A method and system for managing engineering project archive information

Abstract

The present invention relates to the field of data processing, and in particular to a method and system for managing engineering project archive information. The method comprises the following steps: obtaining an engineering project archive and performing a hash transformation to obtain a standard sequence; obtaining coordinate points corresponding to the standard sequence; obtaining a descriptor for the standard sequence in the dimension; using the set consisting of the standard sequences as a parent node, calculating the degree of isolation for each dimension, and dividing the standard sequences in the set corresponding to the parent node into two categories based on the descriptor of the dimension with the greatest degree of isolation, and using the set consisting of data in each category as the child nodes of the parent node; performing classification cutoff control using the maximum degree of isolation to obtain a number of leaf nodes; and managing the engineering project archives corresponding to the data in the leaf nodes as a category. Through accurate classification processing, the efficiency and accuracy of engineering project archive management are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for managing engineering project archive information. Background Art

[0002] Project archives contain digital data from all stages of a project. These archives serve as a crucial basis for project quality control, maintenance operations, legal proceedings, and historical research. Therefore, project archives play an important role. However, current project archive management is rather chaotic, resulting in low search efficiency. To improve this efficiency, a categorized management approach is being implemented for project archives.

[0003] Traditionally, project archives have been classified based on information similarity. However, some similar project archives may have low information similarity but strong correlations. For example, project data from the same phase of different projects can differ, and using this data similarity can easily separate design files into two categories. Furthermore, project data from the same phase of different projects can be correlated, so this correlation can be used to separate project data from different projects at the same phase. Therefore, the focus of this paper is to improve the accuracy of project archive classification by combining correlation information.

[0004] The patent application document with publication number CN117332023A discloses an industrial data governance method based on data classification management. The classification method in the patent application document does not take into account the correlation between data, and therefore the classification method in the patent application document cannot solve the technical problem of the present invention. Summary of the Invention

[0005] In order to solve the problem of how to combine correlation information to improve the classification accuracy of engineering project archives, the present invention provides an engineering project archive information management method and system.

[0006] In a first aspect, the present invention provides a method for managing project archive information, which adopts the following technical solution:

[0007] A method for managing project archive information, comprising the steps of:

[0008] Obtain the project files and perform hash transformation to obtain the standard sequence;

[0009] Obtaining coordinate points corresponding to the standard sequence; obtaining the value of any dimension of the coordinate points of the standard sequence and the angle between the line connecting the coordinate point and the origin and the coordinate axis corresponding to the dimension, and using the vector formed by the value and the angle as the descriptor of the standard sequence in the dimension;

[0010] Take the set of standard sequences as the parent node and calculate the isolation degree of each dimension , arrange the adjacent distances of all standard sequences in descending order to obtain a distance sequence, and take the standard sequence corresponding to the distance before the mutation point in the distance sequence as the boundary standard sequence, Indicates the The adjacent distance of a standard sequence, Indicates the The number of standard sequences between two adjacent boundary standard sequences is: Indicates the degree of connectivity and interruption of the polyline formed by all boundary standard sequences, represents the number of boundary criteria sequences, represents the variance of all data in the set, Represents the hyperbolic tangent function. Based on the descriptor of the dimension with the largest degree of isolation, the standard sequences in the set corresponding to the parent node are divided into two categories, and the sets composed of data in each category are used as the child nodes of the parent node.

[0011] In response to the maximum isolation degree being greater than a preset threshold, the child node is used as a new parent node, and the data in the set corresponding to the new parent node is classified and processed to obtain the child nodes of the new parent node; in response to the maximum isolation degree being not greater than the preset threshold, the loop ends, and a number of leaf nodes are obtained; the engineering project files corresponding to the data in the leaf nodes are managed as a class;

[0012] The present invention improves the management efficiency of engineering project archives by classifying and processing engineering project archives; further, when classifying and processing engineering project archives, the descriptor of each dimension is used as the classification basis, so that the classification not only takes into account the value of each dimension, but also takes into account the correlation between dimensions; further, in the process of classification, the category isolation of the data in each dimension is analyzed, and the dimension with a greater degree of isolation is selected for classification processing, thereby improving the accuracy of classification; further, in the process of classification, the category isolation degree is used to realize adaptive control of classification, thereby preventing over-classification or under-classification; further, when calculating the isolation degree, the degree of connectivity interruption is introduced to accurately reflect the situation where the data is separated by interval points with a larger distance, thereby improving the accuracy of the category isolation measurement; further, when calculating the isolation degree, the number of standard sequences between two adjacent boundary standard sequences is introduced to reflect the continuity of interval points with a larger distance, thereby improving the accuracy of the category isolation measurement.

