File resource multi-dimensional integration analysis method

Abstract

The invention discloses an archive resource multi-dimensional integration analysis method, relates to the technical field of resource classification and integration, solves the problem of large index process difference caused by feature difference of different archive resources, and further forms a polygon to be integrated by constructing a verification polygon and determining feature nodes based on feature data columns. Compared with a traditional numerical variance processing mode, the innovative geometric model construction mode can achieve integration and classification of archive resources more visually and rapidly; according to the expression of the polygon in the preset storage area, the archive resources with similar characteristics can be efficiently classified into one class, the classification efficiency is greatly improved, and the requirement for rapid classification in large-scale archive resource management is met.

Description

Technical Field

[0001] This invention relates to the field of resource classification and integration technology, specifically to a method for multidimensional integration and analysis of archival resources. Background Art

[0002] In today's digital age, the scale of archival resources is expanding rapidly at an unprecedented rate; the number of archives accumulated by government agencies, enterprises and institutions, as well as various scientific research and cultural organizations, is growing explosively; from paper documents to electronic files, from structured data to unstructured text, images, audio, video and other diverse forms, the types of archival resources are becoming increasingly complex. Application CN114742462A discloses a big data-based archive security management system, including an archive information collection module, an archive information integration module, and an archive data retrieval module. The archive information collection module is used to collect information that enterprises need to archive. The archive information integration module is used to integrate the collected enterprise information before archiving. The archive data retrieval module is used to manage the archives that need to be retrieved securely. By setting up the archive information collection module, the archive information of various departments that enterprises need to preserve can be entered into the archive storage database. The paper information is converted into electronic archive information through the OCR recognition module, thereby realizing the scientific and effective management of archive resources.

[0003] During the multidimensional integration and analysis of archival resources, they are stored in different storage spaces based on their classification. However, due to the different characteristics of different archival resources, the indexing process varies greatly in the subsequent resource indexing process, which affects the specific indexing progress and increases the indexing time. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-dimensional integrated analysis method for archival resources, which solves the problem of significant differences in the indexing process caused by the differences in the characteristics of different archival resources.

