A multi-dimensional integrated analysis method for archival resources

Abstract

The present invention discloses a multi-dimensional integration analysis method for archival resources, and the present invention relates to the technical field of resource classification and integration, and solves the problem that the difference in characteristics of different archival resources leads to large differences in indexing processes. The present invention constructs a verification polygon and determines a characteristic node based on a characteristic data column, thereby forming a polygon to be integrated. Compared with a traditional numerical variance processing method, this innovative geometric model construction method can realize the integrated classification of archival resources more intuitively and quickly; according to the performance of the polygon in a preset storage area, archival resources with similar characteristics can be efficiently classified into one category, which greatly improves the classification efficiency and meets the demand for rapid classification in large-scale archival resource management.

Description

Technical Field

[0001] The present invention relates to the technical field of resource classification and integration, and in particular to a multi-dimensional integration and analysis method for archive resources. Background Art

[0002] In today's digital age, the scale of archival resources is expanding at an unprecedented rate. Whether it is government agencies, enterprises, institutions, or various scientific research and cultural organizations, the number of archives accumulated is growing explosively. From paper documents to electronic files, from structured data to unstructured text, images, audio, video and other multiple forms, the types of archival resources are becoming more and more complex.

[0003] The application with publication number CN114742462A discloses an archive security management system based on big data, including an archive information collection module, an archive information integration module and an archive data calling module. The archive information collection module is used to collect information that the enterprise needs to archive, the archive information integration module is used to integrate the collected enterprise information before archiving, and the archive data calling module is used to perform security management on archives that need to be called. By providing the archive information collection module, the archive information integration module and the archive data calling module, the archive information of each department that the enterprise needs to save can be entered into the archive storage database, and the paper information can be converted into electronic archive information through the OCR recognition module, so as to realize scientific and effective management of archive resources.

[0004] In the process of multi-dimensional integrated analysis and processing, its archival resources are stored in different storage spaces based on the categories to which they belong. However, with this type of classification processing method, different archival resources have different characteristics. In the subsequent resource indexing process, the differences in the characteristics of different archival resources lead to large differences in the indexing process, which affects the specific indexing progress and increases the indexing time. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a multi-dimensional integrated analysis method for archival resources, which solves the problem of large differences in indexing processes caused by differences in the characteristics of different archival resources.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-dimensional integrated analysis method of archive resources, comprising the following steps:

[0007] Step 1: Confirm different archive contents associated with different data types from different archive resources, confirm the content characteristics of different archive contents in different archive resources and record them. The specific method is as follows:

[0008] 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;

[0009] 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 data type and the archive content, and different data types correspond to different coding features;

[0010] 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 performance of different polygons to be integrated, integrate and classify several different archive resources. The specific method is as follows:

[0011] 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:

[0012] 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.

[0013] 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;

[0014] Connect the endpoints of adjacent bisectors to generate verification polygons for several groups of archive resources in this batch;

[0015] 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 in the verification polygon, and the characteristic nodes associated with the content features are locked on the corresponding bisectors, and the characteristic nodes associated with the 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 the characteristic data columns, and the different polygons to be integrated associated with different archival resources are confirmed in turn;

[0016] 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;

[0017] 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

[0018] The polygons to be integrated belonging to the same selected area are recorded as polygons with the same characteristics, and the 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;

[0019] 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. The specific method is as follows:

[0020] 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 ;

[0021] Use: R k ÷Z k =B k Confirm the feature ratio B associated with the corresponding content feature k ;

[0022] 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;

[0023] 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.

[0024] Preferably, the encryption circle is confirmed in the following manner:

[0025] 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;

[0026] 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.

[0027] The present invention provides a multi-dimensional integrated analysis method for archive resources. Compared with the prior art, it has the following beneficial effects:

[0028] The present invention constructs a check polygon and determines the characteristic nodes based on the characteristic data column to form a polygon to be integrated. This innovative geometric model construction method can realize the integrated classification of archival resources more intuitively and quickly than the traditional numerical variance processing method; according to the performance of the polygon in the preset storage area, archival resources with similar characteristics can be efficiently classified into one category, which greatly improves the classification efficiency and meets the demand for rapid classification in large-scale archival resource management;

[0029] The encryption circle is generated based on the content feature ratio of the characteristic data column of the archival resource, and the time feature line is determined in combination with the archival storage time, and then the encryption key is obtained. This encryption method makes full use of the characteristics and storage time information of the archival resource itself. Each archival resource has its own unique encryption key, which greatly improves the security and pertinence 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 external illegal acquirers 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

[0030] Figure 1 It is a schematic diagram of the process of the present invention;

[0031] Figure 2 It is a schematic diagram for confirming the polygons to be integrated in the archive resources of the present invention. DETAILED DESCRIPTION

[0032] 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 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 creative work are within the scope of protection of the present invention.

