A real-time dynamic high-precision walking navigation method, device and related components

Abstract

The present invention discloses a real-time dynamic high-precision walking navigation method, device and related components, the method comprising: obtaining a user's starting position and target position in an indoor place; generating an optimal walking route containing at least one feature point according to the starting position and target position; obtaining the user's displacement information when the user travels according to the optimal walking route; and correcting and optimizing the optimal walking route according to the displacement information until the user reaches the target position. The present invention generates an optimal walking navigation route for the user in an indoor place, and corrects and optimizes the optimal walking navigation route according to the user's displacement information, so as to achieve the effect of improving the navigation accuracy of indoor walking navigation.

Description

Technical Field

[0001] The present invention relates to the field of navigation technology, and in particular to a real-time dynamic high-precision walking navigation method, device and related components. Background Art

[0002] In daily work and life, whether it is a business visit or a private gathering, people spend much more time indoors than outdoors. Especially in large indoor places in the city center (such as super-large shopping malls, transportation hubs, and urban complexes, etc.), people often get lost and find it difficult to find their destinations (such as shops, restaurants, etc.). Most of the guide signs provided in general places can only allow users to check the place map on the spot and remember the route to the destination in their minds. The query equipment in some places will provide printing services, output a small note with route prompts for users to carry with them for reference, but the pure text description, including the attached miniature floor plan, is still not intuitive and convenient enough, and cannot efficiently guide users to their destinations in complex environments. Although there are many technical solutions for indoor navigation, they all have certain defects, resulting in unsatisfactory navigation effects.

[0003] For example, based on the on-site navigation service provided by a large number of information query terminals installed in the venue, users query the target location they want to go to on a terminal with venue facility information and obtain the route from the terminal. However, since the terminal is physically fixed at one point, the user can only remember the route information. Some advanced solutions, such as those mentioned above, can print out navigation information and allow users to retrieve the latest navigation information on each terminal. However, such solutions are ultimately accompanied by high terminal installation and maintenance costs, and users must rely on personally going to a terminal to perform query operations. Terminals installed in public places are used in turn by people coming and going, which also brings challenges to public health protection.

[0004] Another example is the simulated navigation service provided by smartphone apps. This solution is similar to porting the navigation information queried by the above terminal to the mobile phone program. Users can set their own starting and end points in the program, and the program will play a purely simulated turn-by-turn navigation animation. However, simulated navigation is not real-time. The turn-by-turn broadcast is completely based on an average speed control icon movement set in advance by the system, which can realize route simulation, but users cannot let the navigation app adapt to their pace and achieve a precise matching high-precision real-time walking navigation effect.

[0005] Another example is based on smartphone apps and a large number of wireless beacons (such as Beacons) set up in indoor places, using wireless communication positioning technology to determine the specific location of users in indoor places. If the beacon layout and algorithm are optimized to the maximum, this solution can achieve real-time navigation with good accuracy. However, for places that provide this service, the large number of beacons required brings huge costs and complexity to the deployment and maintenance of the entire system, and the signal interference between different beacons will also affect the actual user experience to varying degrees.

[0006] Another example is street view VR / AR navigation based on smartphone apps. This presentation method requires users to watch the screen in real time so that the app can capture the surrounding scenes through the phone camera to achieve positioning. It is potentially unsafe for users to watch their phones for a long time while walking. This navigation method not only brings huge time and economic costs for the acquisition and production of street view map data in the early stage, but also puts higher requirements on the performance and power consumption of smartphones. Summary of the invention

[0007] The embodiments of the present invention provide a real-time dynamic high-precision pedestrian navigation method, device, computer equipment and storage medium, aiming to improve the navigation accuracy of indoor pedestrian navigation.

[0008] In a first aspect, an embodiment of the present invention provides a real-time dynamic high-precision walking navigation method, comprising:

[0009] Obtain the user's starting position and target position in the indoor location;

[0010] generating an optimal walking route including at least one feature point according to the starting position and the target position;

[0011] When the user walks along the optimal walking route, obtaining displacement information of the user;

[0012] The optimal walking route is modified and optimized according to the displacement information until the user reaches the target location.

[0013] In a second aspect, an embodiment of the present invention provides a real-time dynamic high-precision walking navigation device, comprising:

[0014] A first acquisition unit, used to acquire a starting position and a target position of a user in an indoor place;

[0015] A first generating unit, configured to generate an optimal walking route including at least one feature point according to the starting position and the target position;

[0016] A second acquisition unit, configured to acquire displacement information of the user when the user travels along the optimal walking route;

[0017] The first correction unit is used to correct and optimize the optimal walking route according to the displacement information until the user reaches the target location.