[0013] Preferably, obtaining the coordinate points corresponding to the standard sequence includes:

[0014] Each dimension in the standard sequence is used as a coordinate axis to construct a coordinate system, and the coordinate points of the standard sequence in the coordinate system are located according to the values of all dimensions in the standard sequence.

[0015] Preferably, the method for obtaining the adjacent distance includes:

[0016] The position of the standard sequence located based on the descriptor is recorded as the single-dimensional position of the standard sequence;

[0017] Preset angle sliding window, use the angle sliding window with the preset angle step size as the step size to rotate and slide around the single-dimensional position of the standard sequence;

[0018] Obtain the adjacent standard sequences of the standard sequence within the angle sliding window and record them as the reference standard sequence. Calculate the mean distance between the descriptors of the standard sequence and all reference standard sequences and record it as the optional adjacent distance of the standard sequence. The maximum value of all optional adjacent distances of the standard sequence is taken as the adjacent distance of the standard sequence.

[0019] The present invention obtains adjacent distances by means of an angle sliding window. Compared with the method of calculating adjacent distances using all adjacent data, it can accurately measure the interval distance on one side and prevent the phenomenon of distance values being equalized due to the introduction of too much adjacent data.

[0020] Preferably, the method for obtaining the mutation point includes:

[0021] Record any adjacent distance in the distance sequence as the target adjacent distance; calculate the difference between the target adjacent distance and the previous adjacent distance and record it as the adjacency difference of the target adjacent distance;

[0022] The absolute value of the difference between the mean of the variance of the adjacent distances before the target adjacent distance and the variance of the adjacent distances after the target adjacent distance and the variance in the distance sequence is recorded as the consistency cutoff degree of the target adjacent distance;

[0023] The product of the adjacency difference of the target adjacent distance and the consistency cutoff degree is taken as the mutation degree;

[0024] The maximum mutation degree is taken as the mutation point.

[0025] When obtaining mutation points, the present invention not only considers the consistency of changes in data before or after the data point, but also considers the mutation of the data point, thereby screening out mutation points more comprehensively and accurately.

[0026] Preferably, the method for obtaining the number of standard sequences between two adjacent boundary standard sequences includes:

[0027] The one-dimensional positions of two adjacent boundary standard sequences are obtained, and the one-dimensional positions of all standard sequences are projected onto the line connecting the one-dimensional positions of the two adjacent boundary standard sequences. The number of projection points between the two boundary standard sequences is obtained as the number of standard sequences existing between the two adjacent boundary standard sequences.

[0028] The present invention obtains the number of standard sequences existing between two adjacent boundary standard sequences by projection, and this method can more accurately reflect the continuity of two adjacent boundary standard sequences.

[0029] Preferably, the method for obtaining the degree of connectivity interruption includes:

[0030] The outer envelope of the single-dimensional position of all standard sequences; connect the single-dimensional positions of every two adjacent boundary standard sequences to obtain a dividing line, and determine whether the two end points of the dividing line are both on the outer envelope. If both end points of the dividing line are on the outer envelope, the degree of connectivity interruption is 1;

[0031] If any endpoint of the demarcation line is not on the outer envelope line, the endpoint not on the envelope line is recorded as the analysis point, and the one-dimensional position fitting straight line of all boundary standard sequences is processed to obtain the truncation length of the fitting straight line within the outer envelope line; the average of the closest distances from the analysis point to the outer envelope line in the extension direction is obtained, and the ratio of 1 minus the average of the closest distances to the truncation length is obtained to obtain the degree of connectivity interruption.

[0032] The present invention accurately measures the data category interval by analyzing the situation where the line connecting the interval points with a large distance divides the data into two isolated areas.

[0033] Preferably, the descriptor based on the dimension with the greatest degree of isolation divides the standard sequences in the set corresponding to the parent node into two categories, including:

[0034] If both end points of the dividing line are on the outer envelope, the dividing line is taken as the final dividing line, and the standard sequence on one side of the final dividing line is taken as one class to obtain the classification result;

[0035] If any endpoint of the dividing line is not on the outer envelope line, the analysis point and the fitting straight line in the extension direction are recorded as the extended straight line. The area between the analysis point and the outer envelope line is spliced together with the dividing line to obtain the final dividing line. The standard sequence on one side of the final dividing line is taken as one class to obtain the classification result.

[0036] Preferably, performing a hash transformation to obtain a standard sequence includes:

[0037] Extract the hash sequence of the project file as the standard sequence.

