Path evaluation and optimization method and device, equipment and storage medium

Abstract

The invention relates to a path evaluation optimization method and device, equipment and a storage medium, and the method comprises the steps: constructing a gravity center shift degree analysis index through a three-axis acceleration observed quantity obtained when a vehicle runs in a target path, carrying out the differential processing of the gravity center shift degree analysis index, and generating a target observation sequence; through a preset mutation point detection algorithm, mutation points and the number of the mutation points of the target observation sequence are extracted; the mutation points are predicted based on small sample prediction of the grey theory, and the mutation degree of the mutation points is determined; a comfort level score about the target path is generated through the number of the mutation points and the mutation degree, the comfort level score about the target path is used for providing a comfort level evaluation index for original navigation path planning of the target path, evaluation and optimization of the original navigation path planning are achieved, the acceleration data safety is guaranteed, and the navigation path planning accuracy is improved. And meanwhile, the driving comfort is further evaluated, the evaluation result is easier to understand, and the purpose of optimizing the navigation path is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of navigation path assisted planning, and particularly relates to a path evaluation and optimization method, device, equipment and storage medium. Background Art

[0002] The comfort evaluation of traditional passenger cars includes vehicle ride comfort, air conditioning performance, vehicle environment and driving operation performance evaluation. With the promotion of the new round of scientific and technological revolution and industrial transformation, in-vehicle sensors are becoming increasingly rich, including sensing devices such as positioning, attitude, video imaging, lidar, millimeter wave radar, etc. Thus, the comfort of vehicles can be evaluated from more aspects. For example, road surface quality evaluation, ride comfort evaluation, navigation path assisted planning, etc.

[0003] Currently, there are some analysis methods for driving behavior, including: an in-vehicle terminal for motor vehicles and a driving behavior judgment method based on microsensors. This system mainly includes microsensors, a satellite positioning system, an OBD interface, a wireless communication module, a main memory, a main processor and a system platform. By collecting the real-time position, driving state and vehicle state of the vehicle, it monitors the driving behavior of motor vehicle drivers, and at the same time judges whether their driving behavior is good through a fixed judgment method. And after the driving ends, it is sent to the system platform through wireless transmission for statistics, and finally an analysis report on the driving behavior of the driver of the current vehicle is obtained to serve units or individuals in need of such reports; and a driving behavior analysis module is used to integrate the information transmitted by the driving recorder analyzed by a positioning system analysis module, a gravity sensing analysis module, an image analysis module and a vehicle data analysis module, and then supplemented by objective information in a road network information database and a traffic data information database to give a warning when an accident occurs to the vehicle and generate a comprehensive accident report.

[0004] The above methods mostly start from the perspective of the driving subject and are used for aspects such as driving behavior habits or accident handling, without considering the influence of other conditions closely related to driving on the driving experience. Moreover, due to the security consideration of surveying and mapping geographic information, the absolute spatial position must be subject to security and confidentiality processing. Especially for gravity anomaly data with an accuracy better than 5 milligals, so the prior art has not fully considered the relevant requirements for the security and confidentiality of surveying and mapping geographic information. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a path evaluation and optimization method, device, equipment and storage medium, to solve the problem that in the existing comfort evaluation methods, most start from the perspective of the driving subject and are used for aspects such as driving behavior habits or accident handling, without considering the influence of the object closely related to driving on the driving experience, nor fully considering the relevant requirements for the security and confidentiality of surveying and mapping geographic information. The specific technical solutions are as follows:

[0006] According to the first aspect of the embodiments of the present application, a path evaluation and optimization method is provided. The method includes:

[0007] Constructing a center of gravity offset degree analysis index through the triaxial acceleration observation data obtained when the vehicle travels on the target path;

[0008] After performing differential processing on the center of gravity offset degree analysis index, generating a target observation sequence;

[0009] Extracting the mutation points and the number of mutation points of the target observation sequence through a preset mutation point detection algorithm;

[0010] Predicting the mutation points based on the small sample prediction of grey theory to determine the mutation degree of the mutation points;

[0011] Generating a comfort score for the target path through the number of mutation points and the mutation degree, wherein the comfort score for the target path is used to provide a comfort evaluation index for the original navigation path planning of the target path, so as to realize the evaluation and optimization of the original navigation path planning.

[0012] Optionally, after generating the comfort score for the target path through the number of mutation points and the mutation degree, the method further includes:

[0013] Generating an evaluation system for the navigation path through the comfort score for the target path, and a corresponding first path identifier is set for each navigation path in the evaluation system for the navigation path;

[0014] Obtaining multiple navigation paths existing between the starting point and the ending point and a corresponding second path identifier for each navigation path;

[0015] Setting a first weight and a navigation path evaluation for each navigation path;

[0016] When it is determined that the second path identifier corresponds to the first path identifier, obtaining the comprehensive comfort score of the navigation path corresponding to the second path identifier in the evaluation system for the navigation path within a preset time period;

[0017] Determining a second weight corresponding to the comprehensive comfort score of the navigation path corresponding to the second path identifier through the first weight, wherein the sum of the first weight and the second weight is 1;

[0018] Generating a target comfort score for the navigation path corresponding to the second path identifier through the first weight, the navigation path evaluation, the comprehensive comfort score and the second weight.

[0019] Optionally, when it is determined that the second path identifier corresponds to the first path identifier, obtaining a comprehensive comfort score of the navigation path corresponding to the second path identifier within a preset time period from an evaluation system for navigation paths includes:

[0020] When it is determined that the second path identifier corresponds to the first path identifier, obtaining a plurality of first comfort scores of the navigation path corresponding to the second path identifier within a preset time period from an evaluation system for navigation paths;

[0021] Setting a third weight for each of the first comfort scores;

[0022] Generating a comprehensive comfort score of the navigation path corresponding to the second path identifier within a preset time period through the weighted sum of the first comfort scores and the third weights.

