A method for address standardization and dynamic management

Abstract

The present invention provides a method for address standardization and dynamic management, belonging to the field of information processing technology. The method comprises: inputting collected address information into a hierarchical entry structure to perform hierarchical division of the address information; converting the unstructured data of each level into initial structured data and splitting it according to the expression combination corresponding to the division level, performing authenticity judgment according to the corresponding expression standards under the expression combination, and constructing an authenticity vector and an authenticity attribute vector; analyzing the existence reliability of the same corresponding elements in the two authenticity vectors and filtering out unreliable elements; establishing an association between the unreliable elements and the unsuccessfully divided portion of the first storage block, retrieving historical standard hierarchical descriptions, and correcting the expressions to obtain a standardized address; determining the historical security information of each transmission interface for transmitting the standardized address and the receiving interface of the big data platform in a specified time period, and establishing a security protection mechanism. The method achieves standardized processing of address information and secure and reliable information transmission.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to an address standardization and dynamic management method. Background Art

[0002] In the digital age, address information, as critical data, is widely used in various business scenarios. However, due to the diversity and non-standardization of address representations, as well as potential errors during data collection, raw address information often suffers from numerous issues, such as unclear hierarchical divisions, disorganized data structures, and incorrect representations. These issues severely impact the effective use and processing efficiency of address information.

[0003] Existing address standardization methods mostly rely on simple rule-matching or template-based processing, which struggles to cope with complex and changing address representations. They are unable to accurately identify and process ambiguous and non-standard address information, resulting in inaccurate standardization results. Furthermore, during the transmission and management of address information, traditional technologies lack effective security protection mechanisms and are unable to dynamically adjust protection strategies based on security risks over time, making it difficult to ensure the security and integrity of address information during transmission and storage.

[0004] Therefore, the present invention proposes a method for address standardization and dynamic management. Summary of the Invention

[0005] The present invention provides an address standardization and dynamic management method to solve the problems of inaccurate standardization and insufficient security protection in existing address information processing, realize accurate standardized processing of address information, and ensure the safe and reliable transmission and management of address information through a dynamic security protection mechanism.

[0006] The present invention provides an address standardization and dynamic management method, comprising:

[0007] Step 1: Input the collected address information into the hierarchical entry framework to perform hierarchical division on the address information, temporarily store the unsuccessfully divided portion in the first storage block, and temporarily store the successfully divided portion in the second storage block;

[0008] Step 2: converting the unstructured data in the second storage block into initial structured data according to the conversion criteria of each division level;

[0009] Step 3: Split the initial structure data according to the expression combination of the corresponding division level, and judge the authenticity of each split structure data according to the corresponding expression standard under the expression combination, and construct an authenticity vector and an authenticity attribute vector, wherein the authenticity vector corresponds one-to-one with the elements of the authenticity attribute vector;

[0010] Step 4: Analyze the existence reliability of the same corresponding element in the authenticity vector and the authenticity attribute vector, and filter out unreliable elements based on the existence reliability;

[0011] Step 5: Establishing an association between the unreliable element and the unsuccessfully divided portion of the first storage block, and retrieving a historical standard hierarchical description based on the hierarchy to which the unreliable element belongs, and modifying the description corresponding to the unreliable element to obtain a standardized address, wherein the standardized address is obtained by sequentially describing the structure from high to low hierarchical levels, and the corresponding last hierarchical level is the lowest hierarchical level in the collected address information;

[0012] Step 6: Determine the historical security information of each transmission interface that transmits the standardized address and the receiving interface of the big data platform in a specified time period, establish a security protection mechanism and dynamically manage it.

[0013] Preferably, the collected address information is input into a hierarchical entry framework to hierarchically divide the address information, including:

[0014] Establish a hierarchical term structure, wherein each level contains corresponding standard terms and synonym libraries, and the hierarchical term structure includes at least country, province, city, district, street, community / village, building, unit, floor and house number;

[0015] Use natural language processing technology to segment the collected address information and obtain several independent words;

[0016] Match each independent word with the standard word and synonym library in the hierarchical word structure, and match them in order from high level to low level to obtain the independent word group under the level with successful matching;

[0017] Determining the remaining words that have not been successfully matched among the plurality of independent words, and semantically associating each of the remaining words with the independent word group under the level of successful matching, and determining the possible level to which the remaining words belong in combination with relevant data in the geographic information database;

[0018] Temporarily storing the independent word groups at the successfully matched level in the second storage block;

[0019] The independent words in the possible level are temporarily stored in the first storage block.

[0020] Preferably, constructing the authenticity vector and the authenticity attribute vector includes:

[0021] Determine a first quantity of split structure data and a combination quantity of the expression combinations at a corresponding division level to obtain a first matching coefficient;

[0022] According to the second matching coefficient of each expression standard under each split structure data in the corresponding division level and the expression combination, it is determined whether the maximum second matching coefficient is 1;

[0023] If it is 1, the corresponding split structure data is determined to be true;

[0024] Otherwise, the corresponding split structure data is determined to be false, and all the second matching coefficients involved are analyzed. In combination with the historical matching failure event set based on each expression standard at the corresponding division level, the authenticity attribute of the corresponding split structure data is determined, wherein the authenticity attribute is related to the possible cause of the corresponding split structure data being false;

[0025] A true-false vector is constructed based on the maximum second matching coefficient corresponding to each split structure data, and at the same time, a true-false attribute corresponding to each split structure data is constructed to obtain a true-false attribute vector.

[0026] Preferably, determining the authenticity of the corresponding split structure data includes:

[0027] Inputting each historical matching failure event in the historical matching failure event set into the event analysis model to obtain a first failure factor, and at the same time, retrieving the matching process log of each historical matching failure event from the historical diary record database and inputting it into the log analysis model to obtain a second failure factor, and establishing a failure factor table corresponding to the historical matching failure event;

[0028] Conduct aggregate analysis on all failure factor tables to establish a complete description framework;

[0029] Perform a unique matching judgment on all second matching coefficients corresponding to the false split structure data, that is, determine whether the second number of second matching coefficients with a value of 0 is N-1. If so, treat the second matching coefficient that is not 0 as the third matching coefficient, and combine the expression standards of the first matching coefficient and the third matching coefficient to construct a search index, where N represents the number of all second matching coefficients under the corresponding split structure data;

[0030] If not, determine the coefficient ratio of the second matching coefficient that is not 0, and construct a search index by combining the expression standard under the second matching coefficient that is not 0 and the first matching coefficient;

[0031] Based on the search index, the possible cause of the location is searched in the complete description framework to obtain the authenticity attribute of the corresponding split structure data.

