Crowdsourcing data driven model and parameter self-learning navigation method and device

Abstract

The invention relates to the technical field of integrated navigation, in particular to a crowdsourcing data driven model and parameter self-learning navigation method and device.The method comprises the steps that crowdsourcing map data of a target navigation system is obtained, and at least one feature factor is extracted; updating the crowdsourcing map data, and generating crowdsourcing map label data; based on the at least one characteristic factor and the crowdsourcing map label data, constructing a preset variance self-learning model, and optimizing position variance information of the target navigation system to obtain a model prediction position variance; and constructing a scaling factor according to the model prediction position variance, and generating a final positioning result of the target navigation system through the scaled model prediction position variance and a preset semi-tight combination architecture. According to the method, the position variance information can be autonomously learned, manual intervention and high-precision reference truth value equipment are not needed, the system model can be continuously optimized through crowdsourcing data updating while high-precision navigation positioning is performed, and the generalization ability and the intelligent level of the system are continuously improved.

Description

Technical Field

[0001] The present application relates to the field of combined navigation technology, and in particular to a navigation method and device of a crowdsourced data driven model and parameter self-learning. Background Art

[0002] With the development of artificial intelligence and Internet of Things technologies, unmanned systems represented by drones and self-driving cars are moving towards intelligence and networking. Positioning is the basis and core of environmental perception and decision-making of unmanned systems, and its accuracy and availability are crucial.

[0003] Among the related technologies, the Global Navigation Satellite System (GNSS), as the most mature positioning technology, has been widely used in the field of vehicle navigation. However, due to the fragility and limitations of satellite radio signals, GNSS faces three major problems: blocking, interference, and deception. In complex scenes such as urban canyons, tree-lined areas, elevated roads, and tunnels, its navigation and positioning performance rapidly declines. As a relative positioning method, the Strapdown Inertial Navigation System (SINS) can provide full navigation information including position, speed, and attitude around the clock and at high frequency. However, due to the error of the inertial measurement unit device, its dead reckoning error will diverge over time. GNSS and SINS are highly complementary. The GNSS / SINS combined navigation formed by the complementary advantages of the two has the characteristics of high accuracy, strong reliability, wide adaptability, and good dynamics. Therefore, it has become an important technical means for navigation and positioning of unmanned systems.

[0004] However, the GNSS / SINS combined navigation system in related technologies mainly relies on model-driven positioning, which makes it difficult to fully extract common features in large-scale data and build complex models. Most algorithms focus on GNSS gross error detection, and lack research on the consistency between GNSS position variance and actual error. In addition, algorithm training schemes usually rely on high-precision reference systems to generate labels. As a result, the accuracy of navigation and positioning results is lacking and certain costs are increased, which needs to be solved urgently. Summary of the invention

[0005] The present application provides a crowdsourced data-driven model and parameter self-learning navigation method and device to solve the problems in the related art that the GNSS / SINS combined navigation system mainly relies on model-driven when achieving positioning, it is difficult to fully extract common features in large-scale data, it is not easy to achieve the construction of complex models, and most algorithms mainly focus on GNSS gross error detection, and the consistency between GNSS position variance and actual error is insufficient. In addition, the algorithm training scheme also usually relies on high-precision reference system to generate labels. As a result, the accuracy of the navigation positioning results is lacking and certain costs are increased.

[0006] The first aspect of the present application provides a navigation method of crowdsourced data driven model and parameter self-learning, comprising the following steps: acquiring crowdsourced map data of a target navigation system, and extracting at least one characteristic factor of the crowdsourced map data; screening map results that meet a preset reliability based on the at least one characteristic factor, and updating the crowdsourced map data according to the map results to generate crowdsourced map label data that meets a preset accuracy; constructing a preset variance self-learning model based on the at least one characteristic factor and the crowdsourced map label data, and optimizing the position variance information of the target navigation system through the preset variance self-learning model to obtain the model predicted position variance of the target navigation system; constructing a scaling factor based on the model predicted position variance to scale the model predicted position variance using the scaling factor to obtain the scaled model predicted variance, and generating the final positioning result of the target navigation system through the scaled model predicted position variance and a preset semi-tight combination architecture.

[0007] Optionally, in one embodiment of the present application, extracting at least one characteristic factor of the crowdsourced map data includes: post-processing and solving the crowdsourced map data to obtain processed crowdsourced map data; and extracting the at least one characteristic factor based on the processed crowdsourced map data using a preset correlation coefficient.

[0008] Optionally, in one embodiment of the present application, the filtering of map results that meet a preset reliability based on the at least one characteristic factor, and updating the crowdsourced map data according to the map results to generate crowdsourced map label data that meet a preset accuracy include: determining the accuracy characteristics and observation update interval of the inertial sensor of the target navigation system using simulated positioning errors; filtering the map results that meet the preset reliability based on the accuracy characteristics, the observation update interval and the at least one characteristic factor; and correcting the divergent errors of the map results to generate crowdsourced map label data that meet the preset accuracy.

[0009] Optionally, in one embodiment of the present application, the preset variance self-learning model is constructed based on the at least one characteristic factor and the crowdsourced map label data, including: expanding the channel of the at least one characteristic factor, and using temporal convolution to aggregate the feature information of the at least one characteristic factor to obtain local features with time dependence; learning contextual information with dependencies from the local features, and based on the contextual information, using an attention mechanism to focus on the feature information to generate position variance information; using the position variance information and the crowdsourced map label data to train and optimize the initial variance self-learning model to obtain the preset variance self-learning model.

[0010] Optionally, in one embodiment of the present application, the final positioning result of the target navigation system is generated by using the scaled model predicted position variance and a preset semi-tight combination architecture, including: updating the data information of the strapdown inertial navigation system according to the map result measurement; fusing the data information and the scaled model predicted position variance through the preset semi-tight combination structure to obtain the final positioning result of the target navigation system.

[0011] The second aspect of the present application provides a navigation device with a crowdsourced data-driven model and parameter self-learning, including: an extraction module, used to obtain crowdsourced map data of a target navigation system, and extract at least one characteristic factor of the crowdsourced map data; an update module, used to screen map results that meet a preset reliability based on the at least one characteristic factor, and update the crowdsourced map data according to the map results to generate crowdsourced map label data that meets a preset accuracy; a construction module, used to construct a preset variance self-learning model based on the at least one characteristic factor and the crowdsourced map label data, and optimize the position variance information of the target navigation system through the preset variance self-learning model to obtain the model predicted position variance of the target navigation system; a positioning module, used to construct a scaling factor according to the model predicted position variance, so as to scale the model predicted position variance using the scaling factor to obtain the scaled model predicted variance, and generate the final positioning result of the target navigation system through the scaled model predicted position variance and a preset semi-tight combination architecture.

