A method, system and storage medium for address positioning correction based on geocoding

Abstract

The present invention belongs to the field of communication positioning technology, and provides an address positioning correction method, system and storage medium based on geocoding. The positioning correction method of the present invention includes: comparing the positioning information of a mobile device with the actual location information and determining whether correction is required; scanning a house coding QR code or photographing a landmark building, uploading it to a server for parsing and obtaining the location information therein; geocoding the location information to obtain longitude and latitude information, and comparing it with the actual location information; determining whether the location information data conforms to the actual address, and presenting the final address text information after performing location correction. The method and system of the present invention correct the location information of houses and landmarks to avoid the problem of mismatch between the actual address and the image watermark address, and the problem of large deviation in positioning information, which greatly improves the work efficiency of banks in reviewing the information of loan companies, while reducing the rework of operators.

Description

Technical Field

[0001] The present invention relates to the field of communication positioning technology, and in particular to a method, system and storage medium for address positioning correction based on geocoding. Background Art

[0002] Currently, many banking-related businesses (such as bank-enterprise reconciliation, micro-loans, and mortgage registration) are conducted outdoors. After these outdoor operations, data review is required. Verification of the delivery of statements and the correct location of the lending company requires on-site photography, along with location watermarks and latitude and longitude for authenticity verification. Currently, mobile device positioning primarily relies on GPS, which offers an accuracy of 5-10 meters outdoors. However, with the increasing use of different scenarios, issues with traditional beacon-based positioning and navigation, such as accuracy and communication with base stations, have become increasingly prominent. These issues are primarily manifested in the following aspects:

[0003] 1. Indoor positioning navigation is affected by the obstruction of building walls, which affects the accuracy of geographic location information.

[0004] 2. Most companies are currently located in office buildings, and the location information is quite biased. This results in many mismatches between the address of the audit materials and the address of the image watermark during the actual review process, resulting in a low approval rate.

[0005] 3. Due to inaccurate positioning, bank operators have to rework frequently, which consumes a lot of time and human resource costs.

[0006] Therefore, we need to develop an address positioning correction method, system and storage medium based on geocoding, which can correct the geographical location information of relevant houses and landmarks, avoid the problem of mismatch between the address of the review materials and the address of the image watermark, improve the work efficiency of banks in reviewing the relevant materials of loan companies, reduce the rework of operators, and make indoor positioning more accurate. Summary of the Invention

[0007] The purpose of the present invention is to provide a method, system and storage medium for address positioning correction based on geocoding to solve the problems existing in the existing positioning technology mentioned in the above background technology.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] In a first aspect, an embodiment of the present invention provides a method for correcting address location based on geocoding, wherein the method comprises the following steps:

[0010] Compare the mobile device's location information with the actual location information to determine whether correction is needed;

[0011] Scan the QR code of the house code or take a photo of a landmark building, upload it to the server for analysis and obtain the location information;

[0012] Geocode the location information to obtain longitude and latitude information, and compare it with the actual location information;

[0013] Determine whether the location information data is consistent with the actual address, and present the final address text information after performing location correction.

[0014] In a second aspect, an embodiment of the present invention provides an address location correction system based on geocoding, the system comprising: a barcode recognition module, an image parsing module, a geocoding module, and a result plotting module.

[0015] The barcode recognition module is used to recognize the house code or house code QR code to obtain the location information therein;

[0016] The image parsing module is used to extract features from captured landmark images and map these features to a neural network for image recognition and classification. After selecting images that match actual landmarks, the location information is obtained.

[0017] The geocoding module is used to geocode the geographical location information obtained in the barcode recognition module or the image analysis module, obtain the longitude and latitude information therein, and perform targeted position correction operations;

[0018] The result plotting module is used to display the coordinates of the final result based on the geocoding data.

[0019] Based on the aforementioned scheme, the house code is a serial number issued by the Housing Authority. It is generally compiled by the local real estate agency according to relevant national regulations. It consists of 25 digits and can be divided into 8 levels. The first level of the house code usually has two codes, which represent the province or autonomous region where the house is located; the second level also has two codes, which represent the city where the house is located; the third level of the house code has a three-digit code, which represents the county or district where the house is located; the eighth level of the house code has six codes, which represent the room in the house, and so on. The house code is usually fixed, unique, and unchanging, equivalent to the identity card number of the house.

