Navigation path determination method and device, storage medium, electronic device and computer program product

Abstract

The invention discloses a navigation path determination method and device, a storage medium, an electronic device and a computer program product, and relates to the field of navigation, the navigation path determination method comprises the following steps: obtaining target data detected by N sensors, N being a positive integer greater than or equal to 2; environment data of a target building and first object data of a target object are determined from the target data, and the N sensors and the target object are located in the target building; based on the environment data and target object data, a navigation path is determined according to a navigation starting point and a navigation ending point, the target object data comprises the first object data and second object data, and the second object data is data determined according to historical navigation data of the target object. By adopting the scheme, the problem that the traditional navigation path determination effect is relatively poor is solved.

Description

Technical Field

[0001] The present application relates to the field of navigation, and more specifically, to a method and device for determining a navigation path, a storage medium, an electronic device, and a computer program product. Background Art

[0002] With the acceleration of urbanization and the continuous development of urban construction, the structures of shopping malls, business districts, office buildings and other buildings are becoming increasingly complex. These buildings often have multiple floors and different functional areas, which can easily make people lost.

[0003] Although traditional building map navigation systems are useful in providing route guidance, they are limited by sensor accuracy and the update speed of map data, so their navigation accuracy is low and they do not take users' walking habits and preferences into consideration, resulting in a poor user experience and a lack of interactivity.

[0004] With regard to the problem that the determination effect of the traditional navigation path in the related technology is poor, no effective solution has been proposed yet.

[0005] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0006] Embodiments of the present invention provide a method and device for determining a navigation path, a storage medium, an electronic device, and a computer program product, so as to at least solve the problem that the determination effect of a conventional navigation path is poor.

[0007] According to one aspect of an embodiment of the present invention, a method for determining a navigation path is provided, comprising: acquiring target data detected by N sensors, wherein N is a positive integer greater than or equal to 2; determining environmental data of a target building and first object data of a target object from the target data, wherein the N sensors and the target object are located in the target building; and determining a navigation path according to a navigation start point and a navigation end point based on the environmental data and the target object data, wherein the target object data includes the first object data and second object data, and the second object data is data determined based on historical navigation data of the target object.

[0008] In an exemplary embodiment, based on the environmental data and the target object data, a navigation path is determined according to a navigation starting point and a navigation end point, including: establishing a three-dimensional model of the target building according to the environmental data; and based on the three-dimensional model and the target object data, determining a navigation path according to a navigation starting point and a navigation end point.

[0009] In an exemplary embodiment, based on the three-dimensional model and the target object data, a navigation path is determined according to a navigation starting point and a navigation end point, including: analyzing the target object data through a machine learning algorithm to obtain a user behavior pattern; based on the three-dimensional model and the user behavior pattern, determining a navigation path according to a navigation starting point and a navigation end point.

[0010] In an exemplary embodiment, a three-dimensional model of the target building is established according to the environmental data, including: constructing a three-dimensional spatial architecture of the target building based on the lidar data in the environmental data; fusing the data detected by different sensors in the environmental data, and determining feature data corresponding to the target building based on the fused data after the fusion processing, wherein the feature data includes: emergency exit locations, stair locations, crowd flow data in different areas, and obstacle distribution; wherein the three-dimensional model includes the three-dimensional spatial architecture and related data of the three-dimensional spatial architecture, and the related data includes the feature data.

[0011] In an exemplary embodiment, after determining the navigation path according to the navigation starting point and the navigation end point based on the environmental data and the target object data, the method further includes: determining the real-time environmental data of the target building and the feedback information of the target object on the navigation path; and adjusting the navigation path in real time according to the real-time environmental data and the feedback information.

[0012] In an exemplary embodiment, after determining the navigation path according to the navigation starting point and the navigation end point based on the environmental data and the target object data, the method further includes: displaying the navigation path through augmented reality technology when the terminal device of the target object supports augmented display technology.

