Indoor multi-floor high-precision navigation method based on NFC (Near Field Communication) and related equipment thereof

Abstract

The invention belongs to the technical field of precise navigation in indoor scenes, and particularly provides an NFC-based indoor multi-floor high-precision navigation method and related equipment thereof, and the method mainly comprises the following steps: obtaining an original identification code of a first near field communication tag read by a mobile terminal, and verifying the original identification code to obtain initial navigation data passing verification; acquiring environment sensing data according to an initial floor code in the initial navigation data, and performing fusion processing on the environment sensing data to obtain an environment partition topological graph; and performing path planning and optimization processing according to the environment partition topological graph to obtain a hierarchical navigation path. The problems that in the prior art, an NFC tag is only used as a map switching trigger signal, environment data is difficult to deeply participate in decision making, a path is difficult to adjust in time during indoor dynamic change, navigation deviation is accumulated in a multi-floor environment, and guidance does not conform to reality are effectively solved, and the navigation accuracy, practicability and user experience in a multi-floor complex scene are improved.

Description

Technical Field

[0001] This application belongs to the field of precision navigation technology in indoor scenarios, and specifically relates to an NFC-based high-precision indoor multi-floor navigation method and related equipment. Background Technology

[0002] Existing indoor multi-floor navigation solutions often use near-field communication tags combined with inertial navigation. By deploying tags at key locations on each floor, mobile terminals can read tag information to confirm the current floor and basic location. Then, relying on pre-set floor map data, inertial navigation is used to achieve path planning and positioning guidance.

[0003] However, near-field communication tags are mostly used as trigger signals for floor map switching, and environmental perception data is collected separately and used only as an auxiliary reference. This makes it difficult for real-time environmental parameters to participate deeply in navigation decisions. When there are dynamic changes such as crowd congestion or temporary barriers indoors, the system is unable to adjust the planned path in a timely manner according to the changes. In complex multi-story environments, this deviation between the path and the actual environment will continue to accumulate, making the navigation guidance inconsistent with the actual situation. Summary of the Invention

[0004] This application provides an NFC-based high-precision indoor multi-floor navigation method and related equipment, which effectively solves the problems in the prior art where NFC tags only serve as map switching trigger signals, environmental data is difficult to deeply participate in decision-making, paths are difficult to adjust in a timely manner when indoor dynamics change, navigation deviations accumulate in multi-floor environments, and guidance does not match reality. It improves the accuracy, practicality and user experience of navigation in complex multi-floor scenarios.

[0005] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, this application provides an NFC-based indoor multi-floor high-precision navigation method, including: The original identification code of the first near-field communication tag read by the mobile terminal is obtained, and the original identification code is verified to obtain the initial navigation data that has passed the verification.

[0006] Environmental sensing data is collected based on the initial floor codes in the initial navigation data, and the environmental sensing data is fused to obtain an environmental partition topology map.

[0007] Based on the environmental partition topology map, path planning and optimization are performed to obtain hierarchical navigation paths.

[0008] Navigation instructions are generated and presented on the hierarchical navigation path to obtain multimodal navigation information.

[0009] The floor switching is triggered by the second near-field communication tag, and the target floor navigation data is obtained by combining the multimodal navigation information.

[0010] The target floor navigation data is processed by cross-floor path fusion and interface update to complete the dynamic update of the navigation system.

[0011] Further, the original identification code is verified to obtain verified initial navigation data, including: The original identification code is checked for format compliance to obtain a label identification code with a standardized format.

[0012] The corresponding initial floor code and map data version number are obtained by performing a mapping relationship query on the label identification code using a database query statement.

[0013] The validity of the map data version number is verified by comparing the version numbers to obtain the initial navigation data that has passed the verification.

[0014] Further, environmental sensor data is collected based on the initial floor codes in the initial navigation data, and the environmental sensor data is fused to obtain an environmental zoning topology map, including: Based on the initial floor codes in the initial navigation data, a data acquisition command is sent to the IoT sensor cluster to obtain the raw environmental sensing data stream.

[0015] Multi-source data time synchronization is performed on the original environmental sensing data stream to obtain an environmental data sequence.

