CN115560753A - Information processing method and device and computer readable storage medium - Google Patents
Information processing method and device and computer readable storage medium Download PDFInfo
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Abstract
The embodiment of the application discloses an information processing method, an information processing device and a computer readable storage medium, and the embodiment of the application displays a target map; acquiring flow information in each detection area; sequencing each detection area according to the sequence of the flow information from high to low; determining the detection area with the highest flow information after sequencing as a target detection area; and determining a driving path of the unmanned equipment according to the starting point of the unmanned equipment and the target detection area. Therefore, the detection areas with the highest activity are determined as the target detection areas according to the flow information in each detection area, and the driving path planning is carried out according to the starting point of the unmanned driving device and the target detection areas, so that the unmanned retail vehicle can automatically drive to the position near the most active target detection area through the shortest route, manual interference is not needed, and the information processing efficiency is greatly improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to an information processing method and apparatus, and a computer-readable storage medium.
Background
The self-service vending vehicle is a mode that a trolley with an automatic driving function is changed into a mobile vending platform, and the self-service vending vehicle is loaded with various life service commodities such as beverages, snacks and the like and then is sold in a specific park such as a park, a square, a district and the like at a specific time.
Among the prior art, can control unmanned retail vehicle and patrol according to fixed route and travel, unmanned vehicle is when patrolling, and the consumer recruits the hand in the plantago, and the vehicle will stop, sweeps the sign indicating number through the cell-phone, just can open the retail cabinet mouth and sell goods. However, since the flow of people changes in real time, the flow of people in many areas is low, and if the tour is still performed, consumption is exhausted, and the efficiency of information processing is low.
Disclosure of Invention
The embodiment of the application provides an information processing method, an information processing device and a computer readable storage medium, which can improve the efficiency of information processing.
In order to solve the above technical problem, an embodiment of the present application provides the following technical solutions:
an information processing method comprising:
displaying a target map, the target map comprising a plurality of detection areas;
acquiring flow information in each detection area;
sequencing each detection area according to the sequence of the flow information from high to low;
determining a detection area with the highest sorted flow information as a target detection area;
and determining a driving path of the unmanned equipment according to the starting point of the unmanned equipment and the target detection area.
An information processing method comprising:
receiving a driving path sent by a server, wherein the driving path is determined by a highest target detection area after the server sorts according to the traffic information in each detection area;
and acquiring a target detection area in the driving path, and driving to the target detection area.
An information processing apparatus comprising:
a display unit for displaying a target map, the target map including a plurality of detection areas and a plurality of road sections, the road sections including a plurality of locations thereon;
a first acquisition unit, configured to acquire traffic information in each detection area;
the sequencing unit is used for sequencing each detection area according to the sequence of the flow information from high to low;
a first determining unit, configured to determine a detection area with the highest ranked traffic information as a target detection area;
and the second determining unit is used for determining the driving path of the unmanned equipment according to the starting point of the unmanned equipment and the target detection area.
In some embodiments, the target map further includes a plurality of road segments including a plurality of locations thereon, and the second determination unit includes:
the acquisition subunit is used for acquiring a target location closest to a target central point of the target detection area in a plurality of road sections;
and the determining subunit is used for determining a driving path according to the starting point of the unmanned equipment and the target point.
In some embodiments, the obtaining subunit is configured to: acquiring a target central point of the target detection area;
calculating the distance between the target center point and a place on each road section to obtain a distance set;
sorting the distance sets in a sequence from small to large;
and determining the place with the minimum distance after sorting as the target place.
In some embodiments, the apparatus further comprises a detection unit for:
detecting whether the target location is in a forbidden area;
when the target location is detected to be in a forbidden area, hiding the target detection area, and returning to sequence each detection area according to the sequence of flow information from high to low;
and when the target location is detected not to be in a forbidden zone, determining a driving path according to a starting point and the target location.
In some embodiments, the first obtaining unit is configured to:
controlling a camera device in each detection area to shoot a target image;
analyzing the target image and determining the character information in the target image;
and counting the figure information to obtain flow information in each detection area.
In some embodiments, the apparatus further comprises:
the second acquisition unit is used for acquiring a preset place of the central point of each detection area in a plurality of road sections, and the distance between the preset place and the central point of the corresponding detection area is smaller than the distance between a non-preset place and the central point of the corresponding detection area;
and the recording unit is used for recording the preset mapping relation between the central point of each detection area and the corresponding preset point.
In some embodiments, the obtaining subunit is configured to:
matching in the preset mapping relation according to the target central point of the target detection area, and determining a preset place corresponding to the target central point;
and determining a preset place corresponding to the target central point as a target place.
In some embodiments, the determining subunit is configured to:
determining a driving route between a starting point of the unmanned equipment and the target position;
acquiring the distance of each driving route, and sequencing according to the sequence of the distances of the driving routes from small to large;
and determining the running route with the minimum distance after sequencing as a target running route, wherein no intersection exists between the target running route and the forbidden area.
In some embodiments, the information processing apparatus further includes an assignment unit configured to:
endowing each road section with a corresponding weight value;
acquiring a target road section which has intersection with a forbidden area, and increasing the weight value of the target road section by a preset value;
the determining subunit is further configured to:
determining a driving route between a starting point of the unmanned equipment and the target position;
acquiring a road section contained in each driving route;
sequentially calculating the sum of the weight values of the road sections contained in each driving route, and determining the sum of the weight values as the distance of each driving route;
sequencing according to the sequence that the distance of the driving route is from small to large;
sequencing according to the sequence of the distances of the driving routes from small to large;
and determining the running route with the minimum distance after sequencing as a target running route, wherein no intersection exists between the target running route and the forbidden area.