[0013] According to another aspect of an embodiment of the present invention, a device for determining a navigation path is also provided, comprising: an acquisition module, used to acquire target data detected by N sensors, wherein N is a positive integer greater than or equal to 2; a first determination module, used to determine environmental data of a target building and first object data of a target object from the target data, wherein the N sensors and the target object are located in the target building; and a second determination module, used to determine a navigation path according to a navigation start point and a navigation end point based on the environmental data and the target object data, wherein the target object data includes the first object data and the second object data, and the second object data is data determined based on historical navigation data of the target object.

[0014] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for determining the navigation path when running.

[0015] According to another aspect of an embodiment of the present invention, there is provided an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method for determining the navigation path through the computer program.

[0016] According to yet another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method for determining the navigation path is implemented.

[0017] Through the present invention, the environment data of the target building and the first object data of the target object are determined from the target data detected by N sensors, and the path from the navigation starting point to the navigation end point is determined based on the determined environment data and target object data. Since multiple sensors are used to obtain environment data and target object data, the path from the navigation starting point to the end point can be determined more accurately, and the historical navigation data of the target object is considered in the target object data, and the path determination result has more historical preference of the target object, thereby improving the determination effect of the navigation path and solving the problem that the determination effect of the traditional navigation path is poor. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 It is a hardware structure block diagram of a mobile terminal of a method for determining a navigation path according to an embodiment of the present invention;

[0021] Figure 2 is a flow chart of a method for determining a navigation path according to an embodiment of the present invention;

[0022] Figure 3 It is a structural block diagram of a navigation path determination device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for determining a navigation path according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, MCU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0026] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the navigation path in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0027] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0028] In this embodiment, a method for determining a navigation path is provided. Figure 2 is a flow chart of a method for determining a navigation path according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps S202-S206:

[0029] Step S202: Acquire target data detected by N sensors, where N is a positive integer greater than or equal to 2;

[0030] Optionally, N sensors are deployed in the target building, where the N sensors include sensors of various types, including but not limited to: cameras, lidars, infrared sensors, ultrasonic sensors, and inertial measurement units (IMU for short).

[0031] Optionally, high-definition cameras can be set at key nodes of the target building, such as elevator entrances, stairwells, and main passages, to identify personnel flow, obstacle locations, navigation signs, etc. through deep learning algorithms. Image processing technology is used to analyze the images captured by the camera in real time to extract the required target data, where the target data includes the environmental data of the target building and the first object data of the target object, such as spatial structure, obstacle location, crowd density, lighting conditions, etc.

[0032] Optionally, infrared sensors can be installed on the top of each floor of the target building to detect temperature changes and heat sources, thereby locating personnel and monitoring the environment.

[0033] Optionally, the map of the target building is analyzed to determine key navigation points, and ultrasonic sensors are deployed at the determined key navigation points, so that the distance to the nearest obstacle is measured using the sound waves emitted by the ultrasonic sensors to accurately locate and avoid the obstacle.

[0034] Optionally, pressure sensors and / or tactile sensors may be used instead of ultrasonic sensors to accurately locate obstacles.

[0035] Step S204: determining environmental data of a target building and first object data of a target object from the target data, wherein the N sensors and the target object are located in the target building;

[0036] Optionally, the environmental data of the target building includes but is not limited to: spatial structure, obstacle location, and crowd density; the first object data of the target object includes but is not limited to: real-time speed of the target object, real-time forward direction of the target object, location of the target object, etc.

[0037] Step S206: Based on the environment data and the target object data, determine the navigation path according to the navigation start point and the navigation end point, wherein the target object data includes the first object data and the second object data, and the second object data is data determined according to the historical navigation data of the target object.

[0038] Optionally, the second object data of the target object includes but is not limited to: a preferred route of the target object.

[0039] Optionally, when the target building is a shopping mall, the second object data includes: the store number that the target object passes by most frequently, the parking space number of the underground parking lot that the target object often uses, the number of the elevator hall that the target object often uses, etc.