[0012] Optionally, in one embodiment of the present application, the extraction module includes: a processing unit, used to post-process and solve the crowd-sourced map data to obtain processed crowd-sourced map data; an extraction unit, used to extract the at least one characteristic factor based on the processed crowd-sourced map data using a preset correlation coefficient.

[0013] Optionally, in one embodiment of the present application, the update module includes: a determination unit, used to determine the accuracy characteristics and observation update interval of the inertial sensor of the target navigation system using simulated positioning errors; a screening unit, used to screen map results that meet a preset reliability based on the accuracy characteristics, the observation update interval and the at least one characteristic factor; and a correction unit, used to correct the divergent error of the map result to generate crowdsourced map label data that meets the preset accuracy.

[0014] Optionally, in one embodiment of the present application, the building module includes: an expansion unit, used to expand the channel of the at least one feature factor, and use temporal convolution to aggregate the feature information of the at least one feature factor to obtain a local feature with time dependence; a learning unit, used to learn contextual information with dependencies from the local features, so as to generate position variance information based on the context information and focusing on the feature information using an attention mechanism; an optimization unit, used to train and optimize an initial variance self-learning model using the position variance information and the crowdsourced map label data to obtain the preset variance self-learning model.

[0015] Optionally, in one embodiment of the present application, the positioning module includes: an updating unit, used to update the data information of the strapdown inertial navigation system according to the map result measurement; a fusion unit, used to fuse the data information and the scaled model predicted position variance through the preset semi-tight combination structure to obtain the final positioning result of the target navigation system.

[0016] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the crowdsourcing data driven model and parameter self-learning navigation method as described in the above embodiments.

[0017] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above crowdsourcing data-driven model and parameter self-learning navigation method.

[0018] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above crowdsourcing data-driven model and parameter self-learning navigation method.

[0019] The embodiment of the present application can generate certain characteristic factors by acquiring crowdsourced map data of the target navigation system, and obtain crowdsourced map label data while updating the data information of the relevant navigation system to train a certain variance self-learning model, and finally use the model prediction variance generated by the variance self-learning model and a certain semi-tight combination architecture to fuse the updated data information to generate a high-precision positioning result. Thus, the autonomous learning of variance information is realized, without the need for manual intervention and high-precision reference truth value equipment, and the system model is continuously optimized through the update of crowdsourced data while achieving high-precision navigation and positioning, and the generalization ability and intelligence level of the system are continuously improved. Thus, the GNSS / SINS combined navigation system in the related art mainly relies on model driving when realizing positioning, it is difficult to fully extract common features in large-scale data, it is not easy to realize the construction of complex models, and most algorithms mainly focus on GNSS gross error detection, and the consistency between GNSS position variance and actual error is insufficient. In addition, the algorithm training scheme also usually relies on high-precision reference system to generate labels, thereby, the accuracy of navigation and positioning results is lacking and certain costs are increased.

[0020] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1 A flowchart of a crowd-sourced data-driven model and parameter self-learning navigation method provided according to an embodiment of the present application;

[0023] Figure 2 A flowchart of feature candidate factor screening for one embodiment of the present application;

[0024] Figure 3 This is a flow chart of the characteristic factor correlation analysis of one embodiment of the present application;

[0025] Figure 4 This is a flowchart of self-generation of label data according to one embodiment of the present application;

[0026] Figure 5 A schematic diagram of the architecture of a variance self-learning model according to an embodiment of the present application;

[0027] Figure 6 A flowchart of adaptive navigation based on semi-tight combination according to an embodiment of the present application;

[0028] Figure 7A flowchart of an intelligent navigation method of a crowdsourced data driven model and parameter self-learning according to an embodiment of the present application;

[0029] Figure 8 A schematic diagram of the structure of a crowdsourcing data driven model and a navigation device for parameter self-learning provided according to an embodiment of the present application;

[0030] Fig. 9 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application.

[0031] Reference numerals:

[0032] 10-Crowd-sourced data driven model and parameter self-learning navigation device: 100-extraction module, 200-update module, 300-construction module and 400-positioning module; 901-memory, 902-processor and 903-communication interface. DETAILED DESCRIPTION

[0033] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0034] The following describes the crowdsourcing data driven model and parameter self-learning navigation method and device of the embodiment of the present application with reference to the accompanying drawings. The GNSS / SINS combined navigation system in the related art mentioned in the above background technology mainly relies on model driving when realizing positioning, and it is difficult to fully extract common features in large-scale data, and it is not easy to realize the construction of complex models. In addition, most algorithms mainly focus on GNSS gross error detection, and the consistency between GNSS position variance and actual error is insufficient. In addition, the algorithm training scheme also usually relies on high-precision reference system to generate labels. Therefore, the accuracy of navigation positioning results is lacking and certain costs are increased. The present application provides a crowdsourcing data driven model and parameter self-learning navigation method, in which a certain characteristic factor can be generated by acquiring crowdsourcing map data of the target navigation system, and the crowdsourcing map label data can be obtained while updating the data information of the relevant navigation system to train a certain variance self-learning model, and finally the model prediction variance generated by the variance self-learning model and a certain semi-tight combination architecture are used to fuse the updated data information to generate a high-precision positioning result. As a result, autonomous learning of variance information is achieved without manual intervention and high-precision reference truth equipment. While achieving high-precision navigation and positioning, the system model is continuously optimized through the update of crowdsourcing data, and the generalization ability and intelligence level of the system are continuously improved. As a result, the problems in the related technology that the GNSS / SINS combined navigation system mainly relies on model-driven when achieving positioning, it is difficult to fully extract common features in large-scale data, it is not easy to achieve the construction of complex models, and most algorithms mainly focus on GNSS gross error detection, and the consistency between GNSS position variance and actual error is insufficient. In addition, the algorithm training scheme also usually relies on high-precision reference systems to generate labels. As a result, the accuracy of the navigation and positioning results is lacking and certain costs are increased.

[0035] Before explaining the crowdsourcing data driven model and parameter self-learning navigation method in the embodiments of the present application, the global navigation satellite system and the strapdown inertial navigation system involved in the embodiments of the present application are first explained.

[0036] As the most mature positioning technology, the Global Navigation Satellite System (GNSS) has been widely used in the field of vehicle navigation. However, due to the fragility and limitations of satellite radio signals, GNSS faces three major problems: blocking, interference, and deception. In complex scenes such as urban canyons, tree-lined areas, elevated roads, and tunnels, its navigation and positioning performance rapidly declines.

[0037] As a relative positioning method, the Strapdown Inertial Navigation System (SINS) can provide full navigation information including position, velocity, and attitude at high frequency around the clock. However, due to the error of the inertial measurement unit device, its dead reckoning error will diverge over time. GNSS and SINS are very complementary. The GNSS / SINS combined navigation formed by the complementary advantages of the two has the characteristics of high accuracy, strong reliability, wide adaptability, and good dynamics. Therefore, it has become an important technical means for navigation and positioning of unmanned systems.

