CN111380530A - Navigation method and related product - Google Patents
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- CN111380530A CN111380530A CN201811633777.6A CN201811633777A CN111380530A CN 111380530 A CN111380530 A CN 111380530A CN 201811633777 A CN201811633777 A CN 201811633777A CN 111380530 A CN111380530 A CN 111380530A
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Abstract
The embodiment of the application discloses a navigation method and a related product, wherein the method comprises the following steps: acquiring a path diagram inside a building and among the buildings; acquiring historical pedestrian flow information in a road map; acquiring current people flow information in a road map, matching the current people flow information with historical people flow information to obtain a matching result, and predicting the change condition of the building people flow within a preset time according to the matching result; and acquiring a travel starting point and a travel destination of the user, and planning a travel path with the minimum pedestrian flow for the user by combining the change condition of the building pedestrian flow. According to the method and the device, the historical pedestrian flow information and the current pedestrian flow information of the path diagram in the building and between the buildings are acquired, the variation condition of the building pedestrian flow within the preset time is predicted, then travel path planning is completed for the user, the path with less pedestrian flow is provided for the user, the travel time of the user is reduced, and the user can conveniently travel.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a navigation method and a related product.
Background
With the rapid development of national economy and the accelerated progress of urbanization, more and more pedestrians and vehicles are in cities, which brings great pressure to traffic and brings great inconvenience to people's trips. Navigation can provide guidance for traveling of the user to a great extent, and inconvenience caused by traffic jam of people is reduced. In the prior art, Global Positioning System (GPS) technology provides assistance for vehicle navigation on highways and roads in cities. However, the pedestrian navigation technology between buildings and in the buildings still needs to be further researched, and how to navigate pedestrians between buildings and between buildings, plan a better path, and reduce the time waste caused by the congestion problem of the pedestrians is a problem to be solved urgently.
Disclosure of Invention
The embodiment of the application provides a navigation method and a related product, so that the change situation of the building pedestrian volume within the preset time is predicted by acquiring historical pedestrian volume information and current pedestrian volume information of a path diagram in and between buildings, the travel path planning is further completed for a user, the path with less pedestrian volume is provided for the user, and the user can conveniently travel.
In a first aspect, an embodiment of the present application provides a navigation method, where the method includes:
acquiring a path diagram inside a building and among the buildings;
acquiring historical pedestrian flow information in the road map;
acquiring current people flow information in the road map, matching the current people flow information with the historical people flow information to obtain a matching result, and predicting the change condition of the building people flow within a preset time according to the matching result;
and acquiring a travel starting point and a travel destination of the user, and planning a travel path with the minimum pedestrian flow for the user by combining the change condition of the building pedestrian flow.
Optionally, the obtaining of the historical pedestrian volume information in the road map includes:
acquiring video sets shot by a plurality of cameras in a monitoring range corresponding to the path diagram in a specified time period to obtain a plurality of video sets;
performing video analysis on each video set in the plurality of video sets to obtain a plurality of video images, wherein each video image in the plurality of video images comprises a time point identifier;
performing position identification on each video image in the plurality of video images to obtain a plurality of positioning video images at each position in a plurality of different positions;
carrying out target identification and time identification on the positioning video image at each position, and determining the number of targets corresponding to each position at different time;
and establishing a corresponding relation of position-time-target number according to the target number corresponding to each position at different time, and determining the historical pedestrian flow information of the path diagram according to the corresponding relation.
Optionally, the matching the current people flow information with the historical people flow information to predict the change of the building people flow within a preset time includes:
acquiring a time period corresponding to the current people flow information, and performing periodic matching according to the time period and the historical people flow information to obtain a plurality of periodic historical people flow information;
acquiring a target quantity change condition corresponding to the current people flow information, and determining the periodic historical people flow information with the highest matching degree with the current people flow information in the plurality of periodic historical people flow information as the target historical flow information according to the target quantity change condition;
and determining that the target historical flow information is the predicted building people flow change condition.
Optionally, the obtaining of the travel starting point and the travel destination of the user and planning the travel path for the user by combining the building people flow variation condition include:
the method comprises the steps of obtaining a travel starting point and a travel destination of a user, and combining a plurality of positions between the travel starting point and the travel destination to form a plurality of reachable paths;
and determining the change condition of the pedestrian flow in each reachable path of the plurality of reachable paths, and setting the reachable path with the least pedestrian flow as a travel path planned for the user.
Optionally, the method further comprises:
calculating to obtain the target quantity average value of each position according to the corresponding relation of the position, the time and the target quantity;
acquiring a first position of which the target quantity average value is smaller than a first preset threshold value, and setting the first position as an infeasible position;
acquiring a second position of which the target quantity average value is larger than the first preset threshold and smaller than a second preset threshold, and setting the second position as a feasible position, wherein the second preset threshold is larger than the first preset threshold;
and when a plurality of positions between the travel starting point and the destination are combined to form a plurality of reachable paths, shielding the infeasible positions and selecting the feasible positions.
Optionally, the performing target recognition and time identifier recognition on the multiple positioning video images at each position includes:
acquiring time point identifiers of a plurality of positioning video images at each position, clustering the plurality of positioning video images according to the time identifiers at a first time interval to obtain a plurality of M time classifications, wherein each time classification in the M time classifications comprises MjPositioning video images in simultaneous segments, wherein j is an integer from 1 to M;
for M included in each time classjRespectively carrying out target identification on the same-time-period positioning video images, and determining that the target number of the ith simultaneous-period positioning video image in the Mj simultaneous-period positioning video images is MjiWherein i is 1 to MjAn integer of (d);
obtaining the repeated target number k of the ith simulcast positioning video image and the (i + 1) th simulcast positioning video imagejiWherein i is 1 to Mj-an integer of 1;
determining the target quantity corresponding to each time classification as
And determining the target quantity corresponding to different times according to the target quantity corresponding to each time classification.