[0023] Optionally, after generating the target comfort score of the navigation path corresponding to the second path identifier through the first weight, the navigation path evaluation, the comprehensive comfort score, and the second weight, it further includes:

[0024] In response to a user's application result display operation, sending the target comfort scores corresponding to multiple navigation paths between the starting point and the ending point to a target application, where the target application displays the target comfort scores corresponding to the multiple navigation paths and a brief description of the multiple navigation paths on a target interface;

[0025] In response to a user's feedback operation on the first path among the multiple navigation paths, adjusting the target comfort score of the first path.

[0026] Optionally, the triaxial acceleration observation quantity is obtained from an inertial measurement unit of a vehicle;

[0027] Extracting the mutation points and the number of mutation points of the target observation sequence through a preset mutation point detection algorithm includes:

[0028] Determining the prior mean square error of the inertial measurement unit by using a preset method, where the preset method includes: an empirical evaluation method, an actual measurement and demonstration method;

[0029] Determining the stability index of the inertial measurement unit through the prior mean square error;

[0030] Extracting the mutation points and the number of mutation points of the target observation sequence through a preset mutation point detection algorithm and the stability index.

[0031] Optionally, generating a comfort score for a target path through the number of mutation points and the degree of mutation includes:

[0032] Set a fourth weight for the number of mutation points and a fifth weight for the degree of mutation, where the sum of the fourth weight and the fifth weight is 1;

[0033] Assign a first score to the weighted result of the number of mutation points and the fourth weight through empirical analysis of measured values, and assign a second score to the weighted result of the degree of mutation and the fifth weight;

[0034] Generate a comfort score for the target path based on the first score and the second score.

[0035] Optionally, after generating the comfort score for the target path based on the first score and the second score, it further includes:

[0036] Pre-construct an evaluation set for the number of mutation points and the degree of mutation;

[0037] Compare the comfort score of the target path with the evaluation set to determine the initial evaluation level of the target path;

[0038] Determine the optimized evaluation level of the target path through fact judgment operations, where the fact judgment operations include user feedback and cloud self-check.

[0039] According to the second aspect of the embodiments of the present application, there is provided a path evaluation and optimization device, the device includes:

[0040] A first construction module, configured to construct a center of gravity offset degree analysis index through triaxial acceleration observation values obtained when a vehicle travels on a target path;

[0041] A first generation module, configured to generate a target observation sequence after performing differential processing on the center of gravity offset degree analysis index;

[0042] A first determination module, configured to extract mutation points and the number of mutation points of the target observation sequence through a preset mutation point detection algorithm;

[0043] A second determination module, configured to predict the mutation points based on small sample prediction of grey theory to determine the degree of mutation of the mutation points;

[0044] A second generation module, configured to generate a comfort score for the target path through the number of mutation points and the degree of mutation, where the comfort score for the target path is used to provide a comfort evaluation index for the original navigation path planning of the target path, and realize the evaluation and optimization of the original navigation path planning.

[0045] According to the third aspect of the embodiments of the present application, there is provided an electronic device, including:

[0046] A processor;

[0047] A memory for storing the processor-executable instructions;

[0048] Wherein, the processor is configured to execute the instructions to implement the path evaluation and optimization method as described in the first aspect.

[0049] According to a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided. When the instructions in the storage medium are executed by a processor of a mobile terminal, the mobile terminal can execute the path evaluation and optimization method as described in the first aspect of the present application.

[0050] The technical solutions provided by the embodiments of the present application may include the following beneficial effects:

[0051] In the present invention, a center-of-gravity offset degree analysis index is constructed by using the three-axis acceleration observation data obtained when the vehicle travels on the target path. After differential processing of the center-of-gravity offset degree analysis index, a target observation sequence is generated; the above differential processing can avoid directly obtaining acceleration data and ensure the security of sensitive data. By generating the center-of-gravity offset degree analysis index through the three-axis acceleration observation data, the conclusion of ride comfort evaluation is easier for users to understand and accept; then, through a preset mutation point detection algorithm, the mutation points and the number of mutation points of the target observation sequence are extracted; based on the small-sample prediction of grey theory, the mutation degree of the mutation points is predicted; a comfort score for the target path is generated through the number of mutation points and the mutation degree; the comfort score for the target path is used to provide a comfort evaluation index for the original navigation path planning of the target path, so as to realize the evaluation and optimization of the original navigation path planning. The above generation of the comfort score for the target path through the number of mutation points and the mutation degree is beneficial to further evaluate the driving comfort and is easier to understand. In the embodiments of the present invention, by constructing the three-axis acceleration observation data into an offset degree index and performing differential processing, the security of the acceleration data is guaranteed. At the same time, the statistical indexes of the number of mutations and the mutation degree are constructed, which is beneficial to further evaluate the driving comfort and is easier to understand, and can also better serve the navigation path optimization. In addition, the embodiments of the present invention perform a comprehensive evaluation based on the existing navigation path planning results supplemented by comfort indexes, providing a path planning concept that takes into account the driving experience, and through the mobilization of the user application end, it is beneficial to improve the user participation and experience, and thus beneficial to improving the customer stickiness.

[0052] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. Description of the Drawings

[0053] The drawings here are incorporated into the specification and form a part of this specification, showing the embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.