[0016] The environmental data sequence is processed into a grid by spatial interpolation algorithm to generate a floor environmental status distribution map.

[0017] An edge detection algorithm is used to identify the regional boundaries of the floor environment status distribution map to obtain an environmental zoning topology map.

[0018] Furthermore, based on the environmental partition topology map, path planning and optimization are performed to obtain hierarchical navigation paths, including: The shortest path is calculated based on the environmental partition topology map to obtain the preliminary navigation path.

[0019] The preliminary navigation path is analyzed for obstacle avoidance using a path feasibility verification method to obtain an optimized feasible path.

[0020] The feasible path is smoothed using the Bézier curve algorithm to generate the final navigation path.

[0021] The final navigation path is classified into different difficulty levels to obtain a graded navigation path.

[0022] Furthermore, navigation instructions are generated and presented on the hierarchical navigation path to obtain multimodal navigation information, including: The hierarchical navigation path is segmented according to a preset distance threshold to obtain a navigation segment sequence.

[0023] By calculating the direction angle, steering instructions are generated from the navigation segment sequence to obtain a detailed navigation instruction set.

[0024] The navigation instruction set is converted into natural language using template filling technology to generate user-readable navigation guidance.

[0025] The navigation guidance is broadcast using a speech synthesis engine to obtain multimodal navigation information.

[0026] Furthermore, floor switching is triggered via a second near-field communication tag, and combined with the multimodal navigation information, target floor navigation data is obtained, including: The raw data of the second near-field communication tag read by the mobile terminal is obtained, and the raw data is verified to obtain a reliable floor switching request.

[0027] The feasibility of the floor switching request is assessed to obtain a floor switching permission signal.

[0028] The floor switching permission signal is used to query the new floor map data to obtain the target floor navigation data.

[0029] Furthermore, the target floor navigation data undergoes cross-floor path fusion and interface update processing to complete the dynamic update of the navigation system, including: A path merging algorithm is used to perform cross-floor path fusion on the target floor navigation data to obtain the updated complete path.

[0030] The navigation interface is refreshed using real-time rendering technology to achieve dynamic updates of the navigation system.

[0031] Secondly, this application provides an NFC-based indoor multi-floor high-precision navigation system, comprising: Tag recognition and verification module: Obtain the original identification code of the first near-field communication tag read by the mobile terminal, verify the original identification code, and obtain the initial navigation data that has passed the verification.

[0032] Environmental data fusion module: Collects environmental sensor data based on the initial floor code in the initial navigation data, and performs fusion processing on the environmental sensor data to obtain an environmental partition topology map.

[0033] Path planning and optimization module: Based on the environmental partition topology map, it performs path planning and optimization to obtain hierarchical navigation paths.

[0034] Navigation instruction generation module: Generates and presents navigation instructions for the hierarchical navigation path to obtain multimodal navigation information.

[0035] Floor switching trigger module: Triggers floor switching through the second near-field communication tag, and obtains target floor navigation data by combining the multimodal navigation information.

[0036] Cross-floor navigation update module: Performs cross-floor path fusion and interface update processing on the target floor navigation data to complete the dynamic update of the navigation system.

[0037] Thirdly, this application provides an NFC-based indoor multi-floor high-precision navigation device, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the steps of the NFC-based indoor multi-floor high-precision navigation method.

[0038] Fourthly, this application provides a readable storage medium storing computer program instructions, which are read and executed by a processor to perform the steps of an NFC-based indoor multi-floor high-precision navigation method.

[0039] Fifthly, this application provides a computer program product, including a computer program or instructions, wherein when the computer program or instructions are executed by a processor, the steps of implementing an NFC-based indoor multi-floor high-precision navigation method are provided.

[0040] The beneficial effects of this application are: This application effectively solves the problems in existing technologies where NFC tags only serve as map switching trigger signals, environmental data is difficult to deeply participate in decision-making, paths are difficult to adjust in a timely manner when indoor dynamic changes occur, navigation deviations accumulate in multi-story environments, and guidance does not match reality. It achieves deep integration of real-time environmental parameters and navigation decisions, can dynamically adapt to indoor changes and optimize cross-story paths, significantly reduces navigation deviations, and improves the accuracy, practicality and user experience of navigation in complex multi-story scenarios.

