CN114020858A

CN114020858A - Method and system for realizing data acquisition and update of walking and riding map

Info

Publication number: CN114020858A
Application number: CN202111309049.1A
Authority: CN
Inventors: 陈文静; 任轶; 闫鸿昌; 张学森; 罗自贤; 王东升
Original assignee: Anhui Yucheng Data Technology Co ltd
Current assignee: Anhui Yucheng Data Technology Co ltd
Priority date: 2021-11-05
Filing date: 2021-11-05
Publication date: 2022-02-08

Abstract

The invention discloses a method and a system for realizing data acquisition and updating of a step riding map, wherein the method comprises the following steps: calibrating a region to be acquired of first map data; acquiring first area characteristic information according to an area to be acquired of first map data, and matching a first data acquisition mode; acquiring a first data acquisition main body set according to a first data acquisition mode, and acquiring a first uploading data set of the first data acquisition main body set based on first portable integrated equipment; inputting the first uploaded data set into a map data proofreading information base for automatic differential comparison to obtain a first comparison result; and according to the first comparison result, carrying out data acquisition and updating on the map information of the area to be acquired of the first map data. The technical problems that in the prior art, the accuracy of map data acquisition is low, the real-time updating rate is low and the user experience is influenced based on walking and riding are solved.

Description

Method and system for realizing data acquisition and update of walking and riding map

Technical Field

The invention relates to the technical field of map acquisition, in particular to a method and a system for realizing data acquisition and updating of a step riding map.

Background

The traditional map industry is labor-intensive in nature, and all data acquisition modes are still data acquisition modes of field data acquisition personnel (field industry) and data processing modes of indoor processing personnel (field industry). The map represents elements such as mountains, rivers, greenbelts, and the like. The method is mainly used for displaying, has extremely low updating requirement and can be obtained at one time. In recent years, modes including vehicle event data recorder crowdsourcing, AI assistance, internal outsourcing and the like appear, but the labor-intensive path dependence cannot be fundamentally eliminated. The emerging market basically has no industrialization process similar to that of similar China for decades, so that the low-end population has extremely low industrial literacy and is difficult to organize a scale internal and external industry team. The core contradiction of map going out of the sea is the contradiction between the map's dependence on labor-intensive work and the local low-industry literacy population. The electronic map is used as a necessary tool for step riding navigation, and the accuracy and precision of the map directly influence the driving safety. In practical application, road conditions often change, such as road construction and traffic sign change, and if a used map is not updated timely, potential safety hazards are brought to vehicle driving.

In the process of implementing the technical scheme of the invention in the embodiment of the present application, the inventor of the present application finds that the above-mentioned technology has at least the following technical problems:

the technical problem that the accuracy of map data acquisition is low and the real-time updating rate is slow based on walking and riding in the prior art, and further the user experience is influenced is solved.

Disclosure of Invention

The embodiment of the application provides a method and a system for realizing data acquisition and updating of a step riding map, and a region to be acquired of first map data is calibrated; acquiring first area characteristic information according to an area to be acquired of first map data, and matching a first data acquisition mode; acquiring a first data acquisition main body set according to a first data acquisition mode, and acquiring a first uploading data set of the first data acquisition main body set based on first portable integrated equipment; inputting the first uploaded data set into a map data proofreading information base for automatic differential comparison to obtain a first comparison result; and according to the first comparison result, carrying out data acquisition and updating on the map information of the area to be acquired of the first map data. The technical problems that in the prior art, the accuracy of map data acquisition is low, the real-time updating rate is low and the user experience is influenced based on walking and riding are solved. The map information of the area to be collected of the first map data is subjected to data collection and updating based on walking and riding, the map data updating is intelligent and timely, the map data updating accuracy is high, the speed is high, the mapping cost is low, and the technical effect of user experience is improved.

In view of the foregoing problems, embodiments of the present application provide a method and system for implementing data collection and update of a step-riding map.

In a first aspect, the present application provides a method for implementing data collection and update of a step riding map, where the method includes: calibrating a region to be acquired of first map data; according to the area to be acquired of the first map data, acquiring first area characteristic information and matching a first data acquisition mode; acquiring a first data acquisition subject set according to the first data acquisition mode, wherein the first data acquisition subject set serves the area to be acquired of the first map data; obtaining a first uploaded data set of the first data collection subject set based on a first portable integrated device; inputting the first uploaded data set into a map data proofreading information base for automatic differential comparison to obtain a first comparison result; and according to the first comparison result, carrying out data acquisition and updating on the map information of the area to be acquired of the first map data.

