Sensitivity determination method and device for geographic information vocabularies

Abstract

The invention discloses a geographic information vocabulary sensitivity determination method and device. The method comprises the steps of obtaining a target geographic information vocabulary; whether the target geographic information vocabulary hits a pre-established geographic information vocabulary database or not is determined, if not, a pre-established geographic information phrase database is obtained, the geographic information phrase database comprises a plurality of geographic information phrases and label information thereof, and the geographic information phrases can form the geographic information vocabulary; the tag information of the geographic information phrase is used for representing the probability that the geographic information vocabulary comprising the geographic information phrase is a sensitive vocabulary or a non-sensitive vocabulary; and performing word segmentation on the target geographic information vocabulary to obtain a plurality of segmented words, matching each segmented word in the plurality of segmented words with the geographic information phrases in the geographic information phrase database, and determining the sensitivity of the target geographic information vocabulary based on the tag information of each geographic information phrase successfully matched with the segmented word in the geographic information phrase database.

Description

Technical Field

[0001] The present application relates to the field of data security technology, and in particular to a method and device for determining the sensitivity of geographic information vocabulary. Background Art

[0002] In the modern information society, geographic information has been widely used in various scenarios, including navigation, location services, social networks, smart cities, etc. However, some of the content contained in geographic information may involve sensitive data such as personal privacy, national security, and commercial secrets. Therefore, how to judge the sensitivity of these geographic information and then take corresponding protection measures has become an important topic in research and technology applications. In related technologies, a database of sensitive geographic information is usually established, and the geographic information to be judged is matched with the geographic information in the database, and whether the geographic information to be judged is sensitive is determined based on the matching results. However, due to the dynamic and real-time nature of geographic information, that is, geographic information will be continuously updated with changes in time, location or environment, therefore, it is difficult to establish a database of sensitive geographic information in a timely and comprehensive manner, resulting in inaccurate judgment results for sensitive geographic information. Summary of the invention

[0003] In a first aspect, an embodiment of the present application provides a method for determining the sensitivity of a geographic information vocabulary, the method comprising: obtaining a target geographic information vocabulary; determining whether the target geographic information vocabulary hits a pre-established geographic information vocabulary database, the geographic information vocabulary database comprising a plurality of geographic information vocabulary, each geographic information vocabulary corresponding to a point of interest, the point of interest representing a unique and definite geographical location, the plurality of geographic information vocabulary comprising sensitive vocabulary and non-sensitive vocabulary; if the target geographic information vocabulary does not hit the geographic information vocabulary database, obtaining a pre-established geographic information phrase database, the geographic information phrase database comprising a plurality of geographic information phrases and their label information, a plurality of geographic information phrases being able to constitute a geographic information vocabulary, the label information of the geographic information phrase being used to indicate the probability that the geographic information vocabulary including the geographic information phrase is a sensitive vocabulary or a non-sensitive vocabulary; segmenting the target geographic information vocabulary to obtain a plurality of segmentations, matching each of the plurality of segmentations with a geographic information phrase in the geographic information phrase database, and determining the sensitivity of the target geographic information vocabulary based on the label information of each geographic information phrase successfully matched with the segmentation in the geographic information phrase database.

[0004] In a second aspect, an embodiment of the present application provides a device for determining the sensitivity of geographic information vocabulary, the device comprising: a first acquisition module for acquiring a target geographic information vocabulary; a determination module for determining whether the target geographic information vocabulary hits a pre-established geographic information vocabulary database, the geographic information vocabulary database comprising a plurality of geographic information vocabulary, each geographic information vocabulary corresponding to a point of interest, the point of interest representing a unique and deterministic geographic location, the plurality of geographic information vocabulary comprising sensitive vocabulary and non-sensitive vocabulary; a second acquisition module for acquiring a pre-established geographic information vocabulary if the target geographic information vocabulary does not hit the geographic information vocabulary database A geographic information phrase database is provided, wherein the geographic information phrase database includes a plurality of geographic information phrases and their label information, wherein a plurality of geographic information phrases can constitute a geographic information vocabulary, and the label information of the geographic information phrase is used to indicate the probability that the geographic information vocabulary including the geographic information phrase is a sensitive word or a non-sensitive word; and a matching module is used to segment the target geographic information vocabulary to obtain a plurality of segmentations, match each of the plurality of segmentations with a geographic information phrase in the geographic information phrase database, and determine the sensitivity of the target geographic information vocabulary based on the label information of each geographic information phrase in the geographic information phrase database that successfully matches the segmentation.

[0005] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present application.

[0006] In a fourth aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present application when executing the computer program.

[0007] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method described in any embodiment of the present application.

[0008] The embodiment of the present application establishes two databases with data granularity. The database with larger data granularity is a geographic information vocabulary database, which includes several geographic information vocabulary, each geographic information vocabulary corresponds to a point of interest, and can represent a unique and definite geographic location; the database with smaller data granularity is a geographic information phrase database, which includes several geographic information phrases and their label information. The geographic information phrase is a part of the geographic information vocabulary, and multiple geographic information phrases can constitute the geographic information vocabulary. The label information can represent the probability that the geographic information vocabulary including the geographic information phrase is a sensitive word or a non-sensitive word. When determining the sensitivity of the target geographic information vocabulary, first determine whether the target geographic information vocabulary hits the geographic information vocabulary database. If not, match multiple segmented words in the target geographic information vocabulary with the geographic information phrases in the geographic information phrase database, and determine the sensitivity of the target geographic information vocabulary based on the label information of each geographic information phrase in the geographic information phrase database that successfully matches the segmented words. Since geographic information phrases are only a part of geographic information vocabulary and their granularity is smaller than that of geographic information vocabulary, geographic information phrases can be used as elements that constitute geographic information vocabulary. Even if the complete geographic information vocabulary has not yet been included in the geographic information vocabulary database, the sensitivity of the geographic information vocabulary can still be estimated based on the elements that constitute the geographic information vocabulary, rather than simply judging all geographic information vocabulary that are not included in the geographic information vocabulary database as non-sensitive words, thereby improving the accuracy of the sensitivity judgment results of geographic information vocabulary.

[0009] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings herein are incorporated into the specification and constitute a part of the present application. These drawings illustrate embodiments consistent with the present application and are used together with the specification to illustrate the technical solutions of the present application.