[0041] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description and the accompanying drawings. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0043] Figure 1 A flowchart illustrating the NFC-based indoor multi-floor high-precision navigation method of this application is shown. Figure 2 A schematic diagram of the module of the NFC-based indoor multi-floor high-precision navigation system of this application is shown. Detailed Implementation

[0044] To address the problems raised in the background technology, this application improves the accuracy, practicality, and user experience of navigation in complex multi-story scenarios by reading and verifying the first near-field communication tag to obtain initial navigation data, combining the initial floor code with environmental sensor data to generate a topology map, planning and optimizing the path to obtain multimodal navigation information, and triggering the update of cross-floor navigation data via the second near-field communication tag.

[0045] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] In some embodiments, such as Figure 1 As shown, this application provides an NFC-based indoor multi-floor high-precision navigation method, including: S1. Obtain the original identification code of the first near-field communication tag read by the mobile terminal, verify the original identification code, and obtain the verified initial navigation data.

[0047] S2. Collect environmental sensor data based on the initial floor codes in the initial navigation data, and perform fusion processing on the environmental sensor data to obtain an environmental zoning topology map.

[0048] S3. Based on the environmental partition topology map, perform path planning and optimization to obtain hierarchical navigation paths.

[0049] S4. Generate and present navigation instructions for the hierarchical navigation path to obtain multimodal navigation information.

[0050] S5. Trigger floor switching via the second near-field communication tag and combine it with multimodal navigation information to obtain target floor navigation data.

[0051] S6. Perform cross-floor path fusion and interface update processing on the target floor navigation data to complete the dynamic update of the navigation system.

[0052] In some embodiments, the original identification code of the near-field communication tag in S1 is used to uniquely identify the physical identity of the NFC tag, and it can be deployed in the building, with the density arranged according to actual needs.

[0053] The original identification code is verified to obtain the initial navigation data that has passed the verification, including: S11. Perform a format compliance check on the original identification code to obtain a label identification code with a standardized format.

[0054] Format compliance checks include checking whether the character type meets the preset requirements and verifying whether the length of the identification code is consistent with the standard format. Integrity checks can be performed on identification codes that meet the character type and length requirements through preset check bit methods, such as CRC32 check. If the check result is consistent with the check bit of the original identification code, a label identification code with the correct format is obtained.

[0055] S12. Use a database query statement to perform a mapping relationship query on the label identification code to obtain the corresponding initial floor code and map data version number.

[0056] Establish a database linking label identification codes and navigation data. The database stores the initial floor code, map data version number, and other related information corresponding to each label identification code.

[0057] Execute the query statement and retrieve the database results. If a matching record is found, the initial floor code and map data version number corresponding to the label identification code will be obtained. If no matching record is found, a data missing message will be returned, and the missing content needs to be supplemented.

[0058] S13. Verify the validity of the map data version number by comparing the version number, and obtain the initial navigation data that has passed the verification.

[0059] Establish a version number database, which stores the latest map data version number corresponding to each floor code.

[0060] Extract the map data version number obtained from S12 And the corresponding floor code, retrieve the latest map data version number corresponding to that floor code from the version number database. If the map data version number is consistent, the corresponding map data and the initial floor code are integrated to obtain the verified initial navigation data; if invalid, the map data update process is triggered and the initial navigation data is generated again.

[0061] In some embodiments, step S2 involves collecting environmental sensing data based on the initial floor codes in the initial navigation data and fusing the environmental sensing data to obtain an environmental zoning topology map, including: S21. Send a data acquisition command to the IoT sensor cluster based on the initial floor code in the initial navigation data to obtain the raw environmental sensing data stream.

[0062] Based on the initial floor code in the initial navigation data, determine the fixed position coordinates of each sensor in the IoT sensor cluster corresponding to that floor.

[0063] An IoT sensor cluster should include at least one of the following sensors: temperature and humidity sensor, light sensor, infrared obstacle sensor, etc.