On the other hand, the application also provides a system for realizing data acquisition and updating of a step riding map, wherein the system comprises: the first calibration unit is used for calibrating a region to be acquired of first map data; the first obtaining unit is used for obtaining first area characteristic information according to the area to be acquired of the first map data and matching a first data acquisition mode; a second obtaining unit, configured to obtain a first data collection subject set according to the first data collection manner, where the first data collection subject set serves the first map data to-be-collected area; a third obtaining unit, configured to obtain a first upload data set of the first data collection subject set based on a first portable integrated device; a fourth obtaining unit, configured to input the first uploaded data set into a map data collation information base for automatic differential comparison, so as to obtain a first comparison result; and the first execution unit is used for carrying out data acquisition and updating on the map information of the area to be acquired of the first map data according to the first comparison result.

On the other hand, the embodiment of the present application further provides a method and a system for acquiring and updating data of a step riding map, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, wherein the processor implements the steps of the method of the first aspect when executing the program.

One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

the embodiment of the application provides a method and a system for realizing data acquisition and updating of a step riding map, wherein a region to be acquired of first map data is calibrated; according to the area to be acquired of the first map data, acquiring first area characteristic information and matching a first data acquisition mode; acquiring a first data acquisition subject set according to the first data acquisition mode, wherein the first data acquisition subject set serves the area to be acquired of the first map data; obtaining a first uploaded data set of the first data collection subject set based on a first portable integrated device; inputting the first uploaded data set into a map data proofreading information base for automatic differential comparison to obtain a first comparison result; and according to the first comparison result, carrying out data acquisition and updating on the map information of the area to be acquired of the first map data. The technical problems that in the prior art, the accuracy of map data acquisition is low, the real-time updating rate is low and the user experience is influenced based on walking and riding are solved. The map information of the area to be collected of the first map data is subjected to data collection and updating based on walking and riding, the map data updating is intelligent and timely, the map data updating accuracy is high, the speed is high, the mapping cost is low, and the technical effect of user experience is improved.

The foregoing is a summary of the present disclosure, and embodiments of the present disclosure are described below to make the technical means of the present disclosure more clearly understood.

Drawings

Fig. 1 is a schematic flowchart of a method for acquiring and updating data of a step-riding map according to an embodiment of the present application;

fig. 2 is a schematic flow chart illustrating updating of a map data collation information base in a method for implementing data collection and updating of a step riding map in an embodiment of the present application;

fig. 3 is a schematic flow chart illustrating updating of the first planned path in a method for acquiring and updating data of a step-by-step riding map according to an embodiment of the present application;

fig. 4 is a schematic flowchart illustrating a process of acquiring a first uploaded data set in a method and system for acquiring and updating data of a step riding map according to an embodiment of the present application;

FIG. 5 is a schematic flow chart illustrating a first dimension reduction dataset obtained in a method for implementing data collection and update of a step riding map according to an embodiment of the present application;

fig. 6 is a schematic flowchart illustrating a process of performing update management on a school proofreading sub-information base in a method for implementing data acquisition and update of a walking map in an embodiment of the present application;

fig. 7 is a schematic flowchart illustrating a process of performing feature classification on a POI data set in a method for implementing data acquisition and update of a step riding map according to an embodiment of the present application;

FIG. 8 is a schematic structural diagram of a system for implementing data collection and update of a step riding map according to an embodiment of the present application;

fig. 9 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.

Description of reference numerals: the system comprises a first calibration unit 11, a first obtaining unit 12, a second obtaining unit 13, a third obtaining unit 14, a fourth obtaining unit 15, a first execution unit 16, an electronic device 300, a memory 301, a processor 302, a communication interface 303, and a bus architecture 304.

Detailed Description

By providing the method and the system for realizing the data acquisition and update of the step riding map, the technical problems that the accuracy of the map data acquisition is low and the real-time update rate is slow based on the step riding in the prior art, and further the user experience is influenced are solved. The map information of the area to be collected of the first map data is subjected to data collection and updating based on walking and riding, the map data updating is intelligent and timely, the map data updating accuracy is high, the speed is high, the mapping cost is low, and the technical effect of user experience is improved.

Hereinafter, example embodiments of the present application will be described in detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and it is to be understood that the present application is not limited by the example embodiments described herein.

Summary of the application

The traditional map industry is labor-intensive in nature, and all data acquisition modes are still data acquisition modes of field data acquisition personnel (field industry) and data processing modes of indoor processing personnel (field industry). The map represents elements such as mountains, rivers, greenbelts, and the like. The method is mainly used for displaying, has extremely low updating requirement and can be obtained at one time. In recent years, modes including vehicle event data recorder crowdsourcing, AI assistance, internal outsourcing and the like appear, but the labor-intensive path dependence cannot be fundamentally eliminated. The emerging market basically has no industrialization process similar to that of similar China for decades, so that the low-end population has extremely low industrial literacy and is difficult to organize a scale internal and external industry team. The core contradiction of map going out of the sea is the contradiction between the map's dependence on labor-intensive work and the local low-industry literacy population. The electronic map is used as a necessary tool for step riding navigation, and the accuracy and precision of the map directly influence the driving safety. In practical application, road conditions often change, such as road construction and traffic sign change, and if a used map is not updated timely, potential safety hazards are brought to driving.

