Outdoor positioning and navigation system and positioning and navigation method combined with terrain analysis

Abstract

The invention relates to the technical field of outdoor navigation, and discloses an outdoor positioning and navigation system and an outdoor positioning and navigation method combined with terrain analysis. The system comprises a topographic data acquisition unit, a topographic feature analysis unit, a positioning data integration unit and a navigation path generation unit. The topographic data acquisition unit is responsible for processing original topographic data, identifying and extracting topographic elements and establishing spatial association; the topographic feature analysis unit calculates a topographic feature complexity index by matching a database template and completes conformity judgment; the positioning data integration unit executes correlation analysis of positioning points and terrain entities, and generates accurate position data by evaluating correlation strength; and the navigation path generation unit performs path feasibility analysis and risk calculation based on the result, and finally outputs a navigation scheme. According to the system, the safety and the positioning precision of a navigation path in a complex field environment are improved by quantifying the terrain feature complexity and intelligently fusing positioning data with the terrain.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of outdoor navigation technology, in particular to an outdoor positioning navigation system and method combined with terrain analysis. BACKGROUND

[0002] The current mainstream outdoor positioning navigation system relies heavily on the simple superposition of global satellite positioning signals and basic electronic maps. This kind of technology is still applicable in urban environments with regular road networks, but its core path planning is only based on simple parameters such as distance and time. Once entering complex natural terrain such as mountains, jungles and canyons, the existing technology faces serious challenges. The defect lies in the inability to deeply understand and quantify the complexity of the terrain, resulting in a significant reduction in navigation reliability.

[0003] The existing technology for processing terrain data is limited to the static display level and only serves as a background layer, lacking the ability to intelligently analyze and assess the difficulty of passing through factors such as slope, ground cover and hydrology. The system cannot automatically identify high-risk terrain such as cliffs and swamps, and is prone to planning unfeasible or unsafe paths. The existing solution for processing positioning data is too isolated, usually directly using raw GPS coordinates with errors. When the signal is blocked by terrain, the positioning point will deviate significantly from the true position, and the system lacks a mechanism for cross-validation and error correction using known terrain entities, resulting in insufficient positioning accuracy and reliability in complex environments.

[0004] There is an urgent need for a technological breakthrough in the field of outdoor navigation that can achieve a shift from passive display of terrain to active analysis of terrain features and resolve the reliability issues of positioning data in complex environments. The present application aims to meet this urgent need by quantifying terrain complexity and achieving intelligent fusion of positioning and terrain. SUMMARY

[0005] The present application aims to provide an outdoor positioning navigation system and method combined with terrain analysis to solve the problems raised in the background technology.

[0006] To achieve the above-mentioned purpose, the present application provides an outdoor positioning navigation system combined with terrain analysis, which comprises: a terrain data acquisition unit, which receives information from outdoor terrain data sources, performs identification and extraction of terrain elements, and maps the extracted elements into pre-set terrain fields while establishing spatial correlation between elements, thereby forming a standardized terrain data unit; a terrain feature analysis unit connected to the terrain data acquisition unit, which receives the standardized terrain data unit, performs a matching process between terrain features and database templates, calculates terrain feature complexity indicators based on the matching results, and completes the compliance determination of terrain features based on the terrain feature complexity indicators, generating terrain feature analysis results; A positioning data integration unit coupled to the terrain feature analysis unit, obtains the terrain feature analysis result and real-time positioning device data, performs correlation analysis of the positioning point and the terrain entity, obtains the positioning fusion accuracy value by evaluating the correlation strength, and further generates the accurate position data; A navigation path generation unit, depending on the output of the positioning data integration unit, combines the destination information provided by the user, performs feasibility analysis of the navigation path, calculates the risk coefficient of the path segment, and finally outputs the navigation path scheme and risk prompt information.

[0007] Preferably, the method for generating the standardized terrain data unit by the terrain data acquisition unit comprises: Collecting original terrain data from multiple outdoor terrain data sources, performing quality verification and format standardization processing on the collected data, and generating a terrain data set in a unified format; Based on the terrain data set in a unified format, performing identification and classification operations on terrain elements, specifically extracting terrain elevation, slope, and landform type elements, and forming a terrain element set; Mapping and matching the terrain element set with the preset terrain field, performing structure conversion and relationship binding on the successfully matched fields, establishing spatial correlation identifiers between fields, and thus generating the standardized terrain data unit.

[0008] Preferably, the process of the terrain feature analysis unit generating the terrain feature analysis result involves: Obtaining the terrain type and feature field in the standardized terrain data unit, performing standardized matching with the field definition library, completing missing fields and unifying semantic expression, and obtaining the standardized terrain feature combination; According to the standardized terrain feature combination, retrieving the matching template in the terrain feature database, extracting the nesting level, logical condition quantity, reference frequency, time effectiveness range, and regional coverage information of the template, and forming the feature template structured data; Based on the feature template structured data, calculating the terrain feature complexity index, and using the terrain feature complexity index to compare the terrain data with the template constraint value item by item, completing the feature compliance condition judgment, and generating the terrain feature analysis result.