In a second aspect, the present application provides a navigation device comprising:
a first acquisition unit for acquiring a path diagram within a building and between buildings;
the second acquisition unit is used for acquiring historical people flow information in the road map;
the prediction unit is used for acquiring current people flow information in the road map, matching the current people flow information with the historical people flow information to obtain a matching result, and predicting the change condition of the building people flow within a preset time according to the matching result;
and the planning unit is used for acquiring a travel starting point and a travel destination of the user and planning a travel path for the user by combining the change condition of the building people flow.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a memory,
A communications interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps of the method of any of the first aspects.
In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the instructions of the steps of the method in the first aspect.
In a fifth aspect, the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform some or all of the steps as described in any one of the methods of the first aspect of the embodiments of the present application. The computer program product may be a software installation package.
It can be seen that, in the embodiment of the application, the path diagrams in the building and between the buildings are firstly obtained; then obtaining historical pedestrian flow information in the road map; then obtaining current people flow information in the road map, matching the current people flow information with historical people flow information to obtain a matching result, and predicting the change condition of the building people flow within a preset time according to the matching result; and finally, acquiring a travel starting point and a travel destination of the user, and planning a travel path with the minimum pedestrian flow for the user by combining the change condition of the building pedestrian flow. In the process, historical pedestrian flow information and current pedestrian flow information of a path diagram in and among buildings are obtained, the variation situation of the pedestrian flow of the buildings within preset time is predicted, and then travel path planning is completed for a user, so that a path with less pedestrian flow is provided for the user, the time consumption caused by selecting a travel path with large pedestrian flow by the user is reduced, and the user can conveniently travel.
Drawings
Reference will now be made in brief to the accompanying drawings, to which embodiments of the present application relate.
Fig. 1A is a navigation method provided in an embodiment of the present application;
fig. 1B is a schematic diagram of a video shot by a plurality of cameras according to an embodiment of the present disclosure;
fig. 1C is a schematic diagram of pedestrian volume information provided in an embodiment of the present application;
FIG. 2 is another navigation method provided by an embodiment of the present application;
FIG. 3 is another navigation method provided by an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a navigation device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, 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 terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The following describes embodiments of the present application in detail.
Referring to fig. 1A, fig. 1A is a schematic flow chart of a navigation method according to an embodiment of the present disclosure, and as shown in fig. 1A, the navigation method includes the following steps.
101. A path map within and between buildings is obtained.
Since the route map between buildings and buildings includes a structure map of buildings, obstacles, passable roads, stairs, corners, elevators, and the like, the route map may be a 3D structure map or a plan structure map, and it is possible to distinguish between a route for pedestrian walking and a route that is impassable. The method for acquiring the path diagrams in the buildings and between the buildings can be acquired according to building design diagrams; or special shooting equipment can be adopted to shoot images in and between buildings for synthesis; and the path graph can be synthesized according to the images shot by monitoring cameras fixedly arranged in the buildings and between the buildings.
102. And acquiring historical pedestrian flow information in the road map.
If the building is a building in use, the information of human activities is available, and the historical people flow information can be determined according to the recorded information of human activities. The historical pedestrian flow information comprises the change of the number of the pedestrians on each road along with the time.
Optionally, the obtaining of the historical pedestrian volume information in the road map includes: acquiring video sets shot by a plurality of cameras in a monitoring range corresponding to the path diagram in a specified time period to obtain a plurality of video sets; performing video analysis on each video set in the plurality of video sets to obtain a plurality of video images, wherein each video image in the plurality of video images comprises a time point identifier; performing position identification on each video image in the plurality of video images to obtain a plurality of positioning video images at each position in a plurality of different positions; carrying out target identification and time identification on the multiple positioning video images at each position, and determining the number of targets corresponding to each position at different time; and establishing a corresponding relation of position-time-target number according to the target number corresponding to each position at different time, and determining the historical pedestrian flow information of the path diagram according to the corresponding relation.
Specifically, the specified time period may be 6: 00-12:00 in the morning of the same day, or 00: 00-23: 59 of the same day, or 00: 00-weekday 23: 59. The monitoring range corresponding to the path diagram can be inside one building, can be between the building and the inside of a plurality of buildings in one unit, and can also be between the building and the inside of all buildings in one cell range. The monitoring ranges comprise a plurality of cameras, and a plurality of video sets can be shot. Each video set comprises a time mark, wherein the time mark can be the corresponding current time when a video is recorded, for example, the time mark of certain video display is 2018/03/02,23:50:09, which indicates that the recording time corresponding to the video picture is 2018, 3, 2,23 pm, 50 min and 9 sec; the continuous recording time of the camera can also be a continuous recording time period, for example, the time mark of certain video display is 2018/03/02,22:09:10, which indicates that the continuous recording time of the video picture is 22 hours, 9 minutes and 10 seconds. The continuous recording time may be divided or may not be divided in the cycle T. For example, when the recording is performed by dividing 24 hours per day, 2018/03/02,22:09:10 indicates that 22 hours, 9 minutes and 10 seconds have been recorded continuously on 3, 2 and 2 days in 2018, and if the recording is not performed, 2018/03/02,22:09:10 indicates that the recording is performed by the camera for 22 hours, 9 minutes and 10 seconds without interruption, possibly starting from 3, 2 and 3 days in 2018, and possibly starting from the previous day.
Analyzing the video to obtain a plurality of video images, wherein when the video images are the same as the video image images, the time marks of the video are the same as the time point marks of the video images. In addition, each video image comprises a plurality of pictures, and according to the pictures, the landmark objects or buildings can be obtained, so that the identification of the corresponding positions of the video images is facilitated. Or adding a camera mark for each video image, and identifying the corresponding position of each video image according to the camera mark. In some cases, more than one camera at the same position can be shot, different cameras shoot the same position from different angles, and then when multiple positioning video images at each position are obtained, the video images shot at different angles at the position are obtained simultaneously. Referring to fig. 1B, fig. 1B is a schematic diagram of a video shot by multiple cameras according to an embodiment of the present disclosure, as shown in fig. 1B, a camera 1 can shoot a video in an a1 area, a camera 2 can shoot an a2 area, an overlapping area A3 exists between the a1 area and the a2 area, and then when a positioning video image corresponding to an a2 position is obtained, positioning video images shot by the camera 1 and the camera 2 are obtained at the same time.