[0020] Wherein, obtaining the house code specifically includes the following steps:

[0021] Check if there is a QR code sticker on the door or nearby wall;

[0022] If so, scan the house information directly with your mobile phone to get the house code.

[0023] If not, you need to log in to the official website of the local community grid management office, enter the corresponding building, address and management agency (such as: xx District xx Street) for inquiry.

[0024] Based on the above solution, the image analysis module includes: an image preprocessing unit, a feature extraction unit, a neural network mapping unit and a recognition and classification unit.

[0025] The image pre-processing unit is configured to: perform a series of processing such as deleting useless information from the original data contained in the uploaded landmark building image, smoothing, binarization, and amplitude normalization, to obtain various features of the landmark image, including but not limited to color, texture, shape, and spatial relationship features;

[0026] The feature extraction unit is configured to: extract various features of the landmark image through a neural network image recognition system, and perform a final feature description and display;

[0027] The neural network mapping unit is configured to: the server inputs the final feature description into the neural network, compares it with the labels stored in the system to form a mapping relationship;

[0028] The above-mentioned recognition and classification unit is configured as follows: the server performs image recognition and classification according to the above-mentioned mapping results, thereby screening out pictures that match the actual landmarks.

[0029] Based on the above solution, the geocoding module includes: a data pre-processing unit, a search and matching unit, and a positioning correction unit.

[0030] The data pre-processing unit is configured to: the server filters and integrates the geographical location information from the databases of various platforms to obtain more data information related to the geographical location; and

[0031] Performing one or more of the following processing methods on the above data information: denoising, standardization, and structuring to obtain the geographic location feature data;

[0032] The search and matching unit is configured to: search for relevant data in the address database by a search engine and match it with the geographical location feature data, thereby accurately extracting the geographical location latitude and longitude information contained in the house code or landmark image;

[0033] The above-mentioned positioning correction unit is configured as follows: the server determines whether the search matching result is consistent with the actual address, and performs targeted position correction operations.

[0034] Based on the above solution, the key to search matching is the segmentation of the input text and the scoring of the recall results. If the segmentation effect is not good, the appropriate records in the database cannot be found; if the scoring rules are not set well, the output results may not be the desired results.

[0035] Based on the above scheme, before displaying the coordinates of the final result, if the search and matching results do not completely match the input address, interpolation (including unilateral offset and end offset) must be performed based on the line feature; if the reference feature is a surface or a point, the center of mass of the surface must be obtained to perform coordinate correction.

[0036] In a third aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the processing method in any implementation of the first or second aspect are implemented.

[0037] It can be seen from the above technical solutions that the present invention has at least the following advantages and positive effects compared with the prior art:

[0038] (1) The present invention processes, filters and integrates the geographic location information obtained from house codes or landmark images, and performs search and matching, so that the final output of the geographic location longitude and latitude information is more accurate, effectively solving the problem of large deviation of indoor positioning information.

[0039] (2) During the image analysis process, the present invention deletes useless information from the original data of the landmark image, performs a series of processing such as smoothing, binarization and amplitude normalization, and obtains various features of the image. Based on this feature, the image that conforms to the actual landmark is screened out, thereby avoiding the problem of mismatch between the audit document address and the image watermark address during the bank's audit. While improving the audit pass rate, it also reduces the operator's rework.

[0040] (3) Before displaying the coordinates of the final result, the present invention corrects the coordinates of the search matching results that do not completely match the input address, thereby ensuring the accuracy of the geographic location information, making it easier for banks to verify the authenticity of the relevant information of the loan companies during the review process, and providing convenience for banks and operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive work, among which:

[0042] Figure 1A flowchart of a geocoding-based address location correction method provided by an embodiment of the present invention is shown;

[0043] Figure 2 A schematic diagram showing a flow chart of an image analysis method provided by an embodiment of the present invention is shown;

[0044] Figure 3 A schematic diagram of a flow chart of a geocoding method provided by an embodiment of the present invention is shown;

[0045] Figure 4 The structure block diagram of an address location correction system based on geocoding provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0046] In order to more clearly illustrate the purpose, technical solutions and advantages of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The example implementation methods can be implemented in various forms and should not be understood as being limited to the examples described herein. On the contrary, these implementation methods are provided to make the present invention more comprehensive and complete, and to fully convey the concepts of the example implementation methods to those skilled in the art.

