a target map, which includes multiple detection areas and multiple road segments, and the road segments include multiple locations; the target map includes a static map, a quasi-dynamic map and a pedestrian distribution map, the quasi-dynamic map is a restricted area, and the pedestrian distribution map is a heat map used to reflect the degree of pedestrian flow concentration; The first acquisition unit is used to acquire traffic information in each detection area; The sorting unit is used to sort each detection area in descending order of traffic information; The first determining unit is used to determine the detection area with the highest traffic information after sorting as the target detection area; The acquisition subunit is used to acquire the target location that is closest to the target center point of the target detection area among multiple road segments; The detection unit is used to detect whether the target location is in a restricted area; When the target location is detected to be in a restricted area, the target detection area is hidden, and the process returns to the step of sorting each detection area in descending order of traffic information. The second determining unit is used to determine the driving path of the autonomous driving device based on the starting point and the target location when the target location is detected to be not in a restricted area. This includes: assigning a corresponding weight value to each road segment; obtaining target road segments that intersect with the restricted area; increasing the weight value of the target road segments by a preset value; determining the driving route between the starting point and the target location; obtaining the road segments included in each driving route; sequentially calculating the sum of the weight values ​​of the road segments included in each driving route; determining the sum of the weight values ​​as the distance of each driving route; and sorting the driving routes according to their distances from smallest to largest; and determining the driving route with the smallest distance after sorting as the target driving route. The patrol unit is used to generate a patrol command when it detects that the unmanned vehicle stays at the target location for a period of time that exceeds a preset threshold; and to send the patrol command to the unmanned vehicle so that the unmanned vehicle can drive around the target detection area within a preset time according to the patrol command.

10. The information processing apparatus according to claim 9, characterized in that, The acquisition subunit is used to: acquire the target center point of the target detection area; Calculate the distances between the target center point and locations on each road segment to obtain a distance set; Sort the distance sets in ascending order; The location with the smallest distance after sorting is determined as the target location.

11. The information processing apparatus according to claim 9, characterized in that, The first acquisition unit is used for: Control the camera device in each detection area to capture target images; Analyze the target image to determine the information about the people in the target image; By analyzing the information of the individuals, traffic flow information is obtained for each detection area.

12. The information processing apparatus according to claim 9, characterized in that, The device further includes: The second acquisition unit is used to acquire the preset location of the center point of each detection area in multiple road segments, wherein the distance between the preset location and the center point of the corresponding detection area is less than the distance between the non-preset location and the center point of the corresponding detection area. The recording unit is used to record the preset mapping relationship between the center point of each detection area and the corresponding preset location.

13. The information processing apparatus according to claim 12, characterized in that, The acquisition subunit is used for: Based on the target center point of the target detection area, a preset location corresponding to the target center point is determined by matching it in the preset mapping relationship. The preset location corresponding to the target center point is determined as the target location.

14. The information processing apparatus according to claim 9, characterized in that, The information processing device further includes a hiding unit, used for: The target detection area is hidden, and the process returns to the step of sorting each detection area in descending order of traffic information.

15. An information processing apparatus employing the device as described in any one of claims 9 to 14, applied to an unmanned driving device, comprising: The receiving unit is used to receive the driving path sent by the server. The driving path is the path determined by the server based on the highest target detection area after sorting the traffic information in each detection area. The driving unit is used to acquire the target detection area in the driving path and drive to the target detection area.

16. The information processing apparatus according to claim 15, characterized in that, The information processing device further includes a patrol unit, used for: Receive patrol instructions sent by the server; According to the patrol instructions, the patrol will travel around the target detection area within a preset time.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions adapted for loading by a processor to perform the steps of the information processing method according to any one of claims 1 to 8.

18. A computer program product, characterized in that, The computer program product includes computer instructions stored in a storage medium; the processor of the computer device reads the computer instructions from the storage medium and executes the computer instructions, causing the computer to perform the steps of the information processing method according to any one of claims 1 to 8.

19. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the information processing method according to any one of claims 1 to 8.

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