[0032] Preferably, analyzing the existence reliability of the same corresponding element in the authenticity vector and the authenticity attribute vector, and filtering out unreliable elements based on the existence reliability, includes:

[0033] Determine the error factor for each authenticity attribute;

[0034]

[0035] in, is the error factor of the wth true or false attribute; n represents the number of possible categories of causes corresponding to the wth true or false attribute; The total number of categories representing all possible causes; represents the category weight of the i-th category reason under the w-th true or false attribute; represents the error probability of converting unstructured data into structured data due to the i-th category reason under the w-th true or false attribute;

[0036] Determine the reliability of the true and false values ​​and error factors of the same corresponding elements;

[0037]

[0038] in, is the existence reliability of the element corresponding to the wth true or false attribute; Indicates the truth value of the element corresponding to the wth truth attribute;

[0039] If the existence reliability is less than the preset reliability, the corresponding split structure data is regarded as an unreliable element.

[0040] Preferably, correcting the expression corresponding to the unreliable element includes:

[0041] Extracting, according to the level to which the unreliable element belongs, from the unsuccessfully divided portion of the first storage block, independent words consistent with the corresponding level, and treating them as first words;

[0042] Obtaining an association relationship based on the semantic relevance and grammatical structure of the description of the unreliable element and each first word, and the position and association of the unreliable element and the first word in the address logic framework according to the overall information logical relationship set according to the corresponding division level;

[0043] A history-related correction rule is determined based on the history standard level description, and the description corresponding to the unreliable element is corrected in combination with the association relationship.

[0044] Preferably, a security protection mechanism is established, including:

[0045] Analyze the historical security information of each transmission interface and receiving interface in a specified time period, determine the set of hazard types and the hazard probability distribution of each hazard type in the corresponding time period, and obtain the information hazard combination at each time point;

[0046] Extracting the input behavior sequence of the receiving interface and each transmission interface corresponding to the transmission interface login terminal at each time point within a specified time period, and obtaining the behavior risk combination at each time point;

[0047] Generate the first safety assessment value, the second safety assessment value, and the comprehensive safety assessment value based on the information risk combination and behavior risk combination at the same time point, and select the maximum safety assessment value for retention;

[0048] The retained maximum safety assessment value is plotted in a time sequence and the value levels are divided according to the safety protection standard to obtain a number of curve segments;

[0049] Based on the current security configuration information of the login end of the transmission interface involved in each curve segment and combined with the value level, a security encryption factor is generated, and an encryption method is set for the continuous sub-time period corresponding to the corresponding curve segment according to the security encryption factor, wherein the encryption method is to scramble the information encoding of the input address information based on the chaos level of the security encryption factor;

[0050] All encryption methods are sorted in order of consecutive sub-time periods to obtain the initial protection mechanism.

[0051] Preferably, after obtaining the initial protection mechanism, the method further includes:

[0052] Determine the segment length of each continuous sub-time period and the resource loss of switching encryption modes between adjacent continuous sub-time periods;

[0053] If the loss of each resource is less than the preset loss, the initial protection mechanism is regarded as a safety protection mechanism;

[0054] Otherwise, the first sub-time period in which the resource loss is greater than or equal to the preset loss is locked. If the security encryption factor of the first sub-time period is greater than the security encryption factor of the previous sub-time period, the corresponding encryption method remains unchanged.

[0055] Otherwise, the encryption mode of the first sub-time period is replaced with the encryption mode of the previous sub-time period, thereby obtaining a security protection mechanism.

[0056] Compared with the prior art, the present invention has the following advantages:

[0057] Through multi-level matching, authenticity judgment and intelligent correction, it can effectively solve problems such as ambiguous addresses and incorrect statements. Based on historical security data and real-time risk assessment, it can realize dynamic adjustment of encryption strategies to ensure data security.

[0058] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0059] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0061] Figure 1 The figure is a flow chart of a method for address standardization and dynamic management in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0063] The present invention provides an address standardization and dynamic management method, such as Figure 1 As shown, including:

[0064] Step 1: Input the collected address information into the hierarchical entry framework to perform hierarchical division on the address information, temporarily store the unsuccessfully divided portion in the first storage block, and temporarily store the successfully divided portion in the second storage block;

[0065] Step 2: converting the unstructured data in the second storage block into initial structured data according to the conversion criteria of each division level;

[0066] Step 3: Split the initial structure data according to the expression combination of the corresponding division level, and judge the authenticity of each split structure data according to the corresponding expression standard under the expression combination, and construct an authenticity vector and an authenticity attribute vector, wherein the authenticity vector corresponds one-to-one with the elements of the authenticity attribute vector;

[0067] Step 4: Analyze the existence reliability of the same corresponding element in the authenticity vector and the authenticity attribute vector, and filter out unreliable elements based on the existence reliability;

[0068] Step 5: Establishing an association between the unreliable element and the unsuccessfully divided portion of the first storage block, and retrieving a historical standard hierarchical description based on the hierarchy to which the unreliable element belongs, and modifying the description corresponding to the unreliable element to obtain a standardized address, wherein the standardized address is obtained by sequentially describing the structure from high to low hierarchical levels, and the corresponding last hierarchical level is the lowest hierarchical level in the collected address information;

[0069] Step 6: Determine the historical security information of each transmission interface that transmits the standardized address and the receiving interface of the big data platform in a specified time period, establish a security protection mechanism and dynamically manage it.

[0070] In this embodiment, the hierarchical entry structure is a multi-level entry system constructed according to address administrative levels or spatial logic.

[0071] In this embodiment, hierarchical division is the process of mapping address information to the corresponding administrative or spatial hierarchy according to the hierarchical term structure. For example, the address No. 88, Jianguo Road, Chaoyang District, Beijing is divided into: Country (China), City (Beijing), District (Chaoyang), Street (Jianguo Road), House Number (No. 88). A two-way longest match algorithm is used in combination with a rule engine to first match the high level (such as country / province), and then match downward layer by layer, using regular expressions to identify digital features such as house numbers and buildings.

[0072] In this embodiment, the first storage block is the address fragment that was not successfully matched in the storage hierarchy division (such as ambiguous place names, misspelled parts). For example, the address Puming District in Shanghai is the part that was not successfully divided and is stored in the first storage block (actually it should be Pudong New Area). A distributed file system (such as HDFS) is used to store the original undivided text, and indexing technology (such as Elasticsearch) is combined to achieve fast retrieval.

[0073] In this embodiment, the second storage block stores structured address data that successfully matches the hierarchical partitioning. For example, if the address "Shenzhen Nanshan District Science and Technology Park South District" is successfully partitioned, "Shenzhen-Nanshan-Science and Technology Park South District" is stored in the second storage block. This structured storage is performed using a relational database (such as PostgreSQL) based on hierarchical fields (such as country, province, and city), supporting SQL queries and transaction management.