[0040] It should be noted that, through the above steps, on the basis of obtaining the environmental data of the target building and the first object data of the target object, combined with the second object data of the target object, it is possible to better adapt to the walking habits and walking preferences of the target object when performing path planning, take more into account the personalized needs of the target object, improve the user experience of the target object, and improve the generation effect of the navigation path.

[0041] In the above steps, the environment data of the target building and the first object data of the target object are determined from the target data detected by the N sensors, and the path from the navigation start point to the navigation end point is determined based on the determined environment data and target object data. Since multiple sensors are used to obtain environment data and target object data, the path from the navigation start point to the end point can be determined more accurately, and the historical navigation data of the target object is considered in the target object data, and the path determination result has more historical preference of the target object, thereby improving the determination effect of the navigation path and solving the problem of poor determination effect of the traditional navigation path.

[0042] In an exemplary embodiment, the above step S206 can be implemented by the following steps S11-S12:

[0043] Step S11: establishing a three-dimensional model of the target building according to the environmental data;

[0044] Optionally, a laser radar and / or radar sensor is deployed inside the target building to scan the target building environment and obtain the laser radar data of the target building, thereby creating or updating a three-dimensional map of the target building environment. The navigation system calculates the distance to obstacles through the time it takes for the laser radar to reflect back, and achieves precise spatial positioning, so as to convert various information in the actual environment into a digital three-dimensional model, and combines the Simultaneous Localization and Mapping (SLAM) algorithm to update the internal map of the building in real time, where the environmental data includes the laser radar data.

[0045] It should be noted that by establishing a three-dimensional model of the target building, the internal structure and characteristics of the building can be presented more intuitively, providing more accurate data support for intelligent building management, navigation systems, etc.

[0046] In an exemplary embodiment, the above step S11 can be implemented by the following steps S111-S112:

[0047] Step S111: constructing a three-dimensional spatial structure of the target building based on the laser radar data in the environmental data;

[0048] Step S112: fusing the data detected by different sensors in the environmental data, and determining the characteristic data corresponding to the target building according to the fused data after fusion processing, wherein the characteristic data includes: emergency exit location, staircase location, crowd flow data in different areas, and obstacle distribution;

[0049] The three-dimensional model includes the three-dimensional space framework and related data of the three-dimensional space framework, and the related data includes the feature data.

[0050] Optionally, the data detected by different sensors in the environmental data are denoised to remove outliers and interference signals, and then the data format is standardized to ensure that the data from different sensors can be analyzed under a unified framework. At the same time, data compression is implemented to reduce the burden of storage and transmission.

[0051] Optionally, the lidar data is used in combination with the visual information captured by the camera to perform texture mapping on the model to increase the realism of the model, and the positions of dynamic obstacles in the environment are updated in real time through infrared sensor and ultrasonic sensor data.

[0052] Optionally, a Kalman filter, a particle filter or other multi-sensor fusion technology is used to fuse the data detected by different sensors in the environmental data to improve the accuracy of positioning and perception.

[0053] It should be noted that Kalman filtering is a filtering algorithm based on probability statistics. It estimates and predicts the system state through the state equation and observation equation of the dynamic system, combined with the dynamic model of the system and the observation data of the sensor. Kalman filtering is applicable to linear dynamic systems and Gaussian noise models, and can effectively fuse information from different sensors to improve the accuracy of positioning and perception.

[0054] It should be noted that particle filtering is a Monte Carlo filtering technology that represents the probability distribution of the system state through randomly sampled particles. According to the sensor observation data, the particle weights are updated and adjusted to finally obtain an estimate of the system state. Particle filtering is suitable for nonlinear systems and non-Gaussian noise models, and can flexibly process complex sensor data and improve the accuracy of positioning and perception.

[0055] It should be noted that by using Kalman filtering, particle filtering or other multi-sensor fusion technologies, the information from different sensors can be effectively fused and processed to eliminate noise and errors, improve the accuracy of positioning and perception, and thus provide more reliable data support for applications such as smart building navigation systems.