[0038] In a second aspect, the present invention provides a project archive information management system, which adopts the following technical solution:

[0039] A project archive information management system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned project archive information management method is implemented.

[0040] By adopting the above technical solution, the above-mentioned engineering project archive information management method is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that a terminal device is made based on the memory and the processor for easy use.

[0041] The present invention has the following technical effects:

[0042] The present invention improves the efficiency of engineering project file management by classifying engineering project files;

[0043] Furthermore, when classifying project files, the descriptor of each dimension is used as the classification basis, so that the classification not only takes into account the value of each dimension, but also takes into account the relationship between dimensions;

[0044] Furthermore, during the classification process, the data's category isolation in each dimension is analyzed, and the dimension with a greater degree of isolation is selected for classification processing, thereby improving the accuracy of classification;

[0045] Furthermore, during the classification process, the degree of class isolation is used to achieve adaptive control of classification, thereby preventing over-classification or under-classification.

[0046] Furthermore, when calculating the degree of isolation, the degree of connectivity interruption is introduced to accurately reflect the situation where the data is separated by interval points with a large distance, thereby improving the accuracy of the category isolation measurement;

[0047] Furthermore, when calculating the degree of isolation, the number of standard sequences existing between two adjacent boundary standard sequences is introduced to reflect the continuity of interval points with a large distance, thereby improving the accuracy of the category isolation measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] By reading the detailed description below with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding numbers represent the same or corresponding parts.

[0049] Figure 1This is a flow chart of a method for managing project archive information according to an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of describing data points provided in an embodiment of the present invention;

[0051] Figure 3 A schematic diagram of the continuous distribution of adjacent interval areas with large distances provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] 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 them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0053] It should be understood that when the terms "first," "second," and the like are used in the claims, description, and drawings of the present invention, they are merely used to distinguish between different objects, rather than to describe a specific order. The terms "comprise" and "comprising" used in the description and claims of the present invention indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0054] The embodiment of the present invention discloses a method for managing project archive information, referring to Figure 1 , including steps S1 to S4:

[0055] S1: Obtain the project file and perform hash transformation to obtain a standard sequence.

[0056] Specifically, the engineering project files are obtained, and a hash algorithm is used to perform hash transformation on the engineering project files to obtain a standard sequence.

[0057] It should be noted that the main information of the project archive can be extracted through the hash algorithm. This method can not only achieve data dimensionality reduction, but also adjust the number of dimensions of all data to the same.

[0058] It should be noted that the project archive contains some text, which can be converted into numbers through the ASCALL table. The present invention performs hash transformation on the converted data.

[0059] S2: Obtain the coordinate points corresponding to the standard sequence; obtain the value of any dimension of the coordinate points of the standard sequence and the angle between the line connecting the coordinate point and the origin and the coordinate axis corresponding to the dimension, and use the vector formed by the value and the angle as the descriptor of the standard sequence in the dimension.

[0060] It's important to note that the correlation between data in different dimensions varies for different types of projects. For example, the correlation between size and strength for a concrete project is different from that for a steel structure project. Therefore, project archives can be categorized based on the correlation between data in different dimensions.

[0061] S20: Obtain coordinate points corresponding to the standard sequence.

[0062] Preferably, as an example, obtaining the coordinate points corresponding to the standard sequence includes:

[0063] Each dimension in the standard sequence is used as a coordinate axis to construct a coordinate system, and the coordinate points of the standard sequence in the coordinate system are located according to the values of all dimensions in the standard sequence.

[0064] S21: Obtain the value of any dimension of the coordinate point of the standard sequence and the angle between the line connecting the coordinate point and the origin and the coordinate axis corresponding to the dimension, and use the vector formed by the value and the angle as the descriptor of the standard sequence in the dimension.

[0065] It is understandable that if Figure 2 The data point description is shown in the schematic diagram. Figure 2 The descriptor contains two dimensions, with the values of the data points in the two dimensions being 3 and 4, respectively. Based on the principle of obtaining tangent values, the angle between the data point and the coordinate axis can reflect the proportional relationship between the values of the two dimensions. Therefore, this embodiment uses the angle between the data point and the coordinate axis to reflect the correlation between different dimensions. The descriptor not only contains the angle value that can describe the correlation between different dimensions, but also contains the data values of the dimensions. Therefore, the descriptor provides a data foundation for accurate segmentation.