In some embodiments, the information processing apparatus further includes a patrol unit configured to:
when the fact that the staying time of the unmanned equipment in the target place is larger than a preset threshold value is detected, a patrol instruction is generated;
and sending the patrol instruction to the unmanned equipment so that the unmanned equipment drives around the target detection area within a preset time according to the patrol instruction.
In some embodiments, the information processing apparatus further includes a hiding unit configured to:
hiding the target detection area, and returning to the step of sequencing each detection area according to the sequence of the flow information from high to low.
An information processing apparatus applied to an unmanned device, comprising:
the receiving unit is used for receiving a driving path sent by the server, wherein the driving path is determined by the highest target detection area after the server carries out sequencing according to the flow information in each detection area;
and the driving unit is used for acquiring a target detection area in the driving path and driving to the target detection area.
In some embodiments, the information processing apparatus further includes a patrol unit configured to:
receiving a patrol instruction sent by a server;
and driving around the target detection area within preset time according to the patrol command.
A computer readable storage medium, storing a plurality of instructions, the instructions being suitable for being loaded by a processor to execute the steps of the information processing method.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps in the information processing method when executing the computer program.
A computer program product or computer program comprising 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 to enable the computer to perform the steps of the information processing method.
The embodiment of the application displays the target map; acquiring flow information in each detection area; sequencing each detection area according to the sequence of the flow information from high to low; determining a detection area with the highest sorted flow information as a target detection area; and determining a driving path of the unmanned equipment according to the starting point of the unmanned equipment and the target detection area. Therefore, the traffic information in each detection area can be sequenced, the detection area with the highest activity is determined as the target detection area, and the driving path planning is carried out according to the starting point and the target detection area of the unmanned equipment, so that the unmanned retail vehicle can automatically drive to the position near the most active target detection area through the shortest route, manual interference is not needed, and the information processing efficiency is greatly improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of a scenario of an information processing system provided in an embodiment of the present application;
FIG. 2 is a schematic flowchart of an information processing method provided in an embodiment of the present application;
FIG. 3 is another schematic flow chart diagram of an information processing method provided in an embodiment of the present application;
fig. 4 is a schematic view of a scene of an information processing method according to an embodiment of the present application;
FIG. 5 is a flowchart illustrating an information processing method according to an embodiment of the present disclosure;
FIG. 6 is a schematic structural diagram of an information processing apparatus provided in an embodiment of the present application;
FIG. 7 is a schematic structural diagram of an information processing apparatus according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides an information processing method, an information processing device and a computer readable storage medium.
Referring to fig. 1, fig. 1 is a schematic view of a scenario of an information processing system according to an embodiment of the present application, including: the system comprises a camera device, an unmanned retail vehicle and a server (the specific number of the camera device and the unmanned retail vehicle is not limited herein), wherein the camera device, the unmanned retail vehicle and the server can be connected through a communication network, and the communication network can comprise a wireless network and a wired network, wherein the wireless network comprises one or more of a wireless wide area network, a wireless local area network, a wireless metropolitan area network and a wireless personal area network. The network includes network entities such as routers, gateways, etc., which are not shown in the figure. The camera and the unmanned retail vehicle can perform information interaction with a server through a communication network, for example, the camera device sends a target image to the server.
The information processing system may include an information processing apparatus, which may be specifically integrated in a computer device, which may be a terminal or a server, and the terminal may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, a smart television, or the like. The information processing method is executed by a server as an example, the server can be an independent physical server, can also be a server cluster or a distributed system formed by a plurality of physical servers, and can also be a cloud server for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN (content delivery network), big data and artificial intelligence platform and the like. As shown in fig. 1, the server may display a target map including a plurality of detection areas; acquiring flow information in each detection area; sequencing each detection area according to the sequence of the flow information from high to low; determining the detection area with the highest flow information after sequencing as a target detection area; and determining a driving path of the unmanned equipment according to the starting point of the unmanned equipment and the target detection area.
The camera device can be installed in each detection area, and the camera device can be a digital high-definition camera and is used for shooting a target image in each detection area and sending the target image to the server in real time through coding compression, so that the server can count the flow information in each detection area in real time according to the target image.
The unmanned retail vehicle is an example of unmanned equipment, and is a mobile vending platform, and is a mode that a trolley with an automatic driving function is changed into the mobile vending platform, the unmanned retail vehicle is loaded with various life service commodities such as beverages, snacks and the like, and then is sold in a specific park such as a park, a square and a cell at a specific time, the unmanned retail vehicle can only run on a road section, the unmanned retail vehicle can share positioning information to a server in real time, or receive a running path sent by the server, and automatically run to a target place according to the running path, so that running in an area with high liveness is realized, consumption is improved, the unmanned retail vehicle can receive the running path sent by the server, and the running path is determined by a highest target detection area after the server sorts according to flow information in each detection area; and acquiring a target detection area in the driving path, and driving to the target detection area.
It should be noted that the scenario diagram of the information processing system shown in fig. 1 is merely an example, and the information processing system and the scenario described in the embodiment of the present application are for more clearly illustrating the technical solution of the embodiment of the present application, and do not form a limitation to the technical solution provided in the embodiment of the present application, and it is known by a person of ordinary skill in the art that the technical solution provided in the embodiment of the present application is also applicable to similar technical problems with the evolution of the information processing system and the occurrence of a new service scenario.
The following are detailed below.
The embodiment of the application provides an information processing method, which can be executed by a terminal or a server, or can be executed by the terminal and the server together; the embodiment of the present application is described by taking an example in which an information processing method is executed by a server.