[0038] As the core element in the process of heterogeneous information fusion, the random model determines the weight of each type of information and significantly affects the accuracy of navigation and positioning results. However, due to the influence of factors such as nonlinearity, non-Gaussianity, unmodeled errors and observational gross errors, the actual errors of observation information and state information will not match their variances in complex scenarios, which is the phenomenon of variance distortion. Variance distortion will lead to unreasonable weight matching between current observation information and historical observation information, and cause more true rejection and false acceptance errors in quality control, while causing false covariance of positioning results, which ultimately affects the accuracy of the navigation and positioning system. The random modeling of SINS has matured. In the GNSS field, a model-driven method is used to analyze and detect gross errors in observation data through empirical thresholds and adaptive filtering algorithms, resulting in incomplete analysis and inability to achieve optimal fusion of GNSS / SINS.

[0039] Therefore, the embodiment of the present application proposes a navigation method of crowdsourcing data-driven model and parameter self-learning.

[0040] Specifically, Figure 1 A flowchart of a crowdsourcing data driven model and parameter self-learning navigation method provided in an embodiment of the present application.

[0041] like Figure 1 As shown, the crowd-sourced data-driven model and parameter self-learning navigation method includes the following steps:

[0042] In step S101, crowdsourced map data of a target navigation system is acquired, and at least one characteristic factor of the crowdsourced map data is extracted.

[0043] It is understandable that crowdsourced map data here can be understood as large-scale map data collected through crowdsourcing, such as GNSS, SINS, odometer and CAN bus data, etc. Characteristic factors here can be understood as characteristic factors related to the observation environment and solution quality of crowdsourced map data, such as signal-to-noise ratio, number of fixed satellites, etc.

[0044] It should be noted that the target navigation system here refers to an intelligent navigation system that uses crowdsourced map data to drive the model and whose parameters can be self-learned. In the embodiment of the present application, it is mainly but not limited to a GNSS navigation system; of course, in actual applications, it can also be adjusted to other navigation systems according to actual conditions. This is only an illustrative explanation without specific limitation.

[0045] In some embodiments, due to the diversity of error sources, a single feature is difficult to fully and accurately characterize the real error of the target navigation system in an actual dynamic scenario. There are many parameter indicators that can indirectly reflect the accuracy of the positioning results of the target navigation system, but not all parameter indicators show an obvious correlation with the positioning results. Therefore, the present application can obtain crowdsourced map data of the target navigation system and extract at least one characteristic factor of the crowdsourced map data, thereby determining whether some new map data can be updated to the target navigation system.

[0046] For example, the embodiments of the present application can collect GNSS and SINS raw observation data through a crowdsourcing client, including but not limited to pseudorange, phase, Doppler, acceleration and angular velocity data. Furthermore, the embodiments of the present application can also upload these crowdsourced map data to a cloud server through the HTTP / HTTPS protocol to perform more sophisticated data processing and extract characteristic factors.

[0047] Optionally, in one embodiment of the present application, extracting at least one characteristic factor of crowdsourced map data includes: post-processing and solving the crowdsourced map data to obtain processed crowdsourced map data; and extracting at least one characteristic factor based on the processed crowdsourced map data using a preset correlation coefficient.

[0048] In some embodiments, when extracting at least one characteristic factor of crowdsourced map data, in order to improve the accuracy of the extraction result, the embodiments of the present application may first perform certain post-processing solutions on the crowdsourced map data. Among them, post-processing solutions here can be understood as further processing, analyzing and converting these data using a series of mathematical methods, algorithms or specific processing procedures after collecting the original observation data to obtain results that better meet actual needs. Because the original observation data is often obtained directly from the observation equipment, these data may contain noise, errors, etc., or the format and content do not meet the data use requirements.

[0049] After obtaining the processed crowdsourced map data, the embodiment of the present application can extract at least one characteristic factor using a preset correlation coefficient. The preset correlation coefficient here can be understood as a coefficient that can measure (calculate) the correlation between the characteristic factor and the observation environment and the solution quality, for example, the Spearman (Spearman's correlation coefficient) correlation coefficient. The Spearman correlation coefficient, also known as the Spearman rank correlation coefficient, is a non-parametric statistic that can be used to measure the strength and direction of the monotonic relationship between two variables.

[0050] For example, Figure 2 This is a flowchart of feature candidate factor screening according to one embodiment of the present application. Figure 2 As shown in the figure, after the original observation data such as GNSS and SINS are uploaded to the cloud server, the cloud server can perform GNSS post-processing and solution. For example, the cloud can, but is not limited to, model GNSS observation values ​​through a dynamic RTK positioning model, select the satellite with the highest elevation angle of different satellite navigation systems as the benchmark, exclude satellite systems with less than 4 visible satellites, and match the common-view satellites between the base station and the mobile station through the satellite PRN number.

[0051] Furthermore, the cloud can also extract features related to the observation environment through Kalman filtering: parameters such as signal-to-noise ratio (C / N0) and multipath effect (MP), as well as features related to solution quality: position precision factor (PDOP), position standard deviation (STD), ambiguity fixed ratio value, fixed number of satellites (NFSAT) and other parameters.

[0052] For example, the calculation formula of the signal-to-noise ratio can be, but is not limited to, expressed as follows:

[0053] C / N0=10log(C / kTB),

[0054] Where C is the signal strength, N0 is the received signal carrier noise intensity, k is the Boltzmann constant, T is the temperature, and B is the equivalent noise bandwidth.

[0055] The multipath effect refers to the fact that there are multiple propagation paths from the satellite to the receiver, which causes interference in the signal at the receiver and ultimately affects the positioning accuracy of the GNSS. In the embodiment of the present application, the multipath error of the three frequency points can be extracted by the geometry-free and ionosphere-free combination. The formula can be, but is not limited to, expressed as follows:

[0056]

[0057] Among them, MP i is the geometry-free and ionosphere-free observation value at the ith frequency point, P i , Li They are the pseudorange and phase observation values ​​at the corresponding frequency points, and α represents β represents In the embodiment of the present application, the multipath value of each frequency point can be calculated by the moving average method, that is, the multipath mean value in the moving window is subtracted from the instantaneous value of the current epoch.

[0058] Other characteristic information such as PDOP, position standard deviation (STD), ambiguity fixation ratio value, fixed number of satellites (NFSAT), etc. can be obtained based on relevant mainstream algorithms.