In view of the above technical problems, the technical solution provided by the present application has the following general idea:

the application provides a method for realizing data acquisition and update of a step riding map, which is applied to a system for realizing data acquisition and update of the step riding map, wherein the method comprises the following steps: calibrating a region to be acquired of first map data; according to the area to be acquired of the first map data, acquiring first area characteristic information and matching a first data acquisition mode; acquiring a first data acquisition subject set according to the first data acquisition mode, wherein the first data acquisition subject set serves the area to be acquired of the first map data; obtaining a first uploaded data set of the first data collection subject set based on a first portable integrated device; inputting the first uploaded data set into a map data proofreading information base for automatic differential comparison to obtain a first comparison result; and according to the first comparison result, carrying out data acquisition and updating on the map information of the area to be acquired of the first map data.

Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.

Example one

As shown in fig. 1, an embodiment of the present application provides a method for acquiring and updating data of a step riding map, where the method includes:

step S100: calibrating a region to be acquired of first map data;

step S200: according to the area to be acquired of the first map data, acquiring first area characteristic information and matching a first data acquisition mode;

specifically, the Map (Map) scientifically reflects the distribution characteristics and the interrelations of natural and socioeconomic phenomena according to a certain rule. The step riding map data are collected and updated by using the portable equipment, most basically, a target area needing map updating is clear, and POI information such as commercial streets, office buildings and the like newly increased or reduced in the area can be better collected and updated only by knowing the target area, so that the system needs to obtain the area to be collected of the map, mark the area and mark the area as the area to be collected of the first map data. And carrying out image acquisition on the area to be acquired based on GPS positioning and high-precision equipment according to the area to be acquired of the first map data, and further carrying out feature extraction on the picture according to the acquired image information of the first area to obtain the feature information of the first area. The first area characteristic information comprises places such as topographic information, urban landscape, terrain, landform, street, branch road, building, public facility, market store, residential door address and the like of the first area. And matching the first data acquisition mode according to the first area characteristic information. For example, if the population density of the collection area is high and a large-scale map collection is not possible, the collection can be performed based on a crowd-sourced mode, and the automatic mapping driven by AI includes: the method is characterized in that tasks are discovered, distributed and priced, a consensus game cross verification mechanism is used for creating a new generation of internet live map which is free of human intervention, full-coverage, quasi-real-time updating and automatic drawing, and huge interior and exterior surveying and mapping cost expenditure of the traditional map businessman is thoroughly eliminated. For example, the APP of high-German panning releases a task needing data acquisition on the platform, if you can just conveniently finish the task, rewards of different money amounts can be received, and then the surveying and mapping expenditure cost can be reduced.

Step S300: acquiring a first data acquisition subject set according to the first data acquisition mode, wherein the first data acquisition subject set serves the area to be acquired of the first map data;

specifically, the system matches the first data acquisition mode according to the characteristic information of the first area, and the acquisition modes are different and the first data acquisition subjects are also different. The first data acquisition main body set serves the first map data to-be-acquired area, so that the data acquisition main body can be ensured to be familiar with the acquisition area sufficiently, and further the authenticity of the acquired data is ensured. For example, for areas where traffic is dense such as commercial streets, office buildings, etc., or where traffic cannot pass through, data collection can only be performed by walking or riding. And take-out rider, the courier of the service of going to the home of delivery to the topography comparison that people's mouth density is big such as trade street, office building, accessible platform crowd-sourced data acquisition task for take-out rider or courier, carry out data acquisition to first region, upload the data at backstage, just need handle, if whole manual identification efficiency is too low, and automatic identification technique can be to effective information wherein, for example: the road sign, the speed limit sign, the lane line sign, the traffic signal lamp and the like are automatically processed, so that the speed of updating the map is improved, and the authenticity of the acquired data is ensured.

Step S400: obtaining a first uploaded data set of the first data collection subject set based on a first portable integrated device;

further, as shown in fig. 4, step S400 in the embodiment of the present application includes:

step S410: according to the first portable integrated equipment, obtaining first POI image information, first POI position information and a first POI contact way in the area to be acquired of the first map data;

step S420: performing feature fusion on the first POI image information, the first POI position information and the first POI contact way to generate a first POI data feature set;

step S430: by analogy, a second POI data feature set is obtained until an Nth POI data feature set is obtained;

step S440: and obtaining the first uploading data set according to the first POI data characteristic set and the Nth POI data characteristic set.