[0056] Step S12: Based on the three-dimensional model and the target object data, determine a navigation path according to a navigation start point and a navigation end point.

[0057] In an exemplary embodiment, the above step S12 can be implemented by the following steps S121-S122:

[0058] Step S121: Analyze the target object data through a machine learning algorithm to obtain a user behavior pattern;

[0059] Optionally, the user's behavior pattern includes, but is not limited to: the target object's commonly used path selection preferences (eg, fewer turns, more direct elevator rides, etc.), the target object's average speed when traveling, etc.

[0060] Step S122: Based on the three-dimensional model and the user behavior pattern, determine a navigation path according to a navigation start point and a navigation end point.

[0061] Optionally, a path with more elevator rides is determined from the three-dimensional model, thereby determining a navigation path from the navigation starting point to the navigation end point in the target building.

[0062] In an exemplary embodiment, after the above step S206, the method further includes the following steps S21-S22:

[0063] Step S21: determining the real-time environmental data of the target building and the feedback information of the target object on the navigation path;

[0064] Step S22: adjusting the navigation path in real time according to the real-time environmental data and the feedback information.

[0065] Optionally, the real-time environmental data of the target building includes but is not limited to: real-time crowd flow data of each area. When it is detected that the real-time crowd flow data of the area passed by the navigation path exceeds a first preset threshold, a first warning message is sent to the target object, and a first instruction of the target object is obtained, wherein the first instruction is used to determine to change to a navigation path that passes through an area with smaller real-time crowd flow data.

[0066] Optionally, the target object's feedback information on the navigation path includes but is not limited to: the target object's real-time forward route, calculating the deviation value between the target object's real-time forward route and the navigation path, when the deviation value exceeds a second preset threshold, sending a second warning message to the target object, and obtaining a second instruction of the target object, wherein the second instruction is used to determine to replace the navigation path with a route that passes through the target object's real-time forward route to reach the navigation end point.

[0067] In an exemplary embodiment, after the above step S206, the method further includes the following steps S31-S32:

[0068] Step S31: determining the real-time speed of the target object, and calculating the travel time of the selected navigation path according to the real-time speed of the target object;

[0069] Step S32: sending the travel time of the selected navigation route to the target object, and obtaining a third instruction from the target object, wherein the third instruction is used to determine to switch to a navigation route with a shorter travel time.

[0070] In an exemplary embodiment, after the above step S206, the method further includes the following steps: displaying the navigation path by using augmented reality technology when the terminal device of the target object supports augmented display technology.

[0071] Optionally, augmented reality (AR) technology can be combined to overlay navigation arrows, route maps and other information in the user's field of view to provide the user with more intuitive navigation guidance.

[0072] In an exemplary embodiment, the method further includes the following steps: automatically switching to a preset emergency navigation path when an emergency signal is detected from the target building.

[0073] It should be noted that by combining the structure and safety specifications of the target building, escape routes for various emergency situations are preset in advance, so that when an emergency situation is sent, the target object can be guided to leave the target building safely and quickly to ensure the life safety of the target object.

[0074] Obviously, the above-described embodiments are only embodiments of a part of the present invention, rather than all embodiments. In order to better understand the above method, the above process is described below in conjunction with embodiments, but it is not intended to limit the technical solutions of the embodiments of the present invention, specifically:

[0075] 1. Collect multi-dimensional perception data;

[0076] (1) Deployment of data collection equipment: Deploy a variety of sensors in the building, such as cameras, lidar, infrared sensors, ultrasonic sensors, inertial measurement units (IMUs), etc.

[0077] Optionally, high-definition cameras can be installed at key points of the building, such as elevator entrances, stairwells, and main passages, to capture visual information for identifying people, obstacles, signboards, etc. Image processing technology can be used to analyze the images captured by the camera in real time to extract useful information. Through deep learning algorithms, personnel flow, obstacle locations, navigation signs, etc. can be identified.

