Urban and rural planning and construction data security sharing method and system

Abstract

The invention relates to the technical field of data processing, in particular to an urban and rural planning and construction data security sharing method and system.The method comprises the steps that firstly, urban and rural planning and construction data are obtained, and different types of urban and rural planning and construction data comprise data files with text data and non-text data; according to the sensitivity capable of reflecting the secret degree of the urban and rural planning and construction data, dividing confidential levels of the urban and rural planning and construction data, determining the confidential levels of the urban and rural planning and construction data, and providing a reference basis for an encryption mode before safe sharing of the data; and finally, based on the confidentiality level of the urban and rural planning and construction data, safely sharing the urban and rural planning and construction data. According to the method, the hierarchy and the sensitivity of the urban and rural planning and construction data can be effectively identified, so that the key data is protected in time, the non-key data is correspondingly protected, the non-key data is prevented from being over-protected, and the sharing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for securely sharing urban and rural planning and construction data. Background Art

[0002] With the acceleration of urbanization and the rapid development of information technology, the types of data involved in urban and rural planning and construction are increasing. These data usually include basic geographic information, socio-economic information, environmental resource information, public facilities construction information, transportation and infrastructure information, etc. In order to improve urban management efficiency and optimize resource allocation, the sharing and collaborative use of relevant data have become particularly important, especially among enterprises, scientific research institutions and the general public. However, urban and rural planning and construction data usually involve a large amount of sensitive information, such as personal privacy, corporate secrets, etc. If this information is not properly protected during the sharing process, it may lead to serious data leakage, abuse and even data tampering.

[0003] In the protection of urban and rural planning and construction data, text data is an important carrier of information transmission. However, text data usually contains both critical information and non-critical information, making the traditional confidentiality level classification method difficult to apply. Unlike objective data such as geographic data and economic data, which can be classified according to established standards and rules, sensitive information in text data may present a more complex distribution form. For example, a text containing a planning scheme may contain both specific land use plans and some environmental impact assessment reports. Some paragraphs may involve sensitive commercial data information, while other parts are public information or low-risk data. The traditional static confidentiality level classification method cannot effectively identify the hierarchy and sensitivity of such information, resulting in some critical data not being protected in time, while non-critical data may be over-protected, affecting the efficiency of data sharing.

[0004] Therefore, it is urgent to develop a method and system for securely sharing urban and rural planning and construction data to solve the above problems. Summary of the invention

[0005] In order to solve the technical problem that the level and sensitivity of urban and rural planning and construction data cannot be effectively identified, some key data may not be protected in time, and non-key data may be over-protected, which affects the efficiency of data sharing, the purpose of the present invention is to provide a method and system for secure sharing of urban and rural planning and construction data. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present invention provides a method for securely sharing urban and rural planning and construction data, the method comprising: Acquire urban and rural planning and construction data, the urban and rural planning and construction data including basic geographic information data, socio-economic data, land use data, environmental resource data, infrastructure data, and building and housing data, and different types of urban and rural planning and construction data include data files with text data and non-text data; According to the sensitivity of the urban and rural planning and construction data, classify the urban and rural planning and construction data into confidentiality levels and determine the confidentiality level of the urban and rural planning and construction data; Based on the confidentiality level of the urban and rural planning and construction data, the urban and rural planning and construction data are securely shared.

[0006] In some embodiments, the classification of the urban and rural planning and construction data into confidentiality levels according to the sensitivity of the urban and rural planning and construction data, and the determination of the confidentiality level of the urban and rural planning and construction data include: The confidentiality level of historical urban and rural planning and construction data is obtained, including top secret and extremely sensitive, confidential and highly sensitive, secret and moderately sensitive, internally disclosed, and externally disclosed; Determining the confidential relevance between the urban and rural planning and construction data and the historical urban and rural planning and construction data; The confidentiality level of the urban and rural planning and construction data is determined based on the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data and the confidentiality level of the historical urban and rural planning and construction data.

[0007] In some embodiments, determining the confidentiality correlation between the urban and rural planning and construction data and historical urban and rural planning and construction data includes: Extracting text data from each data file in the urban and rural planning and construction data; Determine the word vector of the text data of each data file in the urban and rural planning and construction data; According to the following formula, the confidential representativeness of the word vector of the text data of each data file in the urban and rural planning and construction data is determined: ; In the formula, Indicates the urban and rural planning and construction data word vectors for data files Confidential representation of Indicates the urban and rural planning and construction data word vectors for data files The inverse document frequency of represents the function of finding similarity, Representing existence and word vectors The same word vector of historical urban and rural planning and construction data The word vector set of text data in the data file, The historical urban and rural planning and construction data The confidentiality level corresponding to the data file is , Indicates the urban and rural planning and construction data The text data of the data file The word vector corresponding to the word, Indicates the urban and rural planning and construction data The ordinal number of the word in the text data of the data file, Indicates the number of data files in urban and rural planning and construction data. Indicates the number of data files in the historical urban and rural planning and construction data; Based on the confidentiality representativeness of the word vector of the text data of each data file in the urban and rural planning and construction data, the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data is determined.