Specifically, the first portable integrated device integrates a CCD camera, a laser and a GPS antenna, and can realize simultaneous high-precision map and three-dimensional image acquisition. And the data acquisition main body of the first area acquires information of the first POI information in the area to be acquired of the first map data by using the first portable integrated equipment. The first POI refers to any non-geographically meaningful point on the map: such as shops, bars, gas stations, hospitals, stations, etc. The first POI information comprises image information, position information, contact information and the like of the first POI. The data acquisition main body acquires the first POI information, further obtains information such as images, positions and contact ways of the first POI information, performs feature fusion on the first POI, enables the described features to be spliced together, performs induction and arrangement, and further generates a first POI data feature set. And sequentially carrying out data acquisition on the second POI, the third POI and the Nth POI, fusing the characteristics of the second POI and the third POI to obtain a second POI data characteristic set until the Nth POI data characteristic set is obtained, uploading all the POI data characteristic sets to a data set, and increasing the data acquisition accuracy and data diversity.

Step S500: inputting the first uploaded data set into a map data proofreading information base for automatic differential comparison to obtain a first comparison result;

step S600: and according to the first comparison result, carrying out data acquisition and updating on the map information of the area to be acquired of the first map data.

Specifically, a data acquisition main body of a first area acquires information of a first POI (point of interest) in an area to be acquired of first map data by using a first portable integrated device, inputs a first uploaded data set of the first data acquisition main body set into a map data proofreading information base after acquiring the first uploaded data set, and performs differential comparison on the acquired data and the map data proofreading information base based on a neural network model, so as to acquire a comparison result. The system acquires and updates data of changed places in the map information of the area to be acquired of the first map data according to the first comparison result, so that the map data is updated intelligently and timely, the map updating speed is high, and the mapping cost expenditure is reduced.

Further, as shown in fig. 2, step S700 in the embodiment of the present application includes:

step S710: acquiring a first data acquisition user according to the first data acquisition main body set;

step S720: acquiring a historical distribution track thermodynamic information set of the first data acquisition user;

step S730: constructing a distribution track thermodynamic trend graph of the first data acquisition user based on time information serving as an x axis of a horizontal coordinate and the historical distribution track thermodynamic information set serving as a y axis of a vertical coordinate;

step S740: performing trend analysis on the distribution track thermodynamic trend graph to generate a newly increased thermodynamic distribution area and an abruptly decreased thermodynamic distribution area;

step S750: acquiring newly added POI information corresponding to the newly added heat distribution area and sudden reduction POI information corresponding to the sudden reduction heat distribution area;

step S760: and updating the map data proofreading information base according to the newly added POI information and the sudden decrease POI information.

Specifically, according to the first data collection subject set, matching is performed, and then a first data collection user is obtained, the first data collection user has to be familiar with a first area to be collected and serves the first map data area to be collected, such as a takeaway rider, so that the topography of a mall and an office building is relatively known, and data collection can be performed by crowdsourcing a data collection task to the takeaway rider. Outsourcing and crowdsourcing personnel who reach the agreement with the enterprise need take with oneself the smart machine to record own relevant motion trail details, and then obtain the historical distribution orbit heating power information of first data acquisition user, arrange all information in order, form historical distribution orbit heating power information set. The distribution track thermodynamic information refers to that track thermodynamic information is enhanced in places with more passing times when a user moves in a first area, the track thermodynamic information is weaker in places with less passing times, and if thermodynamic distribution in different periods is weakened or enhanced, the places are subjected to key collection. And constructing a distribution track thermodynamic trend graph of the first data acquisition user based on time information serving as an x axis of a horizontal coordinate and a historical distribution track thermodynamic information set serving as a y axis of a vertical coordinate, carrying out trend analysis on the distribution track thermodynamic trend graph, and generating a newly-added thermodynamic distribution area and an abruptly-reduced thermodynamic distribution area. Analyzing the thermodynamic diagram of the user driving track, if a lot of user traveling tracks are suddenly increased at a certain position or the heat of a certain road section suddenly disappears, which probably means that a new road or an expired road is added, at the moment, targeted collection can be arranged, and if a map database compares certain data of information by mistake, error reporting processing can be selected. The system updates the map data proofreading information base according to POI information provided by a user, thereby ensuring the authenticity and accuracy of the map data.

Further, as shown in fig. 3, step S800 in the embodiment of the present application includes:

step S810: obtaining a first planned path for the first data acquisition user to go to a first destination based on the map data proofreading information base;

step S820: obtaining estimated traffic data according to the first planned path, wherein the estimated traffic data comprises estimated traffic time and estimated traffic intersection information;

step S830: acquiring actual passing time and actual passing intersection information of the first data acquisition user to the first destination based on the first portable integrated equipment;

step S840: carrying out data fusion on the actual passing time and the actual passing turnout information to generate actual running track data;

step S850: uploading the actual running track data to the map data proofreading information base, and performing automatic difference comparison with the estimated traffic data to generate first deviation data;

step S860: judging whether the first deviation data meets a preset deviation threshold value;

step S870: and if the first deviation data does not meet the preset deviation threshold, updating the first planned path according to the actual running track data.