After a plurality of positioning video images corresponding to each position are identified, target identification and time point identification are carried out on the plurality of positioning video images, wherein the target identification refers to the process that a special target (or a type of target) is distinguished from other targets (or other types of targets). It includes the identification of both two very similar objects and the identification of one type of object with another type of object. The target recognition may be recognition of a person or an object, and since there is a possibility of a vehicle passing between buildings, the target recognition may be performed on the vehicle. And simultaneously identifying the time point identifications in the positioning video images so as to determine the number of the targets corresponding to the position at each time point.
Optionally, the target recognition and the time point identification recognition are performed on a plurality of positioning video images at each position, including: acquiring time point identifications of a plurality of positioning video images at each position, clustering the plurality of positioning video images according to a first time interval according to the time identifications to obtain M time classifications, wherein each time classification in the M time classifications comprises MjPositioning video images in simultaneous segments, wherein j is an integer from 1 to M; for M included in each time classjRespectively carrying out target identification on the simultaneous positioning video images and determining MjThe number of targets of the ith simultaneous positioning video image in the simultaneous positioning video images is MiiWherein i is 1 to MjAn integer of (d); obtaining the repeated target number k of the ith simulcast positioning video image and the (i + 1) th simulcast positioning video imagejiWherein i is 1 to Mj-an integer of 1; determining the number of targets corresponding to each time classification as
According to eachAnd determining the target quantity corresponding to different times by the target quantity corresponding to the time classification.
Specifically, assuming that there are a plurality of positioning video images at the acquisition position a3, the time points on the positioning video images are identified as D/XX: YY: ZZ, where D denotes the date when the video image was captured, XX denotes the hour when the video image was captured, YY denotes the minute, and ZZ denotes the second. Because the camera continuously shoots the video, the obtained video image can be intercepted according to the fixed time interval, the video image can be classified according to the fixed time interval T to obtain a plurality of time classifications, and each time classification comprises a plurality of simultaneous positioning video images. Or, when performing video analysis, in order to reduce the amount of calculation, first, the quality of the video image is evaluated, and a high-quality video image is selected for subsequent operations. Wherein the image quality evaluation index may include, but is not limited to: mean gray scale, mean square error, entropy, edge preservation, signal-to-noise ratio, and the like. It can be defined that the larger the resulting image quality evaluation value is, the better the image quality is. Clustering is carried out on a plurality of video images obtained after quality evaluation according to a first time interval, and firstly, the time point identification is digitized. The method of digitizing includes converting the time point into a time stamp, reserving the time point as 3 values in a 60 system, and the like. If the time point identifications are converted into the time stamps, if each time point identification is not completely the same, the time stamps are different, the first time interval is assumed to be 60, the first time interval is 1 minute, and the video images corresponding to the time point identifications with the time interval smaller than 1 minute are gathered into the same cluster. If the time points are reserved as 3 values in the 60 system, the time points are firstly classified according to the date, and then the specific time of the same day is reserved as XX, YY and ZZ, because the first time interval is a small value, only YY and ZZ can be reserved, and then clustering is carried out according to the first time interval of 1 minute. The adopted clustering algorithm may be a partition method, such as a k-means algorithm, etc., or a density-based clustering algorithm, such as a DBSCAN algorithm, an OPTICS algorithm, etc., or a model-based algorithm, a graph theory clustering method, etc., and preferably, the clustering in this embodiment is performed by using the density-based DBSCAN algorithm.
Clustering a plurality of positioning video images at each position to obtain M time classifications, wherein each time classification comprises MjThe simultaneous segment positioning video image can be referred to table 1, as shown in table 1:
TABLE 1 target number table in each time class
In terms of time, including M1Positioning video images of simultaneous segments, wherein the number of targets in each positioning video image is M1iI.e. the number of targets in each simultaneous segment-oriented video image is M in turn11，M12，M13，…M1iSumming the target number in each simultaneous positioning video image in the first time classification to obtain the target number in all positioning video images corresponding to the time classification 1, wherein the summation formula is as follows:
in this summation, since multiple image acquisitions of the same target are also included between different localized video images, such targets are repetitive targets and need to be screened. Suppose the number of repeated objects in every two same-time-interval positioning video images is | M1i-M1(i+1)|＝k1iThen all the number of repeated targets in time class 1 are:
finally, the final target number in the acquisition time class 1 is:
it can be seen that, in the embodiment of the present application, the positioning video images at each position are clustered according to the time point identifiers, the positioning video images in the first time interval are gathered into the same cluster to form a plurality of time classifications, then a plurality of same-time-period positioning video images in each time classification are subjected to target identification, and the identified targets are deduplicated and summed to obtain all target numbers in the same time classification. In the process, the positioning video images are clustered according to the time point identifications, so that the video images at the same position at the same time point are gathered in the same classification, the target number in the classification is obtained, namely the current time corresponding to the pedestrian flow of the place is obtained, the pedestrian flows of a plurality of time classifications are obtained, the data of the pedestrian flow changing according to the time is formed, and the accuracy of obtaining the historical pedestrian flow is improved.