[0009] Preferably, the step of the positioning data integration unit generating accurate position data comprises: Receiving the terrain feature analysis result and real-time positioning device data, extracting the positioning point field and the terrain entity field, performing entity categorization and deduplication processing, and generating a positioning shared entity set; Performing entity occurrence frequency statistics and category labeling on the positioning shared entity set, recording the distribution information of the entity in different terrain features, and obtaining the entity distribution structure data; Based on the entity distribution structure data, a positioning fusion accuracy value is calculated, and a graph structure centered on a positioning point and referenced to a terrain entity is constructed using the positioning fusion accuracy value, and accurate position data is generated through node relationship mapping.

[0010] Preferably, the navigation path generation unit outputs the navigation path scheme and the risk prompt information, and the operation comprises: Based on the accurate position data, a coordinate matching of a path starting point and a path ending point is performed in combination with a user destination field, terrain obstacles and path feasibility are analyzed, and a preliminary path set is generated; A risk coefficient calculation process is called for the preliminary path set, a risk level is evaluated segment by segment, a low-risk path segment is screened and connected, and an optimized path scheme is formed; The optimized path scheme is subjected to smoothing processing and node adjustment, and the risk prompt information is integrated, and a final navigation path scheme and risk prompt information are generated.

[0011] Preferably, the data quality verification and format standardization processing of the terrain data collection unit further comprises: The collected original terrain data is subjected to noise filtering and outlier detection to ensure that the data quality meets a preset threshold, and clean terrain data is generated; The clean terrain data is converted into a unified coordinate system and data format, and format verification and integrity check are performed, and a standardized terrain data set is output.

[0012] Preferably, the feature template matching process of the terrain feature analysis unit additionally involves: The retrieved feature templates are prioritized and conflict resolved to ensure the accuracy and consistency of template matching; Based on the prioritization results, the template application order is dynamically adjusted to optimize the feature complexity index calculation efficiency.

[0013] Preferably, the entity distribution structure data analysis of the positioning data integration unit further comprises: The spatial distance and topological relationship between entities are calculated to enhance the accuracy of correlation strength evaluation; The enhanced correlation strength is used to update the positioning fusion accuracy value, and the generation process of the accurate position data is iteratively optimized.

[0014] Preferably, the risk coefficient calculation process of the navigation path generation unit further comprises: Real-time environmental data such as weather conditions and light intensity are integrated to dynamically adjust the risk coefficient evaluation parameters; Based on the dynamically adjusted parameters, the path segment risk is recalculated to ensure the real-time and accuracy of the risk prompt information.

[0015] Preferably, the present application also includes an outdoor positioning navigation method combined with terrain analysis, which comprises all the modules and method processes of the outdoor positioning navigation system combined with terrain analysis as described above.

[0016] Compared with the prior art, the present application has the following beneficial effects: By performing the matching process of the terrain feature and the database template and calculating the terrain feature complexity index, quantitative evaluation of the terrain is realized. This evaluation is no longer a simple terrain type identification, but a dynamic scalar value reflecting the difficulty of terrain commuting is generated. The index enables the system to objectively compare and classify the terrain of different areas, providing a deeper decision basis for path planning beyond distance and time. Based on the complexity index, compliance is determined, which can automatically identify high-risk or impassable terrain units, thereby actively avoiding these areas in the path generation stage, improving the safety and practicality of the navigation scheme.

[0017] By performing the correlation analysis of the positioning point and the terrain entity, the discrete satellite positioning coordinates are linked with the continuous geographic spatial information. The spatial relationship between the positioning point and the adjacent terrain entity is evaluated and the correlation strength is obtained, which is essentially a cross-validation of multi-source data. When the GPS positioning point deviates significantly from the terrain entity it should belong to, the correlation strength value will decrease, and the system can determine that the positioning data has low reliability. Conversely, positioning points with high correlation strength are given higher confidence. This mechanism enables the system to perceive and evaluate the quality of positioning data, rather than simply using raw data, thereby outputting more stable and accurate position information in poor signal environments. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The working principle diagram of the outdoor positioning navigation system combined with terrain analysis described in the present application; Figure 2 The flowchart of the working process of the terrain data acquisition unit to generate standardized terrain data units; Figure 3 The flowchart of the working process of the terrain feature analysis unit to generate terrain feature analysis results; Figure 4 The correlation diagram of terrain obstacle distribution and path feasibility impact in the preliminary path generation stage; Figure 5 The correlation analysis diagram of the final risk coefficient of environmental impact factors under different outdoor activity modes. DETAILED DESCRIPTION

[0019] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0020] With reference to Figure 1 The present application provides an outdoor positioning navigation system combined with terrain analysis, which is composed of a terrain data acquisition unit, a terrain feature analysis unit, a positioning data integration unit and a navigation path generation unit. The terrain data acquisition unit is responsible for receiving information of outdoor terrain data sources, performing identification and extraction operations of terrain elements, and mapping the extracted elements to the preset terrain fields, while establishing the spatial correlation between the elements, thereby forming a standardized terrain data unit. The terrain feature analysis unit is connected to the terrain data acquisition unit, receives the standardized terrain data unit, performs the matching process of terrain features and database templates, calculates the terrain feature complexity index according to the matching result, and completes the compliance determination of terrain features based on the index, and generates the terrain feature analysis result. The positioning data integration unit is coupled to the terrain feature analysis unit, obtains the terrain feature analysis result and real-time positioning device data, performs correlation analysis of positioning points and terrain entities, obtains the positioning fusion accuracy value through the evaluation of correlation strength, and further generates accurate position data. The navigation path generation unit depends on the output of the positioning data integration unit, combines the destination information provided by the user, performs the feasibility analysis of the navigation path, calculates the risk coefficient of the path segment, and finally outputs the navigation path scheme and risk prompt information.

[0021] Embodiment 1: With reference to Figure 2 The method for generating the standardized terrain data unit by the terrain data acquisition unit includes collecting original terrain data from multiple outdoor terrain data sources, performing quality verification and format standardization processing on the collected data, and generating a terrain data collection in a unified format. The quality verification and format standardization processing further includes noise filtering and outlier detection on the collected original terrain data to ensure that the data quality meets the preset threshold, and to generate clean terrain data; converting the clean terrain data into a unified coordinate system and data format, performing format verification and integrity check, and outputting the standardized terrain data collection. Based on the terrain data collection in a unified format, the identification and classification operations of terrain elements are performed, and the terrain elevation, slope and landform type elements are specifically extracted, and a terrain element collection is formed; the terrain element collection is mapped and matched with the preset terrain field, the structure conversion and relationship binding are performed on the matched field, the spatial correlation identifier between the fields is established, and the standardized terrain data unit is generated.

[0022] In practice, the terrain data acquisition unit collects raw terrain data from multiple outdoor terrain data sources, including satellite remote sensing data, aerial photogrammetry data, and ground sensor network data. The collected raw terrain data undergoes quality verification, which includes noise filtering and outlier detection. Noise filtering employs an outlier identification method based on statistical distribution, while outlier detection is achieved by comparing the effective range of terrain parameters, ensuring that the data quality meets preset thresholds and generating clean terrain data.

[0023] In practice, clean terrain data is converted into a unified coordinate system and data format. The unified coordinate system adopts the geocentric coordinate system, and the data format is converted into a standardized raster data structure. The converted data undergoes format verification and integrity checks. Format verification confirms that the data fields are complete and the values ​​are valid, while integrity checks ensure that there are no missing data areas, ultimately producing a standardized terrain data set. Based on the standardized terrain data set, terrain feature identification and classification operations are performed. The identification operation uses edge detection and region growing algorithms to extract terrain contours from the raster data. The classification operation clusters pixels into different categories based on terrain morphological features. Specifically, the extracted terrain features include terrain elevation, slope, and landform type, forming a terrain feature set.

[0024] In practice, the terrain feature set is mapped and matched with preset terrain fields. These preset terrain fields are stored in an XML file configured in the system, containing field names, data types, and constraints. For example, the field "Elevation" corresponds to floating-point elevation values, and "SlopeClass" corresponds to integer slope classification codes. The mapping and matching process is completed by parsing the attribute table structure of the terrain features and performing precise string matching of names and data types with the preset terrain fields. For successfully matched fields, structural transformation and relationship binding are performed. Structural transformation converts the vector geometric data of the features into Well-KnownText format conforming to the OGC Simple Feature Specification. Relationship binding is achieved by calculating the spatial topological relationships between features and assigning unique spatial association identifiers. These spatial association identifiers are managed using geocoding based on the Google S2 library, thereby generating standardized terrain data units. These standardized terrain data units, as structured data objects containing geometry, attributes, and spatial relationships, are output to subsequent terrain feature parsing units for further processing.

[0025] Optionally, the outlier identification method based on statistical distribution used in the noise filtering process has a determination rule expressed by the following formula: in: Indicators representing data volatility This represents the total number of data points. This represents the value of a single data point. This represents the average value of the dataset. When a data point... Value exceeds threshold At that time, the data point was marked as noise and filtered, with a threshold coefficient. The system presets the terrain type based on the specific terrain.

[0026] It is understandable that in the identification and classification of terrain features, the seed point selection of the region growing algorithm is based on the local extreme points of terrain elevation, and the growth criteria are based on the elevation difference between pixels and the consistency of gradient direction. In some embodiments, the classification of landform type features is based on the landform classification system specified in the national standard "Specifications for Electronic Map Data of Geographic Information Public Service Platform", classifying each identified terrain region into categories such as hills, mountains, and plains. In some embodiments, the establishment of spatial association identifiers utilizes spatial connection operations of the spatial database to logically link terrain feature records with adjacent, contained, or intersecting relationships.

[0027] Optionally, the data format conversion in the format standardization process involves converting the geometric coordinates of vector data sources to a unified geodetic coordinate system and reconstructing the attribute table into a fixed-field tabular format. In practice, the preset thresholds for quality verification include a reasonable range of elevation values, the non-negativity of slope values, and the validity of landform type coding. Any data record that does not meet the thresholds will be discarded or marked as pending processing.