[0066] S3: Take the set consisting of standard sequences as the parent node, calculate the isolation degree of each dimension, divide the standard sequences in the set corresponding to the parent node into two categories based on the descriptor of the dimension with the largest isolation degree, and take the set consisting of data in each category as the child nodes of the parent node.

[0067] S30: Taking the set consisting of the standard sequences as the parent node, the isolation degree of each dimension is calculated.

[0068] It should be noted that the standard sequence contains data of multiple dimensions, and some dimensions not only do not improve the classification effect, but may even interfere with the classification effect. In order to prevent interference from some dimensions, this embodiment adopts a hierarchical classification method for classification processing, so that dimensions with better classification capabilities can play a greater role in classification.

[0069] It should be further explained that in order to achieve better classification results, it is necessary to select dimensions with stronger classification capabilities. When a dimension has stronger classification capabilities, the inter-class separation of data points in that dimension is larger. The classification capabilities of a dimension can be judged by analyzing the inter-class separation of the dimension.

[0070] Preferably, as an example, a set of standard sequences is used as a parent node, and the isolation degree of each dimension is calculated, including:

[0071]

[0072] Among them, the adjacent distances of all standard sequences are arranged in descending order to obtain a distance sequence, and the standard sequence corresponding to the distance before the mutation point in the distance sequence is used as the boundary standard sequence. Indicates the The adjacent distance of a standard sequence, represents the set of adjacent distances, represents the variance of all data in the set, Indicates the The number of standard sequences between two adjacent boundary standard sequences, represents the number of boundary criteria sequences, Indicates the degree of connectivity and interruption of the polyline formed by all boundary standard sequences, represents the hyperbolic tangent function, which is used for normalization. Indicates the degree of isolation in each dimension.

[0073] It is understandable that when the inter-class interval is large, the distance between adjacent data in the same class is small, and the distance between adjacent data in different classes is large. Therefore, the variance of the distance between adjacent data should be large overall, that is, When the inter-class interval is large, there is a clear classification boundary between the categories, and the classification boundary can separate different categories. The obvious classification boundary is mainly manifested in the continuous distribution of adjacent large interval areas. It reflects the continuous distribution of adjacent interval areas with large distances, such as Figure 3 It shows a schematic diagram of the continuous distribution of intervals with large adjacent distances; in addition, whether the classification boundary can separate different categories is mainly reflected in whether the classification boundary can interrupt the connectivity of different categories. It reflects the situation where classification boundaries interrupt the connectivity of different categories.

[0074] It should be noted that the above isolation degree calculation method can better evaluate the classification ability without the need for data classification processing, thus saving the classification process and improving the classification efficiency.

[0075] The above embodiments involve adjacent distances, mutation points, the number of standard sequences between two adjacent boundary standard sequences, and connectivity interruption degree indices. The following describes methods for determining these indices.

[0076] First, the method of obtaining adjacent distances is introduced.

[0077] Optionally, as an example, a method for obtaining the adjacent distance includes:

[0078] The position of the standard sequence located based on the value in the descriptor is recorded as the single-dimensional position of the standard sequence. A neighborhood radius is preset. Based on the single-dimensional position, standard sequences within the preset neighborhood radius of a standard sequence are obtained as neighboring standard sequences. The average of the distances between the standard sequence and the neighboring standard sequences is taken as the neighboring distance of the standard sequence. This embodiment uses a preset neighborhood radius of 10 as an example; other embodiments may use other values and are not specifically limited in this embodiment.

[0079] It should be noted that the data points on the category boundary are only at a large distance from some adjacent data points. The adjacent distances calculated in the above way can be easily reduced by averaging.

[0080] Preferably, as an example, the method for obtaining the adjacent distance includes:

[0081] Preset angle sliding window, use the angle sliding window with the preset angle step size as the step size to rotate and slide around the single-dimensional position of the standard sequence;

[0082] Obtain the adjacent standard sequences of the standard sequence within the angle sliding window and record them as the reference standard sequence. Calculate the mean distance between the descriptors of the standard sequence and all reference standard sequences and record it as the optional adjacent distance of the standard sequence. The maximum value of all optional adjacent distances of the standard sequence is taken as the adjacent distance of the standard sequence.

[0083] This embodiment uses the 180-degree area enclosed by the two rays as the preset angle sliding window. Other embodiments may use other methods to set the angle sliding window, and this embodiment does not specifically limit this. This embodiment uses the preset angle step size as 1. Other embodiments may use other values, and this embodiment does not specifically limit this. This embodiment uses the standard sequences within the preset radius of the single-dimensional position of the standard sequence as the adjacent standard sequences.