[0005] To achieve the above objectives, the present invention provides a method for multidimensional integration and analysis of archival resources, comprising the following steps: Step 1: Identify the different archival contents associated with different data types from different archival resources, identify and record the content characteristics of different archival contents from different archival resources, specifically as follows: For a single set of archival resources, identify the different archival contents associated with different data types, determine the total number of characteristic characters corresponding to different archival contents, and label them as ZS. i-k, where i represents different archive resources and k represents different data types; Confirm the preset encoding characteristics of the corresponding data type, using: ZS i-k × Coding feature = Content feature, confirming the content features associated with the corresponding data type and the archive content. Different data types correspond to different coding features. Step Two: Based on the content characteristics associated with different archival resources and their contents, identify the characteristic data columns associated with the corresponding archival resources and construct a set of verification polygons. Determine the feature nodes of the characteristic data columns on the verification polygons, then lock the polygons to be integrated associated with the characteristic data columns. Based on the feature representation of different polygons to be integrated, integrate and classify several different archival resources. The specific method is as follows: The feature data columns associated with different archival resources are labeled, and the relevant verification polygons are constructed based on the total number G of different data types: Randomly select a set of center points on the plane, and extend a set of bisectors from these center points. Then, using these center points as the center, divide the circle around the center point into G bisectors. The data types associated with each different bisector are different. From the feature data columns associated with several archival resources, identify several sets of content features associated with the corresponding data type, select the maximum value from the several sets of content features, take the maximum value as the total value of the corresponding dividing line, and divide the dividing line equally according to this total value. Connect the endpoints of adjacent dividing lines to generate a verification polygon for several sets of archive resources in this batch; Based on the feature data columns associated with different archival resources, the bisectors associated with the corresponding content features are identified on the bisectors set within the verification polygon, and the feature nodes associated with this content feature are locked on the corresponding bisectors. The feature nodes associated with different content features within the feature data column are confirmed in sequence, and several groups of adjacent feature nodes are connected to confirm the polygon to be integrated belonging to this feature data column. The different polygons to be integrated associated with different archival resources are confirmed in sequence. Based on the total number of storage areas H preset in this batch, the verification polygon is divided into H polygon regions: the different dividing lines within the verification polygon are divided into H equal segments, the dividing nodes associated with the segments are confirmed, and starting from the set center point, the associated dividing nodes are connected outwards in sequence to confirm the different dividing regions. This verification polygon is then divided into H polygon regions, with different polygon regions corresponding to different storage areas. Identify and record the total length data of different sides of the polygon to be integrated in different polygon regions as L. q Where q represents different polygonal regions, and L qThe polygonal region associated with max is called the selected region, and its L... q max is a set of total length data L q The maximum value; Polygons belonging to the same selected area to be integrated are recorded as polygons with the same characteristics, and multiple sets of archive resources associated with polygons with the same characteristics are recorded as archive resources with the same characteristics. Archive resources with the same characteristics are stored in the same storage area. Step 3: Based on the different archive resources stored in the same storage area, and considering the different content characteristics associated with each archive resource, identify the encryption key associated with that archive resource and encrypt it accordingly. The specific method is as follows: S31. Based on the feature data columns associated with different archival resources, confirm the proportion of content features within the corresponding feature data columns: label the different content features within the feature data columns as R. k Where k represents different content features, the sum of several content features in the feature data column is then confirmed and labeled as Z. k ; Using: R k ÷Z k =B k Confirm the proportion of features associated with the corresponding content features (B) k ; S32, Based on the proportion of features B k Sort the data from smallest to largest to determine the percentage sequence. Based on this percentage sequence and the storage time associated with this file resource, determine a set of encrypted circles. S33. Based on the baseline, dividing line and time feature line marked within the encryption circle, the area between adjacent line segments is recorded as the feature area. Starting from the baseline, following the clockwise confirmation direction, the area of ​​several feature areas is compared to determine the ratio sequence. The ratios from front to back are extracted from the ratio sequence, and the extracted ratios are reordered according to the sorting method of the ratio sequence to confirm the encryption key belonging to this archive resource.

[0006] Preferably, the method for confirming the encrypted circle is as follows: A set of circles with radius R is randomly generated, where R is a preset value. The center of the circle is connected to the top vertex of the circumference to confirm the baseline. Based on the baseline and rotating clockwise, the first set of radiation regions is confirmed. The area of ​​the radiation region is the same as the proportion of the first set of features in the proportion sequence. Similarly, based on the proportion of different features associated in the proportion sequence and the clockwise confirmation direction, the circle is divided into several radiation regions of different areas, and the dividing line between adjacent radiation regions is recorded. Next, confirm the storage time associated with this file resource, select the hour and minute times, confirm the specific positions of the hour and minute times on the clock, and use the baseline of the encrypted circle as the 0th hour to perform position mapping, confirm the time mapping point, connect the center of the circle with the mapping point, and confirm the two sets of time feature lines.

[0007] This invention provides a method for multidimensional integrated analysis of archival resources. Compared with existing technologies, it has the following advantages: This invention constructs a verification polygon and determines feature nodes based on feature data columns to form a polygon to be integrated. This innovative geometric model construction method can achieve the integration and classification of archival resources more intuitively and quickly compared with traditional numerical variance processing methods. Based on the performance of the polygon in the preset storage area, archival resources with similar characteristics can be efficiently grouped into one category, greatly improving classification efficiency and meeting the needs of rapid classification in large-scale archival resource management. An encryption circle is generated based on the content feature proportions of archival resource characteristic data columns, and a time feature line is determined by combining the archive storage time, thereby deriving the encryption key. This encryption method fully utilizes the characteristics and storage time information of the archival resources themselves. Each archival resource has its own unique encryption key, which greatly improves the security and specificity of encryption. The encryption key is generated by processing the area ratio between adjacent line segments. Its encryption logic is complex, making it difficult for unauthorized external access to crack, effectively protecting the privacy and security of archival resources, and meeting the application scenarios with high requirements for the confidentiality of archival resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 Schematic diagram of the process of the present invention; Figure 2 This is a schematic diagram illustrating the confirmation of polygons to be integrated from the archival resources of this invention. DETAILED DESCRIPTION