[0018] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the real-time dynamic high-precision walking navigation method as described in the first aspect is implemented.

[0019] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the real-time dynamic high-precision walking navigation method as described in the first aspect is implemented.

[0020] The embodiment of the present invention provides a real-time dynamic high-precision walking navigation method, device, computer equipment and storage medium, the method comprising: obtaining a user's starting position and target position in an indoor place; generating an optimal walking route containing at least one feature point according to the starting position and target position; when the user travels according to the optimal walking route, obtaining the user's displacement information; and modifying and optimizing the optimal walking route according to the displacement information until the user reaches the target position. The embodiment of the present invention generates an optimal walking navigation route for the user in an indoor place, and modifies and optimizes the optimal walking navigation route according to the user's displacement information, so as to achieve the effect of improving the navigation accuracy of indoor walking navigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0022] Figure 1 A flowchart of a real-time dynamic high-precision walking navigation method provided by an embodiment of the present invention;

[0023] Figure 2 A schematic diagram of a sub-process of a real-time dynamic high-precision walking navigation method provided by an embodiment of the present invention;

[0024] Figure 3 A schematic block diagram of a real-time dynamic high-precision walking navigation device provided by an embodiment of the present invention;

[0025] Figure 4 A sub-schematic block diagram of a real-time dynamic high-precision walking navigation device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0028] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0029] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0030] See below Figure 1 , Figure 1 A flowchart of a real-time dynamic high-precision walking navigation method provided by an embodiment of the present invention specifically includes: steps S101 to S104.

[0031] S101, obtaining a starting position and a target position of a user in an indoor place;

[0032] S102, generating an optimal walking route including at least one feature point according to the starting position and the target position;

[0033] S103, when the user walks along the optimal walking route, obtaining displacement information of the user;

[0034] S104: Correct and optimize the optimal walking route according to the displacement information until the user reaches the target location.

[0035] In this embodiment, when the user is in an indoor place, the user's starting position, i.e., the starting point of departure, and the user's target position, i.e., the destination of arrival, are first obtained, and then the best walking route is generated according to the starting position and the target position, so that the user can walk from the starting position to the target position according to the best walking route, and the best walking route contains at least one feature point that is easy for the user to identify, such as the name of a store in a shopping mall, etc. When the user starts to go to the destination according to the best walking route, the user's displacement information is compared with the best walking route to determine whether the user is still on the best walking route, and when the user deviates from the best walking route, the best walking route is corrected and optimized in real time to ensure that the user can smoothly reach the target location according to the corrected and optimized best walking route. In addition, the indoor places described in this embodiment include but are not limited to indoor places such as shopping malls, parking lots, indoor stadiums, etc., and can also refer to walkable spaces between adjacent indoor places, such as walking spaces between two buildings, etc. The feature points refer to facilities and landmarks in indoor places that have a traffic function or reference significance and whose positions are not easily changed (including but not limited to: gates, intersections, staircases, elevators, escalator entrances, escalator exits, store doors, etc.).

[0036] The walking navigation method provided in this embodiment has the advantages of simplicity, high precision, and real-time. Different from the "analog navigation" currently available on the market, the walking navigation method provided in this embodiment is based on common sensor data such as compasses and accelerometers commonly used in current smartphones, combined with multiple azimuth and distance value calculation methods, as well as timely correction solutions for possible cumulative errors. It can achieve sub-meter-level precision real-time walking turn-by-turn navigation requirements, thereby improving the navigation accuracy of indoor navigation.

[0037] Furthermore, when the user starts to move according to the best walking route, navigation guidance matching the user's moving speed is provided, such as icon movement speed and voice broadcast timing, to guide the user to walk to the next feature point or target location conveniently and smoothly.

[0038] In one embodiment, the step S101 includes:

[0039] Obtaining the current position of the user in the indoor place, determining the starting feature point closest to the current position, and using the starting feature point as the starting position of the user;

[0040] The location to be reached by the user is obtained, a termination feature point closest to the location to be reached is determined, and the termination feature point is used as the target location of the user.