After determining the target number corresponding to each position at different time, establishing a corresponding relationship between the position and the time and the target number, where the time can be determined according to the time classification, for example, the corresponding time range in the time classification 1 is a cluster which is centered at 12:30:11 in the same day and takes 1 minute as a first preset time interval, then the time which can be set in the time classification 1 is: 12:30:11. I.e. the central point of the same class cluster is taken as the time corresponding to this class. In the embodiment of the present application, a density-based clustering algorithm is adopted, so that only a time point with high density of surrounding data becomes a central point. This process is shown in table 2, as shown in table 2:
TABLE 2 position-time-target number correspondence Table
| Position 1 | Position 2 | Position 3 | Position 4 | |
| 12:30:11 | 10 | 18 | 30 | 2 |
| 12:32:10 | 26 | 28 | 51 | 10 |
| 12:34:30 | 30 | 40 | 40 | 29 |
| 12:36:05 | 15 | 20 | 26 | 35 |
And then determining the historical pedestrian volume information of the path graph according to the corresponding relation in the table 2. Because the traffic information is dynamically changed with time, different traffic information graphs can be obtained according to different time periods, please refer to fig. 1C, fig. 1C is a traffic information graph provided by the embodiment of the present application, as shown in (a) of fig. 1C, a path graph includes several points, namely point 1, point 2, point 3, and point 4, to form a path 1, and at different time points, the traffic information of the path 1 is as shown in (b) of fig. 1C, the target number can be classified, less than 10 people are sparse, represented by double dotted lines, 10-19 people are normal, represented by the combination of dotted lines and solid lines, more than 20-39 people are abundant, represented by double solid lines, more than 40 people are congested, and represented by a filling matrix.
103. And acquiring current people flow information in the road map, matching the current people flow information with the historical people flow information to obtain a matching result, and predicting the change condition of the building people flow within a preset time according to the matching result.
According to the above, the historical pedestrian volume information of the route map is already obtained, and then the current pedestrian volume information needs to be obtained. The method for acquiring the current people flow information is the same as the method for acquiring the historical people flow information, and the difference is that the time for acquiring the video image is a current time period, for example, the current time is 12:00:00, and the acquired current people flow information is the people flow information obtained by analyzing the video image acquired by the video shot by the camera at 11:58:00-12:00: 00. And then matching the current people flow information with historical people flow information, wherein the matching comprises the matching of paths, the matching of people flow and the matching of time.
Optionally, matching the current people flow information with the historical people flow information, predicting the change situation of the building people flow within the preset time, including: acquiring a time period corresponding to the current people flow information, and performing periodic matching according to the time period and the historical people flow information to acquire a plurality of periodic historical people flow information; acquiring a target quantity change condition corresponding to the current pedestrian flow information, and determining the periodic historical pedestrian flow information with the highest matching degree with the current pedestrian flow information in the plurality of periodic historical pedestrian flow information as the target historical pedestrian flow information according to the target quantity change condition; and determining that the target historical flow information is the predicted building people flow change condition.
Specifically, a time period corresponding to the current pedestrian volume information is obtained, and then periodic matching is performed according to the current time period and the historical pedestrian volume information, for example, the current time period is 11:58:00-12:00:00, so that the historical pedestrian volume information corresponding to each day with the time between 11:58:00-12:00:00 can be intercepted and the periodic matching is performed, and a plurality of pieces of periodic historical pedestrian volume information are obtained. Because of the possibility of the flow of people in or between buildings varying periodically, on a daily basisThe flow of people is large at off-duty time and small at on-duty time, and the prediction accuracy can be improved by carrying out periodic matching. And after the time matching is completed, further acquiring target historical pedestrian volume information with the highest matching degree from the periodic historical pedestrian volume information according to the target quantity change condition corresponding to the current pedestrian volume information. For example, when the current time period is 11:58:00-12:00:00, the people flow rate of the place 1 is changed to 8-12-20, and the plurality of pieces of periodic historical people flow rate information are as follows: 12, month 1, 11:58:10, 20-20-16 of the change of the flow rate of people; 12 months and 2 days, 11:59:10, and the change of the human flow is 10-13-20; 12 months and 5 days at the rate of 12:00:01, and the change of the human flow is 10-10-15. When the current people flow information of the site 1 is matched with the periodic historical people flow information, firstly, each person flow is matched, then, the average value of a plurality of people flow matching values is calculated, and a first matching degree is obtained
Then, the change degree of the human flow is matched to obtain a second matching degree
Wherein a1-a2-a3 is the current people flow change of the site 1, b1-b2-b3 is the historical people flow change of the site 1, the first matching degree and the second matching degree are summed to obtain the final people flow information matching degree, and the smaller the sum value is, the higher the matching degree is. After calculation, it can be known that the matching degree of the current people flow information and the people flow change of 12 months and 2 days is the highest, and then the historical people flow information of 12 months and 2 days can be used as the target historical people flow information.
After the target historical people flow information is obtained, determining a time period corresponding to the change situation of the building people flow needing to be predicted, for example, predicting the change situation of the building people flow half an hour after 12:00:00, and then obtaining the change situation of the historical people flow half an hour after 11:59:10 on 2 days of 12 months as a prediction result.
Therefore, in the embodiment of the application, the prediction result of the previous pedestrian flow information is obtained by adopting the matching condition of the historical pedestrian flow information and the current pedestrian flow information, so that the prediction of the pedestrian flow information can be completed, and the navigation is further performed for the user. In the process, the possibility that the current time can be matched with the history at the same time is high, so that the period matching is firstly carried out, but in the same period, the people flow information is different, so that the periodic historical people flow information with the highest matching degree with the current people flow information is selected for prediction, and the accuracy of the prediction result is improved.
104. And acquiring a travel starting point and a travel destination of the user, and planning a travel path with the minimum pedestrian flow for the user by combining the change condition of the building pedestrian flow.
After the people flow information of each place is predicted, path planning needs to be carried out for the user. Firstly, a travel starting point and a destination of a user are obtained, wherein the travel starting point can be a current position located by the user or a travel starting point input by the user, and the destination can also be a current position located by the user or a destination input by the user. Multiple paths may be arranged between the same travel starting point and the same travel destination, and the path with the least pedestrian flow is selected, so that road congestion can be prevented, and time consumption can be reduced.