[0011] Figure 1 It is a flowchart of a method for determining the sensitivity of geographic information vocabulary according to an embodiment of the present application.

[0012] Figure 2 It is a schematic diagram of the geographic information vocabulary database of an embodiment of the present application.

[0013] Figure 3 It is a schematic diagram of the geographic information phrase database of an embodiment of the present application.

[0014] Figure 4 It is a schematic diagram of the system architecture of an embodiment of the present application.

[0015] Figure 5It is a schematic diagram of a method for generating a compliant weak positive word library comparison table according to an embodiment of the present application.

[0016] Figure 6 It is a schematic diagram of the overall process of an embodiment of the present application.

[0017] Figure 7 It is a block diagram of a device for determining the sensitivity of geographic information vocabulary according to an embodiment of the present application.

[0018] Figure 8 It is a schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0020] The terms used in this application are only for the purpose of describing specific embodiments, and are not intended to limit the application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a variety of.

[0021] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0022] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application and to make the above-mentioned purposes, features and advantages of the embodiments of the present application more obvious and understandable, the technical solutions in the embodiments of the present application are further described in detail below in conjunction with the accompanying drawings.

[0023] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0024] Many application scenarios may involve the collection of geographic information, which may include sensitive information. In order to improve data security, it is necessary to make sensitivity judgments on geographic information. For example, in intelligent driving scenarios, vehicle-mounted sensors (such as GPS, radar, cameras, etc.) can be used to collect geographic information of the surrounding environment to plan routes and avoid obstacles. The collected geographic information may include the names of some confidential organizations, which are not suitable for public disclosure. Therefore, it is necessary to filter out the names of these confidential organizations from the collected data through sensitivity judgment and perform desensitization processing.

[0025] In the related art, a database of sensitive geographic information is usually established, and the geographic information to be identified is matched with the geographic information in the database, and whether the geographic information to be identified is sensitive is determined based on the matching result. For example, if the database of sensitive geographic information includes "XX military base", when the geographic information to be identified includes "XX military base", the identified geographic information successfully matches the "XX military base" in the database of sensitive geographic information, and thus it can be determined as sensitive geographic information.

[0026] However, geographic information may be continuously updated with time, location or environmental changes. For example, according to the development of national conditions, the state may secretly add some new administrative agencies in a certain place. The update of sensitive geographic information database often lags behind the update of geographic information itself. If the geographic information corresponding to the newly added administrative agency is not included in the sensitive geographic information database in time, it is impossible to accurately determine the sensitivity of the actually collected geographic information through database matching.

[0027] Based on this, this application provides a method for determining the sensitivity of geographic information vocabulary, see Figure 1 , the method comprising:

[0028] Step S11: Acquire target geographic information vocabulary;

[0029] Step S12: determining whether the target geographic information vocabulary hits a pre-established geographic information vocabulary database, wherein the geographic information vocabulary database includes a plurality of geographic information vocabulary, each geographic information vocabulary corresponds to a point of interest, and the point of interest represents a unique and definite geographical location, and the plurality of geographic information vocabulary includes sensitive vocabulary and non-sensitive vocabulary;

[0030] Step S13: if the target geographic information vocabulary does not hit the geographic information vocabulary database, obtain a pre-established geographic information phrase database, wherein the geographic information phrase database includes a plurality of geographic information phrases and their label information, and a plurality of geographic information phrases can constitute a geographic information vocabulary, and the label information of the geographic information phrase is used to indicate the probability that the geographic information vocabulary including the geographic information phrase is a sensitive word or a non-sensitive word;

[0031] Step S14: Segment the target geographic information vocabulary to obtain multiple segmentations, match each of the multiple segmentations with a geographic information phrase in the geographic information phrase database, and determine the sensitivity of the target geographic information vocabulary based on the label information of each geographic information phrase that successfully matches the segmentation in the geographic information phrase database.

[0032] Taking into account that sensitive geographic information vocabulary (i.e., sensitive words) often include specified elements, the present application establishes a geographic information vocabulary database based on the complete geographic information vocabulary, and at the same time, establishes a geographic information phrase database based on the elements that constitute the geographic information vocabulary (i.e., geographic information phrases). When the target geographic information vocabulary to be judged fails to hit the geographic information vocabulary database, the target geographic information vocabulary is segmented, and the multiple segmented words obtained are matched with the elements that constitute the geographic information vocabulary, and the sensitivity of the target geographic information vocabulary is judged in the element dimension. Compared with the method of simply relying on the geographic information vocabulary database to judge the sensitivity of the target geographic information vocabulary, the present application can effectively improve the accuracy of the sensitivity judgment results of the geographic information vocabulary when the geographic information vocabulary database is not updated in a timely manner. The specific implementation method of the present application is illustrated below.

[0033] In step S11, the target geographic information vocabulary may be obtained. The target geographic information vocabulary corresponds to a point of interest (POI), and POI represents a unique and definite geographic location. In other words, the target geographic information vocabulary corresponds to a unique and definite geographic location. For example, the target geographic information vocabulary may be an address, such as "XX Province XX City XX District XX Street XX", or a unique and definite surface or building name, such as "Forbidden City", "Eiffel Tower", etc.

[0034] The method of obtaining the target geographic information vocabulary can vary according to the actual application scenario. For example, in the intelligent driving scenario, the target geographic information vocabulary can be collected by the vehicle-mounted sensors. In the intelligent traffic management system, the surveillance cameras installed at the intersection or highway can be used to identify the traffic flow, road conditions and other information in combination with computer vision technology, so as to obtain the target geographic information vocabulary. In the application of maps and location services, the target geographic information vocabulary can be obtained by connecting to the interface of the open map service.

[0035] In step S12, a pre-established geographic information vocabulary database may be obtained, wherein the geographic information vocabulary database includes a plurality of geographic information vocabulary, each of which corresponds to a point of interest. The plurality of geographic information vocabulary in the geographic information vocabulary database may include sensitive vocabulary and non-sensitive vocabulary. Among them, sensitive vocabulary refers to geographic information vocabulary that is sensitive and cannot be disclosed, and non-sensitive vocabulary refers to geographic information vocabulary that is not sensitive and can be disclosed.