[0064] A standardized data acquisition command is sent to the sensor cluster. This command includes parameters such as data acquisition frequency, acquisition duration, and data type. The sensor cluster is instructed to acquire environmental data in real time according to these parameters. The environmental data is bound to the corresponding fixed-position coordinates of the sensors. The acquired environmental data is received wirelessly to obtain the raw environmental sensing data stream. Each data point in the raw environmental sensing data stream includes a sensor identifier and the fixed-position coordinates of the sensor. Collected data values Collection timestamp Where i represents the index of the data entry, The timestamp of the acquisition of the i-th data point in the original environmental sensing data stream is represented by j, where j represents the index of the individual sensor. This represents the data value collected by the j-th sensor. This represents the coordinates of the fixed position of the j-th sensor.

[0065] S22. Perform multi-source data time synchronization on the original environmental sensor data stream to obtain the environmental data sequence.

[0066] Establish a unified time reference for the navigation system and obtain the time offset of each sensor. Then, the timestamp of each environmental data collection is calibrated to obtain the calibrated timestamp. ;in, This represents the timestamp after calibration of the i-th environmental data. This represents the time offset of the j-th sensor.

[0067] After calibration, according to the calibrated timestamp Sort all data in ascending order and remove data with duplicate or abnormal timestamps to obtain the environmental data sequence.

[0068] S23. The environmental data sequence is processed into a grid by spatial interpolation algorithm to generate a floor environmental status distribution map.

[0069] Based on the physical dimensions of the floors, a two-dimensional plane coordinate system is established, and this coordinate system is divided into equally sized grid cells. Each grid cell corresponds to a unique coordinate. Then, the coordinates of the k-th grid cell are... .

[0070] The spatial interpolation method is used to estimate the environmental data value of each grid cell. Specifically, for each grid cell, all sensors within a certain range around it are selected, and the arithmetic mean of the data values ​​collected by these sensors is calculated as the grid estimate.

[0071] After estimating all grid cells, the coordinates of each grid cell are associated with the corresponding estimated data values ​​to generate a floor environmental status distribution map.

[0072] S24. Use the edge detection algorithm to identify the regional boundaries of the floor environment status distribution map and obtain the environmental zoning topology map.

[0073] By mapping environmental data values ​​to grayscale values, the floor environmental status distribution map is converted into a grayscale image. An edge detection algorithm, such as the Canny edge detection algorithm, is used to process the grayscale image. A Gaussian filter is applied to smooth the image, eliminating noise interference. The gradient intensity G(x,y) and gradient direction of each pixel in the image are calculated. .

[0074] Using gradient direction Non-maximum suppression is performed, retaining only pixels with the most dramatic gray-level changes along the gradient direction and discarding other pixels to refine the edges; a high threshold is set. and low threshold The gradient intensity G(x,y) is superimposed Preserve strong edges, below Non-edge removal, between and If a weak edge is connected to a strong edge, it is preserved, resulting in an edge image. The high threshold can be selected by analyzing the image gradient intensity distribution and choosing a critical value that distinguishes obvious edges from noise. low threshold Based on high threshold Determined proportionally, such as a high threshold. of or This is used to preserve potential weak edges associated with strong edges.

[0075] Based on the edge image connection contour, combined with gradient information, the boundaries of areas such as channels and obstacles are identified, and the physical structure of the floors is associated to generate an environmental zoning topology map.

[0076] In some embodiments, the path planning and optimization process in S3 based on the environmental partition topology map to obtain a hierarchical navigation path includes: S31. Calculate the shortest path based on the environmental partition topology map to obtain the preliminary navigation path.

[0077] Using the first near-field communication tag as the starting point and the user-inputted or preset location as the ending point, mark the specific coordinates and areas of both in the environmental partition topology map.

[0078] The region center point and channel turning point in the environmental partition topology map are set as vertices V, and the passable paths between regions are set as edges E connecting the vertices. The path length is used as the weight of the edge to construct an undirected graph G=(V,E). Dijkstra's algorithm is used to calculate the shortest path from the starting vertex to the ending vertex. Finally, it is mapped back to the physical space to obtain a preliminary navigation path composed of continuous coordinate points, which includes the position information of each key node on the path.