[0009] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0010] First embodiment See also Figure 1 This application provides a method for multidimensional integration and analysis of archival resources, including the following steps: Step 1: Confirm the characteristics of several sets of archival resources associated with this batch. Identify the different archival contents associated with different data types from different archival resources. Confirm and record the content characteristics of different archival contents from different archival resources. Specifically, different types of data content have different characteristic character conversions when performing binary encoding conversion. For example, for text format data content, a single text character is generally associated with four sets of binary encoding characters. Different data contents with different data formats are associated with different binary codes. The specific methods for recording the content characteristics of different archival resources and contents are as follows: For a single set of archival resources, identify the different archival contents associated with different data types, determine the total number of characteristic characters corresponding to different archival contents, and label them as ZS. i-k , where i represents different archive resources and k represents different data types; Confirm the preset encoding characteristics of the corresponding data type, using: ZS i-k × Coding feature = Content feature, confirming the content features associated with the corresponding data type and the file content. Different data types correspond to different coding features, all of which are formulated in advance by the operators based on experience. Specifically, different archive contents correspond to different data types, and the characteristic characters associated with different data types are all different. Therefore, each different archive resource has different content characteristics in different data types. Feature records can be made based on the content characteristics of different data types, which makes it convenient to classify different archive resources in this batch. Step 2: Based on the content characteristics associated with different archival resources and their contents, identify the characteristic data columns associated with the corresponding archival resources and construct a set of verification polygons. Determine the feature nodes of the characteristic data columns on the verification polygons, and then lock the polygons to be integrated associated with the characteristic data columns. Based on the feature representation of different polygons to be integrated, integrate and classify several different archival resources. The specific sub-steps for integration and classification are as follows: S21. Label the feature data columns associated with different archival resources, and construct the relevant verification polygons based on the total number G of different data types: Randomly select a set of center points on the plane, and extend a set of bisectors from these center points. Then, using these center points as the center, divide the circle around the center point into G bisectors. The data types associated with each different bisector are different. From the feature data columns associated with several archival resources, identify several sets of content features associated with the corresponding data type, select the maximum value from the several sets of content features, use the maximum value as the total value of the corresponding dividing line, and divide the dividing line into equal parts according to this total value. Specifically, different dividing lines correspond to different data types, and the total measurement length of the dividing line is the maximum value associated with the corresponding content feature. Divide the dividing line into equal parts according to the total measurement length, and then each division scale corresponds to a different scale parameter. Connect the endpoints of adjacent dividing lines to generate a verification polygon for several sets of archive resources in this batch; S22. Based on the feature data columns associated with different archival resources, on the bisectors set within the verification polygon, confirm the bisectors associated with the corresponding content features, and lock the feature nodes associated with this content feature on the corresponding bisectors. Confirm the feature nodes associated with different content features within the feature data column in sequence, and connect several groups of adjacent feature nodes to confirm the polygon to be integrated belonging to this feature data column. S23. Confirm the different polygons to be integrated associated with different archival resources in sequence; S24. Based on the total number of storage areas H preset in this batch, the verification polygon is divided into H polygon regions: the different dividing lines in the verification polygon are divided into H equal segments, the dividing nodes associated with the dividing segments are confirmed, and the corresponding dividing nodes are connected outward from the set center point to confirm the different dividing regions. The verification polygon is then divided into H polygon regions, and different polygon regions correspond to different storage areas. S25. Identify the total length data of different sides of the polygon to be integrated located in different polygon regions and record it as L. q Where q represents different polygonal regions, and L q The polygonal region associated with max is called the selected region, and its L... q max is a set of total length data L q The maximum value, specifically, if there exist the same L q If the maximum value is set, then a set of associated polygon regions can be randomly selected for calibration of the selected region. S26. The polygons to be integrated that belong to the same selected area are recorded as polygons with the same feature, and the multiple sets of archive resources associated with the polygons with the same feature are recorded as archive resources with the same feature. Archive resources with the same feature are stored in the same storage area. Specifically, the feature data columns associated with different archive resources are different. Different feature data columns have different polygon features when verifying polygons. Based on the multiple manifestations of the corresponding polygons, the specific classification of the corresponding polygons can be locked. Based on the specific feature classification, archive resources with relative clustering features can be effectively