Optionally, obtaining a travel starting point and a travel destination of the user, and planning a travel path for the user by combining with a change situation of the building traffic, including: the method comprises the steps of obtaining a travel starting point and a travel destination of a user, and combining a plurality of positions between the travel starting point and the travel destination to form a plurality of reachable paths; and determining the change condition of the pedestrian flow in each of the multiple reachable paths, and setting the reachable path with the least pedestrian flow as the trip path planned for the user.
Specifically, a travel starting point and a travel ending point comprise a plurality of positions, the positions can be combined into a plurality of different reachable paths, and when the travel path of the user is planned, the path with the least pedestrian volume, which is the path with the least pedestrian volume, is selected as the travel path of the user. In addition, the detection of the current people flow information is dynamic, so that the target historical flow information obtained by matching may also change, and the people flow information of each reachable path also correspondingly changes, so that the travel path planned for the user also dynamically changes, and the travel path with the least people flow is adjusted for the user at any time.
Optionally, the method further includes: calculating to obtain the target quantity average value of each position according to the corresponding relation of the position, the time and the target quantity; acquiring a first position of which the target quantity average value is smaller than a first preset threshold value, and setting the first position as an infeasible position; acquiring a second position of which the target quantity average value is larger than a first preset threshold and smaller than a second preset threshold, and setting the second position as a feasible position, wherein the second preset threshold is larger than the first preset threshold; when a plurality of positions between a travel starting point and a travel destination are combined to form a plurality of reachable paths, the infeasible positions are shielded, and the feasible positions are selected.
Specifically, the target number average value of each location needs to sum the target numbers obtained at different time periods of each location, and then obtain an average value, for example, the target numbers corresponding to M time classifications of location A3 in table 1, then obtain a sum of the target numbers corresponding to M time classifications, and then divide by M to obtain an average value, which is the target number average value corresponding to location A3, where the formula is
ceil () is a ceiling function. For example, the corresponding relationship of position-time-target number shown in table 2 is obtained, and the target number average value of each position is: ravg(site 1) ═ ceil ((10+26+30+15)/4) ═ 21, Ravg(site 2) ═ ceil ((18+28+40+20)/4) ═ 27, Ravg(site 3) ═ ceil ((30+51+40+26)/4) ═ 21, Ravg (site 4) ═ ceil ((2+10+29+35)/4) ═ 19.
Assuming that the first preset threshold is 2, if the average value of the target number of the first positions is less than 2, which indicates that the first positions are rarely accompanied by a person, and may be an emergency channel, or a channel which is not opened basically, the first position is set as an infeasible position, and the first position is not considered when planning a travel route for the user. Assuming that the second preset threshold is 10, the average value of the target number is in a second position between 2 and 10, the number of the passing persons is small, but the position belongs to a position capable of normally passing, and for such a position, the reachable path composed of the second position can be prioritized when planning the route for the user without acquiring the current traffic information.
As can be seen, in the embodiment of the present application, by acquiring a first position at which the average value of the target number is smaller than a first preset threshold and a second position at which the average value of the target number is between the first preset threshold and a second preset threshold, when a travel path is planned for a user, the first position which may not be passed through is automatically shielded, and the second position at which the target number is sparse is preferentially selected, so as to plan the travel path for the user more efficiently.
In the embodiment of the application, a path diagram inside a building and among the buildings is obtained firstly; then obtaining historical pedestrian flow information in the road map; then obtaining current people flow information in the road map, matching the current people flow information with historical people flow information to obtain a matching result, and predicting the change condition of the building people flow within a preset time according to the matching result; and finally, acquiring a travel starting point and a travel destination of the user, and planning a travel path with the minimum pedestrian flow for the user by combining the change condition of the building pedestrian flow. In the process, historical pedestrian flow information and current pedestrian flow information of a path diagram in and among buildings are obtained, the variation situation of the pedestrian flow of the buildings within preset time is predicted, and then travel path planning is completed for a user, so that a path with less pedestrian flow is provided for the user, the time consumption caused by selecting a travel path with large pedestrian flow by the user is reduced, and the user can conveniently travel.
Referring to fig. 2, fig. 2 is a schematic flow chart of another navigation method according to an embodiment of the present application, and as shown in fig. 2, the navigation method includes the following steps:
201. acquiring a path diagram inside a building and among the buildings;
202. acquiring a plurality of video sets shot by a plurality of cameras in a monitoring range corresponding to the path diagram in a specified time period to obtain a plurality of video sets, wherein the plurality of video sets comprise time marks corresponding to shooting time;
203. performing video analysis on each video set in the plurality of video sets to obtain a plurality of video images, wherein each video image in the plurality of video images comprises a time point identifier;
204. performing position identification on each video image in the plurality of video images to obtain a plurality of positioning video images at each position in a plurality of different positions;
205. performing target identification and time point identification on the multiple positioning video images at each position, and determining the number of targets corresponding to each position at different time;
206. establishing a corresponding relation of position-time-target number according to the target number corresponding to each position at different time, and determining historical pedestrian flow information of the path diagram according to the corresponding relation;
207. acquiring a time period corresponding to the current people flow information, and performing periodic matching according to the time period and the historical people flow information to obtain a plurality of periodic historical people flow information;
208. acquiring a target quantity change condition corresponding to the current people flow information, and determining the periodic historical people flow information with the highest matching degree with the current people flow information in the plurality of periodic historical people flow information as the target historical flow information according to the target quantity change condition;
209. predicting the change condition of the building pedestrian flow according to the target historical flow information;
210. the method comprises the steps of obtaining a travel starting point and a travel destination of a user, and combining a plurality of positions between the travel starting point and the travel destination to form a plurality of reachable paths;
211. and determining the change condition of the pedestrian flow in each reachable path of the plurality of reachable paths, and setting the reachable path with the least pedestrian flow as a travel path planned for the user.