Specifically, the system determines path information to be acquired based on a map data collation information base. The first planned path of the first destination refers to path matching conducted by a system for going to the destination. And acquiring data of the first planned path matching user, and estimating the traffic data of the path based on the neural network model so as to obtain estimated traffic data. The estimated traffic data comprises estimated traffic time and estimated traffic fork information. After a path to a destination is opened, a user acquires data of the passing time and the passing turnout information to the first destination based on acquisition equipment such as a portable CCD camera, a laser instrument and a GPS antenna, and the acquired data are fused to generate actual traveling track data. And uploading the actual driving track data to a map data proofreading information base, and calculating deviation values of all data by the system to obtain first deviation data and comparing the first deviation data with a preset deviation threshold value. The preset deviation threshold is obtained according to historical deviation value data. If the first deviation data meets a preset deviation threshold value, the actual traffic data is similar to the estimated traffic data, and the first planned path is not updated; if the first deviation data does not meet the preset deviation threshold value, the traffic information is inconsistent, and the content needing to be updated is quickly found according to the actual running track data to update the first planned path, so that the accuracy of the map acquisition information is ensured.

Further, as shown in fig. 5, step S900 in the embodiment of the present application includes:

step S910: obtaining a first feature data set according to the first uploading data set;

step S920: performing centralized processing on the first characteristic data set to obtain a second characteristic data set;

step S930: obtaining a first covariance matrix of the second feature data set;

step S940: calculating the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;

step S950: and projecting the first feature data set to the first feature vector to obtain a first dimension reduction data set, wherein the first dimension reduction data set is the feature data set obtained after dimension reduction of the first feature data set.

Specifically, the feature data of the first uploaded data set is subjected to numerical processing, a feature data set matrix is constructed, and the first feature data set is obtained. And then carrying out centralization processing on each feature data in the first feature data set, firstly solving an average value of each feature in the first feature data set, then subtracting the average value of each feature from each feature for all samples, and then obtaining a new feature value, wherein the second feature data set is formed by the new feature values, and is a data matrix. By the covariance formula:

and operating the second characteristic data set to obtain a first covariance matrix of the second characteristic data set. Wherein the feature data in the second feature data set is the feature data; is the average value of the characteristic data; the total amount of sample data in the second feature data set. Then, through matrix operation, the eigenvalue and the eigenvector of the first covariance matrix are solved, and each eigenvalue corresponds to one eigenvector. And selecting the largest first K characteristic values and the corresponding characteristic vectors from the obtained first characteristic vectors, and projecting the original characteristics in the first characteristic data set onto the selected characteristic vectors to obtain the first characteristic data set after dimension reduction. The feature data in the database are subjected to dimensionality reduction processing through a principal component analysis method, and redundant data are removed on the premise of ensuring the information quantity, so that the sample quantity of the feature data in the database is reduced, the loss of the information quantity after dimensionality reduction is minimum, and the operation speed of a training model on the data is accelerated.

Further, as shown in fig. 6, step S1000 in the embodiment of the present application includes:

step S1010: obtaining a POI data set in the map data proofreading information base;

step S1020: carrying out feature classification on the POI data set to generate a first feature data set, a second feature data set and an Mth feature data set;

step S1030: constructing a first map data proofreading sub-information base based on the first characteristic data set until an Mth map data proofreading sub-information base is constructed based on the Mth data characteristic set;

step S1040: and updating and managing the first map data proofreading sub-information base until the Mth map data proofreading sub-information base respectively.

Specifically, all places are thoroughly distributed and databased, and a POI data set in a map data proofreading information base is obtained, wherein the POI data set firstly covers information of restaurants, supermarkets, hotels and banks, shopping malls, office buildings, landmark buildings and the like at two sides of a street. The method comprises the steps of carrying out feature classification on a POI data set, generating a first feature data set based on Euclidean distance, sequentially calculating to obtain a second feature data set till an Mth feature data set, constructing a corresponding first map data proofreading sub-information base according to the first feature data set, the second feature data set till the Mth feature data set based on the feature data set, uploading data to a system by a user, rapidly finding contents needing to be updated by carrying out automatic difference comparison, and carrying out updating management on the map data proofreading sub-information base. By carrying out feature classification on POI data, the system can quickly position the content to be updated, and further management of the information base is realized.

Further, as shown in fig. 7, step S1020 in the embodiment of the present application includes:

step S1021: defining the POI data set as N sample points;

step S1022: based on the N sample points, randomly selecting K central points;

step S1023: calculating the distance between the N sample points and each central point of the K to obtain a Euclidean distance data set;

step S1024: obtaining a POI classification data set according to the Euclidean distance data set, wherein the POI classification data set is the shortest distance in the Euclidean distance data set;

step S1025: and obtaining a first classification result according to the POI classification data set, and performing feature classification on the POI data set.

