Address mapping methods, apparatus, electronic devices, and storage media based on encoding libraries

Abstract

This invention provides an address mapping method, apparatus, electronic device, and storage medium based on a geocoding library. The method involves: acquiring address query information; parsing the address query information to obtain address information features; matching the address information features against a geocoding library to obtain a candidate address list corresponding to the address information features; evaluating the matching degree of the candidate addresses in the candidate address list; and selecting the candidate address with the highest matching degree score as the target candidate address. This invention can obtain the target candidate address during the address query process by matching the parsed query information with data in the geocoding library, thus ensuring the accuracy of address matching. Furthermore, it can automatically select one from the matching results as the final address matching result based on pre-set scoring rules, thereby achieving automated address matching and reducing the degree of manual intervention. This invention can be applied to the field of geographic information system technology.

Description

Technical Field

[0001] This invention relates to the field of geographic information system technology, and in particular to an address mapping method, apparatus, electronic device, and storage medium based on an encoding library. Background Technology

[0002] Address matching is the process of establishing a correspondence between a textual description of an address and its spatial geographic coordinates. Its purpose is to quickly find matching addresses based on the user's input address and return the search results to the user as an online service. In the field of electronic maps, address retrieval and query services are important functions of online map services. Address matching converts the user-input address into a geographic location, allowing it to be displayed on an electronic map and enabling users to easily understand the geographical location of the address.

[0003] However, conventional address database construction and corresponding text parsing methods cannot accurately identify the unique text forms in certain professional fields, resulting in existing address retrieval methods lacking support for personalized retrieval applications in certain professional fields.

[0004] In conclusion, the problems existing in the current technology urgently need to be solved. Summary of the Invention

[0005] This invention provides an address mapping method, apparatus, electronic device, and storage medium based on an encoding library, which addresses the shortcomings of existing technologies in accurately recognizing unique text formats in certain professional fields and improves the accuracy of retrieval.

[0006] This invention provides an address mapping method based on an encoding library, comprising:

[0007] Get address lookup information;

[0008] The address query information is parsed to obtain address information features;

[0009] Based on the address information features, a matching process is performed in the geocoding database to obtain a candidate address list corresponding to the address information features;

[0010] The matching degree of the candidate addresses in the candidate address list is evaluated;

[0011] The candidate address with the highest matching score is selected as the target candidate address.

[0012] The step of parsing the address query information to obtain address information features includes:

[0013] The address query information is segmented to obtain address phrase information;

[0014] Keyword extraction is performed on the address phrase information to obtain address information features.

[0015] According to the address mapping method based on a coding library provided by the present invention, the step of matching the address information features in the geocoding library to obtain a candidate address list corresponding to the address information features includes:

[0016] Construct a mapping relationship between the address information features and the candidate addresses;

[0017] Based on the mapping relationship, a list of candidate addresses corresponding to the address information features is matched in the geocoding library.

[0018] According to the address mapping method based on an encoding library provided by the present invention, the step of evaluating the matching degree of candidate addresses in the candidate address list includes:

[0019] Determine the similarity between the address information features of the address query information and the address information features of the candidate addresses;

[0020] Determine the completeness of the address information features in the address query information;

[0021] The matching degree is evaluated based on the similarity and completeness to obtain a matching degree score.

[0022] According to the address mapping method based on an encoding library provided by the present invention, the method further includes:

[0023] Obtain geographic coordinate information;

[0024] The target address corresponding to the geographic coordinate information is obtained by matching the geographic coordinate information in the geocoding database.

[0025] Generate the address environment information for the target address.

[0026] According to the address mapping method based on an encoding library provided by the present invention, after the step of evaluating the matching degree of candidate addresses in the candidate address list, the method further includes:

[0027] The matching score is compared with a preset score;

[0028] Remove candidate addresses whose matching score is lower than the preset score from the candidate address list;

[0029] The candidate address list is sorted according to the matching score;

[0030] The sorted list of candidate addresses is then visualized.