[0078] Optionally, a LiDAR sensor is deployed inside the building to scan the surrounding environment and create or update a three-dimensional map of the environment. The distance to obstacles is calculated by the time it takes for the LiDAR to reflect back, achieving precise spatial positioning. Combined with the SLAM (Simultaneous Localization and Mapping) algorithm, the internal map of the building is updated in real time.

[0079] Optionally, infrared sensors can be installed in key areas to monitor human body heat and assist in locating personnel. The temperature distribution inside the building can be analyzed through the data from infrared sensors for environmental regulation and safety monitoring.

[0080] Optionally, ultrasonic sensors are deployed on navigation robots or key navigation points. The sound waves emitted by ultrasonic sensors are used to measure the distance to the nearest obstacle. Combined with ultrasonic sensor data, the navigation path of the robot or personnel is optimized to avoid collisions.

[0081] Optionally, an IMU is integrated into the mobile device or navigation robot. The IMU detects the motion state of the device, including acceleration and rotation angle. Combined with IMU data, it assists other sensor information to improve positioning accuracy and navigation stability. Inertial Measurement Unit (IMU): detects acceleration and rotation to assist positioning and navigation.

[0082] (2) Data collection: real-time collection of building environment data to improve the accuracy and real-time performance of navigation data, including but not limited to spatial structure, obstacle location, crowd density, lighting conditions, etc.;

[0083] (3) Monitoring user behavior: using sensors to monitor user behavior patterns, such as walking speed, direction preference, etc.

[0084] It should be noted that radar sensors can be used instead of lidar, and pressure sensors or tactile sensors can be used instead of ultrasonic sensors.

[0085] 2. Use data fusion algorithm to fuse the data;

[0086] (1) Conduct environmental modeling and data analysis;

[0087] The collected raw data is denoised to remove outliers and interference signals. Then, the data format is standardized to ensure that data from different sensors can be analyzed under a unified framework. At the same time, data compression is implemented to reduce the burden of storage and transmission.

[0088] The three-dimensional spatial model of the building is constructed using LiDAR data. Then, the model is texture mapped by combining it with the visual information captured by the camera to increase the realism of the model. Then, the position of dynamic obstacles in the environment is updated in real time through infrared sensor and ultrasonic sensor data.

[0089] (2) Data fusion: Integrate data from different sensors to improve the system’s perception accuracy and robustness;

[0090] Design data fusion algorithms to integrate data from cameras, lidar, infrared sensors, ultrasonic sensors, and IMUs. Use Kalman filtering, particle filtering, or other multi-sensor fusion techniques to improve positioning and perception accuracy.

[0091] (3) Data analysis: extracting useful information from the fused data for navigation optimization and decision support;

[0092] Apply machine learning algorithms such as classification, clustering, and regression to analyze personnel flow patterns, common paths, etc. Use pattern recognition technology to identify key features in the environment, such as emergency exits, stair locations, etc. Through data analysis, predict future personnel flow trends and environmental changes.

[0093] It should be noted that neural networks or deep learning methods, such as convolutional neural networks (CNN) or recurrent neural networks (RNN), can also be used to perform multi-dimensional data fusion.

[0094] It should be noted that a two-dimensional topological diagram may also be used to represent the environment, and the dynamics of the environment may be reflected through topological changes.

[0095] 3. Conduct adaptive learning algorithm training;

[0096] (1) User behavior learning: Use machine learning algorithms, such as deep learning, cluster analysis, and genetic algorithms, to analyze user behavior data and learn user behavior patterns;

[0097] (2) Personalized preference identification: Identify the user's personalized preferences, such as route selection and speed preference, based on the user's historical navigation data;

[0098] (3) Model optimization: Dynamically adjust the learning model and optimize the navigation strategy based on user feedback and real-time navigation data;

[0099] It should be noted that transfer learning or meta-learning algorithms can also be used to use the knowledge learned in other similar environments to optimize the navigation strategy of the current environment.