Referring to fig. 2, fig. 2 is a schematic flow chart of an information processing method according to an embodiment of the present disclosure. The information processing method includes:
in step 101, a target map is displayed.
In the related art, the unmanned retail vehicle adopts a static map as an unmanned vehicle scheduling basis, the static map may be composed of a sensor feature point map and a road network map, and the sensor feature point map is the most basic data constituting the map, usually a laser radar point cloud map or a visual feature point map of a camera. The road network map identifies specific semantic information contained in the sensor feature point map, and comprises road network data such as road driving edges and link relations and traffic identification data such as one-way traffic and speed limit contained in roads.
It can be seen that the static map cannot reflect the change of the drivable area of the actual road and the change of the traffic volume related to the operation, and in the actual operation process, the driving rules of the unmanned retail vehicle are often influenced by the changes of road maintenance, rainy and snowy weather, operation time and the like, and the static map cannot reflect the changes, so that the unmanned retail vehicle cannot normally patrol, the manual remote control is often needed for scheduling, and the operation efficiency is extremely low.
Therefore, the embodiment of the application also introduces a quasi-dynamic map and a people flow distribution diagram on the basis of the static map. The quasi-dynamic map can be understood as a forbidden area, the forbidden area can cause the change of driving rules due to road maintenance, rain and snow weather change and the like, such as the change of traffic forbidding, route adjustment and the like in the area, the change of local congestion caused by performance, promotion activities and the like or the change of space-time rules brought by operation, such as the condition that tide is set for one-way traffic control in the process of opening and closing a park in the morning and at night, and the quasi-dynamic map can be set by operation and maintenance personnel according to the actual condition. The people flow distribution can be a people flow dynamic distribution graph reflected by a target image shot by the camera device, for example, in the form of thermodynamic diagram, and the people flow aggregation degree can be directly reflected.
The target map can be directly displayed, the target map can be composed of a plurality of detection areas and a plurality of road sections, the plurality of detection areas are composed of rectangular grids of divided areas and represent the actual area of a garden, each detection area contains a certain range of human flow, the plurality of road sections are a plurality of line segments connected with each other, each road section comprises a plurality of places, the plurality of places are connected to form a road section, the places are measured by longitude and latitude, the places are connected in series to generate the road sections, and the road sections are distributed in the plurality of detection areas.
In step 102, traffic information in each detection area is obtained.
Wherein, every detection area is the tray in the real garden, and this flow information can be for people flow information, and flow information is bigger, explains that this detection area's liveness is bigger, and this flow information is smaller, explains that this detection area's liveness is smaller. The flow information can directly influence the operation of the unmanned retail vehicle, and in order to increase the retail amount of the unmanned retail vehicle, the unmanned retail vehicle can be driven to a detection area with larger flow information as much as possible.
In an embodiment, the number of the mobile phone signals in each detection area may be obtained, where the larger the number is, the more users in the detection area are represented, the larger the traffic information is, and the smaller the number is, the fewer users in the detection area are represented, and the smaller the traffic information is.
In some embodiments, the step of acquiring traffic information in each detection area may include:
(1) Controlling a camera device in each detection area to shoot a target image;
(2) Analyzing the target image and determining the character information in the target image;
(3) And counting the person information to obtain flow information in each detection area.
Wherein, all can assemble camera device in this each detection area, this camera device includes at least one camera, and this camera device can take a complete shot down the target image that whole detection area corresponds.
Further, the human body information in the target image may be identified through a Convolutional Neural Network (CNN), where the human body information may be human body feature information, that is, features represented by human body shapes, and the Convolutional Neural network may be constructed by simulating a visual perception (visual perception) mechanism of a living being, so that the Convolutional Neural network may be used to identify the human body feature information in the target image, and may be selected in a rectangular or circular identification box.
Therefore, the people flow information in each detection area can be obtained according to the number of the human body characteristic information.
In step 103, each detection area is sorted according to the order of the flow information from high to low.
In one embodiment, each detection area can be expressed in a thermodynamic diagram form, and the darker the detection area with higher flow information, the lighter the detection area with lower flow information, so that the most active detection area can be found simply and intuitively.
In step 104, the detection area with the highest traffic information after sorting is determined as the target detection area.
In order to maximize the retail amount of the unmanned retail vehicle, it is necessary to determine a detection area with the highest pedestrian flow activity, that is, the highest sequenced flow information, and determine the detection area with the highest sequenced flow information as a target detection area, so as to implement subsequent scheduling of the unmanned retail vehicle to the target detection area for sale.
In step 105, a travel path of the unmanned aerial vehicle is determined based on the start point of the unmanned aerial vehicle and the target detection area.
The target detection area is a detection area with the highest traffic information after sorting, that is, the people flow activity in the target detection area is the largest, the unmanned device (that is, the unmanned retail vehicle) can be made to approach the target detection area as much as possible, and the optimal mode is to drive into the target detection area, so that the driving path of the unmanned device can be determined according to the starting point (that is, the current stop point of the unmanned device) of the unmanned device and the target detection area, for example, a suitable stop point can be found around the target detection area as the end point, planning is performed according to the starting point and the stop point, and at least one driving path from the starting point to the stop point is determined.
In some embodiments, the determining of the driving path of the unmanned aerial device according to the start point of the unmanned aerial device and the target detection area may include:
(1) Acquiring a target location closest to a target central point of the target detection area in a plurality of road sections;
(2) And determining a driving path according to the starting point of the unmanned equipment and the target position.