[0054] Figure 1 It is a flowchart of steps of a path evaluation and optimization method shown according to an exemplary embodiment;

[0055] Figure 2 is Figure 1 The comfort score flowchart in a path evaluation and optimization method shown according to an exemplary embodiment shown;

[0056] Figure 3 It is a flowchart of steps of another path evaluation and optimization method shown according to an exemplary embodiment;

[0057] Figure 4 is Figure 3 The schematic diagram of supplementing comfort index based on the existing navigation path planning in another path evaluation and optimization method shown according to an exemplary embodiment shown;

[0058] Figure 5 is Figure 3 The schematic diagram of the system composition of path evaluation in another path evaluation and optimization method shown according to an exemplary embodiment shown;

[0059] Figure 6 It is a block diagram of a path evaluation and optimization device shown according to an exemplary embodiment;

[0060] Figure 7 It is a block diagram of an electronic device shown according to an exemplary embodiment. Detailed implementation manners

[0061] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0062] The first implementation manner of the present application relates to a path evaluation and optimization method, Figure 1 It is a flowchart of a path evaluation and optimization method shown according to an exemplary embodiment, as Figure 1 shown, and includes the following steps:

[0063] Step 101, constructing a center of gravity offset degree analysis index through the triaxial acceleration observation quantities obtained when the vehicle travels on the target path.

[0064] The triaxial acceleration observation values in the embodiments of the present invention are obtained from the inertial measurement unit of the vehicle provided at the vehicle end. However, due to security considerations for mapping geographical information, the absolute spatial position must be subject to confidentiality and security processing, so this result cannot be directly displayed. Therefore, in the embodiments of the present invention, after obtaining the triaxial acceleration observation values, they are constructed into a center of gravity offset degree analysis index.

[0065] In the embodiments of the present invention, the obtained triaxial acceleration observation values are respectively named G x , G y , G z , where G x points to the left of the vehicle traveling direction, G y points to the vehicle traveling direction, G z is the direction of gravitational acceleration, and the axis system relationship of the XYZ triaxial acceleration forms a right-handed system.

[0066] After obtaining the triaxial acceleration observation values, first perform three-component synthesis on the triaxial acceleration observation values to generate a center of gravity offset degree analysis index (see Formula 1):

[0067]

[0068] It can be seen that the final value of the center of gravity offset degree analysis index has nothing to do with the direction settings of the XYZ axes and only relates to their numerical values. This can make the subsequent obtained scoring results independent of the axis system relationship of the acceleration observation values, and thus be more easily understood by users. In addition, in the embodiments of the present invention, positioning data is also obtained according to the vehicle-end GNSS (Global Navigation Satellite System) differential positioning result. Similarly, due to security considerations for mapping geographical information, the obtained positioning data is also preprocessed. Here, it can be processed according to a preset deflection algorithm for the vehicle model of this range. Usually, the deflection algorithms corresponding to the same range of vehicle models are the same. By performing deflection processing on the positioning data and differential processing on the gravitational acceleration data, cloud transmission and processing of sensitive data can be avoided, and data security is guaranteed.

[0069] Step 102: After performing differential processing on the center of gravity offset degree analysis index, generate a target observation sequence.

[0070] In the embodiments of the present invention, after obtaining the center of gravity offset degree analysis index, since this data is directly constructed from the triaxial acceleration observation values, in order to avoid cloud transmission and processing of sensitive data and further guarantee data security, the embodiments of the present invention also perform differential processing on the center of gravity offset degree analysis index (see Formula 2):

[0071]

[0072] Among them, G i is the center of gravity offset degree analysis index at the i-th position among multiple center of gravity offset degree analysis indexes, and t i is the time when G i is obtained. The corresponding G i-1 is the center of gravity offset degree analysis index at the i - 1 position (the previous position of the i-th position) among multiple center of gravity offset degree analysis indexes, and t i-1 is the time when G i-1 is obtained, and it is also the previous moment of t i . The target observation sequence is constructed with ΔG i and t, and a time series graph of ΔG i -t can also be generated for the convenience of users' observation and analysis.

[0073] Step 103: Extract the mutation points and the number of mutation points of the target observation sequence through a preset mutation point detection algorithm.

[0074] In the embodiment of the present invention, after obtaining the positioning and attitude data at the vehicle end, preprocessing and construction steps are performed to obtain the center of gravity offset degree analysis index, and the obtained center of gravity offset degree analysis index is sent to the cloud for data processing, including center of gravity skewness time series analysis and mapping, mutation detection based on the prior mean square error, and small sample prediction based on the grey theory GM(1,1). Among them, the center of gravity skewness time series analysis and mapping means using the generated center of gravity offset degree analysis index and the corresponding t as inputs to generate a center of gravity offset degree time series graph (ΔG i -t). At the same time, the prior mean square error of the center of gravity offset degree analysis index is obtained by using the empirical evaluation or actual measurement and demonstration method. Since the center of gravity offset degree analysis index is generated from the collected triaxial acceleration observation data, and the triaxial acceleration observation data is collected by the acceleration sensor at the vehicle end and transmitted to the inertial measurement unit, the obtained prior mean square error can also be regarded as the prior mean square error of the acceleration sensor. Then, the stability index of the center of gravity offset degree analysis index (i.e., the stability index of the acceleration sensor) is set according to the design requirements. Finally, the mutation points and the number of mutation points in the center of gravity offset degree time series graph are determined through a mature mutation point detection algorithm. The specific steps include:

[0075] Determine the prior mean square error of the inertial measurement unit by using a preset method, and the preset method includes: empirical evaluation method, actual measurement and demonstration method;

[0076] Determine the stability index of the inertial measurement unit through the prior mean square error;

[0077] Extract the mutation points and the number of mutation points of the target observation sequence through a preset mutation point detection algorithm and the stability index.

[0078] Among them, the prior mean square error of the inertial measurement unit obtained by the preset method refers to the prior mean square error of the centroid offset analysis index obtained by the inertial measurement unit under ideal conditions and the actually calculated centroid offset analysis index preset during design, which can be represented by the parameter δ. The stability index is the stability index of the inertial measurement unit and can be set based on the prior mean square error. For example, it can be set to 2 times (2δ) or 1.5 times (1.5δ) of the prior mean square error. The present invention does not make specific limitations here. After obtaining the stability index, the mutation points in the target observation sequence can be judged according to this stability index and through the preset mutation point detection algorithm, that is, the point corresponding to ΔG greater than the stability index is the mutation point.