[0079] S32. Obstacle avoidance analysis is performed on the preliminary navigation path using the path feasibility verification method to obtain the optimized feasible path.

[0080] The initial navigation path is decomposed into multiple continuous path segments. Combining the fixed obstacle information in the environmental partition topology map and real-time environmental data, each path segment is verified to see if it passes through an obstacle area or if there are temporary obstacles. If there are obstacles, A* planning is used to plan around the obstacles. After integration, an optimized feasible path is obtained, which includes the coordinate range and traffic status of each segment.

[0081] The A* programming method for bypassing obstacles involves starting from the beginning of the path segment and ending at the end, setting obstacles as impassable, and using a heuristic function to guide the search to ensure smooth connection between the sub-path and the original path.

[0082] S33. Use the Bézier curve algorithm to smooth the feasible path and generate the final navigation path.

[0083] All turning points are extracted from the feasible path, and the straight line segments between adjacent turning points are smoothed using the Bézier curve algorithm to obtain the final navigation path, effectively eliminating sharp corners.

[0084] S34. Classify the difficulty of the final navigation path to obtain a graded navigation path.

[0085] Obtain or calculate the slope in the final navigation path ,width Obstacle density ; , This refers to the width data of the corresponding channel in the generated environment partitioning topology map. ;in, Represents the height difference between the start and end points of the path. Represents the horizontal distance of the path. This represents the total number of obstacles within a rectangular area extending to both sides of the path's center, with the specified length as an example. The specified length is, for example, 0.5 meters. This represents the area of ​​the rectangular region.

[0086] Collect walking feedback data from a specified number of users of different ages in various indoor environments, and statistically analyze the critical values ​​of "easy", "moderate" and "difficult" for each parameter. Take the average value to determine the first-level threshold and the second-level threshold. Rate each path according to the graded thresholds: below the first-level threshold is "easy", between the two-level thresholds is "moderate", and above the second-level threshold is "difficult". Integrate the walking difficulty grade and coordinate range to obtain the graded navigation path.

[0087] In some embodiments, S4 involves generating and presenting navigation instructions for the hierarchical navigation path to obtain multimodal navigation information, including: S41. Perform path segmentation processing on the hierarchical navigation path according to the preset distance threshold to obtain the navigation segment sequence.

[0088] Determine the overall starting point in the hierarchical navigation path and overall endpoint Coordinates, calculate the total path length.

[0089] Set preset distance threshold ,from Initially, extract segments along the hierarchical navigation path with a length not exceeding [a certain value]. Independent navigation segments, if the remaining length of the last segment is insufficient If it is not a navigation segment, then it is directly retained as the last independent navigation segment, and the starting coordinates of each independent navigation segment are recorded. and endpoint coordinates The navigation segments are arranged in spatial order, along with corresponding levels of walking difficulty.

[0090] Distance threshold It can be obtained by multiplying the average walking speed of a specified number of users in an indoor environment with the average reaction time to navigation instructions.

[0091] S42. By calculating the direction angle, the navigation segment sequence is used to generate steering instructions, resulting in a detailed navigation instruction set.

[0092] With true north as 0° and clockwise as the positive direction, calculate the direction angle for the k-th independent navigation segment in the navigation segment sequence. , If the calculation result is negative, add 360° to convert it to an angle value between 0° and 360°, and then calculate the steering angle between two adjacent independent navigation segments. , ,like Then, it is corrected by adding or subtracting 360°; among which, , These represent the x and y coordinates of the starting point of the k-th independent navigation segment, respectively. , These represent the x and y coordinates of the endpoint of the k-th independent navigation segment, respectively. , These represent the azimuth angles of two adjacent independent navigation segments.

[0093] Generate a navigation segment for each individual segment, including travel distance and direction angle. A set of navigation instructions that classifies turning directions and walking difficulty.

[0094] S43. Use template filling technology to perform natural language conversion on the navigation instruction set to generate user-readable navigation guidance.