classified quickly. Compared with the numerical variance processing method, its classification method is faster and the classification efficiency is higher. Combine Figure 2 The total number of data types is six, so a set of six-endpoint verification polygons is constructed. There are six equal-dividing lines inside the verification polygons. Different equal-dividing lines are associated with different scales, and each different equal-dividing line is associated with a different data type and a different measurement length. According to the feature sequence associated with the corresponding archive resource, the corresponding feature nodes are identified on the corresponding equal-dividing lines. Then, the feature nodes are connected to identify the polygon to be integrated associated with this archive resource. Then, based on the polygon areas identified in sequence inside the verification polygon (that is, the areas contained between adjacent dotted lines), the specific division of the corresponding polygon to be integrated is determined to perform feature classification and achieve the overall processing effect of quickly determining the resource classification. Step 3: Based on the different archive resources stored in the same storage area, and considering the different content characteristics associated with each archive resource, identify the encryption key associated with that archive resource, and encrypt it accordingly. The specific encryption method is as follows: S31. Based on the feature data columns associated with different archival resources, confirm the proportion of content features within the corresponding feature data columns: label the different content features within the feature data columns as R. k Where k represents different content features, the sum of several content features in the feature data column is then confirmed and labeled as Z. k ; Using: R k ÷Z k =B k Confirm the proportion of features associated with the corresponding content features (B) k ; S32, Based on the proportion of features B kSort the circles from smallest to largest to confirm the proportion sequence. Based on this proportion sequence, confirm a set of encrypted circles: randomly generate a set of circles with radius R, where R is a preset value, and the specific value is determined by the operator based on experience. Connect the center of the circle to the top vertex of the circle to confirm the baseline. Based on the baseline, rotate clockwise to confirm the first set of radiation areas. The area of ​​the radiation area is consistent with the proportion of the first set of features in the proportion sequence. And so on. Based on the different feature proportions associated in the proportion sequence and the clockwise confirmation direction, divide the circle into several radiation areas of different areas and record the dividing lines between adjacent radiation areas. Next, confirm the storage time associated with this file resource, select the hour and minute times, confirm the specific positions of the hour and minute times on the clock, and use the baseline of the encryption circle as the 0th hour to perform position mapping, confirm the time mapping point, connect the center of the circle with the mapping point, confirm the two sets of time feature lines, the encryption circle includes not only the dividing line, but also the corresponding time feature lines, and the time scale associated with the time lines can be found on the ring to find the corresponding time point. Then, the corresponding storage time can be combined with the encryption circle to determine the corresponding overall encryption circle, thereby performing the specific mapping of the time feature lines; S33. Based on the baseline, dividing lines, and time feature lines marked within the encryption circle, the area between adjacent line segments is recorded as the feature area. Starting from the baseline, following the clockwise confirmation direction, the area ratios of several feature areas are processed to confirm the ratio sequence. The ratios from front to back are extracted from the ratio sequence, and the extracted ratios are reordered according to the sorting method of the ratio sequence to confirm the encryption key belonging to this file resource. For example, after a specific segmentation process, the feature areas include six groups. After analyzing the area ratio of the six groups of feature areas, the resulting ratio sequence is {12:21:6:8:10:11}. After extracting the confirmed ratios, the encryption key is confirmed based on the set ratios. The encryption key is represented as: 1221681011. The execution code involved in its encryption process is as follows: #S31: Confirm the content feature ratio within the corresponding feature data column: def calculate_feature_ratio(feature_data): #Store different content characteristics and their quantity content_features={} forfeatureinfeature_data: iffeaturenotincontent_features: content_features[feature]=0 content_features[feature] += 1 total_features=len(feature_data) feature_ratios={} fork,countincontent_features.items(): feature_ratios[k]=count / total_features returnfeature_ratios #S32: Generate encryption circles and time feature lines based on feature proportions defgenerate_encryption_circle(feature_ratios,storage_time): # Randomly generate a set of circles with radius R, where R is a preset value. R=10 # This assumes a default value of 10, which can be modified according to the actual situation. #Sorting by Percentage sorted_ratios=sorted(feature_ratios.values()) #Delineation of radiation zones division_lines=[ current_angle=0 forratioinsorted_ratios: #Calculate the angle of the current radiation area angle = ratio * 360 #Record the angle of the separator line current_angle += angle division_lines.append(current_angle) #Processing storage time hour, minute = storage_time #Calculate the angle corresponding to time on a clock face hour_angle=(hour%12)*30+(minute / 60)*30 minute_angle = minute * 6 #Using the baseline of the encrypted circle as time 0, perform position mapping. time_feature_lines=[hour_angle,minute_angle] returndivision_lines,time_feature_lines #S33: Generate encryption key defgenerate_encryption_key(division_lines,time_feature_lines): all_lines=[0]+division_lines+time_feature_lines all_lines.sort() #Calculate the ratio of the areas between adjacent line segments area_ratios=[ foriinrange(len(all_lines)-1): Since the radius of a circle is fixed, the ratio of their areas equals the ratio of their angles. ratio=(all_lines[i+1]-all_lines[i]) / 360 area_ratios.append(ratio) # Sort by comparison values sorted_ratios=sorted(area_ratios) return sorted_ratios.