[0100] 4. Navigation path planning and optimization;

[0101] (1) Real-time path planning: Based on the current environment data and user behavior patterns, the optimal navigation path is planned in real time, and the graph search algorithm is applied to calculate the shortest path from the starting point to the end point;

[0102] It should be noted that a rule-based expert system or a fuzzy logic controller may also be used to generate a navigation path.

[0103] (2) Dynamic adjustment: Considering factors such as population density and obstacle distribution, the route is adjusted dynamically to avoid congested areas. The route planning is adjusted dynamically based on the user's real-time behavior changes and feedback.

[0104] (3) Emergency response: When an emergency is detected, the system presets multiple escape routes for emergency situations based on building structure and safety regulations, automatically switches to the preset emergency route, and guides users to evacuate safely.

[0105] 5. Interactive navigation interface display;

[0106] (1) Augmented reality navigation: Combined with AR technology, navigation arrows, route maps and other information are superimposed in the user's field of view to provide users with intuitive navigation guidance;

[0107] (2) Voice and gesture interaction: Supports voice recognition and gesture control, allowing users to interact with the navigation system through natural interaction;

[0108] (3) Information feedback: Provide an immediate feedback mechanism where users can report problems or provide suggestions for improvement;

[0109] It should be noted that in addition to providing a touch screen or voice-controlled user interaction interface, gesture recognition or brain-computer interface technology can also be used as an interaction means.

[0110] 6. System feedback and continuous optimization;

[0111] (1) User feedback collection: Collect user feedback on navigation services through the user interaction interface;

[0112] (2) System performance monitoring: real-time monitoring of navigation system accuracy and user satisfaction;

[0113] (3) Iterative update: Based on the collected data and feedback, the navigation algorithm and environmental model are continuously updated to improve system performance;

[0114] It should be noted that in addition to improving system robustness through multi-sensor design, redundant system design can also be adopted, that is, deploying multiple identical or similar sensors on key functions to ensure that the system can still work normally when some sensors fail.

[0115] It should be noted that in addition to optimizing energy use based on environmental models and user behavior, predictive maintenance algorithms can also be used to schedule energy use based on the building's usage history and predicted maintenance needs.

[0116] In summary, this method adopts a smart building navigation optimization system based on multi-dimensional perception and adaptive learning, which is different from the existing technical means, effectively improving the accuracy and real-time performance of navigation data; improving the emergency response capability of the navigation system in emergency situations; enhancing the interactivity and user experience of the navigation system; and solving the problem that the existing navigation system cannot adapt to the personalized needs of users.

[0117] It should be noted that this application also has the following advantages:

[0118] 1. High-precision environmental perception;

[0119] By integrating multiple sensors (such as cameras, lidar, infrared sensors, etc.), the system can achieve high-precision perception of the internal environment of a building, including the real-time location of dynamic obstacles, crowd density, and environmental changes.

[0120] Compared with traditional single or dual sensor systems, the multi-dimensional perception technology adopted by the present invention can provide richer and more accurate environmental information, thereby improving the accuracy and reliability of navigation.

[0121] 2. Real-time dynamic path planning;

[0122] The system can dynamically adjust and optimize the navigation path based on the multi-dimensional perception data collected in real time, avoid congested areas, and provide a faster route.

[0123] Compared with a fixed path planning system, the adaptive learning algorithm of the present invention can optimize the path according to real-time data, thereby improving the flexibility and efficiency of navigation.

[0124] 3. Adaptive learning ability;

[0125] The system has adaptive learning capabilities and can automatically adjust sensor parameters and navigation strategies based on historical data and user feedback to provide personalized navigation services.

[0126] The adaptive learning mechanism of the present invention enables the system to continuously learn from experience and optimize its performance, rather than just relying on preset rules or static data.

[0127] 4. Highly integrated user interface;

[0128] The system provides a user-friendly interactive interface that allows users to easily input destinations and preferences, while collecting user feedback for system optimization.