[0036] In some embodiments, label information may be added to each geographic information vocabulary in the geographic information vocabulary database to indicate whether the geographic information vocabulary is a sensitive vocabulary or a non-sensitive vocabulary. In other embodiments, different geographic information vocabulary databases may be established for sensitive vocabulary and non-sensitive vocabulary, respectively. For example, a positive vocabulary database and a negative vocabulary database may be established, wherein the geographic information vocabulary included in the positive vocabulary database are all non-sensitive vocabulary, and the geographic information vocabulary in the negative vocabulary database are all sensitive vocabulary.

[0037] The geographic information vocabulary database can also be updated at a certain frequency, for example, deleting existing geographic information vocabulary in the geographic information vocabulary database, adding new geographic information vocabulary to the geographic information vocabulary database, and / or changing the type of geographic information vocabulary, such as migrating non-sensitive vocabulary in the positive vocabulary database to the negative vocabulary database.

[0038] In some embodiments, the geographic information vocabulary database also includes version information for indicating the current version of the geographic information vocabulary database. The contents of different versions of the geographic information vocabulary database may be different. By adding version information, it is convenient to manage the updated geographic information vocabulary databases.

[0039] In some embodiments, the geographic information vocabulary database also includes a file flag, which is used to indicate the validity of the geographic information vocabulary database. When in use, the file flag in the geographic information vocabulary database can be read first, and the read file flag can be matched with a preset valid file flag. If the match is successful, it means that the geographic information vocabulary database is a valid geographic information vocabulary database, so that the sensitivity judgment of the target geographic information vocabulary can be performed based on the geographic information vocabulary database.

[0040] In some embodiments, each geographic information vocabulary in the geographic information vocabulary database can be encrypted. Specifically, the geographic information vocabulary can be encrypted into an irreversible digital unique identifier through encryption technology. Encryption methods include but are not limited to hash algorithms, hash encryption, symmetric encryption algorithms or asymmetric encryption algorithms. Further, during encryption, since the geographic information vocabulary in the negative vocabulary database is not public, only the geographic information vocabulary in the negative vocabulary database can be encrypted. Alternatively, both the geographic information vocabulary in the positive vocabulary database and the geographic information vocabulary in the negative vocabulary database can be encrypted.

[0041] The geographic information vocabulary database of some embodiments is as follows Figure 2 As shown, it includes header information and table data, wherein the header information includes file mark and file version information, and the table data is used to record geographic information vocabulary, and each geographic information vocabulary corresponds to the corresponding digital unique identifier. In the example where the geographic information vocabulary database includes a positive vocabulary database and a negative vocabulary database, both the positive vocabulary database and the negative vocabulary database can be used Figure 2 The data structure shown.

[0042] After obtaining the target geographic information vocabulary, it can be determined whether the target geographic information vocabulary hits the geographic information vocabulary database. If the target geographic information vocabulary is the same as any geographic information vocabulary in the geographic information vocabulary database, it can be determined that the target geographic information vocabulary hits the geographic information vocabulary database. If the target geographic information vocabulary is different from each geographic information vocabulary in the geographic information vocabulary database, it can be determined that the target geographic information vocabulary does not hit the geographic information vocabulary database.

[0043] In the example where the geographic information vocabulary database includes a positive vocabulary database and a negative vocabulary database, it can be determined first whether the target geographic information vocabulary hits the positive vocabulary database, and if not, it is then determined whether the target geographic information vocabulary hits the negative vocabulary database. Alternatively, it can also be determined first whether the target geographic information vocabulary hits the negative vocabulary database, and if not, it is then determined whether the target geographic information vocabulary hits the positive vocabulary database. Alternatively, the steps of determining whether the target geographic information vocabulary hits the positive vocabulary database and determining whether the target geographic information vocabulary hits the negative vocabulary database can be performed simultaneously.

[0044] If the target geographic information vocabulary hits the geographic information vocabulary database, the sensitivity of the target geographic information vocabulary can be determined based on the type of geographic information vocabulary that the target geographic information vocabulary hits in the geographic information vocabulary database. For example, if the type of the hit geographic information vocabulary is a sensitive word, the target geographic information vocabulary can be determined to be a sensitive word. If the type of the hit geographic information vocabulary is a non-sensitive word, the target geographic information vocabulary can be determined to be a non-sensitive word.

[0045] In the example where the geographic information vocabulary database includes a positive vocabulary database and a negative vocabulary database, if the target geographic information vocabulary hits a non-sensitive vocabulary in the positive vocabulary database, the target geographic information vocabulary can be directly determined to be a non-sensitive vocabulary. Similarly, if the target geographic information vocabulary hits a non-sensitive vocabulary in the negative vocabulary database, the target geographic information vocabulary can be directly determined to be a sensitive vocabulary.

[0046] If the target geographic information vocabulary does not hit the positive vocabulary database nor the negative vocabulary database, it can be determined that the target geographic information vocabulary does not hit the geographic information vocabulary database. At this time, step S13 can be executed.

[0047] In some embodiments, each geographic information vocabulary in the geographic information vocabulary database is encrypted in advance. In this case, the target geographic information vocabulary can be encrypted based on the encryption method of each geographic information vocabulary in the geographic information vocabulary database, and then it is determined whether the encrypted target geographic information vocabulary hits the pre-established geographic information vocabulary database. In this way, the risk of leakage of geographic information vocabulary in the geographic information vocabulary database can be reduced.

[0048] In step S13, a pre-established geographic information phrase database may be obtained, and the geographic information phrase database includes several geographic information phrases and their label information. Among them, the geographic information phrase is an element that constitutes the geographic information vocabulary, and multiple geographic information phrases can constitute the geographic information vocabulary. The geographic information vocabulary includes but is not limited to at least some of the following elements: elements representing countries, elements representing provinces, elements representing cities, elements representing districts, elements representing streets, elements representing building information (such as the name or number of the building), elements representing the name of scenic spots, etc. For example, a geographic information vocabulary may be "XX Province XX City XX Street XX Park". Each geographic information phrase may include one or more of the above elements, for example, one geographic information phrase may be "XX Park", another geographic information phrase may be "XX City XX Street", and another geographic information phrase may be "XX Province XX City". The geographic information phrase corresponds to one or more regions, and each region may include multiple points of interest, that is, the geographical location corresponding to the geographic information phrase is non-unique and uncertain. For example, the geographic information phrase "XX Park" may be either XX Park in City A or XX Park in City B. For another example, the geographic information phrase "XX city, XX province" includes multiple points of interest such as the city's government building, shopping mall, zoo, etc.