[0041] In this embodiment, the starting feature point closest to the user's current position is used as the user's starting position, and the ending feature point closest to the user's position to be reached is used as the user's target position. The user's current position and the position to be reached can be obtained by the user's own declaration, or can be actively obtained with the user's permission. For example, the user's current position is automatically identified outdoors (such as the gate of the venue, the sky garden of the venue, or the position near the window indoors) where the satellite positioning signal is strong enough; or the user's position is automatically obtained based on the connection between the user's terminal and the distributed antenna in the wireless communication room; or the "positioning QR code" posted in the indoor venue is scanned, and the QR code has an independent code and is mapped to the specific position corresponding to the QR code. In a specific application scenario, if the starting feature point closest to the user's current position and the ending feature point closest to the user's position to be reached are the same feature point, the starting feature point second closest to the user's current position can be used as the user's starting position, and the ending feature point second closest to the user's position to be reached can be used as the user's target position, so as to generate the best walking route. Of course, if the distance between the user's current location and the location to be reached is short, the user can be prompted to pay attention to the surroundings, or the user can be prompted by voice that the target location is nearby, etc., to guide the user to arrive smoothly.

[0042] In one embodiment, the step S102 includes:

[0043] Obtain all feature points in the indoor location where the user is located, and set an identification number for each feature point;

[0044] Connect adjacent feature points to form corresponding feature edges, and assign corresponding attributes to the corresponding feature edges according to the attributes of the feature points;

[0045] The attribute of each characteristic edge is quantified into a weight, and then the optimal walking route is generated based on the A-star algorithm and the weight of each characteristic edge, with the starting position as the starting point and the target position as the end point.

[0046] In this embodiment, in order to generate the best walking route, all feature points in the indoor place where the user is located can be obtained first, such as facilities and landmarks in the indoor place that have a traffic function or reference significance and whose positions are not easily changed (including but not limited to: gates, intersections, staircases, elevators, escalator entrances, escalator exits, store doors, etc.), and an independent identification number is set for each feature point, and the corresponding spatial coordinates and information on whether they are adjacent to other such facilities and feature points can also be set. The connection between two feature points constitutes a corresponding feature edge, and the feature edge is assigned corresponding attributes. For example, if the feature edge represents two points that can be walked to and fro, the actual walking distance between the two points will be recorded; if the feature edge represents an escalator, the direction of the escalator, the length of the escalator, and the running speed of the escalator (if available) will be recorded; if the feature edge represents a vertical elevator, it can be statistically analyzed by on-site time period surveys, or the average elevator ride time and average waiting time can be recorded through the operation data provided by the venue management office.

[0047] The attributes of the above feature edges will be converted into quantifiable weights. It should be noted that the weight is mainly based on the walking distance. For feature edges that do not involve walking (such as escalators or vertical elevators), the time spent can be converted into weights. The weight composition of walking distance and time spent in the entire graph can also depend on the user's subjective preference (such as the shortest route time or the shortest walking distance, etc.).

[0048] In one embodiment, if Figure 2 As shown, the attribute of each characteristic edge is quantified into a weight, and then the optimal walking route is generated based on the A-star algorithm and the weight of each characteristic edge with the starting position as the starting point and the target position as the end point, including:

[0049] S201, obtaining a characteristic path of an indoor location where a user is located, and determining a positional relationship between the characteristic path and the starting position and the target position;

[0050] S202: when the characteristic path includes the starting position and / or the target position, or when the characteristic path is between the starting position and the target position, generating an optimal walking route by combining the starting position, the target position and the characteristic path;

[0051] S203, when the characteristic path does not include the starting position or the target position, or when the characteristic path is not located between the starting position and the target position, or when the characteristic path is not acquired, acquiring all characteristic points between the starting position and the target position;

[0052] S204, taking the starting position as the starting point, using an estimation function to calculate the optimal score for each feature point adjacent to the starting position, the formula is as follows:

[0053] f(n)=g(n)+h(n)

[0054] Wherein, f(n) is the preferred score of the nth feature point adjacent to the starting position, g(n) is the weight of the feature edge obtained by connecting the starting position with the nth feature point adjacent to the starting position, and h(n) is the estimated weight from the nth feature point adjacent to the starting position to the target position;

[0055] S205, selecting a feature point with the smallest merit score from all adjacent feature points at the starting position as a candidate feature point;

[0056] S206, and so on, until a candidate feature point adjacent to the target position is selected, and then a route that sequentially connects the starting position, the target position, and all the candidate feature points is used as the optimal walking route.