[0031] According to the address mapping method based on an encoding library provided by the present invention, the method further includes:

[0032] Based on the address query information, obtain the search count of the keywords;

[0033] Compare the number of searches with the preset number of searches;

[0034] Keywords whose search count exceeds the preset number will be visualized.

[0035] The present invention also provides an address mapping device based on an encoding library, comprising:

[0036] The information acquisition unit is used to acquire address query information;

[0037] An information parsing unit is used to parse the address query information to obtain address information features;

[0038] A matching unit is used to perform matching in a geocoding database based on the address information features to obtain candidate addresses corresponding to the address information features.

[0039] A scoring unit is used to score the matching degree of the candidate addresses;

[0040] The filtering unit is used to select the candidate address with the highest matching score as the target candidate address.

[0041] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the address mapping method based on the encoding library as described above.

[0042] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the address mapping method based on the encoding library as described above.

[0043] This invention provides an address mapping method, apparatus, electronic device, and storage medium based on a geocoding library. The method involves: acquiring address query information; parsing the address query information to obtain address information features; matching the address information features against a geocoding library to obtain a candidate address list corresponding to the address information features; evaluating the matching degree of the candidate addresses in the candidate address list; and selecting the candidate address with the highest matching degree score as the target candidate address. This invention can obtain the target candidate address during the address query process by matching the parsed query information with data in the geocoding library, thus ensuring the accuracy of address matching. Furthermore, it can automatically select one from the matching results as the final address matching result based on pre-set scoring rules, thereby achieving automated address matching and reducing the degree of manual intervention. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating the address mapping method based on an encoding library provided by the present invention;

[0046] Figure 2 This is a schematic diagram of the structure of the address mapping device based on the encoding library provided by the present invention;

[0047] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0049] The rapid advancements in computer, communication, and network technologies have facilitated the widespread application of information technology across all sectors of the national economy and people's livelihoods. Digital technologies based on geographic information services have penetrated every aspect of people's learning, work, and life. As an information technology oriented towards the fields of geography or earth sciences, Geographic Information Systems (GIS) bridge the gap between information technology and earth sciences.

[0050] Traditional address matching methods typically employ keyword-based precise or fuzzy matching. For large-scale or wide-ranging place name and address data, this approach is slow and struggles to meet the needs of online electronic map services. Furthermore, the presence of ambiguous words and out-of-vocabulary terms further contributes to low accuracy, resulting in diverse and often undesirable match outcomes. Moreover, existing address retrieval methods lack support for personalized search applications in specific professional fields because conventional address database construction and corresponding text parsing methods cannot accurately identify the unique text formats specific to these fields.

[0051] To address the problems of unintelligent and inaccurate address retrieval results in existing address retrieval methods, this invention proposes an address mapping method based on an encoding library to improve retrieval accuracy. This address mapping method based on an encoding library, as follows... Figure 1 As shown, including but not limited to the following steps:

[0052] Step 110: Obtain address query information.

[0053] In step 110, the address query information refers to the information entered by the user in the search input field using input devices such as keyboards and buttons. Specifically, in this embodiment, there are no restrictions on the acquisition channel of the address query information. The address query information can be obtained directly from the application software, or it can be obtained from other electronic devices and computer systems through data transmission interfaces or remote communication transmission.

[0054] Furthermore, users input keywords for the search location in the address input field using the keyboard or other keys. When the keywords entered in the address input field change, the search function is triggered, and the search results are displayed as supplementary keywords in a drop-down menu in the search bar for the user to select. A keyword change means that the entered keyword has changed compared to the previously entered keyword; when it changes, the search function is triggered. A change is also considered when the input keyword appears instead of being previously entered.

[0055] Furthermore, address lookup information can also include voice lookup information. For voice lookup information, text feature extraction is required. Specifically, the voice lookup information can be processed into text using Automatic Speech Recognition (ASR) technology to recognize the voice query information and obtain its text content. Then, the text feature information of the text content can be extracted. For example, the text content of the voice query information can be converted into structured data, such as vectors, using natural language processing technology, and the resulting structured data can be used as text feature information.

[0056] Step 120: Parse the address query information to obtain address information features.