[0084] It can be understood that the adjacent distances calculated by the adjacent standard sequences within the angle sliding window can prevent the problem of averaging reducing the distance value.

[0085] Then the method of obtaining mutation points is introduced.

[0086] Optionally, as an example, a method for obtaining a mutation point includes:

[0087] Any adjacent distance in the distance sequence is recorded as the target adjacent distance; the difference between the target adjacent distance and the previous adjacent distance is calculated and recorded as the adjacent difference of the target adjacent distance, and the adjacent distance with the largest adjacent difference is taken as the mutation point.

[0088] It should be noted that because the distances between adjacent data at the class boundary are larger than the distances between adjacent data within a class, there will be a sudden change from the corresponding distances at the class boundary to the adjacent distances within the class. The distances at the class boundary and the distances within the class can be distinguished by the mutation point. A mutation point should be a point where the change occurs suddenly, that is, the fluctuations before and after the data points before and after this point should be small. Therefore, mutation points cannot be accurately identified by simply looking at data differences.

[0089] Preferably, as an example, the method for obtaining the mutation point includes:

[0090] Record any adjacent distance in the distance sequence as the target adjacent distance; calculate the difference between the target adjacent distance and the previous adjacent distance and record it as the adjacency difference of the target adjacent distance;

[0091] The absolute value of the difference between the mean of the variance of the adjacent distances before the target adjacent distance and the variance of the adjacent distances after the target adjacent distance and the variance in the distance sequence is recorded as the consistency cutoff degree of the target adjacent distance;

[0092] The product of the adjacency difference of the target adjacent distance and the consistency cutoff degree is taken as the mutation degree;

[0093] The maximum mutation degree is taken as the mutation point.

[0094] It can be understood that by combining the consistency of the fluctuation of the previous and next data points and the drastic situation of the data fluctuation, the mutation point can be accurately extracted.

[0095] Then, a method for obtaining the number of standard sequences between two adjacent boundary standard sequences is introduced.

[0096] Optionally, as an example, a method for obtaining the number of standard sequences between two adjacent boundary standard sequences includes:

[0097] The single-dimensional positions of two adjacent boundary standard sequences are connected, and the number of single-dimensional positions of the standard sequences passed by the connection line is regarded as the number of standard sequences existing between the two adjacent boundary standard sequences.

[0098] It should be noted that there are some two adjacent boundary standard sequences with more standard sequences, but the connecting line does not pass through them. Therefore, this method cannot accurately reflect the number of standard sequences between the two adjacent boundary standard sequences.

[0099] Preferably, as an example, a method for obtaining the number of standard sequences between two adjacent boundary standard sequences includes:

[0100] The one-dimensional positions of two adjacent boundary standard sequences are obtained, and the one-dimensional positions of all standard sequences are projected onto the line connecting the one-dimensional positions of the two adjacent boundary standard sequences. The number of projection points between the two boundary standard sequences is obtained as the number of standard sequences existing between the two adjacent boundary standard sequences.

[0101] It can be understood that the projection method can more accurately screen out the standard sequences between two adjacent boundary standard sequences, and the method of determining the number of standard sequences between two adjacent boundary standard sequences is more accurate.

[0102] Finally, the method of obtaining the degree of connectivity is introduced.

[0103] Preferably, as an example, the method for obtaining the degree of connectivity includes:

[0104] Obtain the outer envelope of the single-dimensional positions of all standard sequences; connect the single-dimensional positions of every two adjacent boundary standard sequences to obtain a boundary line, and determine whether both end points of the boundary line are on the outer envelope. If both end points of the boundary line are on the outer envelope, the degree of connectivity of the line formed by all boundary standard sequences is 1;

[0105] If any endpoint of the demarcation line is not on the outer envelope line, the endpoint not on the envelope line is recorded as an analysis point, and a straight line is fitted to the one-dimensional position of all boundary standard sequences to obtain the truncation length of the fitting straight line within the outer envelope line; the closest distance from each analysis point to the outer envelope line in the extension direction is obtained, and the average of the closest distances from all analysis points to the outer envelope line in the extension direction is obtained. The degree of connectivity interruption is obtained by subtracting the average of the closest distances from 1 to the truncation length.

[0106] In this embodiment, the distance between the two intersection points of the fitting straight line and the outer envelope is used as the cutoff length.

[0107] It can be understood that the situation where the connected domain composed of all standard sequences is divided into two isolated connected domains by analyzing the dividing line can more accurately reflect the degree of connectivity interruption.

[0108] S31: Based on the descriptor of the dimension with the greatest degree of isolation, the standard sequences in the set corