Method and device for assessing geographic information risk sensitivity of internet social media

Abstract

The present invention discloses a method and device for assessing geographic information risk sensitivity in internet social media. The method comprises: collecting original published content including textual descriptions of geographic information and user comment data from internet social media, and preprocessing the data; extracting information from the conversational data using natural language processing technology: geographic subject, geographic location information, sensitive regional coverage type, sensitive application type, and timeliness; merging the geographic location information and sensitive regional coverage type belonging to the same geographic subject in the extracted information; performing a confidentiality quantitative assessment for each geographic subject based on the dimensions of sensitive application type, sensitive regional coverage type, geographic location, and timeliness; and calculating a quantitative risk sensitivity result for each geographic subject in the conversational data based on the confidentiality quantitative assessment value of each dimension and its risk impact weight. The present invention is capable of identifying and determining geographic information security risks involved in internet social media information.

Description

Technical Field

[0001] The present invention belongs to the field of geographic information security, and specifically relates to a method and device for assessing geographic information risk sensitivity of Internet social media. Background Art

[0002] In recent years, with the rapid development of the internet, especially mobile internet, social media has become a crucial means of communication and interaction. In a world where everyone is a video recorder and a locator, geospatial information security is becoming increasingly ubiquitous. This is primarily manifested in the ubiquity of geospatial information collection and dissemination, as well as the multimodality of geospatial data entities. Beyond traditional basic surveying and mapping data files, the explosive emergence of multimodal social media platforms (such as WeChat, Weibo, and TikTok) has created even deeper risks to geospatial information security during the casual sharing and interaction of information. While the geospatial information contained in this ubiquitous data may not be highly sensitive from an individual perspective, the interconnected geospatial information within large amounts of multimodal data can lead to serious leaks of sensitive geospatial information, posing significant security risks. Therefore, identifying geospatial information risks and safeguarding the security of social media data on the internet is urgent. Summary of the Invention

[0003] The present invention provides a geographic information risk sensitivity assessment method and device for Internet social media. By analyzing the text information of Internet social media, the relevant geographic subjects and the address accuracy, application type, confidential coverage area type and timeliness of the information that affect the sensitive risks of the geographic subjects are extracted, and the dimensions of the impact of the sensitive risks of the geographic subjects are calculated to finally obtain the risk sensitivity assessment results of the geographic subject information, providing an important basis for further geographic information security risk management work.

[0004] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0005] A method for assessing geographic information risk sensitivity of Internet social media includes:

[0006] Step 1: Collect original content published on Internet social media, including geographic information text descriptions, and their user comments. Each original content and all its comments are recorded as a dialogue corpus, and each collected dialogue corpus is preprocessed.

[0007] Step 2: Using natural language processing technology, extract information from the preprocessed dialogue data, including: geographic entities, geographic location information of each geographic entity, sensitive regional coverage type of each geographic location information, sensitive application type of each geographic entity, and timeliness of each geographic entity;

[0008] Step 3: Merge the geographical location information and sensitive area coverage types belonging to the same geographical subject in the extracted information;

[0009] Step 4: For each geographic entity, calculate the confidentiality quantitative assessment value based on the four dimensions of sensitive application type, sensitive regional coverage type, geographical location, and timeliness;

[0010] Step 5: For each geographical entity, calculate the final risk sensitivity quantification result based on the confidentiality quantitative assessment value of each dimension and its risk impact weight.

[0011] Furthermore, step 1 of pre-processing the collected data includes: completing the abbreviations of the geographic location information therein, and correcting grammatical errors and typos therein.

[0012] Furthermore, step 2 specifically includes:

[0013] S2-1, using natural language processing engines to identify geographic entities in the original published content of the dialogue corpus and its corresponding set of all address location information ; Among them, country, province, city name, street name, house number, GPS location and direction information are all geographic location information;

[0014] S2-2, for identified geographical entities , analyze the sensitive application types of the original published content of the geographical subject, including the three preset sensitive application types, which are recorded as 、 、 ;

[0015] S2-3, for geographical entities Each geographical location information, identify the sensitive area coverage type, including important types , non-public type , non-public shape types ;

[0016] S2-4: For each comment in the dialogue data, execute steps S2-1, S2-2, and S2-3 to obtain the geographical subject, the geographical location information set P of the geographical subject, and the sensitive application type. , and the sensitive area coverage type of each geographic location information ;

[0017] S2-5. Identify the original content and the release time of each comment, and sort by release date to obtain the timeliness of the corresponding geographic subject information. , ;in Indicates the distance from the statistical time.