[0057] In step 120, to match the address query information with the geocoding database, feature extraction is required to obtain address information features. Specifically, the address query information can be segmented at the sentence level to obtain multiple sentences. Then, each sentence is segmented into words to obtain the word groups that make up the sentence. Specifically, a dictionary-based word segmentation algorithm, a grammar- and rule-based word segmentation algorithm, or a statistical word segmentation algorithm can be used. In this embodiment, no specific limitation is made on the word segmentation method.

[0058] Step 130: Match the address information features in the geocoding database to obtain a list of candidate addresses corresponding to the address information features.

[0059] In step 130, a mapping relationship between address information features and candidate addresses in the geocoding database can be constructed. By inputting address information features, the corresponding candidate addresses can be matched according to the constructed mapping relationship, forming a candidate address list. Specifically, word embedding vectors can be used. Word embedding vectors are obtained by mapping words to a vector space with a unified lower dimension. Strategies for generating such mappings include neural networks, dimensionality reduction of word co-occurrence matrices, probabilistic models, and interpretable knowledge base methods.

[0060] Step 140: Evaluate the matching degree of the candidate addresses in the candidate address list.

[0061] In step 140, in order to determine the credibility of each candidate address in the candidate address list, it is necessary to evaluate the matching degree of each candidate address.

[0062] Step 150: Select the candidate address with the highest matching score as the target candidate address.

[0063] In steps 140 and 150, the matching degree of the candidate addresses in the candidate address list needs to be evaluated. In some embodiments, the scoring task in this application can be set as a regression task, with the scoring result represented by a specific numerical value. In this case, machine learning algorithms such as linear regression, logistic regression, gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) can be selected to predict the numerical value of the scoring result. Specifically, by inputting the feature information of the address query information and the candidate address into the scoring model, the score corresponding to the candidate address can be obtained directly. This score is the matching degree result. The magnitude of the score is used to characterize the credibility of the candidate address. To facilitate unified quantitative comparison, the output score can be constrained to a specified range by adjusting the hyperparameters of the model, such as a score range of 0 to 100. Within this score range, the larger the score, the higher the credibility of the candidate address; conversely, the smaller the score, the lower the credibility of the candidate address. Similarly, the regression prediction method with the above-described output score is only used to illustrate the principle of the embodiments of this application. The actual implementation process can also be flexibly set as needed.

[0064] The scoring models described above for performing classification or regression tasks need to be trained before use. Here, we take a scoring model for performing classification tasks as an example to briefly explain the training process. When training the scoring model, a batch of address query data with classification labels can be obtained. The classification labels represent which scoring category the corresponding address query data belongs to, and the specific data format of the classification labels can be consistent with the aforementioned scoring results. The address query data is input into the initialized scoring model, and the scoring model will output the scoring result for that address query data, which is recorded here as the training scoring result. Based on the training scoring result and classification label corresponding to each address query data, the loss value for predicting each spoken sample data can be determined. In this embodiment, the loss value can be determined using a 0-1 loss function. When the scoring model predicts correctly, i.e., when the training scoring result and classification label corresponding to the spoken sample data are consistent, the loss value is recorded as 0; when the scoring model predicts incorrectly, i.e., when the training scoring result and classification label corresponding to the spoken sample data are inconsistent, the loss value is recorded as 1. Of course, the actual loss value calculation method is not limited to the above-mentioned loss function; for example, the squared difference loss function and the cross-entropy loss function are also optional types. By constraining the scoring model to minimize the sum of the loss values ​​predicted from multiple spoken language samples, the scoring model can be trained through backpropagation to update its internal parameters, thus obtaining a well-trained scoring model.

[0065] As a further optional embodiment, parsing the address query information to obtain address information features includes:

[0066] The address query information is segmented to obtain address phrase information;

[0067] Keyword extraction is performed on the address phrase information to obtain address information features.