[0047] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present invention. However, it will be appreciated by those skilled in the art that the technical solutions of the present invention can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present invention.

[0048] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0049] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0050] The present invention will be described in detail below with reference to specific embodiments:

[0051] Example 1

[0052] like Figure 1 As shown, this embodiment provides an address location correction method based on geocoding, and the specific steps of the method are as follows:

[0053] S1: Compare the mobile device's location information with the actual location information and determine whether correction is needed;

[0054] Preferably, in this embodiment, when bank operators review the location of a loan enterprise, they can first use mobile devices to locate and obtain location information, and compare it with the current actual location information to determine whether the target geographic location information needs to be corrected. If the deviation is within a negligible range, the application will be directly approved; if the deviation is large, subsequent location correction operations will be required.

[0055] S2: Scan the QR code of the house code or take a photo of the landmark building, upload it to the server for analysis and obtain the location information;

[0056] Preferably, the house code refers to a characteristic combination code assigned to the basic unit of the house according to the relevant house coding rules, which is used for distinguishing identification and business management. How to establish a scientific and reasonable house code plays an important role in the collection and use of house information. Scientific house coding is not only used for identification, but more importantly, it has certain important information, such as spatial location, area, orientation relationship, and features that are easy to remember and compare.

[0057] Specifically, scanning a property code QR code typically displays four types of information: property address, property code, contact number (for the relevant jurisdiction's integrated management station), and online services (website). Bank auditors parse the property code information on a server to obtain the target address's location data (e.g., province, city, district, town, township, street, building, and house number). They then use geocoding to accurately retrieve the address's latitude and longitude.

[0058] Preferably, in this embodiment, the server parses the landmark building image to obtain the location information thereof using a neural network image recognition technology and relying on the GPS positioning information in the image file format Exif.

[0059] Specifically, after the server identifies GPS location information in an image of a landmark building, it feeds the data and image into a neural network system, which extracts the image's features. The system then classifies the image based on a comparison between the extracted image features and stored labels. After the neural network identifies images that match actual landmarks, it interprets the image and returns a human-readable sentence (i.e., address text), thereby obtaining the geographic location information of the building or landmark.

[0060] S3: geocoding the location information to obtain latitude and longitude information, and comparing it with the actual location information;

[0061] Preferably, in this embodiment, the server obtains the longitude and latitude information of the geographic location through the geocoding process, which includes three steps: data preprocessing, search and matching, and plotting.

[0062] Specifically, when the server preprocesses geographic location information, it needs to structure and standardize the address text. This process requires massive data and dictionaries as underlying data support. In addition to self-collection, massive data can also be filtered and integrated across various platforms; in addition to daily server accumulation, dictionaries can also be mined and generated within the system database. Furthermore, after the server obtains the characteristic data information of the target geographic location through the data preprocessing step, it activates a search engine to search for relevant data and matches it with the description information of the location coordinates stored in the database, thereby identifying the geographic target corresponding to the address information. If precise positioning is impossible, the geographic target within a certain range that matches the address information is determined. Preferably, the geographic target in this embodiment is expressed as a coordinate vector in the form of a point, line, or surface. In this way, bank operators can accurately retrieve the geographic location latitude and longitude information contained in the house code or landmark image.

[0063] In this embodiment, the actual location information is compared with the acquired information, primarily by comparing the latitude and longitude deviations. Bank staff can accurately determine the target address's location based on the difference between the two, effectively resolving the issue of mismatches between the address in the audited document and the image watermark during bank review.

[0064] S4: Determine whether the location information data is consistent with the actual address, and present the final address text information after performing location correction.