[0057] In this embodiment, when generating the best walking route according to the weight of each characteristic edge, a unique path in the indoor place where the user is located, i.e., the characteristic path, may be first obtained, such as the only connecting bridge between two buildings or a direct elevator across multiple floors. If the above characteristic path exists in the indoor place where the user is located, and the user's starting position and target position are included in the characteristic path, the characteristic path between the starting position and the target position may be used as the best walking route; if the characteristic path is between the user's starting position and the target position, the characteristic path may be used as part of the final best walking route; if the characteristic path only includes the user's starting position or the target position, the characteristic path may also be used as part of the final best walking route. For example, the user's starting position is the first floor of building A, and the target position is the tenth floor of building B adjacent to building A, and there is a connecting bridge (i.e., the characteristic path) between buildings A and B on the fifth floor. Since the connecting bridge does not include the starting position and the target position, but is located between the starting position and the target position, the connecting bridge can be used as part of the optimal walking route, that is, the generated optimal walking route can be walking from the 1st floor of Building A to the 5th floor of Building A, passing through the connecting bridge to the 5th floor of Building B, and then walking from the 5th floor of Building B to the 10th floor of Building B.

[0058] If the characteristic path of the indoor place cannot be obtained, or there is no direct positional relationship between the characteristic path and the starting position and the target position (for example, the characteristic path includes the starting position or the target position, etc.), the A-star algorithm can be used to obtain the candidate feature points between the user's starting position and the target position. Specifically, the user's starting position is used as the starting point, and the next feature point is selected through the estimation function, that is, the candidate feature point adjacent to the starting position. Here, since there may be multiple adjacent feature points at the starting position, the feature edge weight between the starting position and each feature point can be used as a selection condition, for example, the adjacent feature point with the smallest corresponding feature edge weight is selected as the candidate feature point. And so on, until the candidate feature point adjacent to the target position is selected, and then the starting position, each candidate feature point and the target position are connected in sequence, the best walking route can be obtained. In a specific embodiment, the estimated weight between the starting position and the target position can be obtained by Euclidean distance calculation.

[0059] In another specific embodiment, step S202 includes: based on the A-star algorithm, generating the best walking route according to the starting position, the target position and the characteristic path. Specifically, when the characteristic path includes the starting position and the target position, the starting position is used as the starting point and the target position is used as the end point, and the characteristic path between the starting position and the target position is used as the best walking route; when the characteristic path includes the starting position or the target position, the starting position is used as the starting point and the target position is used as the end point, and the characteristic path portion corresponding to the starting position or the target position is used as a part of the best walking route, and the estimation function is used to calculate the optimal score for the feature points of the remaining part, so as to select candidate feature points according to the optimal score to form the best walking route; when the characteristic path is located between the starting position and the target position, the characteristic path is used as a part of the best walking route, and the estimation function is used to calculate the optimal score for each feature point of the remaining part, so as to select candidate feature points according to the optimal score to form the best walking route.

[0060] For example, the user's starting position is the 1st floor of building A, and the target position is the 10th floor of building B adjacent to building A. At the same time, there is a bridge connecting buildings A and B on the 5th floor (i.e., the characteristic path). Then the bridge is used as part of the optimal walking route, and the A-star algorithm is used to obtain candidate feature points from the 1st floor of building A to the 5th floor of building A and candidate feature points from the 5th floor of building B to the 10th floor of building B, thereby generating the optimal walking route from the 1st floor of building A to the 10th floor of building B.

[0061] In one embodiment, the step S103 includes:

[0062] Acquire the walking distance of the user through the terminal currently held by the user, and use the walking distance as the displacement information of the user;

[0063] Alternatively, the number of steps of the user is obtained through the terminal currently held by the user, and the average stride length of the user is determined in combination with the personal information of the user, and then the walking distance of the user is calculated according to the number of steps and the average stride length, and the walking distance is used as the displacement information of the user;

[0064] Alternatively, the user's average walking speed is determined through the user's personal information, and the user's walking time is obtained, and then the user's walking distance is calculated according to the average stride length and the walking time, and the walking distance is used as the user's displacement information.

[0065] In this embodiment, when obtaining the user's displacement information, if the terminal held by the user (such as a smart phone, etc.) can provide the user's walking distance, the walking distance of the user terminal can be directly obtained and used as the user's displacement information. If the terminal held by the user does not have the function of directly providing the walking distance, the number of walking steps of the user can be obtained from the user's terminal first, and then the user's displacement information is determined in combination with the user's personal information (i.e., the average stride length). It can be understood that when obtaining the walking distance from the user's terminal, the user's permission and authorization are required, and the existing terminal devices can usually provide the function of walking steps. Of course, if the number of walking steps of the user cannot be obtained from the user's terminal, the user's walking time can be obtained, and then the user's displacement information is determined in combination with the user's personal information (i.e., the average stride length).