[0068] In this embodiment, the address query information can be segmented at the sentence level to obtain multiple statements. Then, each statement is segmented into words to obtain the word groups that make up the statement, i.e., address word group information. For example, the address query information includes the statement "I want to go to Haizhu Wanda," which, after segmentation, becomes the word group "I, want, go, Haizhu, Wanda." Various segmentation algorithms can be used here. For instance, in some embodiments, a dictionary-based segmentation algorithm can be used, first segmenting the statement into words according to the dictionary, and then finding the optimal combination of words. In other embodiments, a character-based segmentation algorithm can be used, first dividing the statement into individual characters, then combining the characters into words, and finding the optimal combination. After segmenting the statement, the word embedding vector corresponding to each word in the word group can be determined using a pre-established dictionary. Of course, in some embodiments, the word embedding vector can be obtained by mapping the words to a unified, lower-dimensional vector space. Strategies for generating such mappings include neural networks, dimensionality reduction of the word co-occurrence matrix, probabilistic models, and interpretable knowledge base methods. For example, for the statement "I want to go to Haizhu Wanda," first, we determine the word embedding vectors corresponding to each word in the statement, and remove non-keywords such as "I," "want," and "go." The word vector corresponding to the word "Haizhu" is (0, 5, 1, 1), and the word vector corresponding to the word "Wanda" is (0, 0, 0, 1). After determining the word embedding vectors corresponding to each word in the phrase "Haizhu, Wanda," we can accumulate these word embedding vectors. The accumulated vector can be recorded as the phrase vector. For example, the phrase vector corresponding to the phrase "Haizhu, Wanda" is (0, 5, 1, 2). By normalizing the phrase vector, we can obtain the vector corresponding to the statement. For example, during normalization, we can set the sum of the elements in the vector corresponding to the statement to 1, so the statement "I want to go to Haizhu Wanda" can be represented by the vector (0, 1, 0.2, 0.4). It is understandable that, by referring to the above method, the vectors corresponding to all statements in the text content of the address query information can be determined. By concatenating these vectors or constructing them into a matrix, structured data containing all feature information of the text content can be obtained. In this embodiment of the application, this structured data can be used as address information features.

[0069] As a further optional embodiment, the step of matching the address information features in a geocoding database to obtain a candidate address list corresponding to the address information features includes:

[0070] Construct a mapping relationship between the address information features and the candidate addresses;

[0071] Based on the mapping relationship, a list of candidate addresses corresponding to the address information features is matched in the geocoding library.

[0072] In this embodiment, to obtain a candidate address list corresponding to address information features, a mapping relationship between address information features and candidate addresses in a geocoding database can be constructed. By inputting address information features, corresponding candidate addresses can be matched according to the constructed mapping relationship, forming a candidate address list. Specifically, the distance between the address information feature vector and the address vector can be calculated, and a threshold distance can be set. When the distance between the address information feature vector and the address vector is less than the threshold distance, the address is selected into the candidate address list.

[0073] As a further optional embodiment, the matching degree evaluation of the candidate addresses in the candidate address list includes:

[0074] Determine the similarity between the address information features of the address query information and the address information features of the candidate addresses;

[0075] Determine the completeness of the address information features in the address query information;

[0076] The matching degree is evaluated based on the similarity and completeness to obtain a matching degree score.

[0077] In this embodiment, during the scoring process, address information features and candidate address features can be input together into the scoring model. The scoring model calculates the similarity between the two and determines the score result of the search based on the magnitude of the similarity. Here, similarity is used to characterize the degree of similarity between address information features and candidate address features, and also reflects the credibility of the search. In this embodiment, similarity can be measured as a percentage. It can be understood that when the similarity value is 100%, the address information features and candidate address features are considered to be completely identical.

[0078] Simultaneously, the completeness of address information features in address query information can be determined. Specifically, during geodatabase encoding, a hierarchical system is implemented. For example, address element levels are divided into eight levels from top to bottom: country, province / municipality, provincial capital / prefecture-level city, district / county, street / township, road area, POI, and detailed address. The system determines whether the address information features contain any of these address element levels, and the completeness of the address information features is determined based on the extent to which they are contained. It can be understood that the more elements contained, the higher the completeness; conversely, the fewer elements contained, the lower the completeness.

[0079] After determining the similarity and completeness, a matching score can be obtained using these metrics. Specifically, a weighted average method can be used to fuse the similarity and completeness features to arrive at the matching score.