[0074] Conversion standards are the rules for mapping address data at each level from unstructured text to structured fields. For example, at the street level, the conversion standard specifies that "XX Road, XX Number" be split into the street name and house number fields; at the community level, the conversion standard specifies that "XX Garden, XX Building" be split into the community name and building number. Using a regular expression template library (such as the Python re module) combined with a rule engine, standardized splitting patterns are preset for different levels and support custom template extensions.

[0075] Unstructured data is address text that is not organized in a fixed format (such as freely entered address descriptions). For example, the address "Building 3, Yard 5, Zhongguancun South Street, Haidian District, Beijing" entered by a user is unstructured data.

[0076] Use a text parser (such as ANTLR) to identify address components in natural language, and combine it with part-of-speech tagging (such as the NLTK library) to distinguish elements such as place names and numbers.

[0077] In this embodiment, the initial structured data is fielded address data (authenticity has not yet been verified) formed after being split according to the conversion standard. For example, the unstructured data No. 1, Huaxia Road, Zhujiang New Town, Tianhe District, Guangzhou is converted into the initial structured data: {City: Guangzhou, District: Tianhe, Street: Huaxia Road, House Number: 1}. The structured fields are stored in JSON / XML format, and automatic conversion from text to objects is achieved through data mapping tools (such as MapStruct).

[0078] Representation combinations are standard combinations of representations at the same address level (e.g., the street level might include road, street, and avenue). For example, the street-level representation combination is {XX Road, XX Street, XX Avenue, XX Lane}. Frequently occurring representation patterns are collected from the historical address database and clustered using a clustering algorithm (such as K-means). These combinations are regularly updated based on new data.

[0079] In this embodiment, the expression standard is to express each legal address expression pattern in the combination.

[0080] Authenticity determination is the process of determining whether the split structured data meets the corresponding representation standards. For example, if the split data for Puming District is compared with the district and county representation standard [correct administrative division name], it is determined to be false because Puming District does not exist. However, Pudong New Area meets the standard and is determined to be true. A rule engine (such as Drools) is used to perform standard matching, combined with semantic similarity algorithms (such as cosine similarity) to handle fuzzy matching scenarios.

[0081] The true-false vector is a vector consisting of the maximum matching coefficient of each split structure data (element value ∈ [0, 1]). For example, after address splitting, three structure data are obtained, and the matching coefficients are 0.95, 0.1, and 1, respectively. Then the true-false vector is [0.95, 0.1, 1]. Vectors are stored in NumPy arrays, which support vectorized calculations (such as matrix multiplication) to improve computational efficiency.

[0082] In this embodiment, the authenticity attribute vector is a vector that records the specific error cause of each fake data (the elements are string descriptions). The string attributes are stored in a List collection and aligned with the authenticity vector index to support fast attribute query.

[0083] In this embodiment, the existence reliability is a comprehensive reliability indicator calculated by combining the true value and the error factor.

[0084] In this embodiment, the historical standard hierarchical description is a database that records historically correct address representations and correction rules at each level. For example, the street-level historical standard description includes rules such as "Jianguo Road" → correct name, and "incorrect street name" → commonly miswritten as "XX Road." A graph database (such as Neo4j) is used to store the relationship between standard representations and error patterns, supporting semantically similar queries.

[0085] In this embodiment, the association relationship is the semantic, grammatical and logical connection between the unreliable element and the undivided part. For example, the unreliable element "Wrongly Named Community" and the undivided part "XX Garden" have a semantic association (referring to the same community), which constitutes a modification relationship grammatically and belongs to the same level logically.

[0086] Implementation: Use dependency parsing (such as Stanford CoreNLP) to parse grammatical relationships, calculate semantic relevance through word vector similarity (such as Sentence-BERT), and determine logical relationships based on address-level logic rules.

[0087] In this embodiment, the standardized address is a canonical address representation arranged in descending order (the lowest level is the lowest level in the original address). For example, the lowest level of the original address is the building number, and the standardized address format is country-province-city-district-street-district-building number. A template engine (such as Velocity) is used to splice standardized fields in hierarchical order, supporting custom output formats (such as full Chinese spelling and English abbreviations).

[0088] In this embodiment, historical security information refers to security logs (such as attack types and vulnerability records) of the transmission and reception interfaces within a specified time period. For example, an interface may have experienced three SQL injection attacks and two identity authentication failures in the past seven days. Interface security events are collected using a log collection system (such as ELKStack), and time series databases (such as InfluxDB) are used to store the time series data.

[0089] The beneficial effects of the above technical solution are: through multi-level matching, authenticity judgment and intelligent correction, it can effectively solve problems such as ambiguous addresses and incorrect statements, and based on historical security data and real-time risk assessment, it can realize dynamic adjustment of encryption strategies to ensure data security.

[0090] The present invention provides an address standardization and dynamic management method, which inputs the collected address information into a hierarchical entry framework to hierarchically divide the address information, including:

[0091] Establish a hierarchical term structure, wherein each level contains corresponding standard terms and synonym libraries, and the hierarchical term structure includes at least country, province, city, district, street, community / village, building, unit, floor and house number;

[0092] Use natural language processing technology to segment the collected address information and obtain several independent words;

[0093] Match each independent word with the standard word and synonym library in the hierarchical word structure, and match them in order from high level to low level to obtain the independent word group under the level with successful matching;

[0094] Determining the remaining words that have not been successfully matched among the plurality of independent words, and semantically associating each of the remaining words with the independent word group under the level of successful matching, and determining the possible level to which the remaining words belong in combination with relevant data in the geographic information database;

[0095] Temporarily storing the independent word groups at the successfully matched level in the second storage block;

[0096] The independent words in the possible level are temporarily stored in the first storage block.

[0097] In this embodiment, knowledge graph technology is used to construct the logical structure of a hierarchical term structure, with hierarchies as nodes, standard terms and synonym libraries as node attributes, and relationships between nodes established. Standard terms are extracted from authoritative address data sources. Word vector models in natural language processing, such as Word2Vec and BERT, are used to calculate the semantic similarity between words and generate a synonym library. The term structure is regularly updated and maintained, and standard terms and synonyms are promptly supplemented and modified based on administrative division adjustments, the emergence of new place names, and other circumstances.

[0098] In this embodiment, during the matching process, when an independent word successfully matches a standard word or a word in a synonym library at a certain level in the hierarchical word structure, the independent word and other independent words that subsequently match successfully at the same level are combined into a set. A combination of sequential traversal and bidirectional longest match algorithms is used. Starting from the national level, each independent word is string matched and semantic similarity is calculated with the standard word and synonym library at that level in turn. If the similarity reaches the set threshold (such as 0.8), it is considered a successful match, and the next independent word is matched at that level; if all words at a certain level fail to match, the next level is entered to continue matching. Caching technology is used to store matched terms and results to reduce repeated calculations and improve matching efficiency.