[0049] Since the geographical location corresponding to the geographic information phrase is non-unique and uncertain, it is not possible to directly determine whether the geographic information phrase is sensitive or non-sensitive. However, the geographic information phrase is an element that constitutes the geographic information vocabulary, and sensitive words usually include specific elements, such as "army", "government building", etc. Therefore, the probability that the geographic information vocabulary including a certain geographic information phrase is a sensitive word can be roughly inferred. That is, label information can be established for each geographic information phrase to indicate the probability that the geographic information vocabulary including the geographic information phrase is a sensitive word or a non-sensitive word. For example, the probability that the geographic information vocabulary including the geographic information phrase "army" is a sensitive word is high, so the probability represented by the label information corresponding to the geographic information phrase "army" is also large, for example, it can be 0.7; and the probability that the geographic information vocabulary including the geographic information phrase "noodle shop" is a sensitive word is high, so the probability represented by the label information corresponding to the geographic information phrase "noodle shop" is also small, for example, it can be 0.1. It can be understood that the numerical values ​​here are only exemplary and are not used to limit the present disclosure.

[0050] In some embodiments, the geographic information phrase database may include a positive phrase database and a negative phrase database. The probability represented by the label information corresponding to the geographic information phrase in the positive phrase database is greater than the probability represented by the label information corresponding to the geographic information phrase in the negative phrase database. In other words, if the target geographic information vocabulary includes the geographic information vocabulary in the positive phrase database, the target geographic information vocabulary has a higher probability of being a sensitive vocabulary; if the target geographic information vocabulary includes the geographic information vocabulary in the negative phrase database, the target geographic information vocabulary has a lower probability of being a sensitive vocabulary.

[0051] The geographic information phrase database may also be updated at a certain frequency, for example, deleting existing geographic information phrases in the geographic information phrase database, adding new geographic information phrases to the geographic information phrase database, and / or changing label information of geographic information phrases.

[0052] In some embodiments, the geographic information phrase database also includes version information for indicating the current version of the geographic information phrase database. The contents of different versions of the geographic information phrase database may be different. By adding version information, it is convenient to manage the updated geographic information phrase databases.

[0053] In some embodiments, the geographic information phrase database also includes a file flag for indicating the validity of the geographic information phrase database. When in use, the file flag in the geographic information phrase database can be read first, and the read file flag can be matched with a preset valid file flag. If the match is successful, it means that the geographic information phrase database is a valid geographic information phrase database, so that sensitivity judgment of the target geographic information vocabulary can be performed based on the geographic information phrase database.

[0054] In some embodiments, the geographic information phrase database may also include a weight of the geographic information phrase database. Different geographic information phrase databases (such as a positive phrase database and a negative phrase database) may use different weights. By setting a weight for a geographic information phrase database, the degree of influence of the label information of the geographic information phrase in the geographic information phrase database on the sensitivity determination result may be adjusted.

[0055] In some embodiments, the geographic information phrase database may also include weights for each geographic information phrase. By setting weights for geographic information phrases (referred to as phrase weights), the degree of influence of the label information corresponding to the geographic information phrases on the sensitivity determination results can be adjusted. For example, the probability that the geographic information vocabulary including the geographic information phrase "army" is a sensitive vocabulary is much higher than the geographic information vocabulary including the geographic information phrase "noodle shop". Therefore, the weight corresponding to the geographic information phrase "army" may be greater than the weight corresponding to the geographic information phrase "noodle shop".

[0056] In some embodiments, each geographic information phrase in the geographic information phrase database, tag information corresponding to the geographic information phrase, weight of the geographic information phrase database, and weight corresponding to the geographic information phrase may all be encrypted.

[0057] The geographic information phrase database of some embodiments is as follows: Figure 3 As shown, it includes header information and table data, wherein the header information includes file flags, file version information and weights of geographic information phrase databases, and the table data is used to record geographic information phrases, label information of geographic information phrases and weights of geographic information phrases. The weights of geographic information phrase databases, geographic information phrases, label information of geographic information phrases and weights of geographic information phrases can all be encrypted data. In the example where the geographic information phrase database includes a positive phrase database and a negative phrase database, both the positive phrase database and the negative phrase database can be encrypted. Figure 3 The data structure shown.

[0058] In step S14, the target geographic information vocabulary may be segmented to obtain multiple segmented words. The multiple segmented words may include geographic information phrases or non-geographic information phrases, including but not limited to names, time, numbers, etc. For example, the target geographic information vocabulary is "Zhang San Noodle House", and the target geographic information vocabulary may be segmented to obtain two segmented words "Zhang San" and "Noodle House", wherein "Zhang San" is a name and "Noodle House" is a geographic information phrase.

[0059] Each of the multiple segmented words may be matched with a geographic information phrase in a geographic information phrase database. In the case where the geographic information phrase database includes a positive phrase database and a negative phrase database, each segmented word may be matched with a geographic information phrase in the positive phrase database first, and if a segmented word fails to match the geographic information phrase in the positive phrase database, the segmented word may be matched with a geographic information phrase in the negative phrase database; or, each segmented word may be matched with a geographic information phrase in the negative phrase database first, and if a segmented word fails to match the geographic information phrase in the negative phrase database, the segmented word may be matched with a geographic information phrase in the positive phrase database; or, each segmented word may be matched with a geographic information phrase in the positive phrase database and a geographic information phrase in the negative phrase database in parallel.

[0060] In some embodiments, each geographic information word and its label information in the geographic information phrase database are encrypted in advance. The geographic information phrase in the geographic information phrase database may be decrypted first, and each of the multiple word segments may be matched with the decrypted geographic information phrase in the geographic information phrase database. In addition, the label information of the geographic information phrase in the geographic information phrase database that successfully matches the multiple word segments may be decrypted, and the sensitivity of the target geographic information word may be determined based on the decrypted label information of each successfully matched geographic information phrase.

[0061] For example, each geographic information phrase in the geographic information phrase database may be decrypted first, and the decrypted geographic information phrase may be matched with a plurality of segmented words included in the target geographic information vocabulary. Assuming that geographic information phrase a in the geographic information phrase database successfully matches a segmented word in the target geographic information vocabulary, the label information of geographic information phrase a may be decrypted, and the sensitivity of the target geographic information vocabulary may be determined based on the decrypted label information of geographic information phrase a.