[0059] Next, the embodiment of the present application may, but is not limited to, use the Spearman coefficient to extract the correlation between each characteristic factor and the GNSS position error. Figure 3 This is a flow chart of the characteristic factor correlation analysis of an embodiment of the present application, such as Figure 3 As shown, the expression of Spearman coefficient ρ can be as follows:

[0060]

[0061] Where F and L represent the characteristic factor and GNSS position error, respectively. F and L are sorted, and the sorted parameter queues are recorded as R(F) and R(L), which are called the ranks of F and L. cov(R(F), R(L)) is the covariance of the ranks, σ R(F) and σ R(L) The value range of the correlation coefficient ρ in the embodiment of the present application can be set to (-1, 1), and when ρ takes a negative value, it indicates a negative correlation.

[0062] Because the characteristic factors such as PDOP, STD, Ratio, NFAST, etc. have a single numerical representation in each epoch. However, C / N0 and MP have multiple numerical representations in each epoch. For example, the C / N0 of each satellite signal received in an epoch can be calculated, and the MP of each received frequency point can be calculated. In order to facilitate the correlation analysis of C / N0, MP and GNSS position error, the embodiment of the present application can extract the eigenvalues ​​of C / N0 and MP by constructing a membership matrix to uniquely represent the C / N0 and MP information of the current epoch. Taking the extraction of the eigenvalue of C / N0 as an example, the membership matrix M is constructed at time t. t It can be expressed as follows:

[0063]

[0064] in, is the membership function. The membership function of the signal-to-noise ratio can be calculated by the following formula:

[0065]

[0066] in, represents the signal-to-noise ratio of satellite j at time t. is the maximum absolute difference between the signal-to-noise ratios of all satellites at time t. The closer the signal-to-noise ratios of two satellites are, the larger the corresponding membership function will be, and the higher the weight will be in the fusion process.

[0067] Then, the embodiment of the present application can calculate the eigenvector V corresponding to the maximum eigenvalue of the membership matrix by the power iteration method. n ) T The contribution weight of each satellite's signal-to-noise ratio to the current epoch's signal-to-noise ratio eigenvalue can be calculated from its corresponding eigenvalue, and the formula can be expressed as follows:

[0068]

[0069] Finally, the signal-to-noise ratio eigenvalue λ of the current epoch is calculated based on the above formula t For correlation analysis, the formula can be expressed as follows:

[0070]

[0071] It should be noted that the MP eigenvalue of the current epoch can also be obtained by constructing the membership matrix as described above, and the calculation method of other characteristic factors can also be set or adjusted according to actual conditions. This is only an example and is not specifically limited.

[0072] Step S102: based on at least one characteristic factor, filter the map results that meet the preset reliability, and update the crowdsourced map data according to the map results to generate crowdsourced map label data that meets the preset accuracy.

[0073] It is understood that the preset reliability here can be understood as a preset reliability limit, and only when certain data in the crowdsourced map data meets the reliability limit can it be further used, such as updating the crowdsourced map data, and the relevant data that meets the reliability can be, but is not limited to, called a map result in the embodiment of the present application. The preset accuracy here also refers to a preset accuracy limit, and only when certain data in the crowdsourced map data meets a certain reliability limit and the accuracy limit can valid crowdsourced map label data be generated.

[0074] In other embodiments, considering that the crowdsourcing collection method has the characteristics of device isomorphism and data scale, after extracting at least one characteristic factor of the map data, the present application can also screen out map results that meet a certain reliability based on at least one characteristic factor to update the crowdsourcing map data. By mining device characteristics and combining processing strategies, crowdsourcing map label data that meets a certain accuracy can be self-generated.

[0075] For example, the present application can screen out reliable GNSS position data based on at least one characteristic factor obtained to perform GNSS / SINS measurement updates, and use FBC smoothing technology to achieve autonomous generation of high-precision crowdsourced map label data.

[0076] This process is further explained below.

[0077] Optionally, in one embodiment of the present application, map results that meet a preset reliability are screened based on at least one characteristic factor, and crowdsourced map data is updated according to the map results to generate crowdsourced map label data that meet a preset accuracy, including: using simulated positioning errors to determine the accuracy characteristics and observation update intervals of the inertial sensors of the target navigation system; screening map results that meet a preset reliability based on the accuracy characteristics, observation update intervals and at least one characteristic factor; and correcting the divergent errors of the map results to generate crowdsourced map label data that meet the preset accuracy.

[0078] In the actual implementation process, when screening map results that meet a certain reliability, the present application can first use the simulated positioning error to determine the observation update interval of the observation data of the inertial sensor of the target navigation system, and then screen the map results that meet a certain reliability through the accuracy characteristics, observation update interval and at least one characteristic factor. Finally, correct the relevant errors of the map results to obtain crowdsourced map label data that meets a certain accuracy.

[0079] For example, the present application can analyze the accuracy characteristics of the inertial sensor of the GNSS navigation system through the cloud to determine the observation update interval. Then, based on the obtained accuracy characteristics, observation update interval and at least one characteristic factor, reliable GNSS position data is selected for GNSS / SINS measurement update, and FBC smoothing technology is used to realize the autonomous generation of high-precision crowdsourced map label data. The specific process can be expressed as follows:

[0080] Since inertial sensors have the ability to maintain high-precision posture during short-term recursion, and the higher the accuracy level of the inertial sensors, the more so. Therefore, in the embodiment of the present application, only sparsely selected GNSS observation information of some epochs can be used for measurement update during GNSS / SINS combined solution. Of course, these epochs should have high precision and high reliability conditions, so as to accurately correct the inertial navigation divergence error.

[0081] Figure 4 This is a flowchart of label data self-generation according to an embodiment of the present application. Figure 4As shown, for large-scale homogeneous data collected by crowdsourcing, the embodiment of the present application can analyze the common characteristics of inertial sensors. By simulating the positioning error of combined navigation under different observation update intervals, the largest observation update interval Δt is selected as much as possible while ensuring the same level of position accuracy for the generation of label data.

[0082] The GNSS result quality Q obtained by cloud processing is graded based on the parameter factors related to the observation environment and the parameter factors related to the solution quality extracted in other embodiments. The parameter factors related to the observation environment include but are not limited to the number of common view satellites (NCSAT) and PDOP; the parameter factors related to the solution quality include but are not limited to the ambiguity fixation situation (IAS), the number of fixed satellites (NFSAT), the ambiguity fixation ratio, the result standard deviation (STD) and the result forward and backward mutual error (SEP).

[0083] For example, the number of common-view satellites indicates the number of satellites that the base station and the mobile station can monitor together in the current epoch; the ambiguity fixation determines whether the result of the current epoch is a fixed solution or a floating-point solution; the forward and backward mutual difference of the result indicates the second norm of the difference between the forward filtering result and the backward filtering result, and the formula can be expressed as follows:

[0084]

[0085] Among them, ΔX, ΔY, and ΔZ are the differences between the forward and backward results in the three directions of X, Y, and Z. The remaining parameter factors can be obtained based on relevant mainstream algorithms.