[0099] In this embodiment, the remaining unmatched words are independent words that have not been successfully matched with any level of standard words or synonyms in the matching process with the hierarchical word structure. These words may fail to match due to reasons such as non-standard expressions, typos, and newly appeared place names.

[0100] Semantic association is to analyze the semantic connection between the remaining unmatched words and the independent word groups under the successfully matched level, and to determine whether the remaining words may be more detailed address information under the matched level, or whether they have a semantic relationship such as inclusion or adjacency with the matched level.

[0101] A geographic information database stores geographic spatial data and related attribute information, including administrative boundaries, road networks, building locations, and other information. By querying a geographic information database, you can obtain the spatial location relationships and geographic features of address information, helping to determine the hierarchical level to which unmatched terms belong.

[0102] The beneficial effects of the above technical solution are: by constructing a hierarchical entry framework and applying natural language processing technology, efficient hierarchical division of address information is achieved. It can accurately identify the hierarchical affiliation of most standardized address information, and store the successfully matched address portions in the second storage block, providing a clear data foundation for subsequent address standardization and structured processing. For unmatched address portions, semantic association analysis and the assistance of a geographic information database are used to determine their possible hierarchical affiliation and store them in the first storage block, providing direction for further address correction and improvement. Overall, this improves the accuracy and efficiency of address information processing, effectively resolving the issue of unclear hierarchical division of address information.

[0103] The present invention provides an address standardization and dynamic management method, which constructs an authenticity vector and an authenticity attribute vector, including:

[0104] Determine a first quantity of split structure data and a combination quantity of the expression combinations at a corresponding division level to obtain a first matching coefficient;

[0105] According to the second matching coefficient of each expression standard under each split structure data in the corresponding division level and the expression combination, it is determined whether the maximum second matching coefficient is 1;

[0106] If it is 1, the corresponding split structure data is determined to be true;

[0107] Otherwise, the corresponding split structure data is determined to be false, and all the second matching coefficients involved are analyzed. In combination with the historical matching failure event set based on each expression standard at the corresponding division level, the authenticity attribute of the corresponding split structure data is determined, wherein the authenticity attribute is related to the possible cause of the corresponding split structure data being false;

[0108] A true-false vector is constructed based on the maximum second matching coefficient corresponding to each split structure data, and at the same time, a true-false attribute corresponding to each split structure data is constructed to obtain a true-false attribute vector.

[0109] In this embodiment, the split structure data is the address information that has been divided into levels and converted into the initial structure data, and is further split according to the expression combination rules of the corresponding division levels to obtain data segments.

[0110] The first number refers to the total number of split structure data at the corresponding division level.

[0111] The expression combination is a set formed by combining various possible expression methods in each address division level to cover the diversity of address expressions at that level.

[0112] The number of combinations is the total number of expression methods contained in the expression combination at the corresponding division level.

[0113] In this embodiment, the first matching coefficient=the first number / the number of combinations.

[0114] In this embodiment, the expression standard is a specific expression form that complies with the standard, is correct, and accurately expresses the address information in each expression combination. The second matching coefficient is a quantitative indicator used to measure the degree of match between each split structure data and each expression standard in the expression combination. Its value range is generally between 0 and 1, with values ​​closer to 1 indicating a higher degree of match; a value of 1 indicates a perfect match.

[0115] The historical matching failure event set is a collection of all matching failure cases recorded at the corresponding division level in the past when matching the split structure data with the expression standard. Each event contains detailed information such as the failed split structure data, the corresponding expression standard, and the reason for failure.

[0116] In this embodiment, for example, the constructed truth vector is [0.9, 0.3, 1], and the truth attribute vector is [incomplete expression, wrong administrative division name, no error].

[0117] The beneficial effect of this technical solution is that by introducing the first and second matching coefficients, the split structure data is analyzed from the two dimensions of quantity and similarity. Combined with the historical matching failure event set, this allows for accurate judgment of the authenticity of the split structure data of address information and identifies the specific causes of false data. The constructed authenticity vector and authenticity attribute vector provide quantitative and structured data support for subsequent comprehensive evaluation of address information, error correction, and data optimization. It can effectively identify errors and non-standard expressions in address information, providing a strong guarantee for address standardization.

[0118] The present invention provides an address standardization and dynamic management method for determining the authenticity of corresponding split structure data, including:

[0119] Inputting each historical matching failure event in the historical matching failure event set into the event analysis model to obtain a first failure factor, and at the same time, retrieving the matching process log of each historical matching failure event from the historical diary record database and inputting it into the log analysis model to obtain a second failure factor, and establishing a failure factor table corresponding to the historical matching failure event;

[0120] Conduct aggregate analysis on all failure factor tables to establish a complete description framework;

[0121] Perform a unique matching judgment on all second matching coefficients corresponding to the false split structure data, that is, determine whether the second number of second matching coefficients with a value of 0 is N-1. If so, treat the second matching coefficient that is not 0 as the third matching coefficient, and combine the expression standards of the first matching coefficient and the third matching coefficient to construct a search index, where N represents the number of all second matching coefficients under the corresponding split structure data;

[0122] If not, determine the coefficient ratio of the second matching coefficient that is not 0, and construct a search index by combining the expression standard under the second matching coefficient that is not 0 and the first matching coefficient;

[0123] Based on the search index, the possible cause of the location is searched in the complete description framework to obtain the authenticity attribute of the corresponding split structure data.

[0124] In this embodiment, the event analysis model is a model constructed based on machine learning or data analysis algorithms, which is used to analyze and extract features of historical matching failure events, and to mine the key factors that lead to matching failure from various information of the events, namely the first failure factor. The first failure factor is the key failure factor obtained after processing the historical matching failure events through the event analysis model, such as incorrect expression of administrative division names and missing key information, etc. These factors are a high-level summary of the failure events.

[0125] The historical diary record database is a database specifically used to store detailed logs of the address information matching process. The log content includes the algorithm steps used during matching, intermediate results of data processing, details of matching attempts with various expression standards, etc. The matching process log is a detailed record of the specific process of each address information matching operation. It fully presents the entire process information from input data to obtaining matching results.

[0126] The log analysis model is a model that parses and analyzes the matching process log. By combing and mining the data in the log, it finds the deep-seated reasons that lead to matching failure and obtains the second failure factor.

[0127] The second failure factor is the failure reason extracted by the log analysis model from the matching process log. For example, when using the cosine similarity algorithm for calculation, the similarity calculation error is caused by the mismatch of word vector dimensions.

[0128] The failure factor table is a table formed by integrating the first failure factor and the second failure factor corresponding to each historical matching failure event. It clearly presents the key failure factor combination of each failure event on an event basis.