[0033] First embodiment

[0034] See also Figure 1 , the present application provides a method for multi-dimensional integrated analysis of archival resources, comprising the following steps:

[0035] Step 1: Confirm the characteristics of several groups of archive resources associated with this batch, confirm different archive contents associated with different data types from different archive resources, confirm and record the content characteristics of different archive contents of different archive resources. Specifically, different types of data contents have different characteristic character conversions when performing binary code conversion. For example, for data content in text format, the binary code characters associated with a single text character are generally four groups, and different data contents in different data formats have different associated binary codes.

[0036] Among them, the specific method of recording the content characteristics of different archive resources and different archive contents is:

[0037] 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;

[0038] 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 data type and the archive content. Different data types correspond to different coding features, which are all prepared in advance by the operator based on experience;

[0039] 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 on different data types, and the characteristics can be recorded based on the content characteristics of different data types, so as to facilitate the classification of different archive resources in this batch;

[0040] 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 performance of different polygons to be integrated, integrate and classify several different archive resources. The specific sub-steps of integration and classification are as follows:

[0041] S21. Calibrate the characteristic data columns associated with different archive resources, and construct the verification polygons according to the total number G of different data types:

[0042] 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.

[0043] From the characteristic data columns associated with several archive resources, several groups of content features associated with the corresponding data types are identified, and the maximum value is selected from the several groups of content features, and the maximum value is used as the total value of the corresponding equal division line, and the equal division line is divided into scales according to the total value. Specifically, different equal division lines correspond to different data types, and the total measurement length of the equal division line is the maximum value associated with the corresponding content feature. The equal division line is divided into scales according to the total measurement length, and different scale parameters correspond to each equal division scale;

[0044] Connect the endpoints of adjacent bisectors to generate verification polygons for several groups of archive resources in this batch;

[0045] S22, according to the characteristic data columns associated with different archive resources, on the bisector set in the verification polygon, confirm the bisector associated with the corresponding content feature, and lock the characteristic node associated with this content feature on the corresponding bisector, confirm the characteristic nodes associated with different content features in the characteristic data column in turn, and connect several groups of adjacent characteristic nodes to confirm the polygon to be integrated belonging to this characteristic data column;

[0046] S23, confirming different polygons to be integrated associated with different archival resources in turn;

[0047] S24, according to the total number of storage areas H preset in this batch, the verification polygon is divided into H polygonal areas: different equally divided lines in the verification polygon are divided into H equally divided segments, the equally divided nodes associated between the equally divided segments are confirmed, and the associated equally divided nodes are connected outward in sequence from the set center point to confirm different equally divided areas, and the verification polygon is divided into H polygonal areas, and different polygonal areas correspond to different storage areas;

[0048] S25, identify the total length data of different sides of the polygon to be integrated located in different polygonal 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, specifically, if there is the same L q max, then a group of associated polygonal areas are randomly selected to calibrate the selected area;

[0049] S26, the polygons to be integrated belonging to the same selected area are recorded as polygons with the same characteristics, and the 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. Specifically, the feature data columns associated with different archive resources are different, and different feature data columns have different polygon features in the verification polygon. Then, based on the multiple expressions of the corresponding polygons, the specific classification of the corresponding polygons can be locked. Based on the specific feature classification, the archive resources with relative clustering characteristics can be effectively classified quickly. Compared with the numerical variance processing method, the classification method is faster and the resulting classification efficiency is higher;

[0050] Combination Figure 2 , the total number of data types is six, so a set of six endpoints of the check polygon is constructed, and there are six bisectors inside the check polygon. Different bisectors are associated with different scales, and each different bisector is associated with a different data type and a different associated measurement length. According to the feature sequence associated with the corresponding archive resource, the corresponding feature node is confirmed on the corresponding bisector, and then the feature node is connected to confirm the polygon to be integrated associated with this archive resource. Then, according to the polygon area confirmed in sequence inside the check polygon (that is, the area contained between adjacent dotted lines), the specific division of the corresponding polygon to be integrated is determined to perform feature classification, so as to achieve the overall processing effect of quickly determining resource classification;

[0051] 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. The specific encryption method is:

[0052] 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 ;

[0053] Use: R k ÷Z k =B k Confirm the feature ratio B associated with the corresponding content feature k ;

[0054] S32, based on feature proportion B k Sort from small to large, confirm the proportion sequence, and confirm a group of encrypted circles based on this proportion sequence: randomly generate a group of circles with a radius of R, where R is a preset value, and its specific value is determined by the operator based on experience. Connect the center of the circle with the upper vertex of the circumference to confirm the baseline. Use the baseline to rotate clockwise to confirm the first group of radiation areas, whose area ratio of the radiation area in the entire circle is consistent with the first group of feature ratios in the proportion sequence. Similarly, according to the different feature ratios 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;

[0055] 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 to perform position mapping, confirm the time mapping point, connect the center of the circle with the mapping point, and confirm two sets of time feature lines. The encryption circle includes not only the dividing line, but also the corresponding time feature line. The time scale associated with the time line can find the corresponding time point on the ring. Then the corresponding storage time can be combined with the encryption circle to determine the corresponding overall encryption circle, so as to perform specific mapping of the time feature line.