[0080] Specifically, in determining the similarity between address information features and candidate address features, in some embodiments, the difference value between the numerical features of the address information features and the candidate address features can be determined first, and then the similarity can be determined based on the difference value. The larger the difference value, the smaller the similarity, and vice versa.

[0081] As a further optional embodiment, the method further includes:

[0082] Obtain geographic coordinate information;

[0083] The target address corresponding to the geographic coordinate information is obtained by matching the geographic coordinate information in the geocoding database.

[0084] Generate the address environment information for the target address.

[0085] In this embodiment, geographic coordinate information includes parameters such as latitude and longitude, radius, and the number of points. By inputting geographic coordinate information, the system matches the corresponding destination address in the geocoding library and then visualizes the destination address. Specifically, inputting parameters such as latitude and longitude, radius, and the number of points returns address-related information for querying. This embodiment includes two reverse query methods: Method 1 is a radius query, which inputs three parameters: latitude and longitude, radius, and the number of points. The returned result is a specified number of address information within a circle centered at the given latitude and longitude, with the radius value being the radius. This includes the address text, latitude and longitude, and address code. Method 2 is a nearest neighbor query, which inputs two parameters: latitude and longitude, and the number of points. The returned result is a specified number of address information closest to the given point, centered at the given latitude and longitude. This includes the address text, latitude and longitude, and address code.

[0086] As a further optional embodiment, after the step of evaluating the matching degree of the candidate addresses in the candidate address list, the method further includes:

[0087] The matching score is compared with a preset score;

[0088] Remove candidate addresses whose matching score is lower than the preset score from the candidate address list;

[0089] The candidate address list is sorted according to the matching score;

[0090] The sorted list of candidate addresses is then visualized.

[0091] In this embodiment, the preset score is used to filter out erroneous addresses that differ significantly from the input content. This preset score can be adjusted according to actual conditions. After scoring the matching degree, addresses below the preset score are removed by comparing the matching degree score with the preset score, thus outputting a new list of candidate addresses. Furthermore, to improve the user experience, the candidate address list can be sorted according to the matching degree score, and addresses with high matching degree can be displayed in the drop-down box of the search input bar. The drop-down box can also visually display the candidate addresses from highest to lowest matching degree for the user to select.

[0092] Furthermore, when visualizing candidate addresses, not only can the text of the candidate addresses be displayed in the search input dropdown menu, but a map can also be displayed in the blank space corresponding to the text. Specifically, a small map can be displayed for each candidate address in the dropdown menu. This small map shows a portion of the area including the candidate address, and users can zoom in on the small map by touching or clicking to confirm whether the candidate address is the address they want to go to. Understandably, when the candidate addresses are close together, multiple candidate addresses can be displayed on a large map, which covers a larger area than the small map mentioned above. For example, if a user searches for "subway station," the candidate address list will be subway stations within a certain range. These search results for nearby subway stations can be displayed together on the same large map for the user to view, thus clearly distinguishing the locations.

[0093] As a further optional embodiment, the method further includes:

[0094] Based on the address query information, obtain the search count of the keywords;

[0095] Compare the number of searches with the preset number of searches;

[0096] Keywords whose search count exceeds the preset number will be visualized.

[0097] To provide travel options for users without a destination, this embodiment can identify popular addresses searched by a large number of people based on address query information. Specifically, address query information can match corresponding target candidate addresses. After the query command is executed, the backend can count the searched keywords and compare the number of searches with a preset number, displaying addresses with a higher preset number of searches as popular keywords. Based on the above, popular keywords can be displayed above the search input bar. Optionally, when visually displaying popular keywords, not only can the text of popular keywords be displayed above the search input bar, but a map of popular keywords can also be displayed in the blank space corresponding to the text.

[0098] The address mapping device based on the encoding library provided by the present invention will be described below. The address mapping device based on the encoding library described below can be referred to in correspondence with the address mapping method based on the encoding library described above.