[0028] Example 2: See Figure 3 The process of generating terrain feature analysis results by the terrain feature analysis unit involves obtaining the terrain type and feature fields from standardized terrain data units, performing normalized matching with the field definition library, completing missing fields, and unifying semantic expressions to obtain standardized terrain feature combinations. Based on the standardized terrain feature combinations, matching templates are retrieved from the terrain feature database, and information on the template's nesting level, number of logical conditions, reference frequency, time range, and geographical coverage is extracted to form structured feature template data. The feature template matching process further involves prioritizing and resolving conflicts among the retrieved feature templates to ensure the accuracy and consistency of template matching; based on the priority ranking results, the template application order is dynamically adjusted to optimize the calculation efficiency of the feature complexity index. Based on the structured feature template data, the terrain feature complexity index is calculated, and the terrain feature complexity index is used to compare the terrain data with the template constraint values ​​item by item to determine the feature conformity and generate the terrain feature analysis results.

[0029] In practical implementation, the terrain feature parsing unit acquires the terrain type and feature fields from the standardized terrain data unit. These include geomorphic classification codes, average elevation zones, and slope classification identifiers. The terrain feature parsing unit then performs a standardized match between the terrain type and feature fields and a field definition library. This library is a database storing metadata for standard terrain fields, including the standard name, data type, value range, unit, and semantic definition of each field. The matching process calculates the similarity between the input field name and the standard names and their synonym lists in the field definition library. For numerical fields, necessary unit conversions are performed to ensure dimensional consistency. Missing fields are filled by searching for the inter-field association rules defined in the field definition library or by using interpolation algorithms to derive them from existing data. Semantic expression is unified by mapping non-standard terms to the standard terms specified in the field definition library, resulting in a standardized combination of terrain features.

[0030] In practice, based on standardized terrain feature combinations, matching templates are retrieved from the terrain feature database. This database consists of multiple templates describing specific terrain features (such as "ridges" and "valleys"), each containing a set of structured judgment conditions for identifying that feature. The nesting level (i.e., the nesting depth of conditional statements), the number of logical conditions (i.e., the total number of independent judgment conditions in the template), reference frequency, time frame, and geographical coverage information of the matching templates are extracted to form structured feature template data. The feature template matching process further involves prioritizing and resolving conflicts among the retrieved feature templates. Priority ranking calculates a priority score for each template based on a pre-defined scoring model. This model comprehensively considers factors such as the template's reference frequency and its geographical coverage matching degree with the current data. Conflict resolution follows the principle of prioritizing templates with higher priority scores, and, in the case of identical scores, prioritizing those with newer time frames, ensuring the accuracy and consistency of template matching. Based on the priority ranking results, the template application order is dynamically adjusted to optimize the calculation efficiency of feature complexity indicators.

[0031] It is understandable that a terrain feature complexity index is calculated based on structured data with feature templates. This index quantifies the complexity of terrain features. The calculation process of the terrain feature complexity index comprehensively considers multiple dimensions in the structured data with feature templates, and its value is expressed by the following formula: in: Indicators representing the complexity of terrain features This represents the total number of template feature parameters involved in the calculation. Indicates the first The weighting factors for each feature parameter are predefined based on the parameter importance. Indicates the first The specific values ​​or hierarchical depth of each feature parameter, such as the number of logical conditions or the number of nested levels, are used to compare the terrain data with the template constraint values ​​item by item using the terrain feature complexity index, thereby determining the feature conformity and generating terrain feature parsing results.

[0032] Optionally, during the normalization matching process of the field definition library, for continuous numerical fields, the min-max normalization method is used to map them to the [0,1] interval; for categorical text fields, one-hot encoding or label encoding is used for unified expression. In some embodiments, the retrieval operation of the feature template database uses a rule-based inference engine, taking the normalized terrain feature combination as input conditions to trigger the corresponding template matching rules. In some embodiments, the priority ranking weight factor is dynamically configured according to the importance level of the terrain region, and the weight of the corresponding template is increased accordingly for regions with higher importance levels.

[0033] Optionally, after the terrain feature complexity index is calculated, the feature conformity determination is achieved by setting a threshold range. If the terrain feature complexity index falls within the preset threshold range, it is determined to conform; otherwise, it is determined to not conform or partially conform. It can be understood that the generated terrain feature parsing result is a structured determination record, containing the terrain unit identifier, the matched template number, the calculated complexity index value, and the final conformity determination conclusion.

[0034] Example 3: The steps of the positioning data integration unit to achieve accurate location data include receiving terrain feature parsing results and real-time positioning device data, extracting positioning point fields and terrain entity fields, performing entity classification and deduplication processing, and generating a positioning shared entity set. The positioning shared entity set is then used to perform entity occurrence count statistics and category labeling, recording the distribution information of entities in different terrain features to obtain entity distribution structure data. Entity distribution structure data analysis also includes calculating the spatial distance and topological relationships between entities to enhance the accuracy of association strength assessment; updating the positioning fusion accuracy value using the enhanced association strength, and iteratively optimizing the generation process of accurate location data. Based on the entity distribution structure data, the positioning fusion accuracy value is calculated, and a graph structure centered on the positioning point and referencing terrain entities is constructed using the positioning fusion accuracy value. Accurate location data is generated through node relationship mapping.