[0008] In some embodiments, determining the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data includes: According to the following formula, based on the storage ratio of the text data of each data file in the urban and rural planning and construction data to the data file, the number of confidential representative words in each data file in the urban and rural planning and construction data is determined: ; In the formula, Indicates the urban and rural planning and construction data The number of confidential representative words in the data file, Indicates the urban and rural planning and construction data The storage ratio of text data in a data file, Indicates the urban and rural planning and construction data The number of word vectors contained in the data file, represents a hyperparameter, with a value of 20; The confidentiality representativeness of the word vectors of the text data of each data file in the urban and rural planning and construction data is sorted from large to small, and the words corresponding to the confidentiality representativeness of the word vectors having the same number as the number of confidential representative words of a data file are selected from the sorted sequence from large to small as the confidential representative words of the data file; The confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data is determined according to the following formula: ; In the formula, Indicates the urban and rural planning and construction data The data files and historical urban and rural planning and construction data confidential correlation between data files, represents a function that finds the cosine similarity between two vectors. Indicates the urban and rural planning and construction data The number of confidential representative words in the data file, The historical urban and rural planning and construction data The word vector corresponding to the lth word of the text data of the data file, l represents the lth word of the historical urban and rural planning and construction data The ordinal number of the word in the text data of a data file.

[0009] In some embodiments, based on the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data and the confidentiality level of the historical urban and rural planning and construction data, determining the confidentiality level of the urban and rural planning and construction data includes: Acquire the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data, wherein the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data includes the confidentiality correlation between each data file in the urban planning and construction data and each data file in the historical urban and rural planning and construction data; By using DBSCAN clustering method, cluster analysis is performed on the confidentiality correlation between each data file in the urban planning and construction data and each data file in the historical urban and rural planning and construction data, wherein the distance between any two data files is the inverse of the confidentiality correlation between the two data files, the scanning radius of the cluster analysis is 5, and the minimum number of included points of the cluster analysis is 20; Determine, based on the cluster analysis result, the comparability of the confidentiality level of a data file in the historical urban and rural planning and construction data to the confidentiality level of each data file in the urban planning and construction data; Based on the comparability, determine the probability that the confidentiality level of a data file in the historical urban and rural planning and construction data is the confidentiality level of each data file in the urban planning and construction data; The confidentiality level corresponding to the probability of obtaining the maximum value is used as the confidentiality level of the data file in the urban and rural planning and construction data.

[0010] In some embodiments, the confidentiality level of a data file in the historical urban and rural planning and construction data is determined to be comparable to the confidentiality level of each data file in the urban planning and construction data according to the following formula: ; In the formula, The first data file set corresponding to the non-outlier point in the cluster of the cluster analysis result Comparability of confidentiality levels of data files, A set of data files corresponding to non-outlier points in the clusters representing the cluster analysis results, represents the normalization function, The first data file set corresponding to the non-outlier point in the cluster of the cluster analysis result The sequence number of the data file, z represents the sequence number of the zth data file in the data file set corresponding to the non-outlier point in the cluster of the cluster analysis result, represents the maximum value function, The first data file set corresponding to the non-outlier point in the cluster of the cluster analysis result The confidential correlation between the zth data file and the zth data file.

[0011] In some embodiments, the probability that the confidentiality level of a data file in the historical urban and rural planning and construction data is the confidentiality level of each data file in the urban planning and construction data is determined according to the following formula: ; In the formula, represents the probability that the confidentiality level of the p-th data file in the data file set corresponding to the non-outlier point in the cluster of the cluster analysis result belongs to the k-th confidentiality level, Indicates the minimum number of points included in the cluster analysis, the value is 20. Indicates A collection of confidential data files. The first data file set corresponding to the non-outlier point in the cluster of the cluster analysis result The confidentiality correlation between the pth data file and the pth data file, k represents the labels of the five confidentiality levels.

[0012] In some embodiments, according to the following formula, the confidentiality level corresponding to the probability of obtaining the maximum value is used as the confidentiality level of the data file in the urban and rural planning and construction data: ; In the formula, Indicates the urban and rural planning and construction data The confidentiality level of the data files, Represents the function of finding the maximum point, and k takes values ​​of 1, 2, ..., 5.

[0013] In some embodiments, the confidentiality level of the urban and rural planning and construction data includes top secret extremely sensitive, confidential highly sensitive, secret medium sensitive, internally disclosed, and externally disclosed. Based on the confidentiality level of the urban and rural planning and construction data, the urban and rural planning and construction data is securely shared, including: For data files with high confidentiality levels in urban and rural planning and construction, they are encrypted using high-strength encryption algorithms and then shared securely to ensure that the data files cannot be cracked. The high confidentiality levels include top secret, extremely sensitive, and confidential, highly sensitive; For data files with medium confidentiality level in urban and rural planning and construction, they are encrypted with medium-strength encryption algorithm and then securely shared to balance security and sharing efficiency. The medium confidentiality level includes secret medium sensitivity; For data files with a low confidentiality level in urban and rural planning and construction, a lightweight encryption algorithm is used to encrypt them and then share them securely to improve sharing efficiency. The low confidentiality level includes internal and external disclosure.