[0079] Step 104: Predict the mutation point based on the small-sample prediction of grey theory to determine the mutation degree of the mutation point.

[0080] Grey theory in the embodiments of the present invention believes that all random variables are grey quantities and grey processes that change within a certain range and within a certain time period. Instead of looking for their statistical laws and probability distributions in data processing, after a certain processing of the original data, it becomes regular time series data. On this basis, a mathematical model is established. Here, we can use GM(1,1) in grey theory to predict the mutation degree of the mutation point.

[0081] Exemplarily, taking the observed value at the previous moment i as the input, GM(1,1) is used to obtain a predicted value that meets the prediction evaluation confidence level. Then, the mutation degree B of the mutation point at position i is calculated through Formula 3:

[0082]

[0083] Step 105: Generate a comfort score for the target path based on the number of mutation points and the mutation degree, where the comfort score for the target path is used to provide a comfort evaluation index for the original navigation path planning of the target path, so as to realize the evaluation and optimization of the original navigation path planning.

[0084] The comfort score of the target path in the embodiments of the present invention is used to provide a comfort evaluation index for the original navigation path planning of the target path, so as to evaluate and optimize the original navigation path planning. Moreover, in the embodiments of the present invention, the comfort score of the target path is generated based on the number of mutation points and the degree of mutation. Therefore, weight coefficients will also be set for the number of mutation points and the degree of mutation respectively. The specific weight values are not specifically limited, but it is required that the weights corresponding to the number of mutation points and the degree of mutation add up to 1. By way of example, let the number of mutation points (which can also be understood as the number of mutations) be A, the degree of mutation be B, the weight of the number of mutation points A be α, and the weight of the degree of mutation B be β, where α + β = 1. Then, based on the empirical analysis of the measured values, the weighted results of the number of mutation points and the fourth weight are given a first score, and the weighted results of the degree of mutation and the fifth weight are given a second score. The two scores are added together to obtain the comfort score of the target path. The specific steps include:

[0085] Set a fourth weight for the number of mutation points and a fifth weight for the degree of mutation, where the sum of the fourth weight and the fifth weight is 1;

[0086] Based on the empirical analysis of the measured values, give a first score to the weighted result of the number of mutation points and the fourth weight, and give a second score to the weighted result of the degree of mutation and the fifth weight;

[0087] Generate the comfort score of the target path through the first score and the second score.

[0088] By way of example, by determining different value ranges of A and B, different scores are given to them. Generally, the larger the value, the worse the road conditions, and the lower the score is set, as shown in the following table:

[0089]

[0090] Furthermore, after obtaining the score, the level of the target path will also be determined according to the score. This requires pre - constructing a corresponding score set. Different standard score ranges corresponding to different levels are set in the score set, and then based on this standard score range, the levels corresponding to different scores are determined. Since the first score and the second score in the generated comfort score are set manually and belong to a kind of fuzzy evaluation. In view of the drawback that the evaluation result may deviate greatly from the actual situation due to the intervention of subjective factors in the fuzzy evaluation process, this system combines cloud self - inspection and user feedback to support the iterative adjustment of weights and sub - factor scores. The specific steps include:

[0091] Pre - construct an evaluation set for the number of mutation points and the degree of mutation;

[0092] Compare the comfort score of the target path with the evaluation set to determine the initial evaluation level of the target path;

[0093] Determine the optimized evaluation level of the target path through the actual situation judgment operation, where the actual situation judgment operation includes user feedback and cloud self-check.

[0094] Exemplarily, the pre-constructed scoring set is V = (V1, V2, V3, V4), V1 ∈: excellent, V2 ∈: good, V3 ∈: medium, V4 ∈: poor. Different score ranges are set for V1, V2, V3, and V4 respectively. The present invention does not make specific limitations here, but only requires that the better the scoring level, the larger the score in the set score range, that is, the score in the scoring set V1 is greater than the score in the scoring set V2, which is greater than the score in the scoring V3, which is greater than the score in the scoring set V4.

[0095] In summary, the process of the comfort score of the present invention is as Figure 2 shown. First, determine the number of mutation points and the degree of mutation, and then establish an evaluation set to facilitate subsequent scoring and rating. Establish weights for the number of mutation points and the degree of mutation and assign scores, requiring the weights to add up to 1. Fuzzy comprehensive evaluation: The sum of the scores assigned to the number of mutation points and the degree of mutation is used as the comfort score for the target path. Then, optimize the comfort score for the target path through the actual situation judgment operation. If it does not conform to the actual situation, optimize the evaluation index; if it conforms to the actual situation, continue to use it.

[0096] The present invention constructs a center of gravity offset degree analysis index through the three-axis acceleration observation data obtained when the vehicle travels on the target path. After differential processing of the center of gravity offset degree analysis index, a target observation sequence is generated; the above differential processing can avoid directly obtaining acceleration data and ensure the security of sensitive data. Generating a center of gravity offset degree analysis index through three-axis acceleration observation data makes the evaluation conclusion of ride comfort easier to be understood and accepted by users; then, through a preset mutation point detection algorithm, the mutation points and the number of mutation points of the target observation sequence are extracted; based on the small sample prediction of the grey theory, the mutation degree of the mutation point is predicted; a comfort score for the target path is generated through the number of mutation points and the degree of mutation. The comfort score for the target path is used to provide a comfort evaluation index for the original navigation path planning of the target path, realizing the evaluation and optimization of the original navigation path planning. The above generation of a comfort score for the target path through the number of mutation points and the degree of mutation is beneficial to further evaluating the driving comfort and is easier to be understood. The embodiment of the present invention constructs the three-axis acceleration observation data into an offset degree index and performs differential processing, so that the security of acceleration data is guaranteed. At the same time, constructing statistical indicators of the number of mutations and the degree of mutation is beneficial to further evaluating the driving comfort and is easier to be understood, and can also better serve the navigation path optimization.