The unmanned equipment can only run on the road section of the road, so that a target point in a plurality of road sections closest to the target point can be found by taking the target point in the target detection area as a reference point, the distance between the target point and the target point is less than the distance between other points on the road section and the target point, namely the target point can be used as the terminal point of the unmanned retail vehicle, and when the unmanned equipment stops at the target point, the maximum operation efficiency can be realized, so that the most appropriate running path can be determined according to the starting point of the unmanned equipment and the target point, and the unmanned equipment is driven to the target point for operation.
In some embodiments, after obtaining a target location closest to a target center point of the target detection area in the plurality of road segments, the method further includes:
(1) Detecting whether the target location is in a forbidden area;
(2) When the target location is detected to be in a forbidden area, hiding the target detection area, and returning to sequence each detection area according to the sequence of the flow information from high to low;
(3) And when the target location is detected not to be in the forbidden area, determining a driving path according to the starting point and the target location.
In the embodiment of the application, since a forbidden area is introduced, it is assumed that the target location is in the forbidden area, and the unmanned retail vehicle cannot drive to the target location, it is required to detect whether the target location is in the forbidden area, when the target location is detected to be in the forbidden area, it is indicated that the unmanned retail vehicle cannot drive to the target location, the target detection area can be hidden, each detection area is sequenced from high to low according to flow information, the detection area with the highest flow information is re-determined, and so on, until the target location is detected not to be in the forbidden area, a step of determining a driving path according to a starting point and the target location is executed.
As can be seen from the above, in the embodiments of the present application, a target map is displayed; acquiring flow information in each detection area; sequencing each detection area according to the sequence of the flow information from high to low; determining the detection area with the highest flow information after sequencing as a target detection area; and determining a driving path of the unmanned equipment according to the starting point of the unmanned equipment and the target detection area. Therefore, the traffic information in each detection area can be sequenced, the detection area with the highest activity is determined as the target detection area, and the driving path planning is carried out according to the starting point and the target detection area of the unmanned equipment, so that the unmanned retail vehicle can automatically drive to the position near the most active target detection area through the shortest route, manual interference is not needed, and the information processing efficiency is greatly improved.
The method described in connection with the above embodiments will be described in further detail below by way of example.
In the present embodiment, an example will be described in which the information processing apparatus is specifically integrated in a server and the unmanned aerial vehicle is an unmanned retail vehicle, and specific reference will be made to the following description.
Referring to fig. 3, fig. 3 is another schematic flow chart of an information processing method according to an embodiment of the present disclosure. The method flow can comprise the following steps:
in step 201, the server displays a target map.
In order to better understand the embodiment of the present application, please refer to fig. 4 together, where fig. 4 is an application scenario diagram of the information processing method provided in the embodiment of the present application, the server may display the target map 10, where the target map 10 includes a plurality of rectangular detection areas, for example, A1 (i.e., rectangular area 11), A2, A3, A4, A5, and the like, and a plurality of road segments, for example, S1, S2, S3, S4, S5, S6, and the like, and it is assumed that the target map covers the park scenic area, each detection area actually represents a land in the scenic area, the road segment is a travelable road segment in the scenic area, the road segment includes a plurality of locations, and each location employs longitude and latitude measurement, and the location is connected in series to generate the plurality of road segments.
In step 202, the server controls the camera in each detection area to capture a target image, analyzes the target image, determines the person information in the target image, and counts the person information to obtain the traffic information in each detection area.
Wherein, all can assemble camera device in each detection area, this camera device can be the camera, can take a complete picture of the target image that whole detection area corresponds through this camera device in real time, and this target image can be whole detection area's two-dimensional image, can have personage, action and scenery on this target image.
Further, the server may recognize the person information in the image, i.e., the human characteristic information specific to the human body, through the CNN, and count the person information, thereby obtaining the information on the number of people in each detection area, i.e., the traffic information.
In step 203, the server sorts each detection area according to the order of the flow information from high to low.
The server may sort each detection area according to a sequence of the flow information from high to low, and screen out the detection area with high activity, for example, in the target image 10, the flow information of the detection area A1 is greater than the detection area A2, the flow information of the detection area A2 is greater than the detection area A3, the flow information of the detection area A3 is greater than the detection area A4, and the flow information of the detection area A4 is greater than the detection area A5.
In step 204, the server determines the detection area with the highest traffic information after sorting as the target detection area.
The server may determine the detection area A1 with the highest traffic information after sorting as a target detection area, where the target detection area A1 is an area with the highest activity of the human stream.
In step 205, the server obtains a preset location of the central point of each detection area in a plurality of road segments.
In order to calculate the time of the destination more quickly, the server may obtain a preset location where the center point in each detection area is closest to the center point of the corresponding detection area in the plurality of road segments in advance, where a distance between the preset location and the center point of the corresponding detection area is smaller than a distance between a non-preset location and the center point of the corresponding detection area.
In step 206, the server records a preset mapping relationship between the central point of each detection area and the corresponding preset point.
The server may record a preset mapping relationship between a central point of each detection area and a corresponding preset point, for example, a mapping relationship between the central point 111 of the detection area 11 (A1) and the preset point B1.
In step 207, the server performs matching in a preset mapping relationship according to the target central point of the target detection area, determines a preset location corresponding to the target central point, and determines the preset location corresponding to the target central point as the target location.
In this embodiment of the application, the server may perform matching in a preset mapping relationship according to the target central point 111 of the target detection area A1, directly determine the preset location B1 corresponding to the target central point 111, directly determine the preset location B1 corresponding to the target central point as a target location closest to the target central point of the target detection area in a plurality of road segments, where B1 is located on the road segment S4.
In step 208, the server detects whether the target site is in a forbidden area.