[0014] In a second aspect, an embodiment of the present invention provides a system for securely sharing urban and rural planning and construction data, the system comprising: An acquisition module is used to acquire urban and rural planning and construction data, wherein the urban and rural planning and construction data includes basic geographic information data, social and economic data, land use data, environmental resource data, infrastructure data, and building and housing data. Different types of urban and rural planning and construction data include data files with text data and non-text data. A determination module, used to classify the urban and rural planning and construction data into confidentiality levels according to the sensitivity of the urban and rural planning and construction data, and determine the confidentiality level of the urban and rural planning and construction data; The sharing module is used to securely share the urban and rural planning and construction data based on the confidentiality level of the urban and rural planning and construction data.

[0015] The present invention has the following beneficial effects: An embodiment of the present invention provides a method and system for securely sharing urban and rural planning and construction data. The method first obtains urban and rural planning and construction data. Different types of urban and rural planning and construction data include data files with text data and non-text data. Then, the urban and rural planning and construction data are classified into confidentiality levels according to the sensitivity that can reflect the confidentiality of the urban and rural planning and construction data, and the confidentiality level of the urban and rural planning and construction data is determined to provide a reference for the encryption method before data security sharing. Finally, based on the confidentiality level of the urban and rural planning and construction data, the urban and rural planning and construction data are securely shared. The method can effectively identify the hierarchy and sensitivity of urban and rural planning and construction data, so that key data is protected in a timely manner, non-key data is protected accordingly, and over-protection of non-key data is avoided, thereby improving sharing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A method flow chart of a method for securely sharing urban and rural planning and construction data provided by an embodiment of the present invention; Figure 2 A rendering of a cluster analysis in a method for securely sharing urban and rural planning and construction data provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a system for securely sharing urban and rural planning and construction data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] The following is a detailed description of a method for securely sharing urban and rural planning and construction data provided by the present invention in conjunction with the accompanying drawings. Figure 1 , the method comprising: Step 101, obtaining urban and rural planning and construction data.

[0021] Among them, urban and rural planning and construction data include basic geographic information data, socio-economic data, land use data, environmental resources data, infrastructure data, and building and housing data. Different types of urban and rural planning and construction data include data files with text data and non-text data.

[0022] Before data security sharing, the urban and rural planning and construction data needs to be encrypted, and the encryption methods for urban and rural planning and construction data of different sensitivities are different. In view of the fact that the confidentiality level of data is not classified, its confidentiality level can be inferred and determined by analyzing its association with the known confidentiality level data, and step 102 is introduced.

[0023] In some embodiments, text data may include urban planning reports, meeting minutes, planning regulations and policy documents, design description documents, socio-economic analysis reports, etc. Non-text data include geographic information data (coordinates, vector maps, remote sensing images), economic data (GDP, employment rate, industry ratio), land use data (GIS data, plot number), building data (building height, area, material type), sensor data (temperature, humidity, flow), etc.

[0024] Step 102: classify the urban and rural planning and construction data into confidentiality levels according to the sensitivity of the urban and rural planning and construction data, and determine the confidentiality level of the urban and rural planning and construction data.

[0025] Sensitivity can reflect the degree of confidentiality of data files. The confidentiality level of urban and rural planning and construction data can provide a reference for the encryption method before data is securely shared.

[0026] In some embodiments, step 102 includes: Step 1021, obtaining the confidentiality level of historical urban and rural planning and construction data, the confidentiality levels including top secret extremely sensitive, confidential highly sensitive, secret medium sensitive, internally disclosed and externally disclosed.

[0027] The confidentiality level of historical urban and rural planning and construction data can provide a basis for the confidentiality level of urban and rural planning and construction data to be classified.

[0028] Step 1022, determining the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data.

[0029] In order to determine whether the confidentiality level of a data file in historical urban and rural planning and construction data can provide a basis for the confidentiality level of a data file in urban and rural planning and construction data, it is necessary to determine the confidentiality correlation between the two.

[0030] In some embodiments, step 1022 includes: Step 10221, extracting text data from each data file in the urban and rural planning and construction data.

[0031] In order to analyze the difference between text data and non-text data in the future, the text data of each data file in the urban and rural planning and construction data is extracted, and the text data is extracted in units of one data file.

[0032] Step 10222, determine the word vector of the text data of each data file in the urban and rural planning and construction data.

[0033] Word vectors can reflect data characteristics.