[0066] In one embodiment, the step count of the user is obtained through the terminal currently held by the user, and the average stride length of the user is determined in combination with the user's personal information, and then the walking distance of the user is calculated according to the step count and the average stride length, and the walking distance is used as the displacement information of the user, including:

[0067] Get the user's height and calculate the user's average stride length based on a rough estimate formula:

[0068] l=h*0.413

[0069] In the formula, l is the average stride length of the user, and h is the height of the user;

[0070] The user's walking distance is calculated according to the following formula, and the walking distance is used as the user's displacement information:

[0071] d=s*l

[0072] Where d is the user's walking distance and s is the user's walking steps.

[0073] In this embodiment, the displacement information of the user is determined by combining the number of steps the user is walking and the average stride length of the user. First, the user's personal information, such as the user's height, weight, age, gender, etc., is obtained, and the user's average stride length is determined by a rough estimation formula for height, and then the user's average stride length is multiplied by the number of steps the user is walking to obtain the user's walking distance. Furthermore, in other embodiments, in order to more accurately calculate the user's average stride length, the user's height, weight, age, and gender can be combined together, so that more accurate displacement information can be obtained.

[0074] In another embodiment, a computational model for calculating the average stride length of a user is constructed. Specifically, first, the number of steps required for a large number of subjects with different physical signs to walk a fixed distance is obtained, and the stride length of the corresponding subject is obtained by dividing the fixed distance by the required number of steps; the personal information such as height, weight, gender, age group, etc. of all subjects are combined into an independent variable matrix X, and the stride length corresponding to the subject is summarized into a dependent variable matrix Y; multiple linear regression is performed to obtain the relationship between the independent variable and the dependent variable Y=XB+U, where B is a matrix containing multiple estimated parameters, and U is a matrix containing errors and residual terms, thereby constructing a computational model. Then, the average stride length of a specified user can be calculated using the above computational model.

[0075] Of course, when the user's average stride length is determined through the user's personal information, and the user's walking time is obtained, and then the user's walking distance is calculated based on the average stride length and walking time, and the walking distance is used as the user's displacement information, the user's average walking speed can also be calculated by combining the user's personal information and using an appropriate rough estimation formula, and then the user's average walking speed is multiplied by the user's walking time to obtain the user's walking distance. A calculation model for calculating the user's average walking speed can also be constructed, and the specific construction process is similar to the calculation model construction process of the average stride length, which will not be repeated here.

[0076] In one embodiment, the step S104 includes:

[0077] Determine the real-time position of the user according to the displacement information, and judge whether the real-time position is on the optimal walking route;

[0078] If the real-time position is not on the optimal walking route, the real-time position is used as a starting point, and an optimal walking route generating step is used to generate a modified walking route with the real-time position as a starting point and the target position as an end point.

[0079] In this embodiment, when the user goes to the target location according to the optimal walking route, the user's real-time location is obtained according to the user's displacement information to determine whether the user deviates from the optimal walking route. Of course, it is also possible to determine whether the user deviates from the optimal walking route by the user's self-reporting of the real-time location.

[0080] When it is determined that the user deviates from the optimal walking route, the above-mentioned optimal walking route generation step can be used to regenerate the optimal walking route with the user's real-time position as the starting point and the target position as the end point, that is, the modified walking route. Specifically, firstly, the user's real-time position is taken as the starting point and the target position is taken as the end point, all feature points between the real-time position and the target position are obtained, and according to the weight of each feature point, feature points with smaller weights are selected as the modified feature points in the modified walking route, and then the modified walking route is generated. Alternatively, the user's real-time position is taken as the starting point, and based on the estimation function, one feature point is selected from the feature points adjacent to the real-time position as a candidate modified feature point, until a candidate modified feature point adjacent to the target position is selected, and then the route after the real-time position, the target position and all the candidate modified feature points are sequentially connected is used as the modified walking route.

[0081] Figure 3 A schematic block diagram of a real-time dynamic high-precision walking navigation device 300 provided by an embodiment of the present invention, the device 300 includes:

[0082] A first acquisition unit 301 is used to acquire a starting position and a target position of a user in an indoor place;

[0083] A first generating unit 302, configured to generate an optimal walking route including at least one feature point according to the starting position and the target position;

[0084] A second acquisition unit 303 is used to acquire displacement information of the user when the user walks along the optimal walking route;

[0085] The first correction unit 304 is used to correct and optimize the optimal walking route according to the displacement information until the user reaches the target location.

[0086] In one embodiment, the first acquiring unit 301 includes:

[0087] A starting position acquisition unit, used to acquire the current position of the user in the indoor place, determine the starting feature point closest to the current position, and use the starting feature point as the starting position of the user;

[0088] The target position acquisition unit is used to acquire the position to be reached by the user, determine the termination feature point closest to the position to be reached, and use the termination feature point as the user's target position.