The detailed description of the steps 201 to 211 may refer to the corresponding description of the clustering method described in the steps 101 to 104, and is not repeated herein.
As can be seen, in the embodiment of the present application, a path diagram inside a building and between buildings is obtained first; then, acquiring a video image according to a video set shot by a monitoring camera image, carrying out position identification and target identification on the video image, determining the number of targets corresponding to each position, and further acquiring historical pedestrian flow information in a road map; then obtaining current people flow information in the road map, matching the current people flow information with historical people flow information to obtain a matching result, and predicting the change condition of the building people flow within a preset time according to the matching result; and finally, acquiring a travel starting point and a travel destination of the user, and planning a travel path with the minimum pedestrian flow for the user by combining the change condition of the building pedestrian flow. In the process, through accurate calculation of the historical pedestrian flow information, the accuracy of predicting the building pedestrian flow change condition within the preset time according to the historical pedestrian flow information is improved, finally, travel path planning is completed for the user according to the pedestrian flow prediction condition, the path with less pedestrian flow is provided for the user, the time consumption of the user due to selection of a travel path with large pedestrian flow is reduced, and the user can conveniently travel.
Referring to fig. 3, fig. 3 is another navigation method according to an embodiment of the present application, as shown in fig. 3, the method includes the following steps:
301. acquiring a path diagram inside a building and among the buildings;
302. performing video analysis on each video set in the plurality of video sets to obtain a plurality of video images, wherein each video image in the plurality of video images comprises a time point identifier;
303. performing position identification on each video image in the plurality of video images to obtain a plurality of positioning video images at each position in a plurality of different positions;
304. performing target identification and time point identification on the multiple positioning video images at each position, and determining the number of targets corresponding to each position at different time;
305. establishing a corresponding relation of position-time-target number according to the target number corresponding to each position at different time, and determining historical pedestrian flow information of the path diagram according to the corresponding relation;
306. calculating to obtain the target quantity average value of each position according to the corresponding relation of the position, the time and the target quantity;
307. acquiring a first position of which the target quantity average value is smaller than a first preset threshold value, and setting the first position as an infeasible position;
308. acquiring a second position of which the target quantity average value is larger than the first preset threshold and smaller than a second preset threshold, and setting the second position as a feasible position, wherein the second preset threshold is larger than the first preset threshold;
309. acquiring a travel starting point and a travel destination of a user, combining a plurality of positions between the travel starting point and the travel destination, shielding the infeasible positions, and selecting the feasible positions to form a plurality of reachable paths;
310. and determining the change condition of the pedestrian flow in each reachable path of the plurality of reachable paths, and setting the reachable path with the least pedestrian flow as a travel path planned for the user.
The above detailed descriptions of steps 301 to 310 may refer to the corresponding descriptions of the clustering methods described in steps 101 to 104, and are not repeated herein.
As can be seen, in the embodiment of the present application, by acquiring a first position at which the average value of the target number is smaller than a first preset threshold and a second position at which the average value of the target number is between the first preset threshold and a second preset threshold, when a travel path is planned for a user, the first position which may not be passed through is automatically shielded, and the second position at which the target number is sparse is preferentially selected, so as to plan the travel path for the user more efficiently.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure, as shown in fig. 4, the electronic device includes a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing the following steps:
acquiring a path diagram inside a building and among the buildings;
acquiring historical pedestrian flow information in the road map;
acquiring current people flow information in the road map, matching the current people flow information with the historical people flow information to obtain a matching result, and predicting the change condition of the building people flow within a preset time according to the matching result;
and acquiring a travel starting point and a travel destination of the user, and planning a travel path with the minimum pedestrian flow for the user by combining the change condition of the building pedestrian flow.
As can be seen, the electronic device first obtains a path diagram inside and between buildings; then obtaining historical pedestrian flow information in the road map; then obtaining current people flow information in the road map, matching the current people flow information with historical people flow information to obtain a matching result, and predicting the change condition of the building people flow within a preset time according to the matching result; and finally, acquiring a travel starting point and a travel destination of the user, and planning a travel path with the minimum pedestrian flow for the user by combining the change condition of the building pedestrian flow. In the process, historical pedestrian flow information and current pedestrian flow information of a path diagram in and among buildings are obtained, the variation situation of the pedestrian flow of the buildings within preset time is predicted, and then travel path planning is completed for a user, so that a path with less pedestrian flow is provided for the user, the time consumption caused by selecting a travel path with large pedestrian flow by the user is reduced, and the user can conveniently travel.
In one possible example, the obtaining of the historical pedestrian volume information in the road map includes:
acquiring a plurality of video sets shot by a plurality of cameras in a monitoring range corresponding to the path diagram in a specified time period to obtain a plurality of video sets, wherein the plurality of video sets comprise time marks corresponding to shooting time;
performing video analysis on each video set in the plurality of video sets to obtain a plurality of video images, wherein each video image in the plurality of video images comprises a time point identifier;
performing position identification on each video image in the plurality of video images to obtain a plurality of positioning video images at each position in a plurality of different positions;
performing target identification and time point identification on the multiple positioning video images at each position, and determining the number of targets corresponding to each position at different time;
and establishing a corresponding relation of position-time-target number according to the target number corresponding to each position at different time, and determining the historical pedestrian flow information of the path diagram according to the corresponding relation.
In one possible example, the matching the current people flow information with the historical people flow information to predict the building people flow variation within a preset time includes:
acquiring a time period corresponding to the current people flow information, and performing periodic matching according to the time period and the historical people flow information to obtain a plurality of periodic historical people flow information;
acquiring a target quantity change condition corresponding to the current people flow information, and determining the periodic historical people flow information with the highest matching degree with the current people flow information in the plurality of periodic historical people flow information as the target historical flow information according to the target quantity change condition;
and predicting the change condition of the building pedestrian flow according to the target historical flow information.