[0034] When considering the confidentiality relevance between two data files, it is necessary to analyze the similarity between the corresponding description data. For data with high description similarity, their confidentiality levels are also more likely to be the same. Therefore, it is necessary to first obtain the word vectors with strong confidentiality representation in each data.

[0035] In some embodiments, the specific implementation method of step 10222 is: using the word segmentation tool jieba, the text information in the urban and rural planning and construction data, and then according to the traditional word2vec, obtain the word vector corresponding to each word in each text data.

[0036] Step 10223, determine the confidentiality representativeness of the word vector of the text data of each data file in the urban and rural planning and construction data according to the following formula: ; In the formula, Indicates the urban and rural planning and construction data word vectors for data files The confidentiality representativeness of reflects the confidentiality of the word vector. Indicates the urban and rural planning and construction data word vectors for data files The inverse document frequency is the frequency of a single word in a single data file divided by the frequency of a single word in all data files. It can reflect the proportion of the word in the data file. represents the function of finding similarity, Representing existence and word vectors The same word vector of historical urban and rural planning and construction data The word vector set of text data in the data file can reflect the word vector The range of the data file, The historical urban and rural planning and construction data The confidentiality level corresponding to a data file reflects the confidentiality of the data file. The value is , the values ​​from small to large represent the confidentiality level from low to high, Indicates the urban and rural planning and construction data The text data of the data file The word vector corresponding to each word can reflect the data characteristics. Indicates the urban and rural planning and construction data The ordinal number of the word in the text data of the data file, Indicates the number of data files in urban and rural planning and construction data. Indicates the number of data files in the historical urban and rural planning and construction data.

[0037] The larger the value is, the greater the proportion of the word in urban and rural planning and construction data is. The larger the value is, the more similar the confidentiality level of the word in the data file with known confidentiality level (the data file of historical urban and rural planning and construction data) and the confidentiality level of the word in multiple data files of urban and rural planning and construction data are, and the stronger the confidentiality representativeness of the word is, that is, The bigger.

[0038] According to the confidentiality representativeness corresponding to each word vector obtained, the confidentiality correlation between the two data files can be calculated. Considering that non-text data (such as maps) has relatively less text data and a relatively large amount of data contained therein, the less text data may contain key information of the corresponding data, such as annotation information in the map, the area to which the map belongs, etc. However, due to the small amount of text, the inverse document frequency of each word vector is small, making the confidentiality representativeness of each word low. Therefore, step 10224 is introduced to solve the above problem.

[0039] Step 10224, based on the confidentiality representativeness of the word vector of the text data of each data file in the urban and rural planning and construction data, determine the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data.

[0040] The confidentiality correlation between urban and rural planning and construction data and historical urban and rural planning and construction data can reflect the similarity of the confidentiality levels of the two data files, so as to predict the confidentiality level of urban and rural planning and construction data in the future.

[0041] In some embodiments, step 10224 includes: (1) According to the following formula, based on the proportion of text data in each data file in the urban and rural planning and construction data to the storage ratio of the data file, the number of confidential representative words in each data file in the urban and rural planning and construction data is determined: ; In the formula, Indicates the urban and rural planning and construction data The number of confidential representative words in a data file reflects the number of confidential representative words in the data file and its value is not zero. Indicates the urban and rural planning and construction data The storage ratio of text data in a data file reflects the proportion of text data in the data file. Indicates the urban and rural planning and construction data The number of word vectors contained in a data file reflects the number of word vectors contained in the data file. Represents a hyperparameter, with a value of 20.

[0042] It should be noted that when At that time, the urban and rural planning and construction data If the number of word vectors in the data file is less than or equal to 20, the urban and rural planning and construction data is determined to be The number of confidential representative words in the data file is ;when At that time, the urban and rural planning and construction data If the number of word vectors in the data file is greater than 20, the urban and rural planning and construction data is determined to be The number of confidential representative words in the data file is the number of confidential representative words in the urban and rural planning and construction data. The product of the storage ratio of text data in the data file and the hyperparameter and the first The storage ratio of non-text data in each data file and the storage ratio of urban and rural planning and construction data The data files contain the sum of the products of the number of word vectors, that is, .

[0043] (2) Sort the confidentiality representativeness of the word vectors of the text data of each data file in the urban and rural planning and construction data from large to small, and select from the sorted sequence the words corresponding to the confidentiality representativeness of the word vectors that are the same number as the number of confidential representative words of a certain data file as the confidential representative words of the data file.

[0044] By sorting the confidentiality representativeness of word vectors from large to small, the confidentiality degree of word vectors can be grasped. In the sorted sequence, words corresponding to the confidentiality representativeness of word vectors with the same number of confidential representative words in a data file are selected from large to small. The result of the number obtained in the above step (1) can be used to select words corresponding to the confidentiality representativeness of word vectors with the same number of confidential representative words in a data file, thereby obtaining the confidentiality representative words of the data file, so as to subsequently calculate the confidentiality correlation between urban and rural planning and construction data and historical urban and rural planning and construction data.