[0018] Furthermore, step 3 specifically includes:

[0019] S3-1. Geographical entities All geographic locations of the location information set Integrate to obtain geographic entities Unique geographic location information ;

[0020] S3-2. Geographical Entities , merge the sensitive area coverage types of all its geographic location information, namely:

[0021] ;

[0022] Where: Indicates the statistical value of the sensitive area coverage type associated with geographic subject s, which are important types Statistics, non-public types Statistical values, non-disclosed shape types Statistical value of; Represents the i-th geographic location information in the original geographic location information set P of the geographic subject The sensitive area coverage type, where the three elements correspond to the important type, non-public type, and non-public shape type respectively; n is the number of geographic location information included in the original geographic location information set P.

[0023] Furthermore, in step 4, for each geographical entity, the confidential quantitative assessment value is calculated based on the sensitive application type as follows:

[0024] First, calculate the risk matrix R of sensitive application types:

[0025] ;

[0026] in: Indicates the number of events of the three sensitivity levels of the preset first sensitive application type, from high to low sensitivity level; Indicates the number of events of the three sensitivity levels of the preset second sensitive application type, from high to low sensitivity levels; Indicates the number of events at three sensitivity levels for the preset third sensitive application type, with sensitivity levels ranging from high to low. The method for counting the number of events at each sensitivity level for each sensitive application type is as follows: the original published content of the dialogue material and each comment are regarded as one speech. Among all speeches related to the geographic subject, if a speech includes the currently counted sensitive application type and belongs to the currently counted sensitivity level, then the number of events at the currently counted sensitivity level for the currently counted sensitive application type is increased by 1.

[0027] Then, construct a sensitive level setting matrix for sensitive application types and weight matrix of sensitive application types :

[0028] , ;

[0029] in: 、 、 They represent the sensitivity values ​​set for different sensitivity levels, sorted from high to low; 、 、 Respectively represent the weight values ​​of the first, second, and third types of preset sensitive applications;

[0030] Finally, according to the three matrices R, 、 , calculate the quantitative evaluation value of geographic subject s in the dimension of sensitive application type :

[0031] .

[0032] Furthermore, in step 4, for each geographical entity, the confidential quantitative assessment value is calculated based on the sensitive area coverage type as follows:

[0033] First, calculate the risk matrix of the sensitive area coverage type :

[0034] ;

[0035] in: Indicates the statistical value of the sensitive area coverage type associated with geographic subject s, which are important types Statistics, non-public types Statistics, non-disclosed shape types Statistical value of;

[0036] Then, construct the sensitive level setting matrix of sensitive area coverage type :

[0037] ;

[0038] in: Indicates important types The sensitivity value of Represents a non-public type The sensitivity value of Non-public shape types Sensitivity value;

[0039] Finally, according to the two matrices constructed above and , calculate the sensitive quantitative assessment value of the geographical coverage type dimension of the geographic subject s :

[0040] .

[0041] Furthermore, step 5 specifically includes:

[0042] S5-1. Constructing geographic entities The one-dimensional horizontal matrix V of the multi-dimensional sensitivity value includes the quantitative evaluation values ​​of the four dimensions of confidential application type, sensitive regional coverage type, geographical location and timeliness:

[0043] ;

[0044] in: They represent the sensitive quantitative assessment values ​​of the sensitive application type dimension, the sensitive regional coverage type dimension, the geographical location dimension, and the timeliness dimension respectively;

[0045] S5-2. Geographical Entities For each sensitive dimension, set the one-dimensional vertical matrix K of its related risk impact weight value, that is:

[0046] ;

[0047] in: They respectively represent the risk impact weight values ​​of sensitive application types, the risk impact weight values ​​of sensitive regional coverage types, the risk impact weight values ​​of geographical locations, and the risk impact weight values ​​of timeliness;

[0048] S5-3. Based on the two matrices S5-1 and S5-2, calculate the final risk sensitivity quantitative result of the geographic entity s :

[0049] .