[0129] Aggregate analysis aggregates, classifies, compiles, and analyzes data from multiple failure feature tables to identify commonalities and patterns across failure events, uncovering common patterns and key factors leading to matching failures. A complete descriptive framework, established through aggregate analysis, provides a comprehensive and systematic description of various matching failure scenarios and their causes. This framework categorizes and integrates failure causes into a structured, logically organized hierarchy, facilitating subsequent querying and use. Data mining clustering algorithms (such as K-means clustering) are used to cluster failure features in the failure feature table, grouping similar failure causes together. Statistical analysis is then combined to calculate metrics such as the frequency and percentage of each failure cause category. Based on the clustering and statistical results, a complete descriptive framework is constructed using a tree or graph structure, clearly presenting each failure cause and their interrelationships. For example, using knowledge graph technology, with failure cause categories as nodes and relationships between categories as edges, a complete visual description framework can be constructed. For example, when performing aggregate analysis on a large number of failure feature tables, we found that reasons such as misspellings of administrative division names and non-standard street name abbreviations frequently occur. These can be aggregated into the category of address name errors. Other reasons, such as improper handling of special characters by the word segmentation algorithm and incorrect parameter settings for semantic similarity calculations, can be aggregated into the category of algorithm processing issues. Through this categorization and integration, a complete description framework can be established, encompassing major categories such as address name errors and algorithm processing issues, as well as subcategories of failure causes.

[0130] The third matching coefficient is that in the unique matching judgment, when the second number of second matching coefficients with a value of 0 is N-1, the second matching coefficient that is not 0 is regarded as the third matching coefficient alone, which represents the highest relative matching degree between the split structure data and a certain expression standard.

[0131] The search index is constructed based on correlation coefficients and representation criteria. It is used to quickly locate and find possible failure reasons for corresponding split-structure data within a complete description framework. Similar to a book's table of contents, it helps the system efficiently find target information. For example, for the pseudo-split-structure data Fusan District (district and county level), the second matching coefficients obtained by matching it with the representation criteria for Fuzhou, Xiamen, and Quanzhou are 0, 0, and 0.2, respectively. In this case, N=3, the second number is 2 (two zero coefficients), satisfying the N-1 condition, and 0.2 is the third matching coefficient. The search index is constructed by combining the first matching coefficient and the representation criterion for Fuzhou. If the second matching coefficients are 0.1, 0.3, and 0.2, respectively, the unique matching condition is not met. The proportion of each non-zero coefficient is calculated (e.g., 0.1 accounts for 20%, 0.3 accounts for 60%, and 0.2 accounts for 20%). The highest proportion, 0.3, and its corresponding representation criteria are selected and combined with the first matching coefficient to construct the search index. Use database query statements (such as SQL SELECT statements) to query the database that stores the complete description framework based on the keywords of the search index to obtain the corresponding authenticity attributes.

[0132] The beneficial effects of the above technical solution are: through in-depth analysis of historical matching failure events and multi-dimensional data mining, a complete and systematic failure cause description framework is established, and a precise search index is constructed in combination with the matching coefficient characteristics of the split structure data, which can efficiently and accurately determine the authenticity attributes of the split structure data, and provide a detailed and reliable basis for the subsequent correction, optimization and address standardization processing of address data.

[0133] The present invention provides an address standardization and dynamic management method, which analyzes the existence reliability of the same corresponding element in the authenticity vector and the authenticity attribute vector, and filters out unreliable elements based on the existence reliability, including:

[0134] Determine the error factor for each authenticity attribute;

[0135]

[0136] in, is the error factor of the wth true or false attribute; n represents the number of possible categories of causes corresponding to the wth true or false attribute; The total number of categories representing all possible causes; represents the category weight of the i-th category reason under the w-th true or false attribute; represents the error probability of converting unstructured data into structured data due to the i-th category reason under the w-th true or false attribute;

[0137] Determine the reliability of the true and false values ​​and error factors of the same corresponding elements;

[0138]

[0139] in, is the existence reliability of the element corresponding to the wth true or false attribute; Indicates the truth value of the element corresponding to the wth truth attribute;

[0140] If the existence reliability is less than the preset reliability, the corresponding split structure data is regarded as an unreliable element.

[0141] In this embodiment, the error factor is calculated comprehensively from two dimensions: the proportion of the number of cause categories and the weighted error probability. It reflects the proportion of the number of possible cause categories corresponding to the wth true or false attribute in the total number of categories, reflecting the coverage of the causes related to the true or false attribute; It is the weighted error probability average of different categories of reasons under the true or false attribute, through the category weight Distinguish the importance of different causes and combine them with the error probability The probability of error caused by this authenticity attribute is reasonably quantified.

[0142] In this embodiment, the sigmoid function can convert and The value of is mapped to a reasonable range to avoid extreme values ​​dominating the results, and then the smaller value of the two is taken through the min function, which reflects the conservative assessment of the impact of authenticity judgment and errors, and more rigorously measures the reliability of the existence of elements.

[0143] In this embodiment, the error factor is a weighted probability that measures errors caused by authenticity attributes. For example, errors in administrative district names can be attributed to two types of causes: 1. Spelling errors (weight 0.6, error probability 0.8); 2. Abbreviation use (weight 0.4, error probability 0.3). Therefore, the error factor = (0.6 × 0.8 + 0.4 × 0.3) / (total weight sum) = 0.6. A Bayesian network is used to model the association between error causes, and the prior probability and conditional probability of each factor are calculated using historical error data.

[0144] In this embodiment, unreliable elements are split structure data with reliability below a threshold (error fields that require focused correction). For example, if the house number field has a matching coefficient of 0.2, an error factor of 0.8, and a reliability of 0.16 < 0.5, it is considered an unreliable element. The screening threshold is automatically determined using a threshold filtering algorithm (such as the Otsu threshold method).

[0145] The beneficial effect of the above technical solution is: quantitative results are obtained through the existence of reliability formulas, and unreliable elements are finally filtered out based on the results, forming a complete closed loop of address data quality control, making the technical process more logical and complete.

[0146] The present invention provides an address standardization and dynamic management method, which corrects the expression corresponding to the unreliable element, including:

[0147] Extracting, according to the level to which the unreliable element belongs, from the unsuccessfully divided portion of the first storage block, independent words consistent with the corresponding level, and treating them as first words;

[0148] Obtaining an association relationship based on the semantic relevance and grammatical structure of the description of the unreliable element and each first word, and the position and association of the unreliable element and the first word in the address logic framework according to the overall information logical relationship set according to the corresponding division level;

[0149] A history-related correction rule is determined based on the history standard level description, and the description corresponding to the unreliable element is corrected in combination with the association relationship.