Specifically, feature classification is carried out on the POI data set, each POI data is defined as a sample point, N sample points are counted, the sample points are distributed on a map in a scattered mode, K central points are selected from the sample points, K is smaller than or equal to N, and the distance between the N sample points and each central point of the K is calculated based on Euclidean distance. The euclidean distance refers to a straight line distance between two points, i.e., the shortest distance. And storing the shortest distance to the first K center point in the grid data, sorting the POIs with the shortest distance to the first K center point to form a first characteristic data set, calculating the distance from each sample point to the K center point by analogy, and classifying all POIs according to the Euclidean distance data set so as to obtain a second characteristic data set till an Mth characteristic data set, thereby realizing the management and classification of the POIs, enabling the system to quickly position the content to be updated and further managing the information base.

To sum up, the method for realizing data acquisition and update of the step riding map provided by the embodiment of the application has the following technical effects:

1. the application provides a method for realizing data acquisition and update of a step riding map, which is applied to a system for realizing data acquisition and update of the step riding map, wherein the method comprises the following steps: calibrating a region to be acquired of first map data; according to the area to be acquired of the first map data, acquiring first area characteristic information and matching a first data acquisition mode; acquiring a first data acquisition subject set according to the first data acquisition mode, wherein the first data acquisition subject set serves the area to be acquired of the first map data; obtaining a first uploaded data set of the first data collection subject set based on a first portable integrated device; inputting the first uploaded data set into a map data proofreading information base for automatic differential comparison to obtain a first comparison result; and according to the first comparison result, carrying out data acquisition and updating on the map information of the area to be acquired of the first map data. The technical problems that in the prior art, the accuracy of map data acquisition is low, the real-time updating rate is low and the user experience is influenced based on walking and riding are solved. The map information of the area to be collected of the first map data is subjected to data collection and updating based on walking and riding, the map data updating is intelligent and timely, the map data updating accuracy is high, the speed is high, the mapping cost is low, and the technical effect of user experience is improved.

2. The feature data in the database are subjected to dimensionality reduction processing through a principal component analysis method, and redundant data are removed on the premise of ensuring the information quantity, so that the sample quantity of the feature data in the database is reduced, the loss of the information quantity after dimensionality reduction is minimum, and the operation speed of a training model on the data is accelerated.

Example two

Based on the same inventive concept as the method for acquiring and updating the step-riding map data in the foregoing embodiment, the present invention further provides a system for acquiring and updating the step-riding map data, as shown in fig. 8, the system includes:

the first calibration unit 11, the first calibration unit 11 is used for calibrating a region to be acquired of first map data;

the first obtaining unit 12, where the first obtaining unit 12 is configured to obtain first area feature information according to the area to be acquired of the first map data, and match a first data acquisition mode;

a second obtaining unit 13, where the second obtaining unit 13 is configured to obtain a first data collection subject set according to the first data collection manner, where the first data collection subject set serves the first map data to-be-collected area;

a third obtaining unit 14, where the third obtaining unit 14 is configured to obtain a first upload data set of the first data collection subject set based on a first portable integrated device;

a fourth obtaining unit 15, where the fourth obtaining unit 15 is configured to input the first upload data set into a map data collation information base for automatic differential comparison, so as to obtain a first comparison result;

a first executing unit 16, where the first executing unit 16 is configured to perform data acquisition and updating on the map information of the area to be acquired of the first map data according to the first comparison result.

Further, the system further comprises:

a fifth obtaining unit, configured to obtain a first data acquisition user according to the first data acquisition subject set;

a sixth obtaining unit, configured to obtain a historical distribution track thermodynamic information set of the first data collection user;

the first construction unit is used for constructing a distribution track thermodynamic trend graph of the first data acquisition user based on time information serving as an x axis of a horizontal coordinate and the historical distribution track thermodynamic information set serving as a y axis of a vertical coordinate;

the second execution unit is used for carrying out trend analysis on the distribution track thermodynamic trend graph to generate a newly-added thermodynamic distribution area and an abrupt-reduced thermodynamic distribution area;

a seventh obtaining unit, configured to obtain new POI information corresponding to the new thermal distribution area and sudden decrease POI information corresponding to the sudden decrease thermal distribution area;

and the third execution unit is used for updating the map data proofreading information base according to the newly added POI information and the sudden reduction POI information.

Further, the system further comprises:

an eighth obtaining unit, configured to obtain, based on the map data collation information base, a first planned path for the first data collection user to travel to a first destination;

a ninth obtaining unit, configured to obtain estimated traffic data according to the first planned path, where the estimated traffic data includes estimated traffic time and estimated traffic intersection information;

a tenth obtaining unit, configured to obtain, based on the first portable integrated device, actual passage time and actual passage intersection information of the first data collection user to the first destination;

the fourth execution unit is used for carrying out data fusion on the actual passing time and the actual passing turnout information to generate actual running track data;

the fifth execution unit is used for uploading the actual traveling track data to the map data proofreading information base, and performing automatic difference comparison with the estimated traffic data to generate first deviation data;

a sixth execution unit, configured to determine whether the first deviation data meets a preset deviation threshold;

a seventh executing unit, configured to update the first planned path according to the actual travel track data if the first deviation data does not meet the preset deviation threshold.