[0129] Compared with systems lacking user interaction or feedback mechanisms, the interactive interface of the present invention enhances the user experience and enables the system to be personalized according to user needs.

[0130] 5. Enhanced system robustness;

[0131] Through data fusion technology and multi-sensor design, the system can continue to provide accurate navigation services even when some sensors fail.

[0132] Compared with systems that rely on a single sensor, the multi-dimensional sensing design of the present invention significantly improves the overall robustness of the system and reduces the risk of service interruption due to sensor failure.

[0133] 6. Improved safety;

[0134] The system can monitor potential security risks in the environment in real time, such as intrusion into unauthorized areas, planning of emergency evacuation routes, etc., and notify security personnel or users in a timely manner.

[0135] Compared with the traditional navigation system, the present invention not only provides navigation services, but also enhances the safety management capability of the building and improves the overall safety.

[0136] This method adopts a smart building navigation optimization system based on multi-dimensional perception and adaptive learning, which is different from the existing technical means, effectively improving the accuracy and real-time performance of navigation data; improving the emergency response capability of the navigation system in emergency situations; enhancing the interactivity and user experience of the navigation system; and solving the problem that the existing navigation system cannot adapt to the personalized needs of users.

[0137] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0138] In the present embodiment, a navigation path determination device is also provided, and the device is used to implement the above-mentioned embodiment and preferred implementation mode, and the description has been made no further. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the equipment described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceived.

[0139] Figure 3 is a structural block diagram of a navigation path determination device according to an embodiment of the present invention, the device comprising:

[0140] An acquisition module 302 is used to acquire target data detected by N sensors, where N is a positive integer greater than or equal to 2;

[0141] A first determination module 304 is used to determine the environment data of a target building and the first object data of a target object from the target data, wherein the N sensors and the target object are located in the target building;

[0142] The second determination module 306 is used to determine the navigation path according to the navigation starting point and the navigation end point based on the environment data and the target object data, wherein the target object data includes the first object data and the second object data, and the second object data is data determined according to the historical navigation data of the target object.

[0143] The above device determines the environment data of the target building and the first object data of the target object from the target data detected by N sensors, and determines the path from the navigation start point to the navigation end point based on the determined environment data and target object data. Since multiple sensors are used to obtain environment data and target object data, the path from the navigation start point to the end point can be determined more accurately, and the historical navigation data of the target object is taken into account in the target object data, and the path determination result has more historical preference of the target object, thereby improving the determination effect of the navigation path and solving the problem of poor determination effect of the traditional navigation path.

[0144] In an exemplary embodiment, the second determination module 306 includes: a construction unit, configured to establish a three-dimensional model of the target building according to the environmental data; and determine a navigation path according to a navigation start point and a navigation end point based on the three-dimensional model and the target object data.

[0145] In an exemplary embodiment, the second determination module 306 includes: an analysis unit, configured to analyze the target object data through a machine learning algorithm to obtain a user behavior pattern; and determine a navigation path according to a navigation starting point and a navigation end point based on the three-dimensional model and the user behavior pattern.

[0146] In an exemplary embodiment, the above-mentioned construction unit is also used to construct a three-dimensional spatial architecture of the target building based on the lidar data in the environmental data; to fuse the data detected by different sensors in the environmental data, and to determine the characteristic data corresponding to the target building based on the fused data after the fusion processing, wherein the characteristic data includes: emergency exit location, stair location, crowd flow data in different areas, and obstacle distribution; wherein the three-dimensional model includes the three-dimensional spatial architecture and related data of the three-dimensional spatial architecture, and the related data includes the characteristic data.

[0147] In an exemplary embodiment, the above-mentioned device also includes: an adjustment module, which is used to determine the real-time environmental data of the target building and the feedback information of the target object on the navigation path after determining the navigation path based on the environmental data and the target object data according to the navigation starting point and the navigation end point; and adjust the navigation path in real time according to the real-time environmental data and the feedback information.