[0065] Preferably, the basis for determining whether the location information data matches the actual address is the result of the search match in step S3. If it matches, the program ends and the geographic location information is returned; if it does not completely match the actual address, the location needs to be corrected in the server.

[0066] Specifically, the focus of search and matching in step S3 is the segmentation of the input text and the scoring of the recall results. If the segmentation effect is not good, the appropriate record in the database cannot be found; if the scoring rules are not set well, the output result may not be the desired result. Furthermore, before the coordinate display of the final result, if the search and matching result does not completely match the actual address entered in the system, the location information needs to be corrected. Among them, the location information correction includes the following two aspects: on the one hand, if the reference element is a line, it is necessary to interpolate based on the line element (including unilateral offset and end offset); on the other hand, if the reference element is a surface or a point, it is necessary to obtain the center of mass of the surface for coordinate correction.

[0067] In this embodiment, the accuracy of the judgment is ensured by determining whether the address matches the actual address based on the search and matching results. Furthermore, for addresses that do not completely match the actual address, bank operators can use appropriate position correction methods based on the actual situation to make the final displayed address text more accurate.

[0068] Example 2

[0069] like Figure 2 As shown, this embodiment provides an image analysis method, which specifically includes the following steps:

[0070] S20: The server recognizes the landmark building image and performs preprocessing;

[0071] In this embodiment, when a bank operator uploads the image of a landmark building to the server, the server will first identify the image, parse it into data information readable by the computer system, and perform a series of processing such as deleting useless information in the original data, smoothing, binarization, and amplitude normalization to obtain preliminary feature data of the image.

[0072] S21: extracting feature data of the image using a neural network image recognition system;

[0073] Preferably, in this embodiment, the neural network image recognition system extracts image features including: a feature extraction part and a non-feature extraction part.

[0074] Specifically, for the feature extraction part, features include but are not limited to color, texture, shape, and spatial relationships. This part primarily leverages human experience to extract pattern features and the classification capabilities of the neural network to identify the target image. For the featureless part, feature extraction is omitted, and the entire image is directly used as the input to the neural network.

[0075] S22: The server maps the image features to a neural network for image recognition and classification, and selects images that match the actual landmarks.

[0076] In this embodiment, preferably, before the server maps the image features to the neural network, it is necessary to build a neural network mapping unit in the system.

[0077] Specifically, a landmark image and the extracted image feature data are fed into a neural network system. After identifying and analyzing the image, the system outputs data related to the landmark image (e.g., the city, region, type, and surrounding environment of the image). The neural network parameters are adjusted and training is repeated until the parameters converge or the maximum number of iterations is reached, at which point the neural network image recognition system data is updated. Finally, the output data is compared with the system's stored labels, allowing the computer to understand the mapping relationship between the landmark image and the system's stored data. In this way, a simple neural network mapping unit is constructed. Furthermore, the neural network image recognition system compares the extracted image features with the server-stored labels, maps them to the neural network, and performs classification processing. After selecting images that match actual landmarks, the system interprets the images and returns a human-readable sentence (i.e., address text), allowing bank operators to obtain the geographic location information of the building or landmark.

[0078] Example 3

[0079] like Figure 3 As shown, this embodiment provides a geocoding method, which specifically includes the following steps:

[0080] S30: Scan the QR code of the house code or take a photo of a landmark building, upload it to the server for analysis and obtain the location information;

[0081] Preferably, after the bank operator uses the server to parse the house code or landmark building, he or she can obtain the location data of the target address (such as: province, city, district, town, township, street, building and house number, etc.). The house code QR code is generally posted on the door or nearby wall. When the house code QR code sticker is unavailable, the bank operator can also log in to the official website of the local community grid management office and enter the corresponding building, address and management agency (such as: xx district xx street) to query. In this embodiment, the server parses the landmark building image to obtain the location information thereof using a neural network system in the image recognition system based on the GPS positioning information in the image file format Exif. The specific steps are the same as those described in Example 2.