[0045] (3) Determine the confidentiality correlation between urban and rural planning and construction data and historical urban and rural planning and construction data according to the following formula: ; In the formula, Indicates the urban and rural planning and construction data The data files and historical urban and rural planning and construction data The confidentiality correlation between two data files can reflect the similarity of the confidentiality of two data files. represents a function that finds the cosine similarity between two vectors. Indicates the urban and rural planning and construction data The number of confidential representative words in a data file and its value is not zero, The historical urban and rural planning and construction data The word vector corresponding to the lth word of the text data of the data file, l represents the lth word of the historical urban and rural planning and construction data The ordinal number of the word in the text data of a data file.

[0046] pass Get the urban and rural planning and construction data All word vectors of the data file and the historical urban and rural planning and construction data The cosine similarity of all word vectors in the data file, adding weights Get the mean of the cosine similarity.

[0047] Step 1023, based on the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data and the confidentiality level of the historical urban and rural planning and construction data, determine the confidentiality level of the urban and rural planning and construction data.

[0048] The determined confidentiality level of urban and rural planning and construction data can provide a reference for the encryption method before data security sharing.

[0049] The confidentiality correlation between any two data files obtained after the above step 1022, for data files with known confidentiality levels, should be high between any two data files with the same confidentiality level. However, considering that the confidential part of the content may not have a text description, there will still be a small number of data files with low correlation at the same confidentiality level. Therefore, step 1023 is introduced to predict the confidentiality level.

[0050] In some embodiments, step 1023 includes: Step 10231, obtaining the confidential correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data, wherein the confidential correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data includes the confidential correlation between each data file in the urban planning and construction data and each data file in the historical urban and rural planning and construction data.

[0051] Step 10232, through the DBSCAN clustering method, a cluster analysis is performed on the confidentiality correlation between each data file in the urban planning and construction data and each data file in the historical urban and rural planning and construction data, wherein the distance between any two data files is taken as the inverse of the confidentiality correlation between the two data files, the scanning radius of the cluster analysis is taken as 5, which reflects the scope of the cluster analysis, and the minimum number of included points of the cluster analysis is taken as 20, which reflects the minimum number of data files for cluster analysis.

[0052] Cluster analysis can obtain the distribution trend of confidentiality correlation between data files, and the data files with stronger confidentiality correlation are more likely to be distributed in the same cluster. The closer the distribution position of a data file is to the cluster center of its corresponding cluster, the closer the confidentiality level of the data file is to the confidentiality level corresponding to the cluster center.

[0053] like Figure 2 As shown, there are still some data files in the edge of the cluster that have low confidentiality relevance to the data files in the cluster, and different clusters represent data files of different confidentiality levels, which require different encryption methods.

[0054] Step 10233, based on the cluster analysis result, determine the comparability of the confidentiality level of a data file in the historical urban and rural planning and construction data with the confidentiality level of each data file in the urban planning and construction data.

[0055] The clustering analysis results can reflect the clustering of data files corresponding to each confidentiality level. When the confidentiality correlation between data files of known confidentiality levels and cluster centers is large, the confidentiality level of data files of unknown confidentiality levels can be predicted with higher comparability for reference.

[0056] In some embodiments, step 10233 is implemented according to the following formula: ; In the formula, The data file set corresponding to the non-outlier points in the cluster representing the cluster analysis results is The comparability of the confidentiality levels of the data files reflects the The reference degree of confidentiality of each data file, The data file set corresponding to the non-outlier points in the cluster representing the cluster analysis result, that is, the data file set in the cluster, represents the normalization function, The data file set corresponding to the non-outlier points in the cluster representing the cluster analysis results is The sequence number of the data file, z represents the sequence number of the zth data file in the data file set corresponding to the non-outlier point in the cluster of the cluster analysis result, represents the maximum value function, The data file set corresponding to the non-outlier points in the cluster representing the cluster analysis results is The confidentiality correlation between the zth data file and the zth data file reflects the similarity of the confidentiality of the two data files.

[0057] when At that time, Data files belong to the set of data files in the cluster At that time, The value of the comparability of the confidentiality level of the data file is a constant value of 1, which means that the first The data file set where the data file is located; At that time, Data files do not belong to the set of data files in the cluster At that time, The value of the comparability of the confidentiality level of the data file is data files and the set of data files belonging to the cluster The normalized value of the maximum confidentiality correlation between the z-th data files, so that Data files do not belong to the set of data files in the cluster When you find the right A collection of data files with data files, The larger the value is, the stronger the confidentiality correlation between the two data files is. The bigger, The larger it is, the stronger the contrast.

[0058] Step 10234, based on comparability, determine the probability that the confidentiality level of a data file in the historical urban and rural planning and construction data is the confidentiality level of each data file in the urban planning and construction data.