[0148] In an exemplary embodiment, the above-mentioned device also includes: a display module, which is used to display the navigation path through augmented reality technology after determining the navigation path according to the navigation starting point and the navigation end point based on the environmental data and the target object data, if the terminal device of the target object supports augmented display technology.

[0149] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0150] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0151] S1, obtaining target data detected by N sensors, where N is a positive integer greater than or equal to 2;

[0152] S2, determining environmental data of a target building and first object data of a target object from the target data, wherein the N sensors and the target object are located in the target building;

[0153] S3, based on the environment data and the target object data, determine the navigation path according to the navigation starting point and the navigation end point, wherein the target object data includes the first object data and the second object data, and the second object data is data determined according to the historical navigation data of the target object.

[0154] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0155] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0156] An embodiment of the present invention further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are performed.

[0157] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0158] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0159] S1, obtaining target data detected by N sensors, where N is a positive integer greater than or equal to 2;

[0160] S2, determining environmental data of a target building and first object data of a target object from the target data, wherein the N sensors and the target object are located in the target building;

[0161] S3, based on the environment data and the target object data, determine the navigation path according to the navigation starting point and the navigation end point, wherein the target object data includes the first object data and the second object data, and the second object data is data determined according to the historical navigation data of the target object.

[0162] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0163] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0164] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0165] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining a navigation path, characterized in that: include: Obtain target data detected by N sensors, where N is a positive integer greater than or equal to 2; Determining environmental data of a target building and first object data of a target object from the target data, wherein the N sensors and the target object are located in the target building; Based on the environment data and the target object data, a navigation path is determined according to a navigation start point and a navigation end point, wherein the target object data includes the first object data and the second object data, and the second object data is data determined according to historical navigation data of the target object.

2. The method according to claim 1, characterized in that Based on the environment data and the target object data, determining a navigation path according to a navigation start point and a navigation end point includes: Establishing a three-dimensional model of the target building according to the environmental data; Based on the three-dimensional model and the target object data, a navigation path is determined according to a navigation start point and a navigation end point.

3. The method according to claim 2, characterized in that Based on the three-dimensional model and the target object data, determining a navigation path according to a navigation start point and a navigation end point includes: Analyze the target object data through a machine learning algorithm to obtain a user behavior pattern; Based on the three-dimensional model and the user behavior pattern, a navigation path is determined according to a navigation starting point and a navigation end point.

4. The method according to claim 2, characterized in that: Establishing a three-dimensional model of the target building according to the environmental data includes: Constructing the three-dimensional spatial structure of the target building based on the laser radar data in the environmental data; fusing the data detected by different sensors in the environmental data, and determining the characteristic data corresponding to the target building according to the fused data after fusion processing, wherein the characteristic data includes: emergency exit location, stair location, crowd flow data in different areas, and obstacle distribution; The three-dimensional model includes the three-dimensional space framework and related data of the three-dimensional space framework, and the related data includes the feature data.

5. The method according to claim 1, characterized in that After determining the navigation path according to the navigation starting point and the navigation end point based on the environment data and the target object data, the method further includes: Determining real-time environmental data of the target building and feedback information of the target object on the navigation path; The navigation path is adjusted in real time according to the real-time environmental data and the feedback information.

6. The method according to claim 1, characterized in that After determining the navigation path according to the navigation starting point and the navigation end point based on the environment data and the target object data, the method further includes: In the case where the terminal device of the target object supports augmented display technology, the navigation path is displayed by using augmented reality technology.

7. A navigation path determination device, characterized in that: include: An acquisition module, used to acquire target data detected by N sensors, where N is a positive integer greater than or equal to 2; A first determination module, configured to determine environmental data of a target building and first object data of a target object from the target data, wherein the N sensors and the target object are located in the target building; The second determination module is used to determine the navigation path according to the navigation starting point and the navigation end point based on the environment data and the target object data, wherein the target object data includes the first object data and the second object data, and the second object data is data determined according to the historical navigation data of the target object.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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