[0086] Then, the embodiment of the present application can achieve the grading of the GNSS result quality Q by setting thresholds for the above parameter factors, for example:

[0087] Q=1 represents a fixed solution with high precision and reliability. It should meet the following conditions at the same time: NCSAT is greater than 15 satellites; IAS is an ambiguity fixed solution; NFSAT is greater than 7 satellites; and the ambiguity fixed ratio value is greater than 5.

[0088] Q=2 represents a fixed solution with general accuracy and general reliability. It should meet the following conditions at the same time: NCSAT is greater than 10; IAS is an ambiguity fixed solution; NFSAT is greater than 4; the ambiguity fixed ratio is greater than 3; SEP is less than 0.5m.

[0089] Q=3 represents a general precision floating point solution, which should simultaneously meet the following conditions: NCSAT greater than 10; PDOP less than 2.5; STD less than 2m; SEP less than 1m.

[0090] Q = 4 represents a solution with low accuracy and reliability. Results that do not meet the above conditions are classified here.

[0091] Forward filtering updates are performed based on the observation update interval and the GNSS result quality Q. All points that satisfy Q=1 are used for measurement updates. When the measurement update interval between the previous and next epochs is greater than Δt, points with Q=2, Q=3, and Q=4 are used to fill in the gaps. During the filling process, the currently selected point can be used as the starting time, and Δt can be taken backward as the new time period to ensure that the interval between points is always less than Δt. For individual time periods, if the interval within the time period is large and the accuracy of all points within the time period is poor, the observation update variance value of the selected midpoint is appropriately increased to avoid erroneous observation updates from causing serious deviations in the inertial navigation recursive values.

[0092] On the basis of forward filtering, FBC smoothing technology is used to suppress the divergence of inertial guidance errors by utilizing the kinematic constraints of the state between epochs, and to generate high-precision tag data based on past, current and future measurements. The calculation formula of FBC can be expressed as follows:

[0093]

[0094] in, and P k / N Represent the smoothed state vector and state error covariance matrix respectively; and P k A represents the state vector and state error covariance matrix after filtering update; k represents the smoothing gain; Φ k represents the state transfer matrix; It is the forecast status The error covariance matrix of , the following table N represents the total number of epochs. Through the above formula, the forward filtering result and the reverse filtering result can be smoothed to obtain the forward and reverse smoothed state vectors X Fk and X Bk , and its corresponding state error covariance matrix is ​​P Fk and P Bk .

[0095] Finally, the weighted fusion of the forward and directional results is realized according to the variance information to obtain the optimal solution result X Ck and its covariance information P Ck As label data, the formula can be expressed as follows:

[0096]

[0097] Step S103, based on at least one characteristic factor and crowdsourced map label data, construct a preset variance self-learning model, and optimize the position variance information of the target navigation system through the preset variance self-learning model to obtain the model predicted position variance of the target navigation system.

[0098] It is understandable that the preset variance self-learning model here can be understood as a pre-built model that can continuously realize variance information self-learning. The variance information can be understood as the position variance information of the navigation system.

[0099] As a possible implementation method, the present application can construct a certain variance self-learning model to realize the autonomous learning of the variance information of the target navigation system without human intervention and high-precision reference truth equipment. While achieving high-precision navigation and positioning, the model is continuously optimized through the update of crowdsourcing data, thereby continuously improving the generalization ability and intelligence level of the target navigation system, and thus optimizing the position variance information of the target navigation system.

[0100] For example, the present application can, but is not limited to, utilize at least one generated characteristic factor and crowdsourced map label data, combine the sensitivity of CNN (convolutional neural network) to local features, the memory of LSTM (long short-term memory network) to time series, and the focus of attention mechanism on characteristic information to construct a certain variance self-learning model to achieve self-learning of variance information. Among them, the characteristic factor and crowdsourced map label data are mainly used to train the variance self-learning model.

[0101] Then, the embodiment of the present application can use the constructed variance self-learning model to generate the model predicted position variance of the target navigation system. Furthermore, as the variance self-learning model itself is continuously optimized, the generated position variance information of the target navigation system, i.e., the model predicted position variance, will also be continuously optimized.

[0102] Next, the construction process of the variance self-learning model in the embodiment of the present application is further explained.

[0103] Optionally, in one embodiment of the present application, a preset variance self-learning model is constructed based on at least one characteristic factor and crowdsourced map label data, and also includes: expanding the channel of at least one characteristic factor, and using temporal convolution to aggregate feature information of at least one characteristic factor to obtain local features with time dependence; learning contextual information with dependencies from local features, and generating position variance information based on the contextual information by focusing feature information using an attention mechanism; and training and optimizing the initial variance self-learning model using the position variance information and crowdsourced map label data to obtain a preset variance self-learning model.

[0104] Based on the relevant descriptions of other embodiments, it can be understood that the present application can, but is not limited to, utilize at least one generated characteristic factor and crowdsourced map label data, combined with CNN (convolutional neural network) sensitivity to local features, LSTM (long short-term memory network) memory of time series and attention mechanism's focus on feature information to construct a certain variance self-learning model to achieve self-learning of variance information. Figure 5Schematic diagram of the architecture of the variance self-learning model of one embodiment of the present application. Figure 5 As shown, the specific construction process can be but not limited to the following:

[0105] In order to fit the mapping relationship between the characteristic factors and the GNSS position variance, a CNN-LSTM network model based on the attention mechanism is first constructed. The parameter configuration of each module can be expressed as follows:

[0106] One-dimensional convolution module (1D-CNN): This module contains two convolution operations, each of which uses ReL u Function is used as activation function. The input feature data structure is L T ×L F , where L F is the number of GNSS feature factors, LT is the length of the time series used. The convolution is modeled as a sliding window process, and its height is the sliding window length L w , the height of the convolution kernel is the same as the number of feature factors, that is, L W ×L F During feature extraction, the convolution kernel slides in the time dimension to achieve time series processing.

[0107] Bidirectional LSTM module (BiLSTM): This module builds an aligned two-layer model based on LSTM, one layer propagates from front to back, and the other layer propagates from back to front. It learns contextual information with dependencies from local features through forward and backward bidirectional reasoning of features. Its internal update method can be expressed as follows:

[0108]

[0109] Among them, the input gate uses the Sigmoid activation function σ(); W i and W c are the input gate and memory gate weight matrices, respectively, i and b c are the biases for the input gate and memory gate respectively. t-1 The hidden state of the previous moment can be obtained by the memory state C of the previous moment t-1 and output status o t-1 Get h t-1 =o t-1 *tanh(C t-1 ).