[0097] The second embodiment of the present application relates to a path evaluation and optimization method. Figure 3is a flowchart of another path evaluation and optimization method shown according to an exemplary embodiment, as Figure 3 shown, and includes the following steps:

[0098] Step 201, construct a center of gravity offset degree analysis index through the three-axis acceleration observation obtained when the vehicle travels on the target path.

[0099] Step 202, after performing differential processing on the center of gravity offset degree analysis index, generate a target observation sequence.

[0100] Step 203, through a preset mutation point detection algorithm, extract the mutation points and the number of mutation points of the target observation sequence.

[0101] Step 204, perform prediction on the mutation points based on the small sample prediction of grey theory to determine the mutation degree of the mutation points.

[0102] Step 205, generate a comfort score for the target path through the number of mutation points and the mutation degree, where the comfort score for the target path is used to provide a comfort evaluation index for the original navigation path planning of the target path, so as to realize the evaluation and optimization of the original navigation path planning.

[0103] It should be noted that in the embodiments of the present invention, the above steps 201 to 205 are referred to the previous discussion and will not be elaborated here.

[0104] Step 206, generate an evaluation system for the navigation path through the comfort score for the target path.

[0105] In the embodiments of the present invention, the comfort scores for the target paths obtained are integrated to obtain an evaluation system for the navigation path. The evaluation system includes the comfort scores of different paths, and different first path identifiers can be set for different paths to facilitate subsequent search through the first path identifiers.

[0106] Step 207, obtain multiple navigation paths existing between the starting point and the ending point and the corresponding second path identifier for each navigation path.

[0107] In the embodiments of the present invention, when the starting point and the ending point are determined, multiple navigation paths can be obtained through the in-vehicle navigation system. Among them, each navigation path is set with a corresponding second path identifier. It should be noted that the path identifier set for the same navigation path is fixed (as the identity information of the path) to facilitate subsequent search through the path identifier.

[0108] Step 208, set a first weight and a navigation path evaluation for each navigation path.

[0109] In the embodiments of the present invention, a first weight and a navigation path evaluation are set for each navigation path according to preset rules or human experience. The evaluation criteria can be based on the mileage of the road, the number of traffic light intersections, the complexity of the road, etc. The present invention does not make specific limitations here.

[0110] Step 209, when it is determined that the second path identifier corresponds to the first path identifier, obtain the comprehensive comfort score of the navigation path corresponding to the second path identifier in a preset time period from the evaluation system for navigation paths.

[0111] In the embodiments of the present invention, search for the second path identifier in the first path identifier of the evaluation system for navigation paths. If they are the same, it indicates that there is a comprehensive comfort score of the navigation path corresponding to the second path identifier in the evaluation system for navigation paths in the preset time period. If they are different, there is no comprehensive comfort score of the navigation path corresponding to the second path identifier in this path, and it needs to be generated according to the path evaluation and optimization method.

[0112] It should be noted that since the comfort scores stored in the evaluation system for navigation paths are the comfort scores at a certain moment (referred to as the first comfort scores), multiple first comfort scores of the navigation path corresponding to the second path identifier can be obtained from the evaluation system for navigation paths in the preset time period. Weights need to be set for each first comfort score, and then a weighted sum is performed based on the set weights and the obtained first comfort scores to obtain the comprehensive comfort score of each navigation path in the preset time period. The specific steps include:

[0113] When it is determined that the second path identifier corresponds to the first path identifier, obtain multiple first comfort scores of the navigation path corresponding to the second path identifier in a preset time period from the evaluation system for navigation paths;

[0114] Set a third weight for each first comfort score;

[0115] Generate the comprehensive comfort score of each navigation path in a preset time period through the weighted sum of the first comfort score and the third weight.

[0116] Exemplarily, set the preset time period to 3 days. When it is determined that the second path identifier corresponds to the first path identifier, obtain the first comfort scores of the navigation path corresponding to the second path identifier in the recent 3 days from the evaluation system for navigation paths according to the road network interruption rule, and let it be V i-1 、V i-2 、V i-3 . Set weights for them respectively. Since the data closer to the present is more effective, the weights are set to decrease in turn (W1 > W2 > W3, W1 + W2 + W3 = 1), V i-1, V i-2 , V i-3 correspond to W1, W2, W3 respectively, and the comprehensive comfort score is V s Obtained through Formula 4:

[0117] V s = V i-1 ×W1 + V i-2 ×W2 + V i-3 ×W3 (4)

[0118] Step 210, determine the second weight corresponding to the comprehensive comfort score of the navigation path corresponding to the second path identifier through the first weight.

[0119] In the embodiment of the present invention, after obtaining the first weight, since the sum of the first weight and the second weight is 1, the second weight corresponding to the comprehensive comfort score of each navigation path is determined through the first weight. For example, if the first weight of a certain navigation path is set as α1, then the corresponding second weight is 1 - α1.

[0120] Step 211, generate the target comfort score of the navigation path corresponding to the second path identifier through the first weight, the navigation path evaluation, the comprehensive comfort score, and the second weight.

[0121] In the embodiment of the present invention, after obtaining the first weight, the navigation path evaluation, the comprehensive comfort score, and the second weight corresponding to the comprehensive comfort score, the target comfort score V of each navigation path is generated through Formula 5 zi , where V s is the comprehensive comfort score of a certain navigation path, α1 is the first weight of a certain navigation path, 1 - α1 is the second weight of the comprehensive comfort score of a certain navigation path, and V Y is the navigation path evaluation set for a certain navigation path:

[0122] V zi = α i ×V Y +(1 - α i )×V S (5)

[0123] For example, as Figure 4 shown, it is set that there are 3 paths from the starting point to the ending point, and the comfort scores obtained by different paths are different. Based on the existing navigation path planning results supplemented with comfort indicators for comprehensive evaluation, a comprehensive score is obtained, providing a path planning concept that takes into account the driving experience.