[0035] In practical implementation, the positioning data integration unit receives terrain feature analysis results and real-time positioning device data. The terrain feature analysis results include conformity judgments for terrain units, while the real-time positioning device data comes from various sensors such as GPS receivers, inertial measurement units, and barometers. The positioning data integration unit extracts positioning point fields and terrain entity fields from the input data. The positioning point fields include latitude and longitude coordinates and altitude, while the terrain entity fields include characteristic terrain features such as ridgelines, valley lines, and steep cliff boundaries. These fields undergo entity classification and deduplication. Entity classification is based on the spatial attributes and semantic tags of the elements. The semantic tags are assigned by the terrain data acquisition unit during the element identification stage. Deduplication removes records with duplicate coordinates or semantic redundancy, generating a shared positioning entity set.

[0036] In practice, the shared entity set is subjected to entity occurrence count statistics and category labeling. Entity occurrence count records the number of times each terrain entity is associated with a location point within a specific geographic grid. Category labeling assigns a type identifier to each entity and records the distribution information of entities in different terrain features, such as the frequency of ridgeline entities in steep and gentle terrain areas, thus obtaining entity distribution structure data. Entity distribution structure data analysis also includes calculating the spatial distance and topological relationship between entities. Spatial distance is calculated using Euclidean distance to determine the straight-line length between the centroids of entities. Topological relationship analysis is based on the nine-intersection model to determine the adjacency, containment, or intersection relationships between entities, enhancing the accuracy of association strength assessment. The association strength score is calculated based on the entity occurrence count, the preset weight corresponding to the type identifier, and the topological relationship analysis results. The weight of the type identifier is preset according to the stability of the entity type in positioning; for example, the weight of ridgeline is greater than that of temporary features. The enhanced correlation strength is used to update the positioning fusion accuracy value, and the generation process of precise location data is iteratively optimized. The iterative optimization adopts the least squares estimation method. By continuously adjusting the estimated coordinates of the positioning point, the weighted distance residual squares between the positioning point and each terrain entity are minimized. The iteration terminates when the coordinate adjustment amount of two consecutive iterations is less than a preset threshold.

[0037] It is understandable that the location fusion accuracy value is calculated based on entity distribution structure data. This accuracy value is a quantitative indicator reflecting the degree of spatial relationship between a location point and terrain entities. The calculation of the location fusion accuracy value integrates the statistical distribution and spatial relationship characteristics of the entities, and its mathematical expression is as follows: in: This indicates the positioning fusion accuracy value. This indicates the total number of valid entities in the location shared entity set. Indicates the first The association strength score of each entity Indicates the first The spatial distance between an entity and the core location of the positioning point is used to construct a graph structure with the positioning point as the center and the terrain entity as the reference. The positioning point serves as the central node of the graph structure. Precise location data is generated through node relationship mapping. Specifically, the node relationship mapping adopts the weighted centroid method, which calculates the coordinates of the precise location data by weighting the coordinates of each terrain entity node according to its association strength score.

[0038] Optionally, during entity classification and deduplication, for entity records that are spatially close but have slightly different semantic descriptions, a spatial clustering algorithm is used to merge them, with the coordinates of the cluster center and the main semantic label as representatives. In some embodiments, the initial value of the association strength score is based on preset weights for the number of times the entity appears and the importance of its type, and is dynamically adjusted according to the topological relationship analysis results during iterative optimization. In some embodiments, the graph structure is constructed using an adjacency list or adjacency matrix data structure to store node and edge information, and the node relationship mapping is achieved by calculating the relative vectors between the location point node and each terrain entity node.

[0039] Optionally, the iterative optimization process for generating precise location data can be configured with convergence conditions. The iteration terminates when the change in the positioning fusion accuracy value obtained from two consecutive iterations is less than a preset threshold, and the precise location data generated in the current round is output. It can be understood that the generated precise location data not only includes optimized latitude and longitude coordinates but also a confidence level represented by the final positioning fusion accuracy value. This data is passed to the navigation path generation unit for subsequent use.

[0040] Example 4: The operation of the navigation path generation unit in outputting the navigation path plan and risk warning information involves matching the coordinates of the path start and end points based on precise location data and the user's destination field, analyzing terrain obstacles and path feasibility, and generating a preliminary path set. The preliminary path set is then processed using a risk coefficient calculation procedure to assess the risk level segment by segment, selecting low-risk path segments and optimizing their connections to form an optimized path plan. The optimized path plan is then smoothed and its nodes adjusted, while integrating risk warning information to generate the final navigation path plan and risk warning information.

[0041] In practice, the navigation path generation unit uses precise location data, which includes optimized geographic coordinates and confidence information, to plan routes in conjunction with the user's destination field. The user's destination field is input by the user through a human-computer interaction interface and can be latitude and longitude coordinates or a place name address. The navigation path generation unit matches the coordinates of the path's starting and ending points. The starting point uses the coordinates from the precise location data, and the ending point uses geocoding services to resolve the user's destination into coordinates. It then analyzes terrain obstacles and path feasibility. Terrain obstacles include impassable areas such as water bodies, steep cliffs, and dense forests. Path feasibility considers factors such as slope and surface cover, which contribute to the cost of passage, and generates a preliminary set of paths.