[0095] Establish a natural language template library containing templates corresponding to navigation command types, such as the template for "go straight" being: "Go straight in the current direction". Meters, the walking difficulty level of this independent navigation segment is [missing information]. ,arrive The template for turning left is: "Walking"; After meters, turn left. The new independent navigation segment has a travel difficulty level of [degree / degree]. "Continue walking in the new direction"; the template for turning right is "Walk". After rice, turn right. The new independent navigation segment has a travel difficulty level of [degree / degree]. "Continue along the new direction," the specifics of which can be adjusted according to actual needs; among them, Represents the length of a single independent navigation segment. This represents the difficulty level of walking in that independent navigation segment. A physical identifier representing the end point of this independent navigation segment. This represents the steering angle corresponding to that independent navigation segment.

[0096] Based on the parameters in the navigation instruction set, the corresponding natural language template is matched according to the turning direction, and the parameters are filled into the corresponding positions of the template to generate a single natural language navigation guide.

[0097] S44. Use a speech synthesis engine to broadcast navigation guidance in voice, and obtain multimodal navigation information.

[0098] The navigation text is preprocessed, including converting Arabic numerals to Chinese numerals and adding pause markers to long sentences exceeding a specified word count.

[0099] Next, the speech synthesis engine is invoked, the preprocessed text is input, the speech parameters are set, and the engine converts the text into WAV format audio data through the speech synthesis algorithm, and adds a timestamp to each audio to match the trigger time of the corresponding independent navigation segment.

[0100] By associating audio data with navigation guidance, multimodal navigation information is generated, which includes text, voice, coordinates, and walking difficulty level.

[0101] In some embodiments, step S5, which triggers floor switching via a second near-field communication tag and combines it with multimodal navigation information to obtain target floor navigation data, includes: S51. Obtain the raw data of the second near-field communication tag read by the mobile terminal, verify the raw data, and obtain a reliable floor switching request.

[0102] The mobile terminal obtains the raw data from the second near-field communication tag. The second near-field communication tag is deployed at floor switching nodes such as elevator entrances and stairwells. Its content is the same as that of the near-field communication tag, both containing a unique tag identification code and verification information.

[0103] The same process as the first near-field communication tag verification in S11-S13 is used to verify the second near-field communication tag. If the verification is successful, a trusted floor switching request is obtained; otherwise, the request is rejected and the user is prompted to reread the tag.

[0104] S52. Perform a feasibility assessment on the floor switching request and obtain a floor switching permission signal.

[0105] Parse the target floor code from the trusted floor switching request to obtain the floor code of the current navigation.

[0106] Query the building floor connectivity database, which stores the connectivity information of elevators and staircases between all floors.

[0107] By combining multimodal navigation information, check whether the current position is in the process of walking on a certain independent navigation segment. If so, it is determined that "interruption is not allowed"; otherwise, it is determined that "interruption is allowed".

[0108] A floor switching permission signal containing the target floor code is generated only when the floors are connected and the "interruption allowed" condition is met.

[0109] S53. Query the new floor map data based on the floor switching permission signal to obtain the target floor navigation data.

[0110] Extract the target floor code from the floor switching permission signal, perform map data query on the target floor according to the query logic of S12, and obtain the environmental zoning topology map of the target floor and the fixed position coordinates of each sensor of the IoT sensor cluster according to the logic of S21-S24 to obtain the target floor navigation data.

[0111] In some embodiments, step S6 involves cross-floor path fusion and interface update processing of the target floor navigation data to complete the dynamic update of the navigation system, including: S61. A path merging algorithm is used to perform cross-floor path fusion on the target floor navigation data to obtain the updated complete path.

[0112] The floor switching node is the end point of the current floor navigation path, that is, the physical location where the user triggers the floor switching, such as the coordinates of the elevator entrance or stairwell, which corresponds to the deployment location of the second near-field communication tag in S51; the starting point of the new floor is defined as the coordinates of the connected point in the new floor that matches the switching point, such as the exit coordinates of the elevator on the new floor.