[0124] It should be noted that in the embodiment of the present invention, the target comfort score of the first path is also adjusted through user feedback. The specific steps include:

[0125] In response to the user's application result display operation, the target comfort scores corresponding to the multiple navigation paths between the starting point and the end point are sent to the target application, wherein the target application displays the target comfort scores corresponding to the multiple navigation paths and a brief description of the multiple navigation paths on the target interface;

[0126] In response to a user's feedback operation on a first path among the multiple navigation paths, a target comfort score of the first path is adjusted.

[0127] Among them, the user's application results display operation includes the user applying on the target application (at this time, the user's needs are taken as the starting point, and relevant information is pushed in a targeted manner), and then the cloud collects and sends the application content to the target application (cloud collection is to organize the comfort evaluation indicators under this path according to the route traveled on that day, and make appropriate brief explanations. The result form is mainly a time series diagram, and the comfort indicators of each partition are marked by segmented coloring); the user's feedback operation for the first path among multiple navigation paths includes: the user feeds back conclusive information to the cloud platform based on the accuracy of the received information and the navigation path optimization recommendation angle. The cloud platform reviews the rationality of relevant indicator parameters based on user feedback and gradually performs iterative optimization).

[0128] The embodiment of the present invention performs a comprehensive evaluation based on the existing navigation path planning results supplemented by comfort indicators, and provides a path planning concept that takes into account the driving experience. By mobilizing the user application end, it is beneficial to improve user participation and experience, and further helps to improve customer stickiness.

[0129] In summary, the system structure of the path evaluation in the embodiment of the present invention is as follows: Figure 5 As shown, the vehicle side includes inputting the data obtained by the positioning and attitude data sensors into the preprocessing module for preprocessing, and then performing data processing and analysis and evaluation in the cloud to obtain a comfort score for the target path, and assisting in optimizing the path through the comfort score for the target path. At the same time, the comfort score for the target path is sent to the application side, so that the user can provide feedback based on the accuracy of the evaluation conclusion, and further assist in optimizing the path. The result of the path auxiliary optimization is transmitted to the vehicle navigation system on the vehicle side through the map publishing module for the driver's reference, or for sharing the driving pleasure after determining the accuracy of the evaluation conclusion.

[0130] The third embodiment of the present application relates to a path evaluation and optimization device, Figure 6 is a flow chart of a path evaluation and optimization device according to an exemplary embodiment. Figure 6 As shown, the following steps are included:

[0131] The first construction module 301 is used to construct a gravity center deviation analysis index through three-axis acceleration observations obtained when the vehicle travels on a target path.

[0132] The first generation module 302 is configured to generate a target observation sequence after performing differential processing on the center of gravity offset analysis index.

[0133] The first determination module 303 is configured to extract the mutation points and the number of mutation points of the target observation sequence through a preset mutation point detection algorithm.

[0134] The second determination module 304 is configured to predict the mutation points based on the small sample prediction of the grey theory and determine the mutation degree of the mutation points.

[0135] The second generation module 305 is configured to generate a comfort score for the target path through the number of mutation points and the mutation degree, where the comfort score for the target path is used to provide a comfort evaluation index for the original navigation path planning of the target path, so as to evaluate and optimize the original navigation path planning.

[0136] Optionally, the path evaluation and optimization device further includes:

[0137] The third generation module is configured to generate an evaluation system for the navigation path through the comfort score for the target path, and a corresponding first path identifier is set for each navigation path in the evaluation system for the navigation path.

[0138] The first acquisition module is configured to acquire multiple navigation paths existing between the starting point and the ending point and the corresponding second path identifier for each navigation path.

[0139] The first setting module is configured to set a first weight and a navigation path evaluation for each navigation path.

[0140] The second acquisition module is configured to, when determining that the second path identifier corresponds to the first path identifier, acquire the comprehensive comfort score of the navigation path corresponding to the second path identifier in a preset time period from the evaluation system for the navigation path.

[0141] The third determination module is configured to determine a second weight corresponding to the comprehensive comfort score of the navigation path corresponding to the second path identifier through the first weight, where the sum of the first weight and the second weight is 1.

[0142] The fourth generation module is configured to generate a target comfort score of the navigation path corresponding to the second path identifier through the first weight, the navigation path evaluation, the comprehensive comfort score, and the second weight.

[0143] Optionally, the second acquisition module further includes:

[0144] The first acquisition sub-module is configured to, when determining that the second path identifier corresponds to the first path identifier, acquire multiple first comfort scores of the navigation path corresponding to the second path identifier in a preset time period from the evaluation system for the navigation path.

[0145] The first setting sub-module is used to set a third weight for each first comfort score.

[0146] The first generation sub-module is used to generate the comprehensive comfort score of the navigation path corresponding to the second path identifier within a preset time period through the weighted sum of the first comfort score and the third weight.

[0147] Optionally, the path evaluation and optimization device further includes:

[0148] The sending module is used to send the target comfort scores corresponding to multiple navigation paths between the starting point and the ending point to the target application in response to the user's application result display operation, where the target application displays the target comfort scores corresponding to multiple navigation paths and a brief description of the multiple navigation paths on the target interface.

[0149] The adjustment module is used to adjust the target comfort score of the first path in response to the user's feedback operation on the first path among the multiple navigation paths.

[0150] Optionally, the triaxial acceleration observation quantity is obtained from the inertial measurement unit of the vehicle;

[0151] The first determination module 303 further includes:

[0152] The first determination sub-module is used to determine the prior mean square error of the inertial measurement unit by using a preset method, and the preset method includes: an empirical evaluation method and an actual measurement and demonstration method.