As shown in fig. 5, the target map further includes a forbidden area 12, which may be a polygon, for example, a rectangle in the embodiment of the present application, and the forbidden area does not allow the unmanned retail vehicle to pass through, so that, in order to avoid the situation that the target location is in the forbidden area and the unmanned retail vehicle cannot reach the target location, the server needs to detect whether the target location is in the forbidden area, when detecting that the target location is in the forbidden area, step 209 is executed, and when detecting that the target location is not in the forbidden area, step 210 is executed.
In step 209, the server hides the target detection area.
When the target location is detected to be in the forbidden area, it is indicated that the unmanned retail vehicle cannot reach the target location near the target detection area and is forbidden to sell, the target detection area needs to be hidden, the process returns to step 203, each detection area is sequenced according to the sequence of the flow information from high to low, and as the detection area with the highest flow information after the last sequence is hidden, the detection area with the second highest flow information after the last sequence is re-determined as the target detection area to obtain a new target location, and so on until the target location is not in the forbidden area.
In step 210, the server assigns a corresponding weight value to each road segment, obtains a target road segment intersecting with the forbidden area, increases the weight value of the target road segment by a preset value, and determines a driving route between the starting point and the target location.
The server may assign a corresponding weight value to each road segment, where the weight value may be an actual length of the road segment.
In order to avoid driving stop caused by the fact that the unmanned retail vehicle passes through the forbidden area, a target road section S3 intersecting with the forbidden area 12 can be obtained, a section 13 thickened on the section S3 in fig. 4 is a section intersecting with the forbidden area 12, in the embodiment of the application, the preset value is a set value for distinguishing a normal road section and a target road section intersecting with the forbidden area, for example, 9999 meters, therefore, 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 is obtained as a starting point and a target location B1, for subsequent calculation, the road section S6 can be divided into sub-road sections S61 and S62 through B0, and the road section S4 can be divided into sub-road sections S41 and S43 through B1.
Further, a first travel route between the start point and the target point is determined, the first travel route being composed of the sections S62, S3, and S41, and a second travel route being composed of the sections S62, S2, S5, and S4.
In step 211, the server obtains the links included in each driving route, sequentially calculates the sum of the weighted values of the links included in each driving route, determines the sum of the weighted values as the distance of each driving route, and sorts the links according to the order of the distances of the driving routes from small to large.
The server may obtain the links included in the first travel route as S62, S3, and S41 and the links included in the second travel route as S62, S2, S5, and S4, calculate a sum of the weight values of the links included in the first travel route and the weight values of the links included in the second travel route, and use the sum of the weight values as the distance of each travel route.
In an actual scenario, the distance of the first travel route is significantly smaller than that of the second travel route, but since S3 in the first travel route has an intersection with the forbidden area, the sum of the weight values of the first travel route is larger than that of the second travel route through the weight value adjustment, so that the unmanned retail vehicle can be prevented from traveling on the first travel route and failing to reach the target location B1.
In step 212, the server determines the ranked travel route having the smallest distance as the target travel route.
The server determines the second running route with the minimum distance after sequencing, the road sections are S62, S2, S5 and S4 as the target running route, the unmanned retail vehicle can run to the target location B1 for operation according to the target running route because the unmanned retail vehicle comprises components sensing surrounding environments such as a camera and a laser radar, and the target location B1 is closest to a target detection area with the highest liveness, so that the operation efficiency can be greatly improved, and the information processing efficiency is improved.
In some embodiments, further comprising:
(1) When the fact that the staying time of the unmanned equipment in the target place is larger than a preset threshold value is detected, a patrol instruction is generated;
(2) And sending the patrol instruction to the unmanned equipment so that the unmanned equipment drives around the target detection area within a preset time according to the patrol instruction.
The preset threshold may be set by a user or preset by a system, for example, 5 minutes or 10 minutes, and the like, and is not limited specifically, after the unmanned retail vehicle travels to a target location according to a target travel route, the unmanned retail vehicle may stay for a preset threshold time to operate, and when it is detected that the unmanned retail vehicle stays for a time greater than the preset threshold at the target location, in order to increase operation efficiency, a patrol command may be generated and sent to the unmanned retail vehicle, and the unmanned retail vehicle may be controlled to patrol and drive around the target detection area within a preset time, for example, 10 minutes, according to the patrol command, so as to further improve operation efficiency.
In some embodiments, after the sending the patrol instruction to the drone, further comprises: and hiding the target detection area, and returning to the step of sequencing each detection area according to the sequence of the flow information from high to low.
After the patrol command is sent to the unmanned retail vehicle by the server, in order to take account of the operation of other hot spot detection areas, the target detection area can be hidden after preset time, and the step 203 is returned, each detection area is reordered according to the sequence of flow information from high to low, and because the detection area with the highest flow information after current ordering is hidden, the detection area with the flow information after current ordering is used as a new target detection area, a new target place planning target driving route is obtained, the unmanned retail vehicle is controlled to drive to the next hot spot detection area, and the operation efficiency is further improved by analogy.
As can be seen from the above, in the embodiments of the present application, a target map is displayed; acquiring flow information in each detection area; sequencing each detection area according to the sequence of the flow information from high to low; determining a detection area with the highest sorted flow information as a target detection area; and determining a driving path of the unmanned equipment according to the starting point of the unmanned equipment and the target detection area. Therefore, the traffic information in each detection area can be sequenced, the detection area with the highest activity is determined as the target detection area, and the driving path planning is carried out according to the starting point and the target detection area of the unmanned equipment, so that the unmanned retail vehicle can automatically drive to the position near the most active target detection area through the shortest route, manual interference is not needed, and the information processing efficiency is greatly improved.