[0050] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor implements the method described above.

[0051] Based on an analysis of internet social media text, this invention extracts relevant geographic entities and the address accuracy, application type, confidential coverage area type, and timeliness of information that impact the sensitive risks of these entities. This method then calculates the dimensions of these extracted geographic entities' impact on sensitive risks, ultimately yielding a risk sensitivity assessment for the geographic entity's information. This method not only uncovers sensitive geographic information hidden within internet social media topics but also employs a more rational and effective approach to assess the risk sensitivity of this geographic information, providing a more scientific and effective quantitative value and providing a basis for further addressing geographic information risks.

[0052] This invention can identify and assess geographic information security risks associated with everyone's participation in online content production, in the context of high-speed internet information dissemination, including mobile internet. This has important practical implications for preventing the leakage of sensitive geographic information. It also provides an effective quantitative assessment method for the sensitivity of geographic information risks in internet media content and commentary. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of the method described in the embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to better understand the technical solution of the present invention, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings, so that those skilled in the art can understand the present invention. It should be understood that the described embodiments and all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0055] This embodiment provides a method for assessing the risk sensitivity of geographic information in Internet social media. The following steps are explained in sequence. Figure 1 shown.

[0056] Step 1: Collect original content and comment data of Internet social media, including text descriptions of geographic information. Each original content and its comment data together constitute a dialogue corpus, and preprocess the collected data.

[0057] S1-1. Targeted completion based on geographic abbreviations. Some published content and user comments contain colloquial abbreviations for certain places or venues, which are not conducive to natural language analysis and may be treated as noise. Therefore, a geographic location information database is used to complete some geographical abbreviations to more accurately identify and understand the geographic location information.

[0058] S1-2. Correct grammatical errors. Users often make grammatical errors or typos when entering content and comments, causing the sentences themselves to not conform to normal semantics and be unable to be recognized by natural language systems, or to be recognized incorrectly. Therefore, a grammatical correction program is needed to automatically improve the sentences to more accurately extract and understand the geographic information in the content and comments.

[0059] Step 2: Use natural language processing technology to extract information from the preprocessed conversation topic corpus, including: geographical entities, geographical location information of each geographical entity, sensitive regional coverage type of each geographical location information, sensitive application type of each geographical entity, and timeliness of each geographical entity.

[0060] S2-1. Use the natural language processing engine to analyze the original content in the dialogue data and identify the geographical subjects in the original content. and its corresponding set of all address location information Geographic location information may include country, province, city name, street name, house number, GPS location and direction information, etc.

[0061] S2-2. Geographical Entities , analyze the sensitive application types of the original published content, including the three preset sensitive application types, which are recorded as 、 、 , with a value of 0 or 1. If the original published content contains the corresponding sensitive application type, the geographic subject The corresponding sensitive type value is 1, otherwise it is 0. For example, a geographical subject The sensitive application types are , indicating a geographic entity The sensitive application types include the preset first and second sensitive application types.

[0062] S2-3. For each geographic location information , identify the sensitive area coverage type of the geographical location, including important types , non-public type , non-public shape types ; 、 、 The value is 0 or 1. If the geographical location has the corresponding sensitive area coverage type, the value is 1, otherwise it is 0. For example, a geographical location information Sensitive area types , indicating geographic location information The sensitive area coverage type is important .

[0063] S2-4: For each comment in the dialogue data, execute steps S2-1, S2-2, and S2-3 to obtain the geographical subject, the geographical location information set P of the geographical subject, and the sensitive application type in each comment. , and the sensitive area coverage type of each geographic location information .

[0064] For comments in which the geographical subject is not identified, the content identified in the comment will be attributed to the geographical subject of the original published content by default.

[0065] S2-5. Identify the original content and the release time of each comment, and sort by release date to obtain the timeliness of the corresponding geographic subject information. , .in Indicates that within one month, Indicates that within 6 months, Indicates that within 12 months, Indicates within 24 months. Information older than two years is of lower value and will not be extracted and will be ignored. By default, the timeliness is determined by the date of the most recent comment. For example, even if the content was published two years ago, if it is commented within one month, it means that the content is still being paid attention to and has timeliness, and the timeliness is .