[0062] It is understandable that the above is only one possible implementation method. In other implementation methods, multiple word segments in the target geographic information vocabulary can be encrypted according to the encryption method of the geographic information phrase, and the encrypted multiple word segments are matched with the geographic information phrases in the geographic information phrase database, and the successfully matched geographic information phrases are screened out, and then the label information of the successfully matched geographic information phrases is decrypted, and the sensitivity of the target geographic information vocabulary is determined based on the decrypted label information. This method can reduce the leakage of geographic information phrases and improve data security.

[0063] After the above matching process, the geographic information phrases in the geographic information phrase database that successfully match each segmentation can be determined, and the sensitivity of the target geographic information vocabulary can be determined based on the determined geographic information phrases. For example, the target geographic information vocabulary is segmented to obtain four segmentations A, B, C, and D, among which A and B successfully match the geographic information phrase a in the positive phrase database, C does not successfully match any geographic information phrase in the geographic information phrase database, and D successfully matches the geographic information phrase d in the negative phrase database. Then, the sensitivity of the target geographic information vocabulary can be determined based on the label information of the geographic information phrase a and the label information of the geographic information phrase d.

[0064] In some embodiments, when matching a segmented word with a geographic information phrase, if the segmented word is the same as a geographic information phrase, it can be determined that the segmented word matches the geographic information phrase successfully. If the segmented word is different from the geographic information phrase, it is determined that the segmented word fails to match the geographic information phrase.

[0065] In other embodiments, the segmentation words and geographic information phrases can be matched in two dimensions, namely, pronunciation and glyph, to obtain the pronunciation similarity and glyph similarity between the segmentation words and the geographic information phrases, determine the total similarity between the segmentation words and the geographic information phrases based on the pronunciation similarity and glyph similarity, and determine whether the segmentation words and the geographic information phrases match based on the total similarity. For example, when the total similarity is greater than a preset similarity threshold, it can be determined that the segmentation words and the geographic information phrases are successfully matched, otherwise, it is determined that the segmentation words and the geographic information phrases fail to match. In this way, the inaccurate matching caused by spelling errors or similar pronunciations can be reduced. For example, when a user uploads the geographic information vocabulary "XX troops" through an interface with an open map service, the "troops" therein is input as "department pair"; or, when the vehicle-mounted voice output module outputs "Luoyang", it is mistakenly recognized as "Luoyang".

[0066] In some embodiments, each of the multiple word segmentations can be matched with a geographic information phrase in a positive phrase database to determine first label information of each first geographic information phrase in the positive phrase database that successfully matches the multiple word segmentations, each of the multiple word segmentations can be matched with a geographic information phrase in a negative phrase database to determine second label information of each second geographic information phrase in the negative phrase database that successfully matches the multiple word segmentations, and based on the first label information of each first geographic information phrase and the second label information of each second geographic information phrase, the sensitivity of the target geographic information vocabulary can be determined.

[0067] Continuing with the previous example, the first geographic information phrase in the positive phrase database that successfully matches the multiple word segmentations includes geographic information phrase a, and the second geographic information phrase in the negative phrase database that successfully matches the multiple word segmentations includes geographic information phrase d. Therefore, the sensitivity of the target geographic information vocabulary can be determined based on the label information of geographic information phrase a and the label information of geographic information phrase d.

[0068] Furthermore, it is also possible to determine a first weight corresponding to the positive phrase database and a second weight corresponding to the negative phrase database, weight the first label information of each first geographic information phrase based on the first weight, and weight the second label information of each second geographic information phrase based on the second weight, and determine the sensitivity of the target geographic information vocabulary based on the weighted first label information of each first geographic information phrase and the weighted second label information of each second geographic information phrase.

[0069] Continuing with the previous example, the label information of geographic information phrase a can be weighted based on the first weight, and the label information of geographic information phrase d can be weighted based on the second weight. Then, based on the weighted label information of geographic information phrase a and the weighted label information of geographic information phrase d, it is determined whether the target geographic information word is a sensitive word.

[0070] Furthermore, before determining the sensitivity of the target geographic information vocabulary based on the first label information of each first geographic information phrase and the second label information of each second geographic information phrase, the weights of each first geographic information phrase and the weights of each second geographic information phrase can also be determined, and the first label information of the corresponding first geographic information phrase can be weighted based on the weights of each first geographic information phrase, and the second label information of the corresponding second geographic information phrase can be weighted based on the weights of each second geographic information phrase.

[0071] In some embodiments, the weight of a geographic information phrase is related to the sensitivity level of the geographic information phrase. The sensitivity level of the geographic information phrase may be determined first, and then the weight of the geographic information phrase may be determined based on the sensitivity level of the geographic information phrase. The higher the sensitivity level of the geographic information phrase, the greater the probability that the geographic information vocabulary including the geographic information phrase is a sensitive vocabulary. The weights corresponding to different sensitivity levels may be different.

[0072] It can be understood that in actual applications, the number of geographic information phrases that successfully match multiple word segments in the target geographic information vocabulary may not be limited to the cases listed in the above examples. However, no matter how many geographic information phrases are successfully matched, the sensitivity of the target geographic information vocabulary can be determined in a manner similar to that in the above embodiments.

[0073] A specific embodiment of the present application and its application scenario are illustrated below with reference to the accompanying drawings.

[0074] This application can be used in the field of intelligent connected vehicles and autonomous driving to perform sensitivity judgment on geographic information vocabulary collected by vehicle-mounted sensors and give judgment results. This application mainly includes three parts: knowledge base, judgment reference materials, and semantic judgment.

[0075] The overall system architecture is as follows Figure 4 As shown, it includes the following parts: a compliant strong positive vocabulary, a compliant strong positive vocabulary comparison table, a compliant strong positive semantic judgment module, a compliant strong negative vocabulary, a compliant strong negative vocabulary comparison table, a compliant strong negative semantic judgment module, a compliant weak positive vocabulary, a compliant weak positive vocabulary comparison table, a compliant weak positive semantic judgment module, a compliant weak negative vocabulary, a compliant weak negative vocabulary comparison table and a compliant weak negative semantic judgment module.

[0076] The knowledge base includes a positive vocabulary database (also called a compliant strong positive vocabulary database), a negative vocabulary database (also called a compliant strong negative vocabulary database), a positive phrase database (also called a compliant weak positive vocabulary database) and a negative phrase database (also called a compliant weak negative vocabulary database). These knowledge bases can be continuously accumulated and updated based on the practical experience of the expert team.

[0077] The geographic information words in the compliant strong positive word library are publicly available, non-sensitive geographic information words related to the field of intelligent connected vehicles and autonomous driving (i.e., the non-sensitive words in the aforementioned embodiment);

[0078] The geographic information words in the compliant strong negative word library are non-public and highly sensitive geographic information words related to the field of intelligent connected vehicles and autonomous driving (i.e., the sensitive words in the aforementioned embodiment);

[0079] The geographic information phrases in the compliant weak positive word library are publicly available and related to the field of intelligent connected vehicles and autonomous driving, but may cause objections when combined with weakly sensitive phrases. Therefore, the label information in the compliant weak positive word library is used to indicate the probability that the geographic information vocabulary including the geographic information phrase is a non-sensitive vocabulary;

[0080] The geographic information phrases in the compliant weak negative vocabulary are weakly sensitive geographic information phrases related to the field of intelligent connected vehicles and autonomous driving. Therefore, the label information in the compliant weak positive vocabulary is used to indicate the probability that the geographic information vocabulary including the geographic information phrases is a sensitive vocabulary.

[0081] The reference materials for judgment include comparison tables and judgment principles. The comparison tables include a compliant strong positive word library comparison table (including several non-sensitive words), a compliant strong negative word library comparison table (including several sensitive words), a compliant weak positive word library comparison table (including several geographic information phrases and their label information, the label information is used to indicate the probability that the geographic information vocabulary including the geographic information phrase is a non-sensitive word) and a compliant weak negative word library comparison table (including several geographic information phrases and their label information, the label information is used to indicate the probability that the geographic information vocabulary including the geographic information phrase is a sensitive word).

[0082] All words in the compliance strong positive word library can be encrypted with commercial secret data to generate irreversible digital unique identifiers, which are stored in a private format file, which is the compliance strong positive word library comparison table. Similarly, all words in the compliance strong negative word library can be encrypted with commercial secret data to generate irreversible digital unique identifiers, which are stored in a private format file, which is the compliance strong negative word library comparison table.

[0083] Based on the practical experience of the expert team, we can continuously accumulate and update the compliant weak positive word library, and determine the sensitivity level of geographic information phrases based on the compliant weak positive word library according to the degree of objection that may arise when combined with geographic information phrases, and assign different weights to geographic information phrases of different sensitivity levels. For example, multiple N sensitivity levels can be divided, each corresponding to N weights. Each time the compliant weak positive word library is updated, the weight of the geographic information phrase can be adjusted in coordination with the compliant weak negative word library. The weight of the geographic information phrase, label information, and weight of the compliant weak positive word library are encrypted and stored in a private format file, namely the compliant weak positive word library comparison table. The specific steps are as follows: Figure 5 shown.

[0084] The processing methods of the compliant weak negative word library and the compliant weak negative word library comparison table are similar and will not be repeated here.

[0085] The various weights involved in the above embodiments can be determined based on the technical judgment rules formed by the continuous accumulation and update of the actual combat experience of the expert team, relying on the compliant weak positive word library scoring table and the compliant weak negative word library scoring table. At the same time, the scoring and judgment links of the semantic judgment module will rely on the scoring judgment principle.

[0086] like Figure 6 As shown, semantic judgment mainly includes the following steps: 1) Compliance strong positive word library judgment; 2) Compliance strong negative word library judgment; 3) Compliance weak positive word library judgment; 4) Compliance weak negative word library judgment; 5) Output judgment result. This judgment result can determine whether the input geographic information vocabulary can be disclosed or transmitted.

[0087] ① Compliance strong word library judgment

[0088] The input target geographic information vocabulary is encrypted with commercial secret data protection technology to generate an irreversible digital unique identifier, and the compliant strong positive word library comparison table is judged whether the digital unique identifier is included. If it is included, the target geographic information vocabulary is determined to be a non-sensitive vocabulary, and the judgment result is returned, and the judgment process ends; if it is not included, the process proceeds to step ② to continue the judgment;

[0089] ② Compliance strong negative word library judgment

[0090] The input target geographic information vocabulary is encrypted with commercial secret data protection technology to generate an irreversible digital unique identifier, and the compliance strong negative vocabulary comparison table is judged to determine whether the digital unique identifier is included. If it is included, the target geographic information vocabulary is determined to be a sensitive word, and the judgment result is returned, and the judgment process ends; if it is not included, go to step ③ to continue the judgment;

[0091] ③ Compliant weak positive word library judgment

[0092] Traverse the compliant weak positive word library scoring table, perform regular matching on the input target geographic information vocabulary and each decrypted geographic information phrase in the compliant weak positive word library scoring table, obtain the weight and probability (i.e. label information) of each geographic information phrase, and cumulatively calculate the probability corresponding to each successfully matched geographic information phrase in the compliant weak positive word library scoring table to obtain the cumulative total score; the dynamic scoring formula is as follows:

[0093]

[0094] Among them, y positive represents the cumulative total score of each successfully matched geographic information phrase in the compliant weak positive word library score table, α1, α2, … ,α N represents the weight of each of the N geographic information phrases in the compliant weak positive word library scoring table, which is determined based on the sensitivity level of the corresponding geographic information phrases, x1, x2, … ,x N Represents the probability of each of the N geographic information phrases in the compliant weak positive word library scoring table.

[0095] ④ Compliance weak negative word library determination

[0096] Traverse the compliant weak negative word library scoring table, perform regular matching on the input target geographic information vocabulary and each decrypted geographic information phrase in the compliant weak negative word library scoring table, obtain the dimension weight and probability (i.e. label information) of each geographic information phrase, and cumulatively calculate the probability corresponding to each successfully matched geographic information phrase in the compliant weak negative word library scoring table to obtain the cumulative total score; the dynamic scoring formula is as follows:

[0097]

[0098] Among them, ynegative represents the cumulative total score of each successfully matched geographic information phrase in the compliant weak negative word library score table, β1, β2, … ,β M represents the weight of each of the M geographic information phrases in the compliant weak negative word library scoring table, which is determined based on the sensitivity level of the corresponding geographic information phrases, y1, y2, … ,y M Represents the probability corresponding to each of the M geographic information phrases in the compliant weak negative vocabulary scoring table.

[0099] ⑤Output judgment result

[0100] For a certain target geographic information word input, its sensitivity is determined based on the cumulative total score of the compliant weak positive word library score and the compliant weak negative word library score. The specific formula is as follows:

[0101]

[0102] Among them, γ positive and γ negative They represent the weight of the compliant weak positive word library and the weight of the compliant weak negative word library, respectively. total Indicates the probability that the target geographic information word is a sensitive word.

[0103] The weights of the compliant weak positive word library and the compliant weak negative word library are used to ensure the consistency of the value domain of the accumulated total score when the knowledge base is dynamically updated. They come from the compliant weak positive word library scoring table file and the compliant weak negative word library scoring table file respectively, and are dynamically updated synchronously with the two word libraries.

[0104] Based on the cumulative total score S total The built-in judgment principle can give a unique judgment result, which is used to indicate whether the target geographic information vocabulary can be disclosed or transmitted. total When it is greater than a certain threshold, the target geographic information vocabulary can be determined as a sensitive vocabulary and cannot be disclosed or transmitted. total When it is less than or equal to the above threshold, the target geographic information vocabulary can be determined as a non-sensitive vocabulary and can be disclosed or transmitted.

[0105] This application builds a multi-dimensional knowledge base based on the long-term practical experience accumulated by the expert team. This knowledge base not only covers a wide range of industry knowledge and experience, but also keeps pace with industry development through continuous updates by the expert team, and can respond to the ever-changing hazard identification needs.

[0106] Based on this multi-dimensional knowledge base, this application further derived judgment reference materials and semantic judgment logic. These judgment reference materials and semantic judgment logic combine the professional knowledge and experience in the industry to comprehensively evaluate the input vocabulary from multiple angles. By scoring the sensitivity of the vocabulary from multiple dimensions, the potential harm of the vocabulary can be more accurately judged.

[0107] In order to better improve the accuracy of the assessment, this application has designed a rigorous and scientific semantic judgment module. This module does not simply perform word matching, but rather evaluates whether different geographic information phrases pose a hazard by assigning different weights and probabilities to different geographic information phrases. A multi-step, comprehensive scoring mechanism is adopted, which comprehensively considers the sensitivity level of the vocabulary, thereby obtaining a more accurate and reliable judgment result.

[0108] like Figure 7 As shown, the present application also provides a device for determining the sensitivity of geographic information vocabulary, the device comprising:

[0109] The first acquisition module 101 is used to acquire target geographic information vocabulary;

[0110] Determination module 102, used to determine whether the target geographic information vocabulary hits a pre-established geographic information vocabulary database, the geographic information vocabulary database includes a plurality of geographic information vocabulary, each geographic information vocabulary corresponds to a point of interest, the point of interest represents a unique and deterministic geographic location, and the plurality of geographic information vocabulary includes sensitive vocabulary and non-sensitive vocabulary;

[0111] The second acquisition module 103 is used to acquire a pre-established geographic information phrase database if the target geographic information vocabulary does not hit the geographic information vocabulary database, wherein the geographic information phrase database includes a plurality of geographic information phrases and their label information, wherein a plurality of geographic information phrases can constitute a geographic information vocabulary, and the label information of the geographic information phrase is used to indicate the probability that the geographic information vocabulary including the geographic information phrase is a sensitive word or a non-sensitive word;

[0112] The matching module 104 is used to segment the target geographic information vocabulary to obtain multiple segmentations, match each of the multiple segmentations with a geographic information phrase in the geographic information phrase database, and determine the sensitivity of the target geographic information vocabulary based on the label information of each geographic information phrase that successfully matches the segmentation in the geographic information phrase database.

[0113] The functions or modules included in the device provided in the present application can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0114] An embodiment of the present application further provides a computer device, which comprises at least a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any of the foregoing embodiments is implemented.

[0115] Figure 8 A more specific hardware structure diagram of a computer device provided in an embodiment of the present application is shown, and the device may include: a processor 201, a memory 202, an input / output interface 203, a communication interface 204, and a bus 205. The processor 201, the memory 202, the input / output interface 203, and the communication interface 204 are connected to each other in communication within the device through the bus 205.

[0116] The processor 201 can be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., for executing relevant programs to implement the technical solutions provided in the embodiments of the present application. The processor 201 can also include a graphics card, which can be a Nvidia titan X graphics card or a 1080Ti graphics card, etc.

[0117] The memory 202 can be implemented in the form of a read-only memory (ROM), a random access memory (RAM), a static storage device, a dynamic storage device, etc. The memory 202 can store an operating system and other application programs. When the technical solution provided in the embodiment of the present application is implemented by software or firmware, the relevant program code is stored in the memory 202 and is called and executed by the processor 201.

[0118] The input / output interface 203 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0119] The communication interface 204 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0120] The bus 205 comprises a pathway for transmitting information between the various components of the device (eg, the processor 201 , the memory 202 , the input / output interface 203 , and the communication interface 204 ).

[0121] It should be noted that, although the above device only shows the processor 201, the memory 202, the input / output interface 203, the communication interface 204 and the bus 205, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiment of the present application, and does not necessarily include all the components shown in the figure.

[0122] An embodiment of the present application provides a computer program product, including a computer program, which implements the method described in any embodiment of the present application when executed by a processor.

[0123] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any of the foregoing embodiments.

[0124] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computer device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0125] Each embodiment in the present application is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment. The device embodiment described above is merely schematic, wherein the modules described as separate components may or may not be physically separated, and the functions of each module can be implemented in the same one or more software and / or hardware when implementing the embodiment scheme of the present application. It is also possible to select some or all of the modules according to actual needs to achieve the purpose of the embodiment scheme. Those of ordinary skill in the art can understand and implement it without paying creative work.

[0126] The above is only a specific implementation of the embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the embodiment of the present application. These improvements and modifications should also be regarded as the protection scope of the embodiment of the present application.