[0059] Through data files of various known confidentiality levels, data files of unknown confidentiality levels are determined. At each confidentiality level, based on comparison, data files of unknown confidentiality levels with a high degree of similarity with data files of known confidentiality levels are searched to obtain the confidentiality level probability of data files of unknown confidentiality levels, so as to reflect the possible degree to which the confidentiality level of the data files of unknown confidentiality levels belongs to each confidentiality level.

[0060] In some embodiments, step 10234 is implemented according to the following formula: ; In the formula, The probability that the confidentiality level of the p-th data file in the data file set corresponding to the non-outlier point in the cluster of the cluster analysis result belongs to the k-th confidentiality level reflects the possibility that the confidentiality level of the p-th data file belongs to the k-th confidentiality level. Indicates the minimum number of points included in the cluster analysis, the value is 20. Indicates A collection of data files with confidentiality level, i.e. The clustering set of confidentiality-level data files, The data file set corresponding to the non-outlier points in the cluster representing the cluster analysis results is The confidentiality correlation between the pth data file and the pth data file reflects the similarity of the confidentiality levels of the two data files, and k represents the labels of the five confidentiality levels. The larger the The greater the confidentiality level of a data file, the greater the reference level. The larger the value is, the more similar the confidentiality of the two data files is, and the more likely it is that the confidentiality level of the pth data file belongs to the kth confidentiality level, that is, The bigger.

[0061] Step 10235, taking the confidentiality level corresponding to the probability of obtaining the maximum value as the confidentiality level of the data file in the urban and rural planning and construction data, thereby obtaining an accurate confidentiality level of the data file.

[0062] In some embodiments, step 10235 is implemented according to the following formula: ; In the formula, Indicates the urban and rural planning and construction data The confidentiality level of the data file, represents the function of finding the maximum point, k takes the value of 1, 2, ..., 5, You can ask for The confidentiality level corresponding to the maximum value is obtained, thereby obtaining the accurate confidentiality level of the data file.

[0063] Step 103, based on the confidentiality level of the urban and rural planning and construction data, the urban and rural planning and construction data are securely shared, thereby ensuring the security of the data to prevent the data from being leaked during sharing.

[0064] In some embodiments, the confidentiality level of the urban and rural planning and construction data includes top secret extremely sensitive, confidential highly sensitive, secret medium sensitive, internally disclosed, and externally disclosed. Based on the confidentiality level of the urban and rural planning and construction data, step 103 includes: Step 1031: For data files with high confidentiality levels in urban and rural planning and construction, a high-strength encryption algorithm is used to encrypt them and then share them securely to ensure that the data files cannot be cracked. The high confidentiality level includes top secret and extremely sensitive. Highly sensitive and confidential .

[0065] Highly confidential data files (top secret and extremely sensitive : Major financial data; confidential and highly sensitive : Future land development and infrastructure construction plans) need to be shared after high-intensity encryption to ensure data security.

[0066] In some embodiments, the high-strength encryption algorithm may be an AES-256 encryption algorithm.

[0067] Step 1032: For data files with a medium confidentiality level in urban and rural planning and construction, a medium-strength encryption algorithm is used to encrypt them and then share them securely to balance security and sharing efficiency. The medium confidentiality level includes secret, medium sensitivity, .

[0068] Medium-level confidentiality data files (Secret Medium Sensitivity : Approved projects, bidding data, environmental assessments) need to be shared after medium-intensity encryption to achieve a balance between security and sharing efficiency.

[0069] In some embodiments, the medium strength encryption algorithm may be an AES-128 encryption algorithm.

[0070] Step 1033: For data files with low confidentiality levels in urban and rural planning and construction, a lightweight encryption algorithm is used to encrypt them and then share them securely to improve sharing efficiency. The low confidentiality level includes internal public and external disclosure .

[0071] Low-level confidentiality data files (open to the public) :Business process, planning and research data, external disclosure : Published planning and public transportation information) need to be shared after lightweight encryption to improve sharing efficiency.

[0072] In some embodiments, the lightweight encryption algorithm may be a DES encryption algorithm.

[0073] In some embodiments, different roles (such as public users, ordinary staff, senior managers, and core decision makers) are defined for different accounts based on the user's identity and needs, and corresponding data access permissions are assigned to ensure that users can only access data within their authority, so that shared data users have access permissions to ensure data security.

[0074] In some embodiments, whether access is allowed is determined based on the user's role and permissions, and the decrypted data is delivered to the user after the access is performed. At the same time, a data access log is recorded to facilitate subsequent auditing and monitoring.

[0075] In summary, the embodiment of the present invention provides a method for securely sharing urban and rural planning and construction data, which can effectively identify the hierarchy and sensitivity of urban and rural planning and construction data, so that key data can be protected in a timely manner and non-key data can be protected accordingly, thereby avoiding excessive protection of non-key data and improving sharing efficiency.