[0066] Through the above steps, we can obtain the following geographic subject set S from the original content and comments of a dialogue corpus: Each geographic entity Corresponding geographic location information set ,Right now ; Each geographic location information The corresponding sensitive area coverage type, namely Each geographic entity The corresponding sensitive application types, namely Each geographic entity The corresponding aging value is , .

[0067] Step 3: For each dialogue corpus, the geographical location information and sensitive area coverage types belonging to the same geographical subject in the extracted information are merged.

[0068] S3-1. Merge the geographical location information sets of the geographical entities.

[0069] Step 2: The relationship between the geographic subject and its geographic location information is: . For geographical entities The geographic location information set P is matched with the address database and the address database matching analysis technology is used to match all the geographic location information. Integrate to obtain geographic entities More accurate unique geographic location information If these address location information sets can be directly located to GPS coordinate points, the coordinate point information will be used as the unique geographical location information of the geographic subject. Finally, the unique correspondence between the geographic subject and the geographical location information is obtained. .

[0070] S3-2: Merge the sensitive area coverage types of each geographical location of the geographical subject identified in S2-3, namely:

[0071] ;

[0072] Where: Represents the i-th geographic location information in the original geographic location information set P of the geographic subject The sensitive area coverage type of the data, where the three elements correspond to the important type, the non-public type, and the non-public shape type respectively; n is the number of geographic location information included in the original geographic location information set P; Indicates the statistical value of the sensitive area coverage type associated with geographic subject s, which are important types Statistics, non-public types Statistical values, non-disclosed shape types Statistical value of .

[0073] After step 3, each geographic subject will obtain 1 geographic location information, 1 sensitive area coverage type, 1 sensitive application type and 1 timeliness measurement value, which can be expressed as .

[0074] Step 4: For each geographical entity, calculate the confidential quantitative assessment value based on the four dimensions of confidential application type, sensitive area coverage type, geographical location and timeliness.

[0075] S4-1. Constructing geographic entities The sensitive application type risk matrix R is constructed, and the sensitive quantitative assessment value of the geographic information application type dimension is calculated:

[0076] First, calculate the risk matrix R of sensitive application types:

[0077] ;

[0078] in: Indicates the number of events of the three sensitivity levels of the preset first sensitive application type, from high to low sensitivity level; Indicates the number of events of the three sensitivity levels of the preset second sensitive application type, from high to low sensitivity levels; This represents the number of events at the three sensitivity levels for the second preset sensitive application type, ranked from high to low. The number of events at each sensitivity level for each sensitive application type is calculated by treating the original content and each comment in the conversation as a single statement. For all statements related to the geographic subject, if a statement includes the currently counted sensitive application type and falls within the currently counted sensitivity level, the number of events at the currently counted sensitivity level for the currently counted sensitive application type is incremented by 1.

[0079] Then, construct a sensitive level setting matrix for sensitive application types and weight matrix of sensitive application types :

[0080] , ;

[0081] in: 、 、 They represent the sensitivity values ​​set for different sensitivity levels, sorted from high to low; 、 、 The weights for the first, second, and third types of sensitive applications are respectively indicated. The sensitivity values ​​for different levels of sensitivity can be set according to specific standards, such as the national standard GB / T 43697-2024, "Data Security Technical Data Classification and Grading Rules."

[0082] Finally, according to the three matrices R, 、 , calculate the quantitative evaluation value of geographic subject s in the dimension of sensitive application type :

[0083] .

[0084] S4-2. Constructing Geographical Subjects Risk matrix for sensitive geographical coverage types , and calculate the sensitive quantitative assessment value of the sensitive area coverage type dimension :

[0085] First, calculate the risk matrix of the sensitive area coverage type :

[0086] ;

[0087] in: Indicates the statistical value of the sensitive area coverage type associated with geographic subject s, which are important types Statistics, non-public types Statistics, non-disclosed shape types The sensitive area coverage types in this embodiment are classified according to preset requirements for statistics, for example, statistics on sensitive area coverage types are based on the "Research on China's Geographic Information Security Policies and Laws".

[0088] Then, construct the sensitive level setting matrix of sensitive area coverage type :

[0089] ;

[0090] in: Indicates important types The sensitivity value of Represents a non-public type The sensitivity value of Non-public shape types Sensitivity value.

[0091] Finally, according to the two matrices constructed above and , calculate the sensitive quantitative assessment value of the geographical coverage type dimension of the geographic subject s :

[0092] .

[0093] S4-3. Based on geographical entities Geographic location information , determine its accuracy range, and assign a confidential quantitative assessment value for the geographic location dimension In practical applications, geographic location information The higher the accuracy, the greater the confidential quantitative assessment value of the geographic location dimension. The bigger.

[0094] S4-4. Based on geographical entities Topic timeliness , assign confidential quantitative evaluation of timeliness dimension. In practical applications, the older the corpus is from the current time, the lower the timeliness is, and the confidential quantitative evaluation value of timeliness dimension is The smaller.

[0095] Step 5: For each geographical entity, calculate the final risk sensitivity quantification result based on the confidentiality quantitative assessment value of each dimension and its risk impact weight.

[0096] S5-1. Construct a geographic entity based on the calculation results of step S4 The one-dimensional horizontal matrix V of the multi-dimensional sensitive values ​​includes the sensitive application type, sensitive regional coverage type, geographical location, and timeliness:

[0097] ;

[0098] in: They represent the sensitive quantitative assessment values ​​of the sensitive application type dimension, the sensitive regional coverage type dimension, the geographical location dimension, and the timeliness dimension respectively;

[0099] S5-2. Geographical Entities For each sensitive dimension, the one-dimensional vertical matrix K of its related risk impact weight value is preset based on the empirical value, that is:

[0100] ;

[0101] in: They respectively represent the risk impact weight values ​​of sensitive application types, the risk impact weight values ​​of sensitive regional coverage types, the risk impact weight values ​​of geographical locations, and the risk impact weight values ​​of timeliness;

[0102] S5-3. Based on the two matrices S5-1 and S5-2, calculate the final risk sensitivity quantitative result of the geographic entity s :

[0103] .

[0104] In summary, the present invention can not only discover sensitive geographic information hidden in the original content and comments of Internet social media, but also adopt a more reasonable and effective way to evaluate the risk sensitivity of geographic information, provide a more scientific and effective quantitative value, and provide a basis for further geographic information risk management.

[0105] The above embodiments are preferred embodiments of the present application. Ordinary technicians in this field can also make various changes or improvements on this basis. Without departing from the overall concept of the present application, these changes or improvements should fall within the scope of protection required by the present application.

Claims

1. A method for assessing geographic information risk sensitivity of Internet social media, characterized in that: include: Step 1: Collect original content published on Internet social media, including geographic information text descriptions, and their user comments. Each original content and all its comments are recorded as a dialogue corpus, and each collected dialogue corpus is preprocessed. Step 2: Using natural language processing technology, extract information from the preprocessed dialogue data, including: geographic entities, geographic location information of each geographic entity, sensitive regional coverage type of each geographic location information, sensitive application type of each geographic entity, and timeliness of each geographic entity; Step 3: Merge the geographical location information and sensitive area coverage types belonging to the same geographical subject in the extracted information; Step 4: For each geographic entity, calculate the confidentiality quantitative assessment value based on the four dimensions of sensitive application type, sensitive regional coverage type, geographical location, and timeliness; Step 5: For each geographical entity, calculate the final risk sensitivity quantification result based on the confidentiality quantitative assessment value of each dimension and its risk impact weight.

2. The method for assessing geographic information risk sensitivity of Internet social media according to claim 1, characterized in that: Step 1 pre-processes the collected data, including: completing the abbreviations of the geographic location information, and correcting grammatical errors and typos.

3. The method for assessing geographic information risk sensitivity of Internet social media according to claim 1, characterized in that: Step 2 specifically includes: S2-1, using natural language processing engines to identify geographic entities in the original published content of the dialogue corpus and its corresponding set of all address location information ; Among them, country, province, city name, street name, house number, GPS location and direction information are all geographic location information; S2-2, for identified geographical entities , analyze the sensitive application types of the original published content of the geographical subject, including the three preset sensitive application types, which are recorded as 、 、 ; S2-3, for geographical entities Each geographical location information, identify the sensitive area coverage type, including important types , non-public type , non-public shape types ; S2-4: For each comment in the dialogue data, execute steps S2-1, S2-2, and S2-3 to obtain the geographical subject, the geographical location information set P of the geographical subject, and the sensitive application type. , and the sensitive area coverage type of each geographic location information ; S2-5. Identify the original content and the release time of each comment, and sort by release date to obtain the timeliness of the corresponding geographic subject information. , ;in Indicates the distance from the statistical time.