[0089] In one embodiment, the first generating unit 302 includes:

[0090] A feature point acquisition unit, used to acquire all feature points in the indoor location where the user is located, and set an identification number for each feature point;

[0091] A feature edge forming unit is used to connect adjacent feature points to form corresponding feature edges, and to assign corresponding attributes to corresponding feature edges according to the attributes of the feature points;

[0092] The attribute quantization unit is used to quantify the attribute of each feature edge into a weight, and then generate an optimal walking route based on the A-star algorithm and the weight of each feature edge with the starting position as the starting point and the target position as the end point.

[0093] In one embodiment, if Figure 4 As shown, the attribute quantization unit includes:

[0094] The characteristic path acquisition unit 401 is used to acquire the characteristic path of the indoor location where the user is located, and determine the positional relationship between the characteristic path and the starting position and the target position;

[0095] A characteristic path combining unit 402 is used to combine the starting position, the target position and the characteristic path to generate an optimal walking route when the characteristic path includes the starting position and / or the target position, or when the characteristic path is between the starting position and the target position;

[0096] A third acquisition unit 403 is configured to acquire all feature points between the starting position and the target position when the feature path does not include the starting position or the target position, or when the feature path is not located between the starting position and the target position, or when the feature path is not acquired;

[0097] The optimal score calculation unit 404 is used to calculate the optimal score of each feature point adjacent to the starting position using the estimation function, with the formula as follows:

[0098] f(n)=g(n)+h(n)

[0099] Wherein, f(n) is the preferred score of the nth feature point adjacent to the starting position, g(n) is the weight of the feature edge obtained by connecting the starting position with the nth feature point adjacent to the starting position, and h(n) is the estimated weight from the nth feature point adjacent to the starting position to the target position;

[0100] A candidate feature point selection unit 405 is used to select a feature point with the smallest merit score from all adjacent feature points at the starting position as a candidate feature point;

[0101] The candidate feature point connecting unit 406 is used to select a candidate feature point adjacent to the target position in the same manner, and then use the route obtained by sequentially connecting the starting position, the target position and all the candidate feature points as the optimal walking route.

[0102] In one embodiment, the second obtaining unit 303 includes:

[0103] a first displacement information acquisition unit, configured to acquire a walking distance of the user through a terminal currently held by the user, and use the walking distance as the displacement information of the user;

[0104] a second displacement information acquisition unit, configured to acquire the number of walking steps of the user through a terminal currently held by the user, determine the average stride length of the user in combination with the user's personal information, and then calculate the walking distance of the user according to the number of walking steps and the average stride length, and use the walking distance as the displacement information of the user;

[0105] The third displacement information acquisition unit is used to determine the user's average walking speed through the user's personal information and obtain the user's walking time, and then calculate the user's walking distance based on the average stride length and walking time, and use the walking distance as the user's displacement information.

[0106] In one embodiment, the second displacement information acquiring unit includes:

[0107] The average stride length calculation unit is used to obtain the user's height value and calculate the user's average stride length based on a rough estimation formula:

[0108] l=h*0.413

[0109] In the formula, l is the average stride length of the user, and h is the height of the user;

[0110] The displacement information setting unit is used to calculate the walking distance of the user according to the following formula, and use the walking distance as the displacement information of the user:

[0111] d=s*l

[0112] Where d is the user's walking distance and s is the user's walking steps.

[0113] In one embodiment, the first correction unit 304 includes:

[0114] a judgment unit, configured to determine the real-time position of the user according to the displacement information, and to judge whether the real-time position is on the optimal walking route;

[0115] The second correction unit is used to, if the real-time position is not on the optimal walking route, use the real-time position as a starting point and use the optimal walking route generation step to generate a corrected walking route with the real-time position as a starting point and the target position as an end point.

[0116] Since the embodiments of the apparatus part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the apparatus part, which will not be repeated here.

[0117] The embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed, the steps provided in the above embodiment can be implemented. The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0118] The embodiment of the present invention also provides a computer device, which may include a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps provided in the above embodiment may be implemented. Of course, the computer device may also include various network interfaces, power supplies and other components.

[0119] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of this application.