[0011] Second embodiment This embodiment mainly focuses on the decryption procedure associated with step three: The specific operations associated with its decryption process are as follows: The operator verifies their identity, and once the identity verification is successful, the decryption program is initiated. Based on the storage records associated with different archive resources within the corresponding storage area, identify the characteristic data columns associated with the corresponding archive resources and the storage time; The feature data column and storage time are processed in the same way as steps S31-S33 to confirm the decryption key associated with the corresponding file resource; The archive resources are then decrypted using this decryption key.

[0012] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0013] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A multi-dimensional integrated analysis method for archival resources, characterized in that: The following steps are involved: Step 1: confirm different archival contents associated with different data types from different archival resources, confirm the content characteristics of different archival contents in different archival resources and record them; Step 2: Based on the content features associated with different archive contents of different archive resources, confirm the feature data columns associated with the corresponding archive resources, and construct a set of verification polygons. Determine the feature nodes of the feature data columns on the verification polygons, and then lock the polygons to be integrated associated with the feature data columns. Based on the feature performances of different polygons to be integrated, integrate and classify several different archive resources; Step 3: According to different archive resources stored in the same storage area, based on different content features associated with the corresponding archive resources, confirm the encryption key associated with the corresponding archive resource and encrypt it.

2. A method for multi-dimensional integration analysis of archive resources according to claim 1, characterized in that: In step 1, the specific method of recording the content characteristics of different archive contents of different archive resources is: Confirm the different archive contents associated with different data types in a single set of archive resources, confirm the total number of characteristic characters corresponding to different archive contents, and mark them as ZS i-k , where i represents different archive resources and k represents different data types; Confirm the preset encoding features for the corresponding data type, using: ZS i-k × Coding feature = content feature, confirm the content feature associated with the corresponding file content corresponding to the data type, and different data types correspond to different coding features.

3. A method for multi-dimensional integration and analysis of archive resources according to claim 1, characterized in that: In step 2, the specific method of constructing the verification polygon is: The characteristic data columns associated with different archival resources are calibrated, and the relevant construction of the verification polygon is performed according to the total number G of different data types: A set of center points are randomly selected on the plane, and a set of bisectors are extended from the center points. Then, with the center points as the center, G bisectors are equally divided around the circumference of the center points. The data types associated with different bisectors are different. From the characteristic data columns associated with the several archive resources, several groups of content characteristics associated with the corresponding data types are identified, and the maximum value is selected from the several groups of content characteristics, and the maximum value is used as the total value of the corresponding equal division line, and the equal division line is divided into equal parts according to the total value; Connect the endpoints of adjacent bisectors to generate verification polygons for several groups of archival resources in this batch.