[0110]

[0111] Among them, the forget gate uses the Sigmoid activation function; W f is the forget gate weight matrix, b fis the bias of the forget gate. Memory cell state C t Update based on the forget gate output.

[0112]

[0113] Among them, the output gate uses the Sigmoid activation function and combines the forward propagation hidden state h t-1 and back-propagated hidden state h′ t-1 Make prediction output; W o is the output gate weight matrix, b o Bias the output gate and update the hidden state based on this.

[0114] Furthermore, the attention mechanism in the embodiment of the present application mainly obtains the attention weight through autonomous prompts and non-autonomous prompts generated by the data itself, and uses the BiLSTM output vector q and the corresponding key k i To generate the attention score function. Among them, the attention weight is mainly but not limited to generated by the softmax function. For the i-th input vector, its attention weight can be expressed by the following formula:

[0115]

[0116] Among them, α is the attention weight, s(q, k i ) is the attention scoring function. It should be noted that there are multiple scoring functions. Here, the additive model is used as an example and is not a specific limitation:

[0117] s(q,k i )=v T tanh(Wq+Uk i ),

[0118] Among them, W, U and v are learnable parameters, which can be optimized and adjusted through model training. Finally, the information selection mechanism is used to integrate all the features, which can be expressed as follows:

[0119]

[0120] The embodiment of the present application can train the variance self-learning network model based on the generated characteristic factors and crowdsourced map label data, and can continuously update the data set for incremental learning of the model through crowdsourcing, continuously optimize and adjust the parameters of the variance self-learning model, improve its generalization ability, and ultimately achieve self-learning of GNSS position variance without human intervention, thereby greatly improving the intelligence level of the present application.

[0121] Step S104, constructing a scaling factor according to the model predicted position variance, scaling the model predicted position variance with the scaling factor to obtain the scaled model predicted position variance, and generating the final positioning result of the target navigation system through the scaled model predicted position variance and a preset semi-tight combination architecture.

[0122] It is understandable that the preset semi-tight combination architecture here can be understood as a pre-set intermediate architecture state between the loosely coupled architecture and the tightly coupled architecture. It can combine the flexibility and maintainability of the loosely coupled architecture with the high efficiency and real-time processing capabilities of the tightly coupled architecture. By optimizing the dependencies and communication methods between components, the "semi-tight combination" architecture can improve the scalability and maintainability of the system while maintaining the overall performance of the system. For example, in a satellite navigation and inertial navigation combination system, the semi-tight combination architecture can give full play to the high-precision positioning information of satellite navigation and the continuous and autonomous navigation capabilities of inertial navigation, and is suitable for scenarios such as vehicle navigation, aerospace, etc. that have high requirements for navigation accuracy and reliability.

[0123] In the actual implementation process, it is considered that although the variance self-learning model can learn the position variance information, it is difficult to regress the non-diagonal elements of the covariance matrix, and the correlation between the parameters is lost. Therefore, after obtaining the model-predicted position variance of the target navigation system through the constructed variance self-learning model, the present application can also construct a scaling factor based on the model prediction variance to scale the model prediction variance using the scaling factor, thereby achieving the correlation between the parameters by adjusting the non-diagonal elements through the scaling factor. Ultimately, the data information of the target navigation system and the data information of other auxiliary navigation systems after adaptive fusion optimization can be achieved through the scaled model prediction variance and a certain semi-tight combination architecture, and finally the final positioning result of the target navigation system is generated.

[0124] Specifically, the embodiment of the present application can use the prediction results of the variance self-learning model, that is, the model predicted position variance, to replace the original GNSS position variance information, and construct a scaling factor to adjust the non-diagonal elements to maintain the correlation between parameters, and then obtain a high-precision positioning result based on the semi-tight combination architecture after fusion optimization, that is, the updated GNSS and SINS information is measured. Figure 6 This is a flowchart of an adaptive navigation based on semi-tight combination according to an embodiment of the present application, as shown in FIG. Figure 6 As shown, the specific process can be expressed as follows:

[0125] The cloud sends the incrementally updated variance self-learning model to the client of the target navigation system. The client extracts characteristic factors in real time and uses the variance self-learning model to predict the GNSS position variance.

[0126] The GNSS position variance predicted by the variance self-learning model only includes the main diagonal information. At this time, the scaling factor λ can be extracted according to the off-diagonal elements in the original GNSS position variance. xx ,λ yy ,λ zz ) T , based on the position variance predicted by the model, the corresponding off-diagonal elements are restored to achieve the transfer of parameter correlation. The formula can be expressed as follows:

[0127]

[0128] in, are the off-diagonal elements of the variance matrix predicted by the network model, are the non-diagonal elements of the original GNSS variance matrix, i, j∈(x, y, z).

[0129] The scaling factor λ can be extracted by the following formula:

[0130]

[0131] After obtaining the scaling factor, the embodiment of the present application can use the scaling factor to scale the model prediction variance to obtain the scaled model prediction variance, thereby maintaining the correlation between the parameters. Further, based on the scaled model prediction variance and a certain semi-tight combination architecture, the updated position variance information of the target navigation system and the updated position variance information of other auxiliary navigation systems are fused to obtain a high-precision positioning result.

[0132] Next, the process of how to fuse the updated position variance information of the target navigation system and the updated position variance information of other auxiliary navigation systems based on the scaled model prediction variance and a certain semi-tight combination architecture in the embodiment of the present application is further explained.

[0133] Optionally, in one embodiment of the present application, the final positioning result of the target navigation system is generated by using the scaled model predicted position variance and a preset semi-tight combination architecture, including: updating the data information of the strapdown inertial navigation system according to the map result measurement; fusing the data information and the scaled model predicted position variance through a preset semi-tight combination structure to obtain the final positioning result of the target navigation system.

[0134] Based on the relevant descriptions of other embodiments, it can be understood that when the embodiment of the present application generates crowdsourced map label data, it uses at least one characteristic factor to screen out map results that meet a certain reliability, and then updates the crowdsourced map data.

[0135] As a possible implementation method, when the crowdsourced map data of the target navigation system is updated, the embodiment of the present application can measure and update the data information of SINS at the same time. Then, a certain semi-tight combination architecture is used to fuse the measured and updated SINS data information and the scaled model prediction variance to obtain the final high-precision positioning result.

[0136] Specifically, Figure 6 As shown, the embodiment of the present application can use the scaled model prediction variance matrix to replace the original GNSS position variance, and use the semi-tight combination architecture to fuse the updated GNSS information and SINS information. Among them, the semi-tight combination navigation parameters include but are not limited to position r e , speed v e , attitude φ, accelerometer bias b a and gyro bias b g , where e represents the ECEF coordinate system. The error differential equation of the navigation state can be expressed as follows:

[0137]

[0138] Among them, δg e is the gravity error vector, is the Earth's rotation angular velocity, ξ r , v and φ are the random walk noise of position, velocity and attitude in the ECEF system, b represents the IMU coordinate system, fe is the output of the accelerometer in the ECEF system, Represents the rotation matrix from the IMU coordinate system to the ECEF coordinate system.

[0139] Based on the position information solved by GNSS and SINS, and using the placement relationship between the two, the following position and velocity observation equations can be constructed for observation update:

[0140]

[0141] in, l b is the position vector of GNSS in the IMU coordinate system, and All of them can be calculated through the mechanical arrangement at the current moment.

[0142] In addition, the semi-tight combination architecture in the embodiment of the present application can also use the short-term dead reckoning results of SINS to assist the quality control and ambiguity resolution in GNSS filtering, and achieve high-precision real-time navigation positioning through the two-way transmission and sharing of information between the two systems.

[0143] After completing the above work, the target navigation system can realize autonomous learning of variance information without human intervention and high-precision reference true value equipment, and can continuously improve the generalization ability of the system through the update and iteration of crowdsourcing data to realize intelligent navigation and positioning with variance self-learning.

[0144] The present application is described in detail with reference to a specific embodiment below.

[0145] Figure 7 This is a flow chart of an intelligent navigation method of a crowdsourced data driven model and parameter self-learning according to an embodiment of the present application, such as Figure 7 As shown:

[0146] (1) Collect large-scale GNSS, SINS and other data through crowdsourcing and upload them to the cloud;

[0147] (2) Perform GNSS post-processing in the cloud and use the Spearman correlation coefficient to extract characteristic factors related to the observation environment and solution quality;

[0148] (3) Reliable GNSS results are selected based on characteristic factors to update GNSS / SINS measurements, and FBC smoothing technology is used to generate high-precision label data;

[0149] (4) Combining CNN’s sensitivity to local features, LSTM’s memory of time series, and attention mechanism’s focus on feature information, a deep learning model is constructed to achieve self-learning of variance information;

[0150] (5) The client replaces the original GNSS variance with the model prediction result and adjusts the off-diagonal elements based on the scaling factor to maintain their correlation. Finally, a semi-tight combination architecture is used to fuse the optimized GNSS and SINS to obtain a high-precision positioning result.

[0151] By updating the above crowdsourcing data, the system model is continuously optimized, the generalization ability and intelligence level of the system are continuously improved, and intelligent navigation and positioning based on variance self-learning is realized.

[0152] According to the crowdsourcing data driven model and parameter self-learning navigation method proposed in the embodiment of the present application, a certain characteristic factor can be generated by acquiring the crowdsourcing map data of the target navigation system, and the crowdsourcing map label data can be obtained to train a certain variance self-learning model while updating the data information of the relevant navigation system. Finally, the model prediction variance generated by the variance self-learning model and a certain semi-tight combination architecture are used to fuse the updated data information to generate a high-precision positioning result. Thus, the autonomous learning of variance information is realized, without the need for manual intervention and high-precision reference truth value equipment, and the system model is continuously optimized through the update of crowdsourcing data while achieving high-precision navigation and positioning, and the generalization ability and intelligence level of the system are continuously improved. Thus, the GNSS / SINS combined navigation system in the related art mainly relies on model driving when realizing positioning, it is difficult to fully extract the common features in large-scale data, it is not easy to realize the construction of complex models, and most algorithms mainly focus on GNSS gross error detection, and the consistency between GNSS position variance and actual error is insufficient. In addition, the algorithm training scheme also usually relies on high-precision reference system to generate labels, thereby, the accuracy of the navigation and positioning results is lacking and a certain cost is increased.

[0153] Next, the crowdsourcing data driven model and parameter self-learning navigation device proposed in the embodiment of the present application will be described with reference to the accompanying drawings.

[0154] Figure 8 It is a structural diagram of a crowdsourcing data driven model and a navigation device with parameter self-learning according to an embodiment of the present application.

[0155] like Figure 8 As shown, the crowd-sourced data driven model and parameter self-learning navigation device 10 includes: an extraction module 100, an update module 200, a construction module 300 and a positioning module 400.

[0156] The extraction module 100 is used to obtain crowdsourced map data of a target navigation system and extract at least one characteristic factor of the crowdsourced map data;

[0157] An updating module 200, configured to screen map results that meet a preset reliability based on at least one characteristic factor, and update crowdsourced map data according to the map results to generate crowdsourced map label data that meets a preset accuracy;

[0158] A construction module 300 is used to construct a preset variance self-learning model based on at least one characteristic factor and crowdsourced map label data, and optimize the position variance information of the target navigation system through the preset variance self-learning model to obtain a model predicted position variance of the target navigation system;

[0159] The positioning module 400 is used to construct a scaling factor according to the model predicted position variance, to scale the model predicted position variance using the scaling factor, to obtain the scaled model predicted position variance, and to generate the final positioning result of the target navigation system through the scaled model predicted position variance and a preset semi-tight combination architecture.

[0160] Optionally, in one embodiment of the present application, the extraction module 100 includes: a processing unit and an extraction unit.

[0161] The processing unit is used for post-processing and solving the crowd-sourced map data to obtain processed crowd-sourced map data;

[0162] The extraction unit is used to extract at least one characteristic factor based on the processed crowd-sourced map data using a preset correlation coefficient.

[0163] Optionally, in one embodiment of the present application, the updating module 200 includes: a determining unit, a screening unit and a correcting unit.

[0164] Wherein, the determination unit is used to determine the accuracy characteristics and observation update interval of the inertial sensor of the target navigation system by using the simulated positioning error;

[0165] A screening unit, used for screening map results that meet a preset reliability based on the accuracy characteristic, the observation update interval and at least one characteristic factor;

[0166] The correction unit is used to correct the divergent errors of the map results to generate crowdsourced map label data that meets the preset accuracy.

[0167] Optionally, in one embodiment of the present application, the construction module 300 includes: an expansion unit, a learning unit and an optimization unit.

[0168] The expansion unit is used to expand the channel of at least one characteristic factor and aggregate the characteristic information of at least one characteristic factor using temporal convolution to obtain a local feature with time dependence;

[0169] A learning unit is used to learn context information with dependencies from local features, so as to generate position variance information by focusing on feature information based on the context information using an attention mechanism;

[0170] The optimization unit is used to train and optimize the initial variance self-learning model using the location variance information and crowdsourced map label data to obtain a preset variance self-learning model.

[0171] Optionally, in one embodiment of the present application, the positioning module 400 includes: an updating unit and a fusion unit.

[0172] Wherein, the updating unit is used to update the data information of the strapdown inertial navigation system according to the map result measurement;

[0173] The fusion unit is used to fuse the data information and the scaled model prediction position variance through a preset semi-tight combination structure to obtain the final positioning result of the target navigation system.