[0120] It should also be noted that, in this specification, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

Claims

1. A real-time dynamic high-precision walking navigation method, characterized in that: include: Obtain the user's starting position and target position in the indoor location; generating an optimal walking route including at least one feature point according to the starting position and the target position; When the user walks along the optimal walking route, obtaining displacement information of the user; Correcting and optimizing the optimal walking route according to the displacement information until the user reaches the target location; When the user walks along the optimal walking route, obtaining the user's displacement information includes: The number of steps of the user is obtained through the terminal currently held by the user, and the average stride length of the user is determined in combination with the personal information of the user, and then the walking distance of the user is calculated according to the number of steps and the average stride length, and the walking distance is used as the displacement information of the user; The method of obtaining the number of walking steps of the user through the terminal currently held by the user, determining the average stride length of the user in combination with the personal information of the user, and then calculating the walking distance of the user according to the number of walking steps and the average stride length, and using the walking distance as the displacement information of the user includes: Get the user's height and calculate the user's average stride length based on a rough estimate formula: l= h * 0.413 In the formula, l is the average stride length of the user, and h is the height of the user; The user's walking distance is calculated according to the following formula, and the walking distance is used as the user's displacement information: d = s * l In the formula, d is the user's walking distance, and s is the user's walking steps; The step of generating an optimal walking route including at least one feature point according to the starting position and the target position includes: Obtain all feature points in the indoor location where the user is located, and set an identification number for each feature point; Connect adjacent feature points to form corresponding feature edges, and assign corresponding attributes to the corresponding feature edges according to the attributes of the feature points; Quantifying the attribute of each characteristic edge into a weight, and then taking the starting position as the starting point and the target position as the end point, generating an optimal walking route based on the A-star algorithm and according to the weight of each characteristic edge; The method of quantifying the attribute of each characteristic edge into a weight, and then taking the starting position as the starting point and the target position as the end point, generating the best walking route based on the A-star algorithm and according to the weight of each characteristic edge, includes: Acquire a characteristic path of the indoor location where the user is located, and determine the positional relationship between the characteristic path and the starting position and the target position; When the characteristic path includes the starting position and / or the target position, or when the characteristic path is between the starting position and the target position, generating an optimal walking route by combining the starting position, the target position and the characteristic path; When the characteristic path includes the starting position and / or the target position, or when the characteristic path is between the starting position and the target position, generating the optimal walking route by combining the starting position, the target position and the characteristic path includes: When the characteristic path includes the starting position and the target position, the starting position is taken as the starting point and the target position is taken as the end point, and the characteristic path between the starting position and the target position is taken as the best walking route; when the characteristic path includes the starting position or the target position, the starting position is taken as the starting point and the target position is taken as the end point, and the characteristic path portion corresponding to the starting position or the target position is taken as a part of the best walking route, and the estimation function is used to calculate the preferential scores for the characteristic points of the remaining part, so as to select candidate characteristic points according to the preferential scores to form the best walking route; when the characteristic path is located between the starting position and the target position, the characteristic path is taken as a part of the best walking route, and the estimation function is used to calculate the preferential scores for each characteristic point of the remaining part, so as to select candidate characteristic points according to the preferential scores to form the best walking route; When the characteristic path does not include the starting position or the target position, or when the characteristic path is not located between the starting position and the target position, or when the characteristic path is not acquired, all characteristic points between the starting position and the target position are acquired; Taking the starting position as the starting point, the estimation function is used to calculate the optimal score for each feature point adjacent to the starting position. The formula is as follows: f(n) = g(n) + h(n) Wherein, f(n) is the preferred score of the nth feature point adjacent to the starting position, g(n) is the weight of the feature edge obtained by connecting the starting position with the nth feature point adjacent to the starting position, and h(n) is the estimated weight from the nth feature point adjacent to the starting position to the target position; Selecting a feature point with the smallest merit score from all adjacent feature points at the starting position as a candidate feature point; until a candidate feature point adjacent to the target position is selected, and then a route that sequentially connects the starting position, the target position, and all the candidate feature points is used as the optimal walking route; The correcting and optimizing the optimal walking route according to the displacement information includes: Determine the real-time position of the user according to the displacement information, and judge whether the real-time position is on the optimal walking route; If the real-time position is not on the optimal walking route, the real-time position is set as the starting point, and the optimal walking route generating step is used to generate a modified walking route with the real-time position as the starting point and the target position as the end point.

2. The walking navigation method according to claim 1, characterized in that: The obtaining of the user's starting position and target position in an indoor place includes: Obtaining the current position of the user in the indoor place, determining the starting feature point closest to the current position, and using the starting feature point as the starting position of the user; The location to be reached by the user is obtained, a termination feature point closest to the location to be reached is determined, and the termination feature point is used as the target location of the user.