In one possible example, the obtaining of the travel starting point and the travel destination of the user and planning the travel path for the user in combination with the building people flow variation condition includes:
the method comprises the steps of obtaining a travel starting point and a travel destination of a user, and combining a plurality of positions between the travel starting point and the travel destination to form a plurality of reachable paths;
and determining the change condition of the pedestrian flow in each reachable path of the plurality of reachable paths, and setting the reachable path with the least pedestrian flow as a travel path planned for the user.
In one possible example, the method further comprises:
calculating to obtain the target quantity average value of each position according to the corresponding relation of the position, the time and the target quantity;
acquiring a first position of which the target quantity average value is smaller than a first preset threshold value, and setting the first position as an infeasible position;
acquiring a second position of which the target quantity average value is larger than the first preset threshold and smaller than a second preset threshold, and setting the second position as a feasible position, wherein the second preset threshold is larger than the first preset threshold;
and when a plurality of positions between the travel starting point and the destination are combined to form a plurality of reachable paths, shielding the infeasible positions and selecting the feasible positions.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a navigation device according to an embodiment of the present application, and as shown in fig. 5, the navigation device 500 includes:
a first obtaining unit 501, configured to obtain a path diagram inside a building and between buildings;
a second obtaining unit 502, configured to obtain historical pedestrian volume information in the road map;
the prediction unit 503 is configured to obtain current pedestrian volume information in the road map, match the current pedestrian volume information with the historical pedestrian volume information to obtain a matching result, and predict a building pedestrian volume change condition within a preset time according to the matching result;
the planning unit 504 is configured to obtain a travel starting point and a travel destination of the user, and plan a travel path for the user according to the building people flow variation condition.
It can be seen that the navigation device first acquires a path map within and between buildings; then obtaining historical pedestrian flow information in the road map; then obtaining current people flow information in the road map, matching the current people flow information with historical people flow information to obtain a matching result, and predicting the change condition of the building people flow within a preset time according to the matching result; and finally, acquiring a travel starting point and a travel destination of the user, and planning a travel path with the minimum pedestrian flow for the user by combining the change condition of the building pedestrian flow. In the process, historical pedestrian flow information and current pedestrian flow information of a path diagram in and among buildings are obtained, the variation situation of the pedestrian flow of the buildings within preset time is predicted, and then travel path planning is completed for a user, so that a path with less pedestrian flow is provided for the user, the time consumption caused by selecting a travel path with large pedestrian flow by the user is reduced, and the user can conveniently travel.
The first obtaining unit 501 may be configured to implement the method described in the step 101, the second obtaining unit 502 may be configured to implement the method described in the step 102, the predicting unit 503 may be configured to implement the method described in the step 103, the planning unit 504 may be configured to implement the method described in the step 104, and so on.
In a possible example, the second obtaining unit 502 is specifically configured to:
acquiring a plurality of video sets shot by a plurality of cameras in a monitoring range corresponding to the path diagram in a specified time period to obtain a plurality of video sets, wherein the plurality of video sets comprise time marks corresponding to shooting time;
performing video analysis on each video set in the plurality of video sets to obtain a plurality of video images, wherein each video image in the plurality of video images comprises a time point identifier;
performing position identification on each video image in the plurality of video images to obtain a plurality of positioning video images at each position in a plurality of different positions;
carrying out target identification and time identification on the positioning video image at each position, and determining the number of targets corresponding to each position at different time;
and establishing a corresponding relation of position-time-target number according to the target number corresponding to each position at different time, and determining the historical pedestrian flow information of the path diagram according to the corresponding relation.
In one possible example, the prediction unit 503 is specifically configured to:
acquiring a time period corresponding to the current people flow information, and performing periodic matching according to the time period and the historical people flow information to obtain a plurality of periodic historical people flow information;
acquiring a target quantity change condition corresponding to the current people flow information, and determining the periodic historical people flow information with the highest matching degree with the current people flow information in the plurality of periodic historical people flow information as the target historical flow information according to the target quantity change condition;
and predicting the change condition of the building pedestrian flow according to the target historical flow information.
In one possible example, the planning unit 504 is specifically configured to:
the method comprises the steps of obtaining a travel starting point and a travel destination of a user, and combining a plurality of positions between the travel starting point and the travel destination to form a plurality of reachable paths;
and determining the change condition of the pedestrian flow in each reachable path of the plurality of reachable paths, and setting the reachable path with the least pedestrian flow as a travel path planned for the user.
In one possible example, the navigation device 500 further comprises a selection unit 505, specifically configured to:
calculating to obtain the target quantity average value of each position according to the corresponding relation of the position, the time and the target quantity;
acquiring a first position of which the target quantity average value is smaller than a first preset threshold value, and setting the first position as an infeasible position;
acquiring a second position of which the target quantity average value is larger than the first preset threshold and smaller than a second preset threshold, and setting the second position as a feasible position, wherein the second preset threshold is larger than the first preset threshold;
and when a plurality of positions between the travel starting point and the destination are combined to form a plurality of reachable paths, shielding the infeasible positions and selecting the feasible positions.
It is to be understood that the functions of the program modules of the navigation device of this embodiment can be specifically implemented according to the method in the foregoing method embodiment, and the specific implementation process thereof can refer to the related description of the foregoing method embodiment, which is not described herein again.
The present application further provides a computer storage medium, where the computer storage medium may store a program, and when the program is executed, the program includes some or all of the steps of any one of the clustering methods described in the above method embodiments.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus (device), or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. A computer program stored/distributed on a suitable medium supplied together with or as part of other hardware, may also take other distributed forms, such as via the Internet or other wired or wireless telecommunication systems.
Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the present application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A method of navigation, the method comprising:
acquiring a path diagram inside a building and among the buildings;
acquiring historical pedestrian flow information in the road map;
acquiring current people flow information in the road map, matching the current people flow information with the historical people flow information to obtain a matching result, and predicting the change condition of the building people flow within a preset time according to the matching result;
and acquiring a travel starting point and a travel destination of the user, and planning a travel path with the minimum pedestrian flow for the user by combining the change condition of the building pedestrian flow.