[0082] S31: The server performs data preprocessing on the geographic location information;

[0083] In this embodiment, data preprocessing preferably includes both data production and processing and address text preprocessing. In addition to processing the massive amount of self-collected data, data production and processing also involves filtering and integrating data from various platforms (such as AutoNavi and Baidu) to obtain more information about the location. The better the data quality and the larger the volume, the more accurate the results. Furthermore, the server applies one or more of the following processing methods, such as "denoising, standardization, and structuring," to the processed data to obtain characteristic data about the location.

[0084] S32: The search engine searches for relevant data in the address database and performs matching, and retrieves the geographical location latitude and longitude information contained in the house code or landmark image;

[0085] In this embodiment, the focus of search matching is on segmenting the input text and scoring the recall results. If the segmentation is not effective, the appropriate records in the database cannot be found; if the scoring rules are not set well, the output results may not be the desired results. Specifically, after the server obtains the characteristic data information of the target geographic location through the data preprocessing step, it will activate the search engine to search for relevant data and match it with the description information of the location coordinates stored in the database, thereby identifying the geographic target corresponding to the address information; if precise positioning is not possible, a certain range of geographic targets matching the address information is determined. The geographic targets are presented as coordinate vectors in the form of points, lines, and surfaces, allowing bank operators to accurately obtain the geographic location latitude and longitude information contained in the house code or landmark image.

[0086] S33: Determine whether the search matching result is consistent with the actual address, and perform targeted location correction operations.

[0087] After obtaining the geographic location latitude and longitude information contained in the house code or landmark image, the bank operator compares it with the actual address data entered into the system. If they match, no correction is required and the procedure ends. If they do not completely match, targeted position correction operations must be performed based on the reference elements before the coordinates of the final result are displayed.

[0088] Example 4

[0089] like Figure 4 As shown, this embodiment provides an address location correction system based on geocoding, which includes: a barcode recognition module 401, an image analysis module 402, a geocoding module 403 and a result plotting module 404. Among them:

[0090] The barcode recognition module 401 is used to recognize the house code or the house code QR code to obtain the location information therein;

[0091] In this embodiment, through the barcode recognition module, the server will parse the location information of the target address house (such as: province, city, district, town, township, street, building and house number, etc.). Bank operators can search in the database based on this information to obtain more geographic location data of the address and its attachments.

[0092] Image parsing module 402 is used to extract features of the captured landmark images and map the features to a neural network for image recognition and classification. After selecting images that match the actual landmarks, the location information is obtained.

[0093] The image analysis module 402 includes: an image pre-processing unit 4021, a feature extraction unit 4022, a neural network mapping unit 4023 and a recognition and classification unit 4024.

[0094] The image pre-processing unit 4021 is configured to: perform a series of processing operations such as deleting useless information from the original data of the uploaded landmark image, smoothing, binarization, and amplitude normalization, to obtain various features of the landmark image, including but not limited to color, texture, shape, and spatial relationship features;

[0095] The feature extraction unit 4022 is configured to: extract various features of the landmark image through a neural network image recognition system, and perform a final feature description and display;

[0096] The neural network mapping unit 4023 is configured to: the server inputs the final feature description into the neural network and compares it with the labels stored in the system to form a mapping relationship;

[0097] The recognition and classification unit 4024 is configured as follows: the server performs image recognition and classification according to the mapping result, thereby screening out pictures that match the actual landmarks.

[0098] The geocoding module 403 is used to geocode the geographical location information obtained in the barcode recognition module or the image analysis module, obtain the longitude and latitude information therein, and perform targeted position correction operations;

[0099] The geocoding module 403 includes a data pre-processing unit 4031, a search and matching unit 4032, and a positioning correction unit 4033.

[0100] The data pre-processing unit 4031 is configured to: the server filters and integrates the geographical location information from the databases of various platforms to obtain more data information related to the geographical location; and

[0101] Performing one or more of the following processing methods on the above data information: denoising, standardization, and structuring to obtain the geographic location feature data;

[0102] The search and matching unit 4032 is configured to: search for relevant data in the address database and match it with the geographic location feature data, thereby accurately extracting the geographic location latitude and longitude information contained in the house code or landmark image;

[0103] The above-mentioned positioning correction unit 4033 is configured as: the server determines whether the search matching result is consistent with the actual address, and performs targeted position correction operations.