[0076] In a second aspect, an embodiment of the present invention provides a system for securely sharing urban and rural planning and construction data. Figure 3 The system comprises: The acquisition module 301 is used to acquire urban and rural planning and construction data. The urban and rural planning and construction data include basic geographic information data, socio-economic data, land use data, environmental resource data, infrastructure data, and building and housing data. Different types of urban and rural planning and construction data include data files with text data and non-text data.

[0077] The determination module 302 is used to classify the confidentiality level of the urban and rural planning and construction data according to the sensitivity of the urban and rural planning and construction data, and determine the confidentiality level of the urban and rural planning and construction data.

[0078] The sharing module 303 is used to securely share the urban and rural planning and construction data based on the confidentiality level of the urban and rural planning and construction data.

[0079] In summary, the embodiment of the present invention provides a system for securely sharing urban and rural planning and construction data. The system can effectively identify the hierarchy and sensitivity of urban and rural planning and construction data, so that key data can be protected in a timely manner and non-key data can be protected accordingly, thereby avoiding excessive protection of non-key data and improving sharing efficiency.

[0080] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for securely sharing urban and rural planning and construction data, characterized in that: The method comprises: Acquire urban and rural planning and construction data, the urban and rural planning and construction data including basic geographic information data, socio-economic data, land use data, environmental resource data, infrastructure data, and building and housing data, and different types of urban and rural planning and construction data include data files with text data and non-text data; According to the sensitivity of the urban and rural planning and construction data, classify the urban and rural planning and construction data into confidentiality levels and determine the confidentiality level of the urban and rural planning and construction data; Based on the confidentiality level of the urban and rural planning and construction data, the urban and rural planning and construction data are securely shared.

2. A method for securely sharing urban and rural planning and construction data according to claim 1, characterized in that: The method of classifying the urban and rural planning and construction data according to the sensitivity of the data and determining the confidentiality level of the urban and rural planning and construction data includes: The confidentiality level of historical urban and rural planning and construction data is obtained, including top secret and extremely sensitive, confidential and highly sensitive, secret and moderately sensitive, internally disclosed, and externally disclosed; Determining the confidential relevance between the urban and rural planning and construction data and the historical urban and rural planning and construction data; The confidentiality level of the urban and rural planning and construction data is determined based on the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data and the confidentiality level of the historical urban and rural planning and construction data.

3. A method for securely sharing urban and rural planning and construction data according to claim 2, characterized in that: The determining of the confidential correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data includes: Extracting text data from each data file in the urban and rural planning and construction data; Determine the word vector of the text data of each data file in the urban and rural planning and construction data; According to the following formula, the confidential representativeness of the word vector of the text data of each data file in the urban and rural planning and construction data is determined: ； In the formula, Indicates the urban and rural planning and construction data word vectors for data files Confidential representation of Indicates the urban and rural planning and construction data word vectors for data files The inverse document frequency of represents the function of finding similarity, Representing existence and word vectors The same word vector of historical urban and rural planning and construction data The word vector set of text data in the data file, The historical urban and rural planning and construction data The confidentiality level corresponding to the data file is , Indicates the urban and rural planning and construction data The text data of the data file The word vector corresponding to the word, Indicates the urban and rural planning and construction data The ordinal number of the word in the text data of the data file, Indicates the number of data files in urban and rural planning and construction data. Indicates the number of data files in the historical urban and rural planning and construction data; Based on the confidentiality representativeness of the word vector of the text data of each data file in the urban and rural planning and construction data, the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data is determined.

4. A method for securely sharing urban and rural planning and construction data according to claim 3, characterized in that: Determine the confidential relevance between the urban and rural planning and construction data and historical urban and rural planning and construction data, including: According to the following formula, based on the storage ratio of the text data of each data file in the urban and rural planning and construction data to the data file, the number of confidential representative words in each data file in the urban and rural planning and construction data is determined: ； In the formula, Indicates the urban and rural planning and construction data The number of confidential representative words in the data file, Indicates the urban and rural planning and construction data The storage ratio of text data in a data file, Indicates the urban and rural planning and construction data The number of word vectors contained in the data file, represents a hyperparameter, with a value of 20; The confidentiality representativeness of the word vectors of the text data of each data file in the urban and rural planning and construction data is sorted from large to small, and the words corresponding to the confidentiality representativeness of the word vectors having the same number as the number of confidential representative words of a data file are selected from the sorted sequence from large to small as the confidential representative words of the data file; The confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data is determined according to the following formula: ； In the formula, Indicates the urban and rural planning and construction data The data files and historical urban and rural planning and construction data confidential correlation between data files, represents a function that finds the cosine similarity between two vectors. Indicates the urban and rural planning and construction data The number of confidential representative words in the data file, The historical urban and rural planning and construction data The word vector corresponding to the lth word of the text data of the data file, l represents the lth word of the historical urban and rural planning and construction data The ordinal number of the word in the text data of a data file.