[0042] In practice, the risk coefficient calculation process is invoked on the initial path set, and this process is performed independently for each path segment. The risk coefficient calculation process assesses the risk level segment by segment, based on terrain complexity, historical accident data, and real-time sensor readings. The risk coefficient of each path segment is calculated using a quantitative model, and its mathematical expression is as follows: in: Indicates the first Risk coefficient of each path segment Indicates the first The terrain steepness factor of each path segment Indicates the first Historical risk factors for each path segment and These are preset weighting coefficients. Terrain steepness factor. Obtained through the following method: Calculate the [number] based on digital elevation model data. The average slope value of the geographical area covered by each path segment is mapped to the interval [0,1] using a linear or nonlinear function. The mapped value is the terrain steepness factor. A higher value indicates a steeper terrain. Historical risk factor. Obtained through the following methods: Accessing a database that stores historical accident records or regional risk level assessments, and querying the... The frequency of historical events or the inherent risk level of the area where each path segment is located are normalized to the interval [0,1]. The normalized value is the historical risk factor. A higher value indicates a higher historical risk. Weighting coefficient. and The system is configured according to different navigation scenarios during initialization, such as in hiking navigation mode. 0.7 is acceptable. A value of 0.3 is acceptable; however, in off-road driving mode, and The value can be adjusted. Low-risk path segments are selected and their connections are optimized. The criterion for low-risk path segments is that the risk coefficient is lower than the safety threshold set by the system. Connection optimization ensures that the selected path segments are geometrically and topologically connected to form an optimized path scheme. See Table 1 for the evaluation parameters of path segment risk levels and their typical values.

[0043] Table 1: Parameters for Calculating Risk Coefficient of Path Segment Optionally, the optimized path scheme undergoes smoothing and node adjustment. Smoothing employs a curve fitting algorithm to reduce sharp turns in the path, while node adjustment optimizes the position of key points to avoid small obstacles. Simultaneously, risk warning information is integrated, generating text or icon alerts based on the final risk level of each path segment, resulting in the final navigation path scheme and risk warning information. In some embodiments, the initial path set is generated using a graph search algorithm, such as A* or Dijkstra's algorithm, using terrain navigability as edge weights. In some embodiments, the connection optimization process checks the turning angle between adjacent low-risk path segments; if the angle is too large, transition path points are inserted to ensure path navigability.

[0044] It is understandable that the integration of risk warning information involves binding the warning text corresponding to different risk levels with the geometric data of the path segments. Optionally, the final navigation path plan is output in the form of a list containing ordered coordinate points, while the risk warning information is associated with each path segment or critical path point in the form of an attribute table. In some embodiments, terrain obstacle analysis is achieved by querying geomorphic type features extracted from standardized terrain data units, such as using water polygons or cliff line layers as obstacle boundaries.

[0045] See Figure 4In the preliminary path generation stage, the quantification of the path feasibility impact coefficient in the terrain obstacle analysis needs to be combined with the coverage area ratio of various terrain obstacles. Specifically, terrain obstacle types include water bodies, steep cliffs, dense forests, swamps, rock piles, and gullies. The coverage area ratio of each type is obtained through statistical analysis of geomorphic elements in standardized terrain data units. The calculation of the path feasibility impact coefficient is based on the obstacle coverage area: when the obstacle coverage area ratio increases, the physical constraints on path passage strengthen, and the feasibility impact coefficient increases accordingly (e.g., when the dense forest obstacle coverage area ratio reaches approximately 23%, the path feasibility impact coefficient is close to 0.9); when the obstacle coverage area ratio decreases, the passage constraints weaken, and the feasibility impact coefficient decreases accordingly (e.g., when the swamp obstacle coverage area ratio is approximately 6.5%, the path feasibility impact coefficient is only about 0.2). In the parameter correlation analysis, the coverage area of ​​different terrain obstacles and their feasibility impact exhibit a non-linear relationship: the high coverage area of ​​dense forest corresponds to the highest feasibility impact coefficient, reflecting its strong restriction on path passage; while the low coverage area of ​​swamp corresponds to a low impact coefficient, reflecting its weak constraint on path feasibility. This associated data provides a quantitative basis for subsequent path feasibility screening. When generating the initial path set, the feasibility impact coefficient corresponding to the proportion of obstacle coverage area should be used as the weight parameter of the path edge and input into a graph search algorithm (such as the A* algorithm) to achieve path planning under terrain constraints.

[0046] Example 5: The risk coefficient calculation process of the navigation path generation unit further includes integrating real-time environmental data such as weather conditions and light intensity, dynamically adjusting the risk coefficient assessment parameters, and recalculating the path segment risk based on the dynamically adjusted parameters to ensure the real-time nature and accuracy of risk warning information.

[0047] In practical implementation, the risk coefficient calculation process of the navigation path generation unit integrates real-time environmental data. This real-time environmental data includes weather conditions and light intensity. Weather condition data comes from a meteorological service interface and includes information such as precipitation, wind speed, and visibility. Light intensity data comes from photosensitive sensors or astronomical calculation models. Risk coefficient assessment parameters are dynamically adjusted. These parameters include terrain weight, historical weight, and environmental correction factors. The dynamic adjustment process updates the values ​​of these parameters based on the comparison between real-time environmental data and preset thresholds. The preset thresholds are critical values ​​set for each environmental parameter; for example, the precipitation threshold is set to 10 mm per hour, the wind speed threshold to 5 meters per second, and the light intensity threshold to 100 lux. When the real-time data exceeds the threshold, the parameter adjustment logic is triggered.

[0048] In practice, the risk of each path segment is recalculated based on dynamically adjusted parameters. The risk coefficient calculation process iterates through every independent path segment in the route plan. The dynamically adjusted parameters are substituted into the risk calculation model, outputting a new risk assessment value for each path segment, ensuring the real-time nature and accuracy of risk warning information. The mathematical expression of the risk coefficient calculation model after integrating environmental factors is as follows: in: This indicates the first time after integrating real-time environmental data. The final risk coefficient of each path segment Indicates the first Environmental impact factors of each route segment This represents the basic risk coefficient calculated from topographical and historical factors. These are the weighting coefficients of environmental factors.

[0049] Environmental impact factors The following steps are used to calculate the following: First, the environmental parameters are normalized and mapped to the [0,1] interval. For example, precipitation... Using min-max normalization, the formula is: ,in: This represents the current precipitation. A preset maximum precipitation threshold (e.g., 50 mm / hour) is set. Next, weights are assigned to the normalized parameters, and a weighted sum is calculated as... The initial value. Specifically, It can be calculated using the following formula: in: , , , These are the normalized values ​​of precipitation, wind speed, visibility, and light intensity, respectively. to These are the corresponding weight coefficients, and they satisfy... Visibility and light intensity are calculated using (1 - normalized value) because lower values ​​indicate a more negative environmental impact. This is understandable, as environmental factor weighting coefficients... The value can be read from a predefined configuration table according to different outdoor activity modes. See Table 2 for the configuration table.

[0050] Table 2: Mapping Relationship between Activity Patterns and Environmental Factor Weighting Coefficients γ In some embodiments, real-time environmental data is acquired periodically from external data sources connected to the device's built-in sensors or a wireless network to ensure the timeliness of the input data. In some embodiments, the operation of dynamically adjusting the risk coefficient assessment parameters is performed by a parameter management module, which maintains a mapping table between parameters and real-time environmental conditions. An example of this mapping table is: when the normalized value of precipitation... At that time, terrain weight Increase the base value by 0.1; when the light intensity is normalized... At that time, historical weight Increase the base value by 0.05.

[0051] Optional, environmental factor weighting coefficients The value can be obtained from the configuration table above based on different outdoor activity modes. It can be understood that the generation logic of risk warning information will be based on the final risk coefficient. Different levels of warning messages are triggered based on the numerical range, for example, by setting: Low risk Medium risk. High risk. Optional, final risk factor. The calculation results are stored along with the geometric information of the route segments. When a user approaches a high-risk section, the navigation system will issue an appropriate risk warning in advance. In some embodiments, the impact of weather condition data on risk will take into account its persistence. For example, the parameter management module can be configured to further adjust the weighting coefficients when high precipitation lasts for more than 30 minutes.

[0052] See Figure 5 Using environmental impact factors as the horizontal axis and the final risk coefficient as the vertical axis, the scatter plot distribution and trend line fitting results of three types of activities—hiking, mountaineering, and night driving—are presented. The slope of the trend line for night driving (0.820) is significantly higher than that for mountaineering (0.520) and hiking (0.418), indicating that environmental impact factors have a stronger positive driving effect on the final risk coefficient in the night driving scenario. The intercepts of the trend lines for the three activities (all close to 0.397-0.398) reflect that the baseline levels of the basic risk coefficients composed of terrain and historical factors are similar across different activity modes. The scatter plot distribution characteristics show that when the environmental impact factor is in the range of 0.1-0.3, the final risk coefficients of the three activities differ little. As the environmental impact factor increases to above 0.4, the final risk coefficient of night driving rises rapidly and becomes significantly different from the other two activities. This is highly consistent with the configuration logic of the environmental factor weight coefficient (γ=0.7 for night driving), verifying the effectiveness of the environmental correction factor in the dynamic adjustment of the risk coefficient.

[0053] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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 process, method, article, or apparatus.