[0153] The second determination sub-module is used to determine the stability index of the inertial measurement unit through the prior mean square error.

[0154] The third determination sub-module is used to extract the mutation points and the number of mutation points of the target observation sequence through a preset mutation point detection algorithm and the stability index.

[0155] Optionally, the second generation module 305 further includes:

[0156] The second setting sub-module is used to set a fourth weight for the number of mutation points and a fifth weight for the degree of mutation, where the sum of the fourth weight and the fifth weight is 1.

[0157] The first assignment sub-module is used to assign a first score to the weighted result of the number of mutation points and the fourth weight through empirical analysis of the measured values, and assign a second score to the weighted result of the degree of mutation and the fifth weight.

[0158] The second generation sub-module is used to generate the comfort score of the target path through the first score and the second score.

[0159] A construction sub-module for pre-constructing an evaluation set regarding the number and degree of mutation points.

[0160] A fourth determination sub-module for comparing the comfort score of the target path with the evaluation set to determine the initial evaluation level regarding the target path.

[0161] A fifth determination sub-module for determining the optimized evaluation level of the target path through fact judgment operations, where the fact judgment operations include user feedback and cloud self-check.

[0162] In the present invention, a centroid offset degree analysis index is constructed based on the three-axis acceleration observation data obtained when the vehicle travels on the target path. After differential processing of the centroid offset degree analysis index, a target observation sequence is generated. The above differential processing can avoid directly obtaining acceleration data and ensure the security of sensitive data. Generating the centroid offset degree analysis index through the three-axis acceleration observation data makes the conclusion of the ride comfort evaluation easier to be understood and accepted by users. Then, through a preset mutation point detection algorithm, the mutation points and the number of mutation points of the target observation sequence are extracted. Based on the small sample prediction of the grey theory, the mutation degree of the mutation points is determined. The comfort score regarding the target path is generated through the number and degree of mutation points. The comfort score regarding the target path is used to provide a comfort evaluation index for the original navigation path planning of the target path, realizing the evaluation and optimization of the original navigation path planning. The above generating the comfort score regarding the target path through the number and degree of mutation points is beneficial to further evaluating the driving comfort and is easier to be understood. In the embodiment of the present invention, by constructing the three-axis acceleration observation data into an offset degree index and performing differential processing, the security of the acceleration data is guaranteed. At the same time, constructing the statistical indexes of the number of mutations and the degree of mutation is beneficial to further evaluating the driving comfort and is easier to be understood, and can also better serve the navigation path optimization. In addition, the embodiment of the present invention conducts a comprehensive evaluation based on the existing navigation path planning result supplemented by the comfort index, providing a path planning concept that takes into account the driving experience, and by mobilizing the user application end, it is beneficial to improve the user participation and experience, and further beneficial to enhancing the customer stickiness.

[0163] Regarding the device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0164] The fourth embodiment of the present application relates to an electronic device, including: a processor; a memory for storing instructions executable by the processor; wherein, the processor is configured to execute the instructions to implement any path evaluation and optimization method.

[0165] Figure 7FIG. 0 is a block diagram of an electronic device 400 shown in accordance with an exemplary embodiment. For example, the electronic device 400 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a gaming console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0166] Referring Figure 7 to FIG. 5, the electronic device 400 may include one or more of the following components: a processing component 402, a memory 404, a power component 406, a multimedia component 408, an audio component 410, an input / output interface 412, a sensor component 414, and a communication component 416.

[0167] The processing component 402 generally controls the overall operation of the device 400, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 402 may include one or more processors 420 to execute instructions to complete all or part of the steps of the above-described methods. In addition, the processing component 402 may include one or more modules to facilitate interaction between the processing component 402 and other components. For example, the processing component 402 may include a multimedia module to facilitate interaction between the multimedia component 408 and the processing component 402.

[0168] The memory 404 is configured to store various types of data to support the operation of the device 400. Examples of such data include instructions for any application or method operating on the device 400, contact data, phone book data, messages, pictures, videos, etc. The memory 404 may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk.

[0169] The power component 406 provides power to the various components of the electronic device 400. The power component 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 400.

[0170] The multimedia component 408 includes a screen that provides an output interface between the electronic device 400 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can sense not only the boundaries of touch or swipe actions, but also detect the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 408 includes a front camera and / or a rear camera. When the electronic device 400 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.

[0171] The audio component 410 is configured to output and / or input audio signals. For example, the audio component 410 includes a microphone (MIC) that is configured to receive external audio signals when the electronic device 400 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 404 or transmitted via the communication component 416. In some embodiments, the audio component 410 further includes a speaker for outputting audio signals.

[0172] The input / output interface 412 provides an interface between the processing component 402 and a peripheral interface module, and the peripheral interface module can be a keyboard, a click wheel, buttons, etc. These buttons can include but are not limited to: a home button, a volume button, a power button, and a lock button.

[0173] The sensor component 414 includes one or more sensors for providing a status assessment of various aspects of the electronic device 400. For example, the sensor component 414 can detect the on / off state of the electronic device 400, the relative positioning of components, such as the display and keypad of the electronic device 400. The sensor component 414 can also detect a change in the position of the electronic device 400 or a component of the electronic device 400, the presence or absence of user contact with the electronic device 400, the orientation or acceleration / deceleration of the electronic device 400, and a change in the temperature of the electronic device 400. The sensor component 414 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor component 414 can also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 414 can further include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0174] The communication component 416 is configured to facilitate communication between the electronic device 400 and other devices in a wired or wireless manner. The electronic device 400 can access a communication standard-based wireless network, such as WiFi, a carrier network (such as 2G, 3G, 4G, or 5G), or a combination thereof. In an exemplary embodiment, the communication component 416 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 416 further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on Radio Frequency Identification (RFID) technology, Infrared Data Association (IrDA) technology, Ultra-Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0175] In an exemplary embodiment, the electronic device 400 can be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above methods.