3. A real-time dynamic high-precision walking navigation device, characterized in that: include: A first acquisition unit, used to acquire a starting position and a target position of a user in an indoor place; A first generating unit, configured to generate an optimal walking route including at least one feature point according to the starting position and the target position; A second acquisition unit, configured to acquire displacement information of the user when the user travels along the optimal walking route; When the user walks along the optimal walking route, obtaining the user's displacement information includes: The number of steps of the user is obtained through the terminal currently held by the user, and the average stride length of the user is determined in combination with the personal information of the user, and then the walking distance of the user is calculated according to the number of steps and the average stride length, and the walking distance is used as the displacement information of the user; The method of obtaining the number of walking steps of the user through the terminal currently held by the user, determining the average stride length of the user in combination with the personal information of the user, and then calculating the walking distance of the user according to the number of walking steps and the average stride length, and using the walking distance as the displacement information of the user includes: Get the user's height and calculate the user's average stride length based on a rough estimate formula: l= h * 0.413 In the formula, l is the average stride length of the user, and h is the height of the user; The user's walking distance is calculated according to the following formula, and the walking distance is used as the user's displacement information: d = s * l In the formula, d is the user's walking distance, and s is the user's walking steps; A first correction unit, configured to correct and optimize the optimal walking route according to the displacement information until the user reaches the target location; The first generating unit comprises: A feature point acquisition unit, used to acquire all feature points in the indoor location where the user is located, and set an identification number for each feature point; A feature edge forming unit is used to connect adjacent feature points to form corresponding feature edges, and to assign corresponding attributes to corresponding feature edges according to the attributes of the feature points; An attribute quantization unit, used for quantifying the attribute of each characteristic edge into a weight, and then generating an optimal walking route based on the A-star algorithm and the weight of each characteristic edge with the starting position as the starting point and the target position as the end point; The attribute quantization unit comprises: A characteristic path acquisition unit, used to acquire a characteristic path of an indoor location where a user is located, and determine a positional relationship between the characteristic path and the starting position and the target position; a characteristic path combining unit, configured to generate an optimal walking route by combining the starting position, the target position and the characteristic path when the starting position and / or the target position are included in the characteristic path or when the characteristic path is between the starting position and the target position; When the characteristic path includes the starting position and / or the target position, or when the characteristic path is between the starting position and the target position, generating the optimal walking route by combining the starting position, the target position and the characteristic path includes: When the characteristic path includes the starting position and the target position, the starting position is taken as the starting point and the target position is taken as the end point, and the characteristic path between the starting position and the target position is taken as the best walking route; when the characteristic path includes the starting position or the target position, the starting position is taken as the starting point and the target position is taken as the end point, and the characteristic path portion corresponding to the starting position or the target position is taken as a part of the best walking route, and the estimation function is used to calculate the preferential scores for the characteristic points of the remaining part, so as to select candidate characteristic points according to the preferential scores to form the best walking route; when the characteristic path is located between the starting position and the target position, the characteristic path is taken as a part of the best walking route, and the estimation function is used to calculate the preferential scores for each characteristic point of the remaining part, so as to select candidate characteristic points according to the preferential scores to form the best walking route; a third acquisition unit, configured to acquire all feature points between the starting position and the target position when the feature path does not include the starting position or the target position, or when the feature path is not located between the starting position and the target position, or when the feature path is not acquired; The optimal score calculation unit is used to calculate the optimal score of each feature point adjacent to the starting position using the estimation function, with the starting position as the starting point, and the formula is as follows: f(n) = g(n) + h(n) Wherein, f(n) is the preferred score of the nth feature point adjacent to the starting position, g(n) is the weight of the feature edge obtained by connecting the starting position with the nth feature point adjacent to the starting position, and h(n) is the estimated weight from the nth feature point adjacent to the starting position to the target position; A candidate feature point selection unit, used to select a feature point with the smallest merit score from all adjacent feature points at the starting position as a candidate feature point; A candidate feature point connecting unit, used to select a candidate feature point adjacent to the target position, and then use a route that sequentially connects the starting position, the target position and all the candidate feature points as the optimal walking route; The first revision unit includes: a judgment unit, configured to determine the real-time position of the user according to the displacement information, and to judge whether the real-time position is on the optimal walking route; The second correction unit is used to set the real-time position as the starting point if the real-time position is not on the optimal walking route, and use the optimal walking route generation step to generate a corrected walking route with the real-time position as the starting point and the target position as the end point.

4. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the real-time dynamic high-precision walking navigation method as claimed in claim 1 or 2 is implemented.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the real-time dynamic high-precision walking navigation method according to claim 1 or 2 is implemented.

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