Further, the system further comprises:

an eleventh obtaining unit, configured to obtain, according to the first portable integrated device, first POI image information, first POI position information, and a first POI contact address in the area to be captured of the first map data;

an eighth execution unit, configured to perform feature fusion on the first POI image information, the first POI location information, and the first POI contact information, and generate a first POI data feature set;

a twelfth obtaining unit, configured to obtain the second POI data feature set by analogy until the nth POI data feature set is obtained;

a thirteenth obtaining unit, configured to obtain the first upload data set according to the first POI data feature set up to the nth POI data feature set.

Further, the system further comprises:

a fourteenth obtaining unit, configured to obtain a first feature data set according to the first upload data set;

a fifteenth obtaining unit, configured to perform centering processing on the first feature data set to obtain a second feature data set;

a sixteenth obtaining unit, configured to obtain a first covariance matrix of the second feature data set;

a seventeenth obtaining unit, configured to perform operation on the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;

an eighteenth obtaining unit, configured to project the first feature data set to the first feature vector to obtain a first dimension reduction data set, where the first dimension reduction data set is a feature data set obtained after dimension reduction of the first feature data set.

Further, the system further comprises:

a nineteenth obtaining unit configured to obtain a POI data set in the map data collation information base;

a ninth execution unit, configured to perform feature classification on the POI data set, and generate a first feature data set, a second feature data set, and up to an mth feature data set;

the second construction unit is used for constructing a first map data correction sub-information base based on the first characteristic data set until an Mth map data correction sub-information base is constructed based on the Mth data characteristic set;

a tenth execution unit, configured to update and manage the first map data collation sub-information base and the mth map data collation sub-information base, respectively.

Further, the system further comprises:

an eleventh execution unit to define the POI data set as N sample points;

a twelfth execution unit, configured to randomly select K center points based on the N sample points;

a nineteenth obtaining unit, configured to perform distance calculation on the N sample points and each center point of the K to obtain an euclidean distance dataset;

a twentieth obtaining unit, configured to obtain, according to the euclidean distance data set, a POI classification data set that is a shortest distance in the euclidean distance data set;

a twenty-first obtaining unit, configured to obtain a first classification result according to the POI classification data set, and perform feature classification on the POI data set.

Exemplary electronic device

The electronic apparatus of the embodiment of the present application is described below with reference to fig. 9.

Based on the same inventive concept as the method for acquiring and updating the step-riding map data in the foregoing embodiment, the embodiment of the present application further provides a system for acquiring and updating the step-riding map data, including: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the method of any of the first aspects.

The electronic device 300 includes: processor 302, communication interface 303, memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein, the communication interface 303, the processor 302 and the memory 301 may be connected to each other through a bus architecture 304; the bus architecture 304 may be a peripheral component interconnect standard bus or an extended industry standard architecture bus, or the like. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.

Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits for controlling the execution of programs in accordance with the teachings of the present application. Communication interface 303, using any transceiver or the like, is used for communicating with other devices or communication networks, such as ethernet, wireless access networks, wireless local area networks, wired access networks, and the like. The memory 301 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read only memory, a read only optical disk or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through a bus architecture 304. The memory may also be integral to the processor.

The memory 301 is used for storing computer-executable instructions for executing the present application, and is controlled by the processor 302 to execute. The processor 302 is configured to execute the computer-executable instructions stored in the memory 301, so as to implement a method for implementing data collection and update of a step riding map provided by the above-mentioned embodiments of the present application.

Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application program codes, which are not specifically limited in the embodiments of the present application.

The embodiment of the application solves the technical problems that in the prior art, the accuracy of map data acquisition is low, the real-time updating rate is slow based on walking and riding, and the user experience is influenced. The map information of the area to be collected of the first map data is subjected to data collection and updating based on walking and riding, the map data updating is intelligent and timely, the map data updating accuracy is high, the speed is high, the mapping cost is low, and the technical effect of user experience is improved.

Those of ordinary skill in the art will understand that: the various numbers of the first, second, etc. mentioned in this application are only used for the convenience of description and are not used to limit the scope of the embodiments of this application, nor to indicate the order of precedence. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any," or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one (one ) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire or wirelessly. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium, an optical medium, a semiconductor medium, or the like.

The various illustrative logical units and circuits described in this application may be implemented or operated upon by design of a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.

The steps of a method or algorithm described in the embodiments herein may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software cells may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be disposed in a terminal. In the alternative, the processor and the storage medium may reside in different components within the terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application.

Accordingly, the specification and figures are merely exemplary of the present application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations.