[0056] 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 a characteristic area. Starting from the baseline, the area of ​​several characteristic areas is processed by ratio according to the clockwise confirmation direction, and the ratio sequence is confirmed. The ratios of the ratio sequence from front to back are extracted, and 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. For example, after a specific segmentation process, the characteristic area includes six groups. After the area ratio analysis of the six groups of characteristic areas is performed, the ratio sequence generated is {12:21:6:8:10:11}. Then, after the confirmed ratio is extracted, the encryption key is confirmed according to the set ratio. The encryption key is expressed in the form of: 1221681011;

[0057] The running code involved in the encryption process is:

[0058] #S31: Confirm the content feature ratio of the corresponding feature data column defcalculate_feature_ratio(feature_data):

[0059] #Store different content features and their quantities

[0060] content_features={}

[0061] forfeatureinfeature_data:

[0062] iffeaturenotincontent_features:

[0063] content_features[feature]=0

[0064] content_features[feature]+=1

[0065] total_features=len(feature_data)

[0066] feature_ratios={}

[0067] fork,countincontent_features.items():

[0068] feature_ratios[k]=count / total_features

[0069] returnfeature_ratios

[0070] #S32: Generate encrypted circles and time feature lines based on feature proportions

[0071] defgenerate_encryption_circle(feature_ratios,storage_time):

[0072] #Randomly generate a set of circles with a radius of R, where R is a preset value

[0073] R=10#Here the default value is assumed to be 10, which can be modified according to actual conditions

[0074] #Rank by percentage

[0075] sorted_ratios=sorted(feature_ratios.values())

[0076] # Divide the radiation area

[0077] division_lines=[]

[0078] current_angle=0

[0079] forratioinsorted_ratios:

[0080] #Calculate the angle of the current radiation area

[0081] angle=ratio*360

[0082] #Record the angle of the dividing line

[0083] current_angle+=angle

[0084] division_lines.append(current_angle)

[0085] #Processing storage time

[0086] hour,minute=storage_time

[0087] #Calculate the angle corresponding to the time on the clock

[0088] hour_angle=(hour%12)*30+(minute / 60)*30

[0089] minute_angle=minute*6

[0090] #Use the baseline of the encrypted circle as time 0 to perform position mapping

[0091] time_feature_lines=[hour_angle,minute_angle]

[0092] returndivision_lines,time_feature_lines

[0093] #S33: Generate encryption key defgenerate_encryption_key(division_lines,time_feature_lines):

[0094] all_lines=[0]+division_lines+time_feature_lines

[0095] all_lines.sort()

[0096] #Calculate the area ratio between adjacent line segments

[0097] area_ratios=[]

[0098] foriinrange(len(all_lines)-1):

[0099] #Since the radius of the circle is fixed, the area ratio is equal to the angle ratio

[0100] ratio=(all_lines[i+1]-all_lines[i]) / 360

[0101] area_ratios.append(ratio)

[0102] # Sort by comparison value

[0103] sorted_ratios=sorted(area_ratios)

[0104] return sorted_ratios.

[0105] Second embodiment

[0106] This embodiment mainly focuses on the decryption program associated with step three:

[0107] The specific operations associated with the decryption program are as follows:

[0108] The operator performs identity verification, and after the identity verification is correct, the decryption program starts;

[0109] 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;

[0110] 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;

[0111] The archive resources are decrypted based on this decryption key.

[0112] Some of the data in the above formulas are numerically calculated by removing their dimensions. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0113] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. 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 performance of different polygons to be integrated, integrate and classify several different archive resources. The specific method of constructing the verification polygons is as follows: The characteristic data columns associated with different archival resources are calibrated, and the verification polygons are constructed 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 point as the center, G bisectors are equally divided around the circumference of the center point. The data types associated with each different bisector 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 archive resources in this batch; 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. The specific method 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.

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 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.

4. A method for multi-dimensional integration analysis of archive resources according to claim 3, 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.

5. The method for multi-dimensional integration and analysis of archive resources according to claim 1, 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.

6. A method for multi-dimensional integration and analysis of archive resources according to claim 5, 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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