[0174] It should be noted that the aforementioned explanation of the crowdsourcing data driven model and parameter self-learning navigation method embodiment is also applicable to the crowdsourcing data driven model and parameter self-learning navigation device of this embodiment, and will not be repeated here.

[0175] According to the crowd-sourced data driven model and parameter self-learning navigation device proposed in the embodiment of the present application, a certain characteristic factor can be generated by acquiring the crowd-sourced map data of the target navigation system, and the crowd-sourced map label data can be obtained to train a certain variance self-learning model while updating the data information of the relevant navigation system. Finally, the model prediction variance generated by the variance self-learning model and a certain semi-tight combination architecture are used to fuse the updated data information to generate a high-precision positioning result. Thus, the autonomous learning of variance information is realized, without the need for manual intervention and high-precision reference truth value equipment, and the system model is continuously optimized through the update of crowd-sourced data while achieving high-precision navigation and positioning, and the generalization ability and intelligence level of the system are continuously improved. Thus, the GNSS / SINS combined navigation system in the related art mainly relies on model driving when realizing positioning, it is difficult to fully extract the common features in large-scale data, it is not easy to realize the construction of complex models, and most algorithms mainly focus on GNSS gross error detection, and the consistency between GNSS position variance and actual error is insufficient. In addition, the algorithm training scheme also usually relies on high-precision reference system to generate labels, thereby, the accuracy of the navigation and positioning results is lacking and a certain cost is increased.

[0176] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0177] A memory 901 , a processor 902 , and a computer program stored in the memory 901 and executable on the processor 902 .

[0178] When the processor 902 executes the program, the crowdsourcing data driven model and parameter self-learning navigation method provided in the above embodiment is implemented.

[0179] Furthermore, the electronic device further comprises:

[0180] The communication interface 903 is used for communication between the memory 901 and the processor 902 .

[0181] The memory 901 is used to store computer programs that can be executed on the processor 902 .

[0182] The memory 901 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0183] If the memory 901, the processor 902 and the communication interface 903 are implemented independently, the communication interface 903, the memory 901 and the processor 902 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0184] Optionally, in a specific implementation, if the memory 901, the processor 902 and the communication interface 903 are integrated on a chip, the memory 901, the processor 902 and the communication interface 903 can communicate with each other through an internal interface.

[0185] The processor 902 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0186] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above crowdsourcing data-driven model and parameter self-learning navigation method.

[0187] An embodiment of the present application also provides a computer program product, including a computer program, which can run computer instructions. When the computer instructions are executed by a processor, the navigation method of the crowdsourcing data-driven model and parameter self-learning provided in the embodiment of the present application is implemented.

[0188] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0189] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0190] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0191] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0192] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0193] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0194] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0195] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A crowd-sourced data-driven model and parameter self-learning navigation method, characterized in that: The following steps are involved: Acquire crowdsourced map data of a target navigation system, and extract at least one characteristic factor of the crowdsourced map data; Screening map results that meet a preset reliability based on the at least one characteristic factor, and updating the crowdsourced map data according to the map results to generate crowdsourced map label data that meets a preset accuracy; Based on the at least one characteristic factor and the crowdsourced map label data, a preset variance self-learning model is constructed, and the position variance information of the target navigation system is optimized by the preset variance self-learning model to obtain a model-predicted position variance of the target navigation system; A scaling factor is constructed according to the model predicted position variance, and the model predicted position variance is scaled by the scaling factor to obtain the scaled model predicted position variance, and a final positioning result of the target navigation system is generated by the scaled model predicted position variance and a preset semi-tight combination architecture.

2. The method according to claim 1, characterized in that The extracting at least one characteristic factor of the crowdsourced map data comprises: Post-processing and solving the crowd-sourced map data to obtain processed crowd-sourced map data; Based on the processed crowd-sourced map data, the at least one characteristic factor is extracted using a preset correlation coefficient.

3. The method according to claim 1, characterized in that The screening of map results that meet a preset reliability based on the at least one characteristic factor, and updating the crowdsourced map data according to the map results to generate crowdsourced map label data that meets a preset accuracy, includes: The simulated positioning error is used to determine the accuracy characteristics and observation update interval of the inertial sensor of the target navigation system; Filtering a map result that meets a preset reliability based on the accuracy characteristic, the observation update interval and the at least one characteristic factor; The divergent errors of the map results are corrected to generate crowdsourced map label data that meets a preset accuracy.

4. The method according to claim 1, characterized in that: The step of constructing a preset variance self-learning model based on the at least one characteristic factor and the crowdsourced map label data includes: Expanding the channel of the at least one characteristic factor, and aggregating the characteristic information of the at least one characteristic factor using temporal convolution to obtain a local feature with time dependence; Learning context information with dependencies from the local features, and focusing on the feature information using an attention mechanism to generate position variance information based on the context information; The location variance information and the crowdsourced map label data are used to train and optimize an initial variance self-learning model to obtain the preset variance self-learning model.

5. The method according to claim 1, characterized in that: The method of generating the final positioning result of the target navigation system by using the scaled model predicted position variance and the preset semi-tight combination architecture includes: Measuring and updating data information of a strapdown inertial navigation system according to the map result; The data information and the scaled model predicted position variance are fused through the preset semi-tight combination structure to obtain a final positioning result of the target navigation system.

6. A crowd-sourced data driven model and parameter self-learning navigation device, characterized in that: include: An extraction module, configured to obtain crowdsourced map data of a target navigation system and extract at least one characteristic factor of the crowdsourced map data; An updating module, configured to screen map results that meet a preset reliability based on the at least one characteristic factor, and update the crowdsourced map data according to the map results to generate crowdsourced map label data that meets a preset accuracy; A construction module, configured to construct a preset variance self-learning model based on the at least one characteristic factor and the crowdsourced map label data, and optimize the position variance information of the target navigation system through the preset variance self-learning model to obtain a model-predicted position variance of the target navigation system; A positioning module is used to construct a scaling factor according to the model predicted position variance, to scale the model predicted position variance using the scaling factor, to obtain the scaled model predicted position variance, and to generate a final positioning result of the target navigation system through the scaled model predicted position variance and a preset semi-tight combination architecture.

7. The device according to claim 6, characterized in that The extraction module comprises: A processing unit, used for post-processing and solving the crowd-sourced map data to obtain processed crowd-sourced map data; The extraction unit is used to extract the at least one characteristic factor based on the processed crowd-sourced map data using a preset correlation coefficient.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the crowdsourcing data driven model and parameter self-learning navigation method as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the crowdsourcing data driven model and parameter self-learning navigation method as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed, it is used to implement the crowdsourcing data driven model and parameter self-learning navigation method as described in any one of claims 1-5.

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