[0113] Using the same logic as the shortest path calculation in S31, the length of the transition path between the floor switching node and the starting point of the new floor is calculated, and the three parts of the path content are spliced ​​together in spatial order: the incomplete navigation path of the current floor, the transition path, and the path planned by the environmental partition topology map in the navigation data of the target floor.

[0114] The path planned by the environmental zoning topology map in the target floor navigation data is a path generated using the same shortest path calculation logic as S31, with the new floor starting point as the starting point and the user-preset or input navigation endpoint as the endpoint.

[0115] Finally, the path smoothing method in S33 is used to smooth the corners at the splicing points, resulting in the updated complete path.

[0116] S62. The navigation interface is refreshed using real-time rendering technology to complete the dynamic update of the navigation system.

[0117] The system removes display elements related to the old floors from the navigation interface, renders the new floor map background, overlays the complete navigation route on the map and marks the difficulty level, such as green for "easy" sections, yellow for "medium" sections, and red for "difficult" sections. It also marks the user's current location and navigation target point, generates voice broadcast, and sends the new interface data to the mobile terminal to complete the dynamic update of the navigation system.

[0118] In some embodiments, such as Figure 2As shown, this application provides an NFC-based indoor multi-floor high-precision navigation system, including: Tag recognition and verification module: Obtain the original identification code of the first near-field communication tag read by the mobile terminal, verify the original identification code, and obtain the initial navigation data that has passed the verification.

[0119] Environmental data fusion module: Collects environmental sensor data based on the initial floor code in the initial navigation data, and performs fusion processing on the environmental sensor data to obtain an environmental partition topology map.

[0120] Path planning and optimization module: Based on the environmental partition topology map, it performs path planning and optimization to obtain hierarchical navigation paths.

[0121] Navigation instruction generation module: Generates and presents navigation instructions for the hierarchical navigation path to obtain multimodal navigation information.

[0122] Floor switching trigger module: Triggers floor switching through the second near-field communication tag, and obtains target floor navigation data by combining the multimodal navigation information.

[0123] Cross-floor navigation update module: Performs cross-floor path fusion and interface update processing on the target floor navigation data to complete the dynamic update of the navigation system.

[0124] In some embodiments, this application provides an NFC-based indoor multi-floor high-precision navigation device, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the steps of the NFC-based indoor multi-floor high-precision navigation method.

[0125] In some embodiments, this application provides a readable storage medium storing computer program instructions, which are read and executed by a processor to perform the steps of an NFC-based indoor multi-floor high-precision navigation method.

[0126] In some embodiments, this application provides a computer program product, including a computer program or instructions, wherein when the computer program or instructions are executed by a processor, the steps of implementing an NFC-based indoor multi-floor high-precision navigation method are provided.

[0127] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0128] In this application, several formulas are calculated by removing dimensions and taking their numerical values. The formulas are established by collecting a large amount of data and simulating the most recent real situation. Some coefficients or weights in the formulas are set by those skilled in the art according to the actual situation, so they will not be elaborated here.

[0129] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0130] Any references to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory.

[0131] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A high-precision indoor multi-floor navigation method based on NFC, characterized in that, include: Obtain the original identification code of the first near-field communication tag read by the mobile terminal, verify the original identification code, and obtain the verified initial navigation data; Environmental sensor data is collected based on the initial floor codes in the initial navigation data, and the environmental sensor data is fused to obtain an environmental zoning topology map. Based on the environmental partition topology map, path planning and optimization are performed to obtain hierarchical navigation paths; The hierarchical navigation path is used to generate and present navigation instructions to obtain multimodal navigation information; Floor switching is triggered by the second near-field communication tag, and the target floor navigation data is obtained by combining the multimodal navigation information. The target floor navigation data is processed by cross-floor path fusion and interface update to complete the dynamic update of the navigation system.

2. The NFC-based indoor multi-floor high-precision navigation method according to claim 1, characterized in that, The original identification code is verified to obtain verified initial navigation data, including: Perform a format compliance check on the original identification code to obtain a label identification code with a standardized format; The corresponding initial floor code and map data version number are obtained by performing a mapping relationship query on the label identification code using a database query statement; The validity of the map data version number is verified by comparing the version numbers to obtain the initial navigation data that has passed the verification.