Furthermore, the server can avoid the forbidden area when designing a target driving route, so that the unmanned retail vehicle can accurately arrive at a target place in the shortest time to operate, and the information processing efficiency is further improved.
The embodiment of the application also provides an information processing method which can be executed by the unmanned equipment.
Referring to fig. 5, fig. 5 is a schematic flowchart of an information processing method according to an embodiment of the present disclosure. The information processing method includes:
in step 301, the travel route transmitted by the server is received.
The unmanned device may be an unmanned retail vehicle, and the unmanned device may receive a travel path sent by the server, where the travel path is determined by a highest target detection area after the server performs ranking according to the traffic information in each detection area, and refer to the above embodiment specifically.
In step 302, a target detection area in the travel path is acquired and the vehicle travels to the target detection area.
The unmanned equipment can use a target detection area in the target detection area as a driving terminal, and drives to the target detection area through the unmanned function to operate, so that the operation efficiency is improved.
In some embodiments, after the sending the patrol instruction to the unmanned device, further comprising:
(1) Receiving a patrol instruction sent by a server;
(2) And driving around the target detection area within preset time according to the patrol command.
The unmanned equipment can receive a patrol instruction sent by the server, and then patrol driving is carried out around the target detection area within a preset time, such as 10 minutes, according to the instruction of the patrol instruction, so that the operation efficiency is improved.
In order to better implement the information processing method provided by the embodiment of the present application, the embodiment of the present application further provides a device based on the information processing method. The terms are the same as those in the above-described information processing method, and details of implementation may refer to the description in the method embodiment.
Referring to fig. 6, fig. 6 is a schematic structural diagram of an information processing apparatus according to an embodiment of the present application, where the information processing apparatus may include a display unit 401, a first obtaining unit 402, a sorting unit 403, a first determining unit 404, and a second determining unit 405, and the information processing apparatus is applied to a terminal or a server.
The display unit 401 is configured to display a target map, where the target map includes a plurality of detection areas and a plurality of road segments, and the road segments include a plurality of locations.
A first obtaining unit 402, configured to obtain traffic information in each detection area.
In some embodiments, the first obtaining unit 402 is configured to:
controlling a camera device in each detection area to shoot a target image;
analyzing the target image and determining the character information in the target image;
and counting the figure information to obtain flow information in each detection area.
A sorting unit 403, configured to sort each detection area according to a sequence of the traffic information from high to low.
A first determining unit 404, configured to determine a detection area with the highest ranked traffic information as a target detection area.
A second determining unit 405 for determining a driving path of the unmanned aerial device according to a starting point of the unmanned aerial device and the target detection area.
In some embodiments, the target map further comprises a plurality of road segments including a plurality of locations thereon, and the second determining unit 405 comprises:
the acquisition subunit is used for acquiring a target place which is closest to a target central point of the target detection area in a plurality of road sections;
and the determining subunit is used for determining a driving path according to the starting point of the unmanned equipment and the target point.
In some embodiments, the obtaining subunit is configured to:
acquiring a target central point of the target detection area;
calculating the distance between the target center point and the point on each road section to obtain a distance set;
sorting the distance sets in a sequence from small to large;
and determining the place with the minimum distance after sorting as the target place.
In some embodiments, the apparatus further comprises:
the second acquisition unit is used for acquiring a preset place of the central point of each detection area in a plurality of road sections, and the distance between the preset place and the central point of the corresponding detection area is smaller than the distance between a non-preset place and the central point of the corresponding detection area;
and the recording unit is used for recording the preset mapping relation between the central point of each detection area and the corresponding preset point.
In some embodiments, the obtaining subunit is configured to:
matching in the preset mapping relation according to the target central point of the target detection area, and determining a preset place corresponding to the target central point;
and determining a preset place corresponding to the target central point as a target place.
In some embodiments, the determining subunit is configured to:
determining a driving route between a starting point of the unmanned equipment and the target position;
acquiring the distance of each driving route, and sequencing according to the sequence of the distances of the driving routes from small to large;
and determining the running route with the minimum distance after sequencing as a target running route, wherein the intersection between the target running route and the forbidden area does not exist.
In some embodiments, the information processing apparatus further includes an assignment unit configured to:
assigning a corresponding weight value to each road section;
acquiring a target road section which has intersection with a forbidden area, and increasing the weight value of the target road section by a preset value;
the determining subunit is further configured to:
determining a driving route between a starting point of the unmanned equipment and the target position;
acquiring a road section contained in each driving route;
sequentially calculating the sum of the weighted values of the road sections contained in each driving route, and determining the sum of the weighted values as the distance of each driving route;
sequencing according to the sequence of the distances of the driving routes from small to large;
sequencing according to the sequence of the distances of the driving routes from small to large;
and determining the running route with the minimum distance after sequencing as a target running route, wherein no intersection exists between the target running route and the forbidden area.
In some embodiments, the information processing apparatus further includes a patrol unit configured to:
when the fact that the staying time of the unmanned equipment in the target place is larger than a preset threshold value is detected, a patrol instruction is generated;
and sending the patrol instruction to the unmanned equipment so that the unmanned equipment drives around the target detection area within a preset time according to the patrol instruction.
In some embodiments, the information processing apparatus further includes a hiding unit configured to:
and hiding the target detection area, and returning to the step of sequencing each detection area according to the sequence of the flow information from high to low.
The above embodiments can be referred to as the previous embodiments, and detailed descriptions thereof are omitted.