[0176] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 404 including instructions, and the above instructions can be executed by a processor 420 of the electronic device 400 to complete the above methods. For example, the non-transitory computer-readable storage medium can be a ROM, Random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0177] Those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include known common knowledge or conventional technical means in the technical field not disclosed in this application. The specification and embodiments are only to be considered as exemplary, and the true scope and spirit of the present invention are pointed out by the following claims.

[0178] It should be noted that the above embodiments illustrate the present invention rather than limit the present invention, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In the unit claims listing several devices, several of these devices can be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words can be interpreted as names. The present invention is not limited to the exact construction described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is limited only by the appended claims.

Claims

1. A path evaluation and optimization method, characterized in that, The method includes: Constructing a center of gravity offset degree analysis index based on the triaxial acceleration observation data obtained when the vehicle travels on the target path; After performing differential processing on the center of gravity offset degree analysis index, generating a target observation sequence; Extracting the mutation points and the number of mutation points of the target observation sequence through a preset mutation point detection algorithm; Predicting the mutation points based on the small sample prediction of grey theory to determine the mutation degree of the mutation points; Generating a comfort score for the target path through the number of mutation points and the mutation degree, wherein the comfort score for the target path is used to provide a comfort evaluation index for the original navigation path planning of the target path, so as to realize the evaluation and optimization of the original navigation path planning.

2. The method according to claim 1, wherein After generating the comfort score for the target path through the number of mutation points and the mutation degree, it further includes: Generating an evaluation system for the navigation path through the comfort score for the target path, and each navigation path in the evaluation system for the navigation path is set with a corresponding first path identifier; Obtaining multiple navigation paths existing between the starting point and the ending point and the corresponding second path identifiers of each navigation path; Setting a first weight and a navigation path evaluation for each navigation path; When it is determined that the second path identifier corresponds to the first path identifier, obtaining the comprehensive comfort score of the navigation path corresponding to the second path identifier in the preset time period from the evaluation system for the navigation path; Determining the second weight corresponding to the comprehensive comfort score of the navigation path corresponding to the second path identifier through the first weight, wherein the sum of the first weight and the second weight is 1; Generating the target comfort score of the navigation path corresponding to the second path identifier through the first weight, the navigation path evaluation, the comprehensive comfort score and the second weight.

3. The method according to claim 2, wherein The step of, when it is determined that the second path identifier corresponds to the first path identifier, obtaining the comprehensive comfort score of the navigation path corresponding to the second path identifier in the preset time period from the evaluation system for the navigation path includes: When it is determined that the second path identifier corresponds to the first path identifier, obtaining multiple first comfort scores of the navigation path corresponding to the second path identifier in the preset time period from the evaluation system for the navigation path; Setting a third weight for each first comfort score; Generating the comprehensive comfort score of the navigation path corresponding to the second path identifier in the preset time period through the weighted sum of the first comfort score and the third weight.

4. The method according to claim 2, wherein After generating the target comfort score of the navigation path corresponding to the second path identifier through the first weight, the navigation path evaluation, the comprehensive comfort score and the second weight, it further includes: In response to the user's application result display operation, sending the target comfort scores corresponding to multiple navigation paths existing between the starting point and the ending point to the target application, wherein the target application is used to display the target comfort scores corresponding to multiple navigation paths and a brief description of multiple navigation paths on the target interface. In response to a user's feedback operation on the first path among multiple navigation paths, adjust the target comfort score of the first path.

5. The method according to claim 1, characterized in that, The triaxial acceleration observation quantity is obtained from the inertial measurement unit of the vehicle; The method of extracting the mutation points and the number of mutation points of the target observation sequence through a preset mutation point detection algorithm includes: Determine the prior mean square error of the inertial measurement unit by a preset method, and the preset method includes: empirical evaluation method, actual measurement and demonstration method; Determine the stability index of the inertial measurement unit through the prior mean square error; Extract the mutation points and the number of mutation points of the target observation sequence through a preset mutation point detection algorithm and the stability index.

6. The method according to claim 1, wherein The method of generating a comfort score for the target path through the number of mutation points and the degree of mutation includes: Set a fourth weight for the number of mutation points and a fifth weight for the degree of mutation, where the sum of the fourth weight and the fifth weight is 1; Assign a first score to the weighted result of the number of mutation points and the fourth weight through empirical analysis of measured values, and assign a second score to the weighted result of the degree of mutation and the fifth weight; Generate a comfort score for the target path through the first score and the second score.

7. The method according to claim 6, wherein After generating the comfort score for the target path through the first score and the second score, it further includes: Pre-construct an evaluation set for the number of mutation points and the degree of mutation; Compare the comfort score of the target path with the evaluation set to determine the initial evaluation level of the target path; Determine the optimized evaluation level of the target path through a fact judgment operation, where the fact judgment operation includes user feedback and cloud self-check.

8. A path evaluation and optimization device, characterized in that, It includes: A first construction module for constructing a center of gravity offset degree analysis index through the triaxial acceleration observation quantity obtained when the vehicle travels on the target path; A first generation module for generating a target observation sequence after performing differential processing on the center of gravity offset degree analysis index; A first determination module for extracting the mutation points and the number of mutation points of the target observation sequence through a preset mutation point detection algorithm; A second determination module for predicting the mutation points based on the small sample prediction of the grey theory to determine the degree of mutation of the mutation points; A second generation module for generating a comfort score for the target path through the number of mutation points and the degree of mutation, where the comfort score for the target path is used to provide a comfort evaluation index for the original navigation path planning of the target path, and realize the evaluation and optimization of the original navigation path planning.

9. An electronic device, characterized in that, It includes: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the instructions to implement the path evaluation and optimization method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the mobile terminal, the mobile terminal can execute the path evaluation and optimization method according to any one of claims 1 to 7.

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