[0150] In this embodiment, semantic relevance is an indicator that measures the degree of similarity between the unreliable element description and the first word at the semantic level. The closer the semantics, the higher the relevance. The grammatical structure is the grammatical composition of the unreliable element description and the first word, including the word's part of speech, phrase structure, etc. The overall information logical relationship is the overall logical rule of the address information at the corresponding level. For example, the address fragments at the district and county level must comply with the administrative division specifications and must be reasonably associated with the upper-level city, lower-level street and other levels in the address logic framework. The address logic framework is constructed according to the address hierarchy. It contains a structured framework of logical associations between address information at each level, and clearly defines the inclusion and subordination relationships between levels such as country, province, city, district and county.

[0151] In this embodiment, the association relationship between the unreliable element and the first term is determined based on semantic relevance, grammatical structure, and overall information logical relationships. These relationships include semantic matching, grammatical adaptation, and logical subordination, and are used to guide the correction of unreliable elements. For example, the unreliable element is Jinghai District (described as Jinghai District), and the first term is Haidian District and Dongcheng District. Calculating semantic relevance: The cosine similarity between the word vectors of Jinghai District and Haidian District is 0.8 (hypothetical), and 0.3 with Dongcheng District. Grammatical structure analysis: All three are constructed as a noun + district, with a grammatical structure matching degree of 1. Determining the overall information logical relationship: Jinghai District (assuming incorrect), Haidian District, and Dongcheng District should all belong to the district and county level of Beijing, and their positions are consistent within the address logic framework. Overall, Jinghai District has a stronger association with Haidian District, which can be described as a high-match relationship with high semantic similarity (correlation 0.8), consistent grammatical structure, and belonging to the same district and county level of Beijing. The association with Dongcheng District is a moderate match, characterized by low semantic relevance (0.3), consistent grammatical structure, and belonging to the same district and county level of Beijing.

[0152] In this embodiment, a historical standard hierarchical description database is constructed and stored in an SQL database or a document database (such as MongoDB). The hierarchy to which the unreliable element belongs, error characteristics (such as semantic errors, grammatical errors), etc. are used as search keywords, and a database query statement is used to retrieve relevant historical standard hierarchical descriptions and correction rules. The retrieved historical standard hierarchical descriptions are analyzed to extract correction rules that are closely related to the current unreliable element. Applicable correction rules can be screened from historical descriptions through text mining techniques (such as keyword matching and rule template matching), and the correction strategy can be determined by combining the association relationship and historical related correction rules. For example, if the association relationship indicates that a first word is the most semantically matched with the unreliable element, and the historical correction rule supports the priority selection of this type of word correction, the unreliable element will be replaced with the corresponding first word. Programming code (such as Python scripts) is used to implement specific replacement and adjustment operations to correct unreliable element descriptions. For example, for the unreliable element Jinghai District, historical standard hierarchical descriptions reveal that at the Beijing district and county level, Jinghai District was previously corrected to Haidian District due to a spelling error. The corresponding historical correction rule is that for errors caused by similar pronunciation or glyphs within the same city, district, or county name, the correct name with the highest semantic relevance is selected for correction. Based on the previously determined associations, Jinghai District and Haidian District share the highest semantic relevance, share the same grammatical structure, and both belong to the Beijing district and county level. Therefore, a correction is performed to correct Jinghai District to Haidian District.

[0153] The beneficial effects of the above technical solution are: by accurately extracting candidate correction words (first words), multi-dimensional analysis of association relationships (semantics, grammar, logic) and combining historical correction experience (historical standard hierarchical descriptions and rules), a scientific and efficient unreliable element correction process is constructed, continuously improving the address standardization processing capabilities.

[0154] The present invention provides an address standardization and dynamic management method, which establishes a security protection mechanism, including:

[0155] Analyze the historical security information of each transmission interface and receiving interface in a specified time period, determine the set of hazard types and the hazard probability distribution of each hazard type in the corresponding time period, and obtain the information hazard combination at each time point;

[0156] Extracting the input behavior sequence of the receiving interface and each transmission interface corresponding to the transmission interface login terminal at each time point within a specified time period, and obtaining the behavior risk combination at each time point;

[0157] Generate the first safety assessment value, the second safety assessment value, and the comprehensive safety assessment value based on the information risk combination and behavior risk combination at the same time point, and select the maximum safety assessment value for retention;

[0158] The retained maximum safety assessment value is plotted in a time sequence and the value levels are divided according to the safety protection standard to obtain a number of curve segments;

[0159] Based on the current security configuration information of the login end of the transmission interface involved in each curve segment and combined with the value level, a security encryption factor is generated, and an encryption method is set for the continuous sub-time period corresponding to the corresponding curve segment according to the security encryption factor, wherein the encryption method is to scramble the information encoding of the input address information based on the chaos level of the security encryption factor;

[0160] All encryption methods are sorted in order of consecutive sub-time periods to obtain the initial protection mechanism.

[0161] In this embodiment, the security encryption factor is a dynamic encryption parameter generated by combining the security assessment value and the current security configuration. For example, a high security assessment value generates a high-level chaos factor (such as a 256-bit random key), while a low assessment value generates a low-level factor (such as a 128-bit key). The factor is dynamically generated using an encryption algorithm (such as AES) combined with a random number generator (such as SHA-256). The factor strength is positively correlated with the security level.

[0162] In this embodiment, the chaos level is an indicator that measures the degree to which the encryption method scrambles address information (the higher the level, the stronger the attack resistance). For example, chaos level 1 is simple substitution encryption, and level 5 is dynamic obfuscation encryption based on neural networks. Different levels of chaos encryption are achieved by combining different encryption algorithms (such as symmetric encryption + asymmetric encryption + hash algorithm). The level is adjusted by parameters such as key complexity and encryption period.

[0163] In this example, resource loss refers to the system resources consumed when switching encryption methods (such as CPU usage and memory bandwidth). For example, when switching from AES-128 to AES-256, if CPU usage increases from 20% to 35%, this is considered increased resource loss. Real-time resource usage data is collected using system monitoring tools (such as Prometheus) to calculate the difference in resource usage before and after the encryption method switch.

[0164] In this embodiment, the transmission interface is the exit end for sending address information, the receiving interface is the entry end for receiving address information, and the specified time period is a pre-set time interval for analyzing security information, such as analyzing the security situation in the past 7 days or 1 month.

[0165] Historical security information is a security-related record of the transmission interface and the receiving interface within a specified time period. It contains information such as attack types (such as SQL injection and malicious code injection), data leakage incidents, and access anomalies (such as unauthorized access attempts). The danger type set is a collection of all danger types identified from historical security information, such as {SQL injection attack, unauthorized access, data tampering}.

[0166] Hazard probability distribution is the probability of each hazard type occurring at different time points within a specified time period.