As can be seen from the above, in the embodiment of the present application, the target map is displayed by the display unit 401; the first acquisition unit 402 acquires traffic information in each detection area; the sorting unit 403 sorts each detection area according to the sequence of the traffic information from high to low; the first determining unit 404 determines the detection area with the highest traffic information after sorting as a target detection area; the second determination unit 405 determines the travel path of the unmanned device according to the start point of the unmanned device and the target detection area. Therefore, the traffic information in each detection area can be sequenced, the detection area with the highest activity is determined as the target detection area, and the driving path planning is carried out according to the starting point and the target detection area of the unmanned equipment, so that the unmanned retail vehicle can automatically drive to the position near the most active target detection area through the shortest route, manual interference is not needed, and the information processing efficiency is greatly improved.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an information processing apparatus according to an embodiment of the present disclosure, where the information processing apparatus may include a receiving unit 501 and a traveling unit 502, and the information processing apparatus is applied to an unmanned device.
A receiving unit 501, configured to receive a driving path sent by a server, where the driving path is determined by a highest target detection area after the server performs ranking according to traffic information in each detection area;
a driving unit 502, configured to acquire a target detection area in the driving route and drive to the target detection area.
In some embodiments, the information processing apparatus further includes a patrol unit configured to:
receiving a patrol instruction sent by a server;
and driving around the target detection area within preset time according to the patrol command.
An embodiment of the present application further provides a computer device, where the computer device may be a server or a terminal, as shown in fig. 8, which shows a schematic structural diagram of a server according to an embodiment of the present application, and specifically:
the computer device may include components such as a processor 601 of one or more processing cores, memory 602 of one or more computer-readable storage media, a power supply 603, and an input unit 604. Those skilled in the art will appreciate that the computer device configuration illustrated in FIG. 8 does not constitute a limitation of computer devices, and may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components. Wherein:
the processor 601 is a control center of the computer device, connects various parts of the whole computer device by various interfaces and lines, performs various functions of the computer device and processes data by running or executing software programs and/or modules stored in the memory 602 and calling data stored in the memory 602, thereby monitoring the computer device as a whole. Alternatively, 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 operating systems, user interfaces, application programs, and the like, and the modem processor mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 601.
The memory 602 may be used to store software programs and modules, and the processor 601 executes various functional applications and data processing by operating 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, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to the use of the server, and the like. Further, the memory 602 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic 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.
The computer device further comprises a power supply 603 for supplying power to each component, and optionally, the power supply 603 may be logically connected to the processor 601 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system. The power supply 603 may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.
The computer device may further include an input unit 604, and the input unit 604 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
Although not shown, the computer device may further include a display unit and the like, which are not described in detail herein. Specifically, in this embodiment, the processor 601 in the computer device loads the executable file corresponding to the process of one or more application programs into the memory 602 according to the following instructions, and the processor 601 runs the application program stored in the memory 602, so as to implement the various method steps provided by the foregoing embodiments, as follows:
displaying a target map, the target map comprising a plurality of detection areas; acquiring flow information in each detection area; sequencing each detection area according to the sequence of the flow information from high to low; determining the detection area with the highest flow information after sequencing as a target detection area; and determining a driving path of the unmanned equipment according to the starting point of the unmanned equipment and the target detection area.
In the above embodiments, the descriptions of the embodiments have respective emphasis, and parts that are not described in detail in a certain embodiment may refer to the above detailed description of the information processing method, and are not described herein again.
As can be seen from the above, the computer device according to the embodiment of the present application may display the target map; acquiring flow information in each detection area; sequencing each detection area according to the sequence of the flow information from high to low; determining the detection area with the highest flow information after sequencing as a target detection area; and determining a driving path of the unmanned equipment according to the starting point of the unmanned equipment and the target detection area. Therefore, the traffic information in each detection area can be sequenced, the detection area with the highest activity is determined as the target detection area, and the driving path planning is carried out according to the starting point and the target detection area of the unmanned equipment, so that the unmanned retail vehicle can automatically drive to the position near the most active target detection area through the shortest route, manual interference is not needed, and the information processing efficiency is greatly improved.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
To this end, embodiments of the present application provide a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any one of the information processing methods provided in the embodiments of the present application. For example, the instructions may perform the steps of:
displaying a target map, the target map comprising a plurality of detection areas; acquiring flow information in each detection area; sequencing each detection area according to the sequence of the flow information from high to low; determining the detection area with the highest flow information after sequencing as a target detection area; and determining a driving path of the unmanned equipment according to the starting point of the unmanned equipment and the target detection area.
According to an aspect of the application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method provided in the various alternative implementations provided by the above embodiments.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
Wherein the computer-readable storage medium may include: read Only Memory (ROM), random Access Memory (RAM), magnetic or optical disks, and the like.
Since the instructions stored in the computer-readable storage medium may execute the steps in any information processing method provided in the embodiments of the present application, beneficial effects that can be achieved by any information processing method provided in the embodiments of the present application may be achieved, for details, see the foregoing embodiments, and are not described herein again.
The foregoing detailed description has provided a method, an apparatus, and a computer-readable storage medium for information processing provided in the embodiments of the present application, and specific examples have been applied herein to explain the principles and implementations of the present application, and the description of the foregoing embodiments is only used to help understand the method and its core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and the application scope may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (15)
1. An information processing method, characterized by comprising:
displaying a target map, the target map comprising a plurality of detection areas;
acquiring flow information in each detection area;
sequencing each detection area according to the sequence of the flow information from high to low;
determining the detection area with the highest flow information after sequencing as a target detection area;
and determining a driving path of the unmanned equipment according to the starting point of the unmanned equipment and the target detection area.