[0054] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An outdoor positioning and navigation system incorporating terrain analysis, characterized in that, The system consists of the following components: The terrain data acquisition unit receives information from outdoor terrain data sources, performs terrain feature identification and extraction operations, maps the extracted features to preset terrain fields, and establishes spatial relationships between features to form standardized terrain data units. The terrain feature parsing unit is connected to the terrain data acquisition unit. It receives the standardized terrain data unit, performs a matching process between terrain features and database templates, calculates the terrain feature complexity index based on the matching results, and completes the conformity judgment of terrain features based on the terrain feature complexity index, generating terrain feature parsing results. The positioning data integration unit is coupled to the terrain feature parsing unit to obtain the terrain feature parsing results and real-time positioning device data, perform correlation analysis between positioning points and terrain entities, and obtain the positioning fusion accuracy value by evaluating the correlation strength, thereby generating accurate location data. The navigation path generation unit, relying on the output of the positioning data integration unit and combining the destination information provided by the user, performs a feasibility analysis of the navigation path, calculates the risk coefficient of the path segment, and finally outputs the navigation path plan and risk warning information.

2. The outdoor positioning and navigation system combining terrain analysis according to claim 1, characterized in that, The method for generating standardized terrain data units by the terrain data acquisition unit includes: Raw terrain data was collected from multiple outdoor terrain data sources. The collected data underwent quality verification and format standardization to generate a terrain data set with a unified format. Based on the unified format terrain data set, terrain feature identification and classification operations are performed, specifically extracting terrain elevation, slope, and landform type features, and forming a terrain feature set. The set of terrain features is mapped and matched with preset terrain fields. The matched fields are structurally transformed and bound to relationships, and spatial association identifiers between fields are established to generate standardized terrain data units.

3. The outdoor positioning and navigation system combining terrain analysis according to claim 1, characterized in that, The process by which the terrain feature parsing unit generates terrain feature parsing results involves: The terrain type and feature fields in the standardized terrain data unit are obtained, and they are matched with the field definition library to complete the missing fields and unify the semantic expression, so as to obtain the standardized terrain feature combination. Based on the standardized terrain feature combination, matching templates are retrieved from the terrain feature database, and the nesting level, number of logical conditions, reference frequency, time range and geographical coverage information of the templates are extracted to form structured feature template data. Based on the structured data of the feature template, the terrain feature complexity index is calculated, and the terrain feature complexity index is used to compare the terrain data with the template constraint values ​​one by one to complete the feature conformity determination and generate terrain feature parsing results.

4. The outdoor positioning and navigation system combining terrain analysis according to claim 1, characterized in that, The steps of the positioning data integration unit to achieve accurate location data include: Receive the terrain feature parsing results and real-time positioning device data, extract the positioning point field and terrain entity field, perform entity classification and deduplication, and generate a positioning shared entity set; Perform entity occurrence count and category labeling on the location shared entity set, record the distribution information of entities in different terrain features, and obtain entity distribution structure data; Based on the entity distribution structure data, the positioning fusion accuracy value is calculated, and the positioning fusion accuracy value is used to construct a graph structure with the positioning point as the center and the terrain entity as the reference. Precise location data is generated through node relationship mapping.

5. The outdoor positioning and navigation system combining terrain analysis according to claim 1, characterized in that, The operation of the navigation path generation unit in outputting navigation path schemes and risk warning information includes: Based on the precise location data and combined with the user destination field, the coordinates of the path start and end points are matched, terrain obstacles and path feasibility are analyzed, and a preliminary set of paths is generated. The risk coefficient calculation process is invoked on the preliminary path set to evaluate the risk level of each segment, select low-risk path segments and optimize their connections to form an optimized path scheme. The optimized path scheme is smoothed and nodes are adjusted, while risk warning information is integrated to generate the final navigation path scheme and risk warning information.

6. The outdoor positioning and navigation system combining terrain analysis according to claim 2, characterized in that, The data quality verification and format standardization processing of the terrain data acquisition unit further includes: The collected raw terrain data is subjected to noise filtering and outlier detection to ensure that the data quality meets the preset threshold and generate clean terrain data. The cleaned terrain data is converted into a unified coordinate system and data format, and format verification and integrity checks are performed to produce a standardized terrain data set.

7. The outdoor positioning and navigation system combining terrain analysis according to claim 3, characterized in that, The feature template matching process of the terrain feature parsing unit additionally involves: Prioritize and resolve conflicts among the retrieved feature templates to ensure the accuracy and consistency of template matching; Based on the priority ranking results, the order of template application is dynamically adjusted to optimize the calculation efficiency of feature complexity index.

8. The outdoor positioning and navigation system combining terrain analysis according to claim 4, characterized in that, The entity distribution structure data analysis of the location data integration unit also includes: Calculate the spatial distance and topological relationships between entities to enhance the accuracy of association strength assessment; The enhanced correlation strength is used to update the positioning fusion accuracy value, and the process of generating precise location data is iteratively optimized.

9. The outdoor positioning and navigation system combining terrain analysis according to claim 5, characterized in that, The risk coefficient calculation process of the navigation path generation unit further includes: Integrate real-time environmental data such as weather conditions and light intensity to dynamically adjust risk coefficient assessment parameters; The risk of the path segment is recalculated based on the dynamically adjusted parameters to ensure the timeliness and accuracy of the risk warning information.

10. An outdoor positioning and navigation method incorporating terrain analysis, characterized in that, It includes all modules and method flows of the outdoor positioning and navigation system combined with terrain analysis as described in any one of claims 1 to 9.

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