Claims

1. A method for determining the sensitivity of geographic information vocabulary, characterized in that: The method comprises: Acquire target geographic information vocabulary; Determine whether the target geographic information vocabulary hits a pre-established geographic information vocabulary database, wherein the geographic information vocabulary database includes a plurality of geographic information vocabulary, each geographic information vocabulary corresponds to a point of interest, the point of interest represents a unique and deterministic geographic location, and the plurality of geographic information vocabulary includes sensitive vocabulary and non-sensitive vocabulary; If the target geographic information vocabulary does not hit the geographic information vocabulary database, a pre-established geographic information phrase database is obtained, wherein the geographic information phrase database includes a plurality of geographic information phrases and their label information, wherein a plurality of geographic information phrases can constitute a geographic information vocabulary, and the label information of the geographic information phrase is used to indicate the probability that the geographic information vocabulary including the geographic information phrase is a sensitive word or a non-sensitive word; The target geographic information vocabulary is segmented to obtain multiple segmented words, each of the multiple segmented words is matched with a geographic information phrase in the geographic information phrase database, and the sensitivity of the target geographic information vocabulary is determined based on the label information of each geographic information phrase successfully matched with the segmented word in the geographic information phrase database.

2. The method according to claim 1, characterized in that: The method further comprises: If the target geographic information vocabulary hits the geographic information vocabulary database, the sensitivity of the target geographic information vocabulary is determined based on the type of geographic information vocabulary hit by the target geographic information vocabulary in the geographic information vocabulary database.

3. The method according to claim 2, characterized in that The geographic information vocabulary database includes a positive vocabulary database and a negative vocabulary database, the positive vocabulary database includes the non-sensitive vocabulary, and the negative vocabulary database includes the sensitive vocabulary; The determining the sensitivity of the target geographic information vocabulary based on the type of geographic information vocabulary hit by the target geographic information vocabulary in the geographic information vocabulary database includes: If the target geographic information vocabulary hits a non-sensitive vocabulary in the positive vocabulary database, determining that the target geographic information vocabulary is a non-sensitive vocabulary; If the target geographic information vocabulary hits a sensitive vocabulary in the negative vocabulary database, it is determined that the target geographic information vocabulary is a sensitive vocabulary.

4. The method according to claim 1, characterized in that: The geographic information phrase database includes a positive phrase database and a negative phrase database, and the probability represented by the label information corresponding to the geographic information phrase in the positive phrase database is greater than the probability represented by the label information corresponding to the geographic information phrase in the negative phrase database; The step of matching each of the plurality of segmented words with a geographic information phrase in the geographic information phrase database, and determining the sensitivity of the target geographic information vocabulary based on label information of each geographic information phrase successfully matched with the segmented word in the geographic information phrase database, comprises: Matching each of the multiple segmented words with a geographic information phrase in the positive phrase database, and determining first label information of each first geographic information phrase in the positive phrase database that successfully matches the multiple segmented words; Matching each of the multiple segmented words with a geographic information phrase in the negative phrase database, and determining second label information of each second geographic information phrase in the negative phrase database that successfully matches the multiple segmented words; The sensitivity of the target geographic information vocabulary is determined based on the first tag information of each first geographic information phrase and the second tag information of each second geographic information phrase.

5. The method according to claim 4, characterized in that The determining the sensitivity of the target geographic information vocabulary based on the first tag information of each first geographic information phrase and the second tag information of each second geographic information phrase includes: Determining a first weight corresponding to the positive phrase database and a second weight corresponding to the negative phrase database; weighting the first tag information of each first geographic information phrase based on the first weight, and weighting the second tag information of each second geographic information phrase based on the second weight; The sensitivity of the target geographic information vocabulary is determined based on the weighted first label information of each first geographic information phrase and the weighted second label information of each second geographic information phrase.

6. The method according to claim 4, characterized in that Before determining the sensitivity of the target geographic information vocabulary based on the first tag information of each first geographic information phrase and the second tag information of each second geographic information phrase, the method further includes: Determine the weight of each first geographic information phrase and the weight of each second geographic information phrase; the weight of the geographic information phrase is related to the sensitivity level of the geographic information phrase, and the higher the sensitivity level of the geographic information phrase, the greater the probability that the geographic information vocabulary including the geographic information phrase is a sensitive vocabulary; The first tag information of the corresponding first geographic information phrases is weighted based on the weights of the respective first geographic information phrases, and the second tag information of the corresponding second geographic information phrases is weighted based on the weights of the respective second geographic information phrases.

7. The method according to claim 1, characterized in that Each geographic information vocabulary in the geographic information vocabulary database is encrypted in advance; The step of determining whether the target geographic information vocabulary hits a pre-established geographic information vocabulary database comprises: Encrypting the target geographic information vocabulary based on the encryption method of each geographic information vocabulary in the geographic information vocabulary database; Determine whether the encrypted target geographic information vocabulary hits a pre-established geographic information vocabulary database.

8. The method according to claim 1, characterized in that Each geographic information word and its label information in the geographic information phrase database is encrypted in advance; The step of matching each of the plurality of segmented words with a geographic information phrase in the geographic information phrase database, and determining the sensitivity of the target geographic information vocabulary based on label information of each geographic information phrase successfully matched with the segmented word in the geographic information phrase database, comprises: Decrypting the geographic information phrases in the geographic information phrase database; Matching each of the plurality of segmented words with the decrypted geographic information phrase in the geographic information phrase database; Decrypting the label information of the geographic information phrases in the geographic information phrase database that successfully match the multiple segmented words; The sensitivity of the target geographic information vocabulary is determined based on the decrypted tag information of each successfully matched geographic information phrase.

9. A device for determining the sensitivity of geographic information vocabulary, characterized in that: The device comprises: The first acquisition module is used to acquire target geographic information vocabulary; A determination module, used to determine whether the target geographic information vocabulary hits a pre-established geographic information vocabulary database, wherein the geographic information vocabulary database includes a plurality of geographic information vocabulary, each geographic information vocabulary corresponds to a point of interest, and the point of interest represents a unique and deterministic geographic location, and the plurality of geographic information vocabulary includes sensitive vocabulary and non-sensitive vocabulary; A second acquisition module is used to acquire a pre-established geographic information phrase database if the target geographic information vocabulary does not hit the geographic information vocabulary database, wherein the geographic information phrase database includes a plurality of geographic information phrases and their label information, wherein a plurality of geographic information phrases can constitute a geographic information vocabulary, and the label information of the geographic information phrase is used to indicate the probability that the geographic information vocabulary including the geographic information phrase is a sensitive word or a non-sensitive word; A matching module is used to segment the target geographic information vocabulary to obtain multiple segmentations, match each of the multiple segmentations with a geographic information phrase in the geographic information phrase database, and determine the sensitivity of the target geographic information vocabulary based on the label information of each geographic information phrase successfully matched with the segmentation in the geographic information phrase database.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

11. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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