2. The information processing method according to claim 1, wherein the target map further includes a plurality of road segments including a plurality of places thereon, and the determining the travel path of the unmanned aerial device based on the start point of the unmanned aerial device and the target detection area includes:
acquiring a target location closest to a target central point of the target detection area in a plurality of road sections;
and determining a driving path according to the starting point of the unmanned equipment and the target position.
3. The information processing method according to claim 2, wherein the obtaining of a target location closest to a target center point of the target detection area in the plurality of road segments includes:
acquiring a target central point of the target detection area;
calculating the distance between the target center point and the point on each road section to obtain a distance set;
sorting the distance sets in a sequence from small to large;
and determining the place with the minimum distance after sorting as the target place.
4. The information processing method according to claim 2, wherein after the obtaining of the target location closest to the target center point of the target detection area in the plurality of road segments, the method further comprises:
detecting whether the target location is in a forbidden area;
when the target location is detected to be in a forbidden area, hiding the target detection area, and returning to the step of sequencing each detection area according to the sequence of the flow information from high to low;
and when the target location is detected not to be in the forbidden area, determining a driving path according to the starting point and the target location.
5. The information processing method according to claim 1, wherein the acquiring traffic information in each detection area includes:
controlling a camera device in each detection area to shoot a target image;
analyzing the target image and determining the character information in the target image;
and counting the figure information to obtain flow information in each detection area.
6. The information processing method according to claim 2, characterized by further comprising:
acquiring a preset place of a central point of each detection area in a plurality of road sections, wherein the distance between the preset place and the central point of the corresponding detection area is less than the distance between a non-preset place and the central point of the corresponding detection area;
and recording a preset mapping relation between the central point of each detection area and the corresponding preset point.
7. The information processing method according to claim 6, wherein the acquiring a target location closest to a target center point of the target detection area in the plurality of road segments includes:
matching in the preset mapping relation according to the target central point of the target detection area, and determining a preset place corresponding to the target central point;
and determining a preset place corresponding to the target central point as a target place.
8. The information processing method according to claim 2, wherein the determining a travel path from a start point of the unmanned aerial vehicle and the target point includes:
determining a driving route between a starting point of the unmanned equipment and the target position;
acquiring the distance of each driving route, and sequencing according to the sequence of the distances of the driving routes from small to large;
and determining the running route with the minimum distance after sequencing as a target running route, wherein no intersection exists between the target running route and the forbidden area.
9. The information processing method according to claim 8, characterized by further comprising:
assigning a corresponding weight value to each road section;
acquiring a target road section which has intersection with a forbidden area, and increasing the weight value of the target road section by a preset value;
the obtaining of the distance of each driving route comprises the following steps:
acquiring a road section contained in each driving route;
and sequentially calculating the sum of the weighted values of the road sections contained in each driving route, and determining the sum of the weighted values as the distance of each driving route.
10. The information processing method according to claim 2, after determining the travel path from the start point of the unmanned aerial vehicle and the target point, comprising:
when the fact that the staying time of the unmanned equipment in the target place is larger than a preset threshold value is detected, a patrol instruction is generated;
and sending the patrol instruction to the unmanned equipment so that the unmanned equipment drives around the target detection area within a preset time according to the patrol instruction.
11. The information processing method according to claim 10, wherein after the transmitting of the patrol instruction to the unmanned aerial device, further comprising:
hiding the target detection area, and returning to the step of sequencing each detection area according to the sequence of the flow information from high to low.
12. An information processing method applied to an unmanned device, the method comprising:
receiving a driving path sent by a server, wherein the driving path is determined by a highest target detection area after the server sorts according to the traffic information in each detection area;
and acquiring a target detection area in the driving path, and driving to the target detection area.
13. The information processing method according to claim 12, characterized by further comprising:
receiving a patrol instruction sent by a server;
and driving around the target detection area within preset time according to the patrol command.
14. An information processing apparatus characterized by comprising:
a display unit for displaying a target map, the target map including a plurality of detection areas and a plurality of road segments, the road segments including a plurality of places thereon;
the first acquisition unit is used for acquiring the traffic information in each detection area;
the sequencing unit is used for sequencing each detection area according to the sequence of the flow information from high to low;
a first determining unit, configured to determine a detection area with the highest ranked traffic information as a target detection area;
and the second determining unit is used for determining the driving path of the unmanned equipment according to the starting point of the unmanned equipment and the target detection area.
15. A computer-readable storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the steps of the information processing method according to any one of claims 1 to 11.
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| CN202110750710.6A CN115560753A (en) | 2021-07-02 | 2021-07-02 | Information processing method and device and computer readable storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116858264A (en) * | 2023-07-10 | 2023-10-10 | 深圳市丰宜科技有限公司 | Path planning method, device, equipment and medium |
| CN116962288A (en) * | 2023-09-21 | 2023-10-27 | 卓望数码技术（深圳）有限公司 | CDN multi-node path-finding optimization method, device, equipment and storage medium |
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2021
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116858264A (en) * | 2023-07-10 | 2023-10-10 | 深圳市丰宜科技有限公司 | Path planning method, device, equipment and medium |
| CN116858264B (en) * | 2023-07-10 | 2024-04-26 | 深圳市丰宜科技有限公司 | Path planning method, device, equipment and medium |
| CN116962288A (en) * | 2023-09-21 | 2023-10-27 | 卓望数码技术（深圳）有限公司 | CDN multi-node path-finding optimization method, device, equipment and storage medium |
| CN116962288B (en) * | 2023-09-21 | 2023-12-05 | 卓望数码技术（深圳）有限公司 | CDN multi-node path-finding optimization method, device, equipment and storage medium |
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