5. A method for securely sharing urban and rural planning and construction data according to claim 2, characterized in that: Determining the confidentiality level of the urban and rural planning and construction data based on the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data and the confidentiality level of the historical urban and rural planning and construction data includes: Acquire the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data, wherein the confidentiality correlation between the urban and rural planning and construction data and the historical urban and rural planning and construction data includes the confidentiality correlation between each data file in the urban planning and construction data and each data file in the historical urban and rural planning and construction data; By using DBSCAN clustering method, cluster analysis is performed on the confidentiality correlation between each data file in the urban planning and construction data and each data file in the historical urban and rural planning and construction data, wherein the distance between any two data files is the inverse of the confidentiality correlation between the two data files, the scanning radius of the cluster analysis is 5, and the minimum number of included points of the cluster analysis is 20; Determine, based on the cluster analysis result, the comparability of the confidentiality level of a data file in the historical urban and rural planning and construction data to the confidentiality level of each data file in the urban planning and construction data; Based on the comparability, determine the probability that the confidentiality level of a data file in the historical urban and rural planning and construction data is the confidentiality level of each data file in the urban planning and construction data; The confidentiality level corresponding to the probability of obtaining the maximum value is used as the confidentiality level of the data file in the urban and rural planning and construction data.

6. A method for securely sharing urban and rural planning and construction data according to claim 5, characterized in that: The confidentiality level of a data file in the historical urban and rural planning and construction data is determined to be comparable to the confidentiality level of each data file in the urban planning and construction data according to the following formula: ； In the formula, The first data file set corresponding to the non-outlier point in the cluster of the cluster analysis result Comparability of confidentiality levels of data files, A set of data files corresponding to non-outlier points in the clusters representing the cluster analysis results, represents the normalization function, The first data file set corresponding to the non-outlier point in the cluster of the cluster analysis result The sequence number of the data file, z represents the sequence number of the zth data file in the data file set corresponding to the non-outlier point in the cluster of the cluster analysis result, represents the maximum value function, The first data file set corresponding to the non-outlier point in the cluster of the cluster analysis result The confidential correlation between the zth data file and the zth data file.

7. A method for securely sharing urban and rural planning and construction data according to claim 6, characterized in that: The probability that the confidentiality level of a data file in the historical urban and rural planning and construction data is the confidentiality level of each data file in the urban planning and construction data is determined according to the following formula: ； In the formula, represents the probability that the confidentiality level of the p-th data file in the data file set corresponding to the non-outlier point in the cluster of the cluster analysis result belongs to the k-th confidentiality level, Indicates the minimum number of points included in the cluster analysis, the value is 20. Indicates A collection of confidential data files. The first data file set corresponding to the non-outlier point in the cluster of the cluster analysis result The confidentiality correlation between the pth data file and the pth data file, k represents the labels of the five confidentiality levels.

8. A method for securely sharing urban and rural planning and construction data according to claim 7, characterized in that: According to the following formula, the confidentiality level corresponding to the probability of obtaining the maximum value is used as the confidentiality level of the data file in the urban and rural planning and construction data: ； In the formula, Indicates the urban and rural planning and construction data The confidentiality level of the data files, Represents the function of finding the maximum point, and k takes values ​​of 1, 2, ..., 5.

9. A method for securely sharing urban and rural planning and construction data according to claim 1, characterized in that: The confidentiality levels of urban and rural planning and construction data include top secret, extremely sensitive, confidential, highly sensitive, secret, moderately sensitive, internally disclosed, and externally disclosed. Based on the confidentiality level of urban and rural planning and construction data, the urban and rural planning and construction data are securely shared, including: For data files with high confidentiality levels in urban and rural planning and construction, they are encrypted using high-strength encryption algorithms and then shared securely to ensure that the data files cannot be cracked. The high confidentiality levels include top secret, extremely sensitive, and confidential, highly sensitive; For data files with a medium confidentiality level in urban and rural planning and construction, they are encrypted using a medium-strength encryption algorithm and then securely shared to balance security and sharing efficiency. The medium confidentiality level includes secrets of medium sensitivity; For data files with a low confidentiality level in urban and rural planning and construction, a lightweight encryption algorithm is used to encrypt them and then share them securely to improve sharing efficiency. The low confidentiality level includes internal and external disclosure.

10. A system for secure sharing of urban and rural planning and construction data, characterized in that: The system comprises: An acquisition module is used to acquire urban and rural planning and construction data, wherein the urban and rural planning and construction data includes basic geographic information data, social and economic data, land use data, environmental resource data, infrastructure data, and building and housing data. Different types of urban and rural planning and construction data include data files with text data and non-text data. A determination module, used to classify the urban and rural planning and construction data into confidentiality levels according to the sensitivity of the urban and rural planning and construction data, and determine the confidentiality level of the urban and rural planning and construction data; The sharing module is used to securely share the urban and rural planning and construction data based on the confidentiality level of the urban and rural planning and construction data.

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