Claims

1. A method for realizing data acquisition and updating of a step riding map is disclosed, wherein the method comprises the following steps:

calibrating a region to be acquired of first map data;

according to the area to be acquired of the first map data, acquiring first area characteristic information and matching a first data acquisition mode;

acquiring a first data acquisition subject set according to the first data acquisition mode, wherein the first data acquisition subject set serves the area to be acquired of the first map data;

obtaining a first uploaded data set of the first data collection subject set based on a first portable integrated device;

inputting the first uploaded data set into a map data proofreading information base for automatic differential comparison to obtain a first comparison result;

and according to the first comparison result, carrying out data acquisition and updating on the map information of the area to be acquired of the first map data.

2. The method of claim 1, wherein the method further comprises:

acquiring a first data acquisition user according to the first data acquisition main body set;

acquiring a historical distribution track thermodynamic information set of the first data acquisition user;

constructing a distribution track thermodynamic trend graph of the first data acquisition user based on time information serving as an x axis of a horizontal coordinate and the historical distribution track thermodynamic information set serving as a y axis of a vertical coordinate;

performing trend analysis on the distribution track thermodynamic trend graph to generate a newly increased thermodynamic distribution area and an abruptly decreased thermodynamic distribution area;

acquiring newly added POI information corresponding to the newly added heat distribution area and sudden reduction POI information corresponding to the sudden reduction heat distribution area;

and updating the map data proofreading information base according to the newly added POI information and the sudden decrease POI information.

3. The method of claim 2, wherein the method further comprises:

obtaining a first planned path for the first data acquisition user to go to a first destination based on the map data proofreading information base;

obtaining estimated traffic data according to the first planned path, wherein the estimated traffic data comprises estimated traffic time and estimated traffic intersection information;

acquiring actual passing time and actual passing intersection information of the first data acquisition user to the first destination based on the first portable integrated equipment;

carrying out data fusion on the actual passing time and the actual passing turnout information to generate actual running track data;

uploading the actual running track data to the map data proofreading information base, and performing automatic difference comparison with the estimated traffic data to generate first deviation data;

judging whether the first deviation data meets a preset deviation threshold value;

and if the first deviation data does not meet the preset deviation threshold, updating the first planned path according to the actual running track data.

4. The method of claim 1, wherein the obtaining a first set of uploaded data for the first set of data acquisition subjects, the method further comprises:

according to the first portable integrated equipment, obtaining first POI image information, first POI position information and a first POI contact way in the area to be acquired of the first map data;

performing feature fusion on the first POI image information, the first POI position information and the first POI contact way to generate a first POI data feature set;

by analogy, a second POI data feature set is obtained until an Nth POI data feature set is obtained;

and obtaining the first uploading data set according to the first POI data characteristic set and the Nth POI data characteristic set.

5. The method of claim 4, wherein the method further comprises:

obtaining a first feature data set according to the first uploading data set;

performing centralized processing on the first characteristic data set to obtain a second characteristic data set;

obtaining a first covariance matrix of the second feature data set;

calculating the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;

and projecting the first feature data set to the first feature vector to obtain a first dimension reduction data set, wherein the first dimension reduction data set is the feature data set obtained after dimension reduction of the first feature data set.

6. The method of claim 3, wherein the method further comprises:

obtaining a POI data set in the map data proofreading information base;

carrying out feature classification on the POI data set to generate a first feature data set, a second feature data set and an Mth feature data set;

constructing a first map data proofreading sub-information base based on the first characteristic data set until an Mth map data proofreading sub-information base is constructed based on the Mth data characteristic set;

and updating and managing the first map data proofreading sub-information base until the Mth map data proofreading sub-information base respectively.

7. The method of claim 6, wherein said feature classifying said set of POI data further comprises:

defining the POI data set as N sample points;

based on the N sample points, randomly selecting K central points;

calculating the distance between the N sample points and each central point of the K to obtain a Euclidean distance data set;

obtaining a POI classification data set according to the Euclidean distance data set, wherein the POI classification data set is the shortest distance in the Euclidean distance data set;

and obtaining a first classification result according to the POI classification data set, and performing feature classification on the POI data set.

8. A system for realizing data acquisition and updating of a step riding map, wherein the system comprises:

the first calibration unit is used for calibrating a region to be acquired of first map data;

the first obtaining unit is used for obtaining first area characteristic information according to the area to be acquired of the first map data and matching a first data acquisition mode;

a second obtaining unit, configured to obtain a first data collection subject set according to the first data collection manner, where the first data collection subject set serves the first map data to-be-collected area;

a third obtaining unit, configured to obtain a first upload data set of the first data collection subject set based on a first portable integrated device;

a fourth obtaining unit, configured to input the first uploaded data set into a map data collation information base for automatic differential comparison, so as to obtain a first comparison result;

and the first execution unit is used for carrying out data acquisition and updating on the map information of the area to be acquired of the first map data according to the first comparison result.

9. A system for realizing data acquisition and update of a step riding map comprises: a processor coupled with a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the method of any of claims 1-7.