[0167] The information hazard combination is the combination of various hazard types and their corresponding probability of occurrence at each point in time.

[0168] In this embodiment, the transmission interface login terminal is a terminal that logs into the transmission interface to perform address information operations, such as a computer terminal used by logistics staff or a handheld device terminal used by delivery personnel.

[0169] The input behavior sequence is the sequence of input operations on address information by the login end of the transmission interface at each time point, including the frequency of input addresses, the number of addresses input at a time, whether the format of the input addresses is abnormal (such as a large number of addresses with incorrect formats being input continuously), and other behaviors.

[0170] A behavior risk combination is a combination of identified risky behaviors and their occurrences in the input behavior sequence at each time point. For example, a behavior risk combination at a certain time point might be (high-frequency address input, 10 entries / minute; incorrectly formatted address input, 3 entries).

[0171] In this embodiment, the first safety assessment value = Σ (hazard type weight × hazard probability);

[0172] The second safety assessment value = basic score - Σ (points deducted for dangerous behavior), where the score ranges from 0 to 1.

[0173] Comprehensive safety assessment value = 0.6 × first safety assessment value + 0.4 × second safety assessment value.

[0174] Safety protection standards are pre-established standards for classifying safety assessment values. For example, a safety assessment value of <0.2 is set as low risk, 0.2-0.6 as medium risk, and >0.6 as high risk.

[0175] The value classification is based on the security protection standards, and the maximum security assessment value at each time point is classified into the corresponding risk level (such as low, medium, and high risk).

[0176] The curve is drawn with time as the horizontal axis and the maximum safety assessment value as the vertical axis. The maximum safety assessment values ​​at each time point are connected into a curve to intuitively show the time change trend of security risks.

[0177] A curve segment is a continuous part of the curve with the same safety assessment value level. Each curve segment corresponds to a continuous period of time and the same risk level.

[0178] The current security configuration information is the current security settings of the transmission interface login end, including firewall rules (such as whether intrusion detection is enabled), encryption protocol version (such as TLS1.2, TLS1.3), identity authentication method (such as password + verification code, biometric recognition), etc.

[0179] Information coding scrambling is to convert the input address information into code in an encrypted manner, disrupt the arrangement and storage form of the original information, and achieve encryption protection, such as converting XX Road in Chaoyang District, Beijing to X2# District Chaoyang North $X Road Yang.

[0180] The continuous sub-time period is the continuous time interval corresponding to the curve segment, and each curve segment corresponds to a continuous sub-time period.

[0181] In this embodiment, a security encryption factor generation model is established, and the security encryption factor is generated through weighted calculation based on the curve segment level (such as a high risk level corresponds to a basic factor of 1.5) and the current security configuration information (such as multiplying the factor by 1.2 when an advanced firewall is enabled).

[0182] Determine the chaos level based on the security encryption factor (e.g., factor > 3 corresponds to chaos level 3), select the corresponding encryption algorithm (e.g., chaos level 3 uses the AES algorithm combined with random permutation), encode and scramble the address information, and write an encryption program (e.g., Python combined with the pycryptodome library) to implement it.

[0183] A corresponding encryption method is associated with each continuous sub-time period, and the encryption policy is stored.

[0184] The beneficial effects of the above technical solution are: from the two dimensions of information risk and behavioral risk, combined with historical data and real-time behavior, the interface security risks are comprehensively evaluated, making the security assessment more accurate, and being able to timely discover different types of security threats. The encryption method is dynamically adjusted according to the security assessment results. High-intensity encryption is used when the security risk is high, and relatively lightweight encryption is adapted when the risk is low. While ensuring security, the system performance can be optimized, and the trend of security risk changes can be intuitively presented by drawing a security assessment curve and dividing the curve segments.

[0185] The present invention provides an address standardization and dynamic management method, which, after obtaining the initial protection mechanism, further includes:

[0186] Determine the segment length of each continuous sub-time period and the resource loss of switching encryption modes between adjacent continuous sub-time periods;

[0187] If the loss of each resource is less than the preset loss, the initial protection mechanism is regarded as a safety protection mechanism;

[0188] Otherwise, the first sub-time period in which the resource loss is greater than or equal to the preset loss is locked. If the security encryption factor of the first sub-time period is greater than the security encryption factor of the previous sub-time period, the corresponding encryption method remains unchanged.

[0189] Otherwise, the encryption mode of the first sub-time period is replaced with the encryption mode of the previous sub-time period, thereby obtaining a security protection mechanism.

[0190] In this embodiment, the preset loss is a pre-set, acceptable threshold for resource loss during encryption mode conversion. This threshold is determined based on factors such as system hardware performance and the service's tolerance for resources. For example, a CPU loss threshold of 15% and a memory loss threshold of 120MB can be set.

[0191] Use system monitoring tools (such as the top command on Linux, Task Manager on Windows, or professional APM tools like Prometheus and Grafana) to collect data such as CPU usage, memory usage, and process execution time before and after the encryption method change. Calculate the difference in resource data before and after the change to determine the resource loss caused by the encryption method change.

[0192] In this embodiment, the segment length of each continuous sub-time period and the resource loss of adjacent transformations are associated and stored. A database (such as MySQL) can be used to establish a data table with fields including the sub-time period ID, segment length, previous encryption method, next encryption method, resource loss (CPU), resource loss (memory), etc., to facilitate subsequent query and analysis.

[0193] The beneficial effects of the above technical solution are: through resource loss analysis and encryption factor judgment, the initial protection mechanism is optimized to create a security protection mechanism that is more adapted to system resources and security requirements. While ensuring the security of address information transmission, the system operation efficiency and stability are improved, and a dynamically adjustable protection barrier is built for the secure transmission of address data.

[0194] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for address standardization and dynamic management, characterized in that: include: Step 1: Input the collected address information into the hierarchical entry framework to perform hierarchical division on the address information, temporarily store the unsuccessfully divided portion in the first storage block, and temporarily store the successfully divided portion in the second storage block; Step 2: converting the unstructured data in the second storage block into initial structured data according to the conversion criteria of each division level; Step 3: Split the initial structure data according to the expression combination of the corresponding division level, and judge the authenticity of each split structure data according to the corresponding expression standard under the expression combination, and construct an authenticity vector and an authenticity attribute vector, wherein the authenticity vector corresponds one-to-one with the elements of the authenticity attribute vector; Step 4: Analyze the existence reliability of the same corresponding element in the authenticity vector and the authenticity attribute vector, and filter out unreliable elements based on the existence reliability; Step 5: Establishing an association between the unreliable element and the unsuccessfully divided portion of the first storage block, and retrieving a historical standard hierarchical description based on the hierarchy to which the unreliable element belongs, and modifying the description corresponding to the unreliable element to obtain a standardized address, wherein the standardized address is obtained by sequentially describing the structure from high to low hierarchical levels, and the corresponding last hierarchical level is the lowest hierarchical level in the collected address information; Step 6: Determine the historical security information of each transmission interface that transmits the standardized address and the receiving interface of the big data platform in a specified time period, establish a security protection mechanism and dynamically manage it.