3. The NFC-based indoor multi-floor high-precision navigation method according to claim 1, characterized in that, Environmental sensor data is collected based on the initial floor codes in the initial navigation data, and the environmental sensor data is fused to obtain an environmental zoning topology map, including: Based on the initial floor code in the initial navigation data, a data acquisition command is sent to the IoT sensor cluster to obtain the raw environmental sensing data stream; Multi-source data time synchronization is performed on the original environmental sensing data stream to obtain an environmental data sequence; The environmental data sequence is processed into a grid by spatial interpolation algorithm to generate a floor environmental status distribution map. An edge detection algorithm is used to identify the regional boundaries of the floor environment status distribution map to obtain an environmental zoning topology map.

4. The NFC-based indoor multi-floor high-precision navigation method according to claim 1, characterized in that, Based on the environmental partition topology map, path planning and optimization are performed to obtain hierarchical navigation paths, including: The shortest path is calculated based on the environmental partition topology map to obtain a preliminary navigation path; The preliminary navigation path is analyzed for obstacle avoidance using a path feasibility verification method to obtain an optimized feasible path. The feasible path is smoothed using the Bézier curve algorithm to generate the final navigation path; The final navigation path is classified into different difficulty levels to obtain a graded navigation path.

5. The NFC-based indoor multi-floor high-precision navigation method according to claim 1, characterized in that, The hierarchical navigation path is used to generate and present navigation instructions to obtain multimodal navigation information, including: The hierarchical navigation path is segmented according to a preset distance threshold to obtain a navigation segment sequence. By calculating the direction angle, steering instructions are generated from the navigation segment sequence to obtain a detailed navigation instruction set; The navigation instruction set is converted into natural language using template filling technology to generate user-readable navigation guidance; The navigation guidance is broadcast using a speech synthesis engine to obtain multimodal navigation information.

6. The NFC-based indoor multi-floor high-precision navigation method according to claim 1, characterized in that, Floor switching is triggered by a second near-field communication tag, and combined with the multimodal navigation information, target floor navigation data is obtained, including: The raw data of the second near-field communication tag read by the mobile terminal is obtained, and the raw data is verified to obtain a reliable floor switching request. The feasibility of the floor switching request is assessed to obtain a floor switching permission signal; The floor switching permission signal is used to query the new floor map data to obtain the target floor navigation data.

7. The NFC-based indoor multi-floor high-precision navigation method according to claim 1, characterized in that, The target floor navigation data undergoes cross-floor path fusion and interface update processing to achieve dynamic updates of the navigation system, including: A path merging algorithm is used to perform cross-floor path fusion on the target floor navigation data to obtain the updated complete path; The navigation interface is refreshed using real-time rendering technology to achieve dynamic updates of the navigation system.

8. An indoor multi-floor high-precision navigation system based on NFC, characterized in that, include: Tag recognition and verification module: acquires the original identification code of the first near-field communication tag read by the mobile terminal, verifies the original identification code, and obtains the initial navigation data that has passed the verification; Environmental data fusion module: Collects environmental sensor data based on the initial floor code in the initial navigation data, and performs fusion processing on the environmental sensor data to obtain an environmental partition topology map; Path planning and optimization module: Performs path planning and optimization based on the environmental partition topology map to obtain hierarchical navigation paths; Navigation instruction generation module: generates and presents navigation instructions for the hierarchical navigation path to obtain multimodal navigation information; Floor switching trigger module: Triggers floor switching through the second near-field communication tag, and obtains target floor navigation data by combining the multimodal navigation information; Cross-floor navigation update module: Performs cross-floor path fusion and interface update processing on the target floor navigation data to complete the dynamic update of the navigation system.

9. An indoor multi-floor high-precision navigation device based on NFC, characterized in that, It includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the steps of the NFC-based indoor multi-floor high-precision navigation method according to any one of claims 1-7.

10. A readable storage medium, characterized in that, The readable storage medium stores computer program instructions, which are read and executed by a processor to perform the steps of the NFC-based indoor multi-floor high-precision navigation method according to any one of claims 1-7.

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