[0099] An address mapping device based on an encoding library, such as Figure 2 As shown, it includes:

[0100] Information acquisition unit 210 is used to acquire address query information;

[0101] Information parsing unit 220 is used to parse the address query information to obtain address information features;

[0102] Matching unit 230 is used to perform matching in a geocoding library based on the address information features to obtain candidate addresses corresponding to the address information features;

[0103] Scoring unit 240 is used to score the matching degree of the candidate address;

[0104] The filtering unit 250 is used to select the candidate address with the highest matching score as the target candidate address.

[0105] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communications interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute an address mapping method based on an encoding library, the method including:

[0106] Get address lookup information;

[0107] The address query information is parsed to obtain address information features;

[0108] Based on the address information features, a matching process is performed in the geocoding database to obtain a candidate address list corresponding to the address information features;

[0109] The matching degree of the candidate addresses in the candidate address list is evaluated;

[0110] The candidate address with the highest matching score is selected as the target candidate address.

[0111] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0112] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the address mapping method based on the encoding library provided by the above methods, the method comprising:

[0113] Get address lookup information;

[0114] The address query information is parsed to obtain address information features;

[0115] Based on the address information features, a matching process is performed in the geocoding database to obtain a candidate address list corresponding to the address information features;

[0116] The matching degree of the candidate addresses in the candidate address list is evaluated;

[0117] The candidate address with the highest matching score is selected as the target candidate address.

[0118] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0119] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for address mapping based on an encoding library, characterized in that, The method comprises: obtaining address query information; parsing the address query information to obtain address information features; matching the address information features in a geographic coding library to obtain a candidate address list corresponding to the address information features; evaluating the matching degree of the candidate addresses in the candidate address list; selecting a candidate address with the highest matching degree score as a target candidate address; the evaluation of the matching degree of the candidate addresses in the candidate address list comprises: determining the similarity between the address information features of the address query information and the address information features of the candidate addresses; determining the completeness of the address information features of the address query information; evaluating the matching degree according to the similarity and the completeness to obtain a matching degree score.

2. The code library based address mapping method of claim 1, wherein, the parsing of the address query information to obtain address information features comprises: segmenting the address query information to obtain address phrase information; extracting keywords from the address phrase information to obtain address information features.

3. The code library based address mapping method of claim 1, wherein, the matching of the address information features in the geographic coding library to obtain a candidate address list corresponding to the address information features comprises: constructing a mapping relationship between the address information features and the candidate addresses; matching the candidate address list corresponding to the address information features in the geographic coding library according to the mapping relationship.

4. The code bank based address mapping method according to any one of claims 1-3, wherein, The method further comprises: obtaining geographic coordinate information; matching the geographic coordinate information in the geographic coding library to obtain a target address corresponding to the geographic coordinate information; generating address environment information of the target address.

5. The code bank based address mapping method according to any one of claims 1-3, wherein, After the step of evaluating the matching degree of the candidate addresses in the candidate address list, the method further comprises: comparing the matching degree score with a preset score; removing candidate addresses with a matching degree score lower than the preset score from the candidate address list; sorting the candidate address list according to the matching degree score; visually displaying the sorted candidate address list.

6. The code bank based address mapping method according to any one of claims 1-3, wherein, The method further comprises: obtaining the search frequency of a keyword according to the address query information; comparing the search frequency with a preset frequency; visually displaying keywords with a search frequency higher than the preset frequency.

7. An address mapping apparatus based on a code library, characterized by, The method comprises: an information acquisition unit configured to obtain address query information; an information parsing unit configured to parse the address query information to obtain address information features; a matching unit configured to match the address information features in a geographic coding library to obtain a candidate address list corresponding to the address information features; a scoring unit configured to evaluate the matching degree of the candidate addresses, comprising: determining the similarity between the address information features of the address query information and the address information features of the candidate addresses; determining the completeness of the address information features of the address query information; evaluating the matching degree according to the similarity and the completeness to obtain a matching degree score; a screening unit configured to select a candidate address with the highest matching degree score as a target candidate address.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the address mapping method based on a coding library according to any one of claims 1 to 6 when executing the program. 9.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the address mapping method based on the encoding library according to any one of claims 1 to 6.

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