2. The address standardization and dynamic management method according to claim 1, characterized in that: Inputting the collected address information into a hierarchical entry framework to hierarchically divide the address information includes: Establish a hierarchical term structure, wherein each level contains corresponding standard terms and synonym libraries, and the hierarchical term structure includes at least country, province, city, district, street, community / village, building, unit, floor and house number; Use natural language processing technology to segment the collected address information and obtain several independent words; Match each independent word with the standard word and synonym library in the hierarchical word structure, and match them in order from high level to low level to obtain the independent word group under the level with successful matching; Determining the remaining words that have not been successfully matched among the plurality of independent words, and semantically associating each of the remaining words with the independent word group under the level of successful matching, and determining the possible level to which the remaining words belong in combination with relevant data in the geographic information database; Temporarily storing the independent word groups at the successfully matched level in the second storage block; The independent words in the possible level are temporarily stored in the first storage block.

3. The address standardization and dynamic management method according to claim 1, characterized in that: Construct the true and false vector and the true and false attribute vector, including: Determine a first quantity of split structure data and a combination quantity of the expression combinations at a corresponding division level to obtain a first matching coefficient; According to the second matching coefficient of each expression standard under each split structure data in the corresponding division level and the expression combination, it is determined whether the maximum second matching coefficient is 1; If it is 1, the corresponding split structure data is determined to be true; Otherwise, the corresponding split structure data is determined to be false, and all the second matching coefficients involved are analyzed. In combination with the historical matching failure event set based on each expression standard at the corresponding division level, the authenticity attribute of the corresponding split structure data is determined, wherein the authenticity attribute is related to the possible cause of the corresponding split structure data being false; A true-false vector is constructed based on the maximum second matching coefficient corresponding to each split structure data, and at the same time, a true-false attribute corresponding to each split structure data is constructed to obtain a true-false attribute vector.

4. The method for address standardization and dynamic management according to claim 3, characterized in that: Determine the authenticity of the corresponding split structure data, including: Inputting each historical matching failure event in the historical matching failure event set into the event analysis model to obtain a first failure factor, and at the same time, retrieving the matching process log of each historical matching failure event from the historical diary record database and inputting it into the log analysis model to obtain a second failure factor, and establishing a failure factor table corresponding to the historical matching failure event; Conduct aggregate analysis on all failure factor tables to establish a complete description framework; Perform a unique matching judgment on all second matching coefficients corresponding to the false split structure data, that is, determine whether the second number of second matching coefficients with a value of 0 is N-1. If so, treat the second matching coefficient that is not 0 as the third matching coefficient, and combine the expression standards of the first matching coefficient and the third matching coefficient to construct a search index, where N represents the number of all second matching coefficients under the corresponding split structure data; If not, determine the coefficient ratio of the second matching coefficient that is not 0, and construct a search index by combining the expression standard under the second matching coefficient that is not 0 and the first matching coefficient; Based on the search index, the possible cause of the location is searched in the complete description framework to obtain the authenticity attribute of the corresponding split structure data.

5. The address standardization and dynamic management method according to claim 1, characterized in that: Analyzing the existence reliability of the same corresponding element in the authenticity vector and the authenticity attribute vector, and filtering out unreliable elements based on the existence reliability, including: Determine the error factor for each authenticity attribute; ； in, is the error factor of the wth true or false attribute; n represents the number of possible categories of causes corresponding to the wth true or false attribute; The total number of categories representing all possible causes; represents the category weight of the i-th category reason under the w-th true or false attribute; represents the error probability of converting unstructured data into structured data due to the i-th category reason under the w-th true or false attribute; Determine the reliability of the true and false values ​​and error factors of the same corresponding elements; ； in, is the existence reliability of the element corresponding to the wth true or false attribute; Indicates the truth value of the element corresponding to the wth truth attribute; If the existence reliability is less than the preset reliability, the corresponding split structure data is regarded as an unreliable element.

6. The method for address standardization and dynamic management according to claim 1, characterized in that: The descriptions of the unreliable elements are modified, including: Extracting, according to the level to which the unreliable element belongs, from the unsuccessfully divided portion of the first storage block, independent words consistent with the corresponding level, and treating them as first words; Obtaining an association relationship based on the semantic relevance and grammatical structure of the description of the unreliable element and each first word, and the position and association of the unreliable element and the first word in the address logic framework according to the overall information logical relationship set according to the corresponding division level; A history-related correction rule is determined based on the history standard level description, and the description corresponding to the unreliable element is corrected in combination with the association relationship.

7. The address standardization and dynamic management method according to claim 1, characterized in that: Establish security protection mechanisms, including: Analyze the historical security information of each transmission interface and receiving interface in a specified time period, determine the set of hazard types and the hazard probability distribution of each hazard type in the corresponding time period, and obtain the information hazard combination at each time point; Extracting the input behavior sequence of the receiving interface and each transmission interface corresponding to the transmission interface login terminal at each time point within a specified time period, and obtaining the behavior risk combination at each time point; Generate the first safety assessment value, the second safety assessment value, and the comprehensive safety assessment value based on the information risk combination and behavior risk combination at the same time point, and select the maximum safety assessment value for retention; The retained maximum safety assessment value is plotted in a time sequence and the value levels are divided according to the safety protection standard to obtain a number of curve segments; Based on the current security configuration information of the login end of the transmission interface involved in each curve segment and combined with the value level, a security encryption factor is generated, and an encryption method is set for the continuous sub-time period corresponding to the corresponding curve segment according to the security encryption factor, wherein the encryption method is to scramble the information encoding of the input address information based on the chaos level of the security encryption factor; All encryption methods are sorted in order of consecutive sub-time periods to obtain the initial protection mechanism.

8. The address standardization and dynamic management method according to claim 7, characterized in that: After obtaining the initial protection mechanism, it also includes: Determine the segment length of each continuous sub-time period and the resource loss of switching encryption modes between adjacent continuous sub-time periods; If the loss of each resource is less than the preset loss, the initial protection mechanism is regarded as a safety protection mechanism; Otherwise, the first sub-time period in which the resource loss is greater than or equal to the preset loss is locked. If the security encryption factor of the first sub-time period is greater than the security encryption factor of the previous sub-time period, the corresponding encryption method remains unchanged. Otherwise, the encryption mode of the first sub-time period is replaced with the encryption mode of the previous sub-time period, thereby obtaining a security protection mechanism.

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