[0104] The result plotting module 404 is used to display the coordinates of the final result based on the geocoding data.

[0105] In this embodiment, the bank operator checks the geocoding result with the actual address information. After confirmation, the procedure ends and the accurate location information of the target address is obtained.

[0106] Those skilled in the art will readily appreciate 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 that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present invention are indicated by the claims. It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and that 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 method for address location correction based on geocoding, characterized in that: include: Compare the mobile device location information with the actual address information to determine whether correction is needed; When correction is required, scan the house code QR code or take a photo of a landmark building, upload it to the server for analysis, and obtain the location information. For the photographed landmark building image, obtaining the location information specifically includes: The server identifies landmark building images and performs preprocessing; Extracting feature data from the preprocessed image using a neural network image recognition system; the feature data includes a feature-extracted portion and a non-feature-extracted portion; for the feature-extracted portion, the features include color, texture, shape, and spatial relationship; for the non-feature-extracted portion, the feature extraction step is omitted, and the entire image is directly used as input to the neural network image recognition system; The server maps the image features to the neural network image recognition system for image recognition and classification, selects pictures that match actual landmarks, and obtains location information therein; Geocoding the location information to obtain longitude and latitude information, and comparing the information with the actual address information; Determine whether the location information is consistent with the actual address information, and if it is not completely consistent, perform location correction and present the final address text information; The position correction is specifically as follows: If the location information does not completely match the actual address information, when the reference element is a line, interpolation needs to be performed based on the line element; if the reference element is a surface or a point, the center of mass of the surface needs to be obtained to perform coordinate correction.

2. The address location correction method based on geocoding according to claim 1, characterized in that: The geocoding of the location information to obtain longitude and latitude information specifically includes the following steps: The server performs data preprocessing on the location information; The search engine searches for relevant data in the address database and matches it, extracting the geographical location latitude and longitude information contained in the house code or landmark image; Determine whether the search matching result is consistent with the actual address information, and display the coordinates of the final result.

3. The address positioning correction method based on geocoding according to claim 1, characterized in that: The server parses the landmark building image to obtain the location information thereof using a neural network image recognition technology and relying on the GPS positioning information in the image file format Exif.

4. The address positioning correction method based on geocoding according to claim 2, characterized in that: The data preprocessing includes two aspects: data production processing and address text preprocessing.

5. The address positioning correction method based on geocoding according to claim 2, characterized in that: The focus of the search matching is on segmenting the input text and scoring the recall results.

6. The method for address location correction based on geocoding according to claim 4, characterized in that: The data production and processing includes processing the massive amount of data collected and screening and obtaining data related to the location information on various platforms.

7. An address location correction system based on geocoding, characterized in that: include: A barcode recognition module is used to recognize a house code or a house code QR code to obtain location information therein; The image parsing module is used to extract the features of the captured landmark images and map them to a neural network for image recognition and classification. After selecting images that match the actual landmarks, the module obtains their location information. The steps of selecting images that match the actual landmarks include: The server identifies landmark building images and performs preprocessing; Extracting feature data from the preprocessed image using a neural network image recognition system; the feature data includes a feature-extracted portion and a non-feature-extracted portion; for the feature-extracted portion, the features include color, texture, shape, and spatial relationship; for the non-feature-extracted portion, the feature extraction step is omitted, and the entire image is directly used as input to the neural network image recognition system; The server maps the image features to the neural network image recognition system for image recognition and classification, and selects pictures that match the actual landmarks; The geocoding module is used to geocode the location information obtained in the barcode recognition module or the image analysis module, obtain the latitude and longitude information therein, and perform targeted location correction operations; wherein the targeted location correction operations are specifically: If the location information does not completely match the actual address information, when the reference element is a line, it is necessary to interpolate based on the line element; if the reference element is a surface or a point, it is necessary to obtain the centroid of the surface to perform coordinate correction; The result plotting module is used to display the coordinates of the final results based on the geocoding data.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the address location correction method according to any one of claims 1 to 6 are implemented.

Images