4. The method for assessing geographic information risk sensitivity of Internet social media according to claim 1, characterized in that: Step 3 specifically includes: S3-1. Geographical entities All geographic locations of the location information set Integrate to obtain geographic entities Unique geographic location information ; S3-2. Geographical Entities , merge the sensitive area coverage types of all its geographic location information, namely: ； Where: Indicates the statistical value of the sensitive area coverage type associated with geographic subject s, which are important types Statistics, non-public types Statistical values, non-disclosed shape types Statistical value of; Represents the i-th geographic location information in the original geographic location information set P of the geographic subject The sensitive area coverage type, where the three elements correspond to the important type, non-public type, and non-public shape type respectively; n is the number of geographic location information included in the original geographic location information set P.

5. The method for assessing geographic information risk sensitivity of Internet social media according to claim 1, characterized in that: In step 4, for each geographic entity, the confidentiality quantitative assessment value is calculated based on the sensitive application type: First, calculate the risk matrix R of sensitive application types: ； in: Indicates the number of events of the three sensitivity levels of the preset first sensitive application type, from high to low sensitivity level; Indicates the number of events of the three sensitivity levels of the preset second sensitive application type, from high to low sensitivity levels; Indicates the number of events at three sensitivity levels for the preset third sensitive application type, with sensitivity levels ranging from high to low. The method for counting the number of events at each sensitivity level for each sensitive application type is as follows: the original content of the dialogue material and each comment are considered as one statement. Among all statements related to the geographic subject, if a statement includes the currently counted sensitive application type and belongs to the currently counted sensitivity level, the number of events at the currently counted sensitivity level for the currently counted sensitive application type is increased by 1. Then, construct a sensitive level setting matrix for sensitive application types and weight matrix of sensitive application types : ， ； in: 、 、 They represent the sensitivity values ​​set for different sensitivity levels, sorted from high to low; 、 、 Respectively represent the weight values ​​of the first, second, and third types of preset sensitive applications; Finally, according to the three matrices R, 、 , calculate the quantitative evaluation value of geographic subject s in the dimension of sensitive application type : 。 6. The method for assessing geographic information risk sensitivity of Internet social media according to claim 1, characterized in that: In step 4, for each geographical entity, the confidential quantitative assessment value is calculated based on the sensitive area coverage type as follows: First, calculate the risk matrix of the sensitive area coverage type : ； in: Indicates the statistical value of the sensitive area coverage type associated with geographic subject s, which are important types Statistics, non-public types Statistics, non-disclosed shape types Statistical value of; Then, construct the sensitive level setting matrix of sensitive area coverage type : ； in: Indicates important types The sensitivity value of Represents a non-public type The sensitivity value of Non-public shape types Sensitivity value; Finally, according to the two matrices constructed above and , calculate the sensitive quantitative assessment value of the geographical coverage type dimension of the geographic subject s : 。 7. The method for assessing geographic information risk sensitivity of Internet social media according to claim 1, characterized in that: Step 5 specifically includes: S5-1. Constructing geographic entities The one-dimensional horizontal matrix V of the multi-dimensional sensitivity value includes the quantitative evaluation values ​​of the four dimensions of confidential application type, sensitive regional coverage type, geographical location and timeliness: ； in: They represent the sensitive quantitative assessment values ​​of the sensitive application type dimension, the sensitive regional coverage type dimension, the geographical location dimension, and the timeliness dimension respectively; S5-2. Geographical Entities For each sensitive dimension, set the one-dimensional vertical matrix K of its related risk impact weight value, that is: ； in: They respectively represent the risk impact weight values ​​of sensitive application types, the risk impact weight values ​​of sensitive regional coverage types, the risk impact weight values ​​of geographical locations, and the risk impact weight values ​​of timeliness; S5-3. Based on the two matrices S5-1 and S5-2, calculate the final risk sensitivity quantitative result of the geographic entity s : 。 8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the computer program is executed by the processor, the processor is caused to implement the method according to any one of claims 1 to 7.

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