4. A method for multi-dimensional integration analysis of archive resources according to claim 3, characterized in that: In the step 2, the specific method of locking the polygon to be integrated associated with the feature data column is: According to the characteristic data columns associated with different archival resources, the bisectors associated with the corresponding content features are confirmed on the bisectors set within the verification polygon, and the characteristic nodes associated with this content feature are locked on the corresponding bisectors. The characteristic nodes associated with different content features in the characteristic data columns are confirmed in turn, and several groups of adjacent characteristic nodes are connected to confirm the polygons to be integrated belonging to this characteristic data column, and the different polygons to be integrated associated with different archival resources are confirmed in turn.

5. A method for multi-dimensional integration and analysis of archive resources according to claim 4, characterized in that: In step 2, the specific method of integrating and classifying a number of different archive resources is as follows: According to the total number of storage areas H preset in this batch, the verification polygon is divided into H polygonal areas: different bisectors in the verification polygon are divided into H equal segments, and the bisector nodes associated with the equal segments are confirmed. Starting from the set center point, the associated bisector nodes are connected outward in sequence, and different bisector areas are confirmed. The verification polygon is divided into H polygonal areas, and different polygonal areas correspond to different storage areas; Identify the total length data of different sides of the polygon to be integrated in different polygon areas and record it as L q , where q represents different polygonal areas, L q The polygonal area associated with max is recorded as the selected area, and its L q max is a number of total length data L q The maximum value of The polygons to be integrated belonging to the same selected area are recorded as polygons with the same characteristics, and multiple groups of archive resources associated with the polygons with the same characteristics are recorded as archive resources with the same characteristics, and the archive resources with the same characteristics are stored in the same storage area.

6. A method for multi-dimensional integration and analysis of archive resources according to claim 1, characterized in that: In step 3, the specific method of encrypting the archive resources is as follows: S31. Based on the characteristic data columns associated with different archive resources, determine the content feature proportions within the corresponding characteristic data columns: mark the different content features within the characteristic data columns as R k , where k represents different content features, and then the sum of several content features in the feature data column is confirmed and marked as Z k ; Use: R k ÷Z k =B k Confirm the feature ratio B associated with the corresponding content feature k ; S32, based on feature proportion B k Sort from small to large to confirm the proportion sequence, and confirm a group of encrypted circles based on the proportion sequence and the storage time associated with the archive resource; S33. Based on the baseline, separation line and time characteristic line marked in the encryption circle, the area included between adjacent line segments is recorded as the characteristic area. Starting from the baseline, the area of ​​several characteristic areas is ratio-processed according to the clockwise confirmation direction to confirm the ratio sequence, and the ratios of the ratio sequence from front to back are extracted. The extracted ratios are re-sorted according to the sorting method of the ratio sequence to confirm the encryption key belonging to this archive resource.

7. A method for multi-dimensional integration and analysis of archive resources according to claim 6, characterized in that: The confirmation method of the encrypted circle is: A group of circles with a radius of R is randomly generated, where R is a preset value. The center of the circle is connected to the upper vertex of the circle to confirm the baseline. The first group of radiation areas is confirmed in a clockwise direction along the baseline. The area ratio of the radiation area in the entire circle is consistent with the first group of feature ratios in the ratio sequence. Similarly, the circle is divided into several radiation areas of different areas according to the different feature ratios associated in the ratio sequence and the clockwise confirmation direction, and the dividing lines between adjacent radiation areas are recorded; Then confirm the storage time associated with this archive resource, select the hour and minute, confirm the specific position of the hour and minute on the clock, and use the baseline of the encryption circle as the 0 time, perform position mapping, confirm the time mapping point, connect the center of the circle with the mapping point, and confirm the two sets of time characteristic lines.

8. A method for multi-dimensional integration and analysis of archive resources according to claim 7, characterized in that: For the encrypted archive resources in step 3, a decryption program is involved, and the decryption method of the decryption program specifically includes: The operator performs identity verification, and after the identity verification is correct, the decryption program starts; According to the storage records associated with different archive resources in the corresponding storage area, confirm the characteristic data columns and storage time associated with the corresponding archive resources; The characteristic data column and storage time are processed in the same manner as steps S31-S33 to confirm the decryption key associated with the corresponding archive resource; The archive resources are decrypted based on this decryption key.

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