2. The method of claim 1, wherein the obtaining historical people flow information in the road map comprises:
acquiring a plurality of video sets shot by a plurality of cameras in a monitoring range corresponding to the path diagram in a specified time period to obtain a plurality of video sets, wherein the plurality of video sets comprise time marks corresponding to shooting time; performing video analysis on each video set in the plurality of video sets to obtain a plurality of video images, wherein each video image in the plurality of video images comprises a time point identifier; performing position identification on each video image in the plurality of video images to obtain a plurality of positioning video images at each position in a plurality of different positions;
performing target identification and time point identification on the multiple positioning video images at each position, and determining the number of targets corresponding to each position at different time;
and establishing a corresponding relation of position-time-target number according to the target number corresponding to each position at different time, and determining the historical pedestrian flow information of the path diagram according to the corresponding relation.
3. The method of claim 2, wherein the matching the current people flow information with the historical people flow information to predict building people flow variation within a preset time comprises:
acquiring a time period corresponding to the current people flow information, and performing periodic matching according to the time period and the historical people flow information to obtain a plurality of periodic historical people flow information;
acquiring a target quantity change condition corresponding to the current people flow information, and determining the periodic historical people flow information with the highest matching degree with the current people flow information in the plurality of periodic historical people flow information as the target historical flow information according to the target quantity change condition;
and predicting the change condition of the building pedestrian flow according to the target historical flow information.
4. The method of claim 3, wherein the obtaining of the travel starting point and the travel destination of the user and the planning of the travel path for the user in combination with the building people flow variation condition comprise:
the method comprises the steps of obtaining a travel starting point and a travel destination of a user, and combining a plurality of positions between the travel starting point and the travel destination to form a plurality of reachable paths;
and determining the change condition of the pedestrian flow in each reachable path of the plurality of reachable paths, and setting the reachable path with the least pedestrian flow as a travel path planned for the user.
5. The method of claim 4, further comprising:
calculating the target quantity average value of each position according to the corresponding relation of the position, the time and the target quantity;
setting the position of the target quantity average value smaller than a first preset threshold value as an infeasible position;
setting the position where the target quantity average value is larger than the first preset threshold value and smaller than a second preset threshold value as a feasible position, wherein the second preset threshold value is larger than the first preset threshold value;
and when a plurality of positions between the travel starting point and the destination are combined to form a plurality of reachable paths, shielding the infeasible positions and selecting the feasible positions.
6. A navigation device, characterized in that the device comprises:
a first acquisition unit for acquiring a path diagram within a building and between buildings;
the second acquisition unit is used for acquiring historical people flow information in the road map;
the prediction unit is used for acquiring current people flow information in the road map, matching the current people flow information with the historical people flow information to obtain a matching result, and predicting the change condition of the building people flow within a preset time according to the matching result;
and the planning unit is used for acquiring a travel starting point and a travel destination of the user and planning a travel path for the user by combining the change condition of the building people flow.
7. The navigation device according to claim 6, wherein the second obtaining unit is specifically configured to:
acquiring a plurality of video sets shot by a plurality of cameras in a monitoring range corresponding to the path diagram in a specified time period to obtain a plurality of video sets, wherein the plurality of video sets comprise time marks corresponding to shooting time; performing video analysis on each video set in the plurality of video sets to obtain a plurality of video images, wherein each video image in the plurality of video images comprises a time point identifier; performing position identification on each video image in the plurality of video images to obtain a plurality of positioning video images at each position in a plurality of different positions;
carrying out target identification and time identification on the positioning video image at each position, and determining the number of targets corresponding to each position at different time;
and establishing a corresponding relation of position-time-target number according to the target number corresponding to each position at different time, and determining the historical pedestrian flow information of the path diagram according to the corresponding relation.
8. The apparatus of claim 7, wherein the prediction unit is specifically configured to:
acquiring a time period corresponding to the current people flow information, and performing periodic matching according to the time period and the historical people flow information to obtain a plurality of periodic historical people flow information;
acquiring a target quantity change condition corresponding to the current people flow information, and determining the periodic historical people flow information with the highest matching degree with the current people flow information in the plurality of periodic historical people flow information as the target historical flow information according to the target quantity change condition;
and predicting the change condition of the building pedestrian flow according to the target historical flow information.
9. An electronic device comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-5.
10. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any one of claims 1-5.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112070936A (en) * | 2020-09-07 | 2020-12-11 | 姜锡忠 | Pedestrian target identification monitoring method and system |
| CN113715020A (en) * | 2021-08-31 | 2021-11-30 | 上海擎朗智能科技有限公司 | Robot traveling method, device, equipment and storage medium |
| CN113747093A (en) * | 2021-09-01 | 2021-12-03 | 深圳Tcl数字技术有限公司 | Volume adjusting method and device, television and storage medium |
| CN114898484A (en) * | 2022-05-24 | 2022-08-12 | 珠海格力电器股份有限公司 | Method for awakening intelligent door lock, sensor, intelligent door lock and electronic equipment |
-
2018
- 2018-12-28 CN CN201811633777.6A patent/CN111380530A/en not_active Withdrawn
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112070936A (en) * | 2020-09-07 | 2020-12-11 | 姜锡忠 | Pedestrian target identification monitoring method and system |
| CN113715020A (en) * | 2021-08-31 | 2021-11-30 | 上海擎朗智能科技有限公司 | Robot traveling method, device, equipment and storage medium |
| CN113747093A (en) * | 2021-09-01 | 2021-12-03 | 深圳Tcl数字技术有限公司 | Volume adjusting method and device, television and storage medium |
| CN114898484A (en) * | 2022-05-24 | 2022-08-12 | 珠海格力电器股份有限公司 | Method for awakening intelligent door lock, sensor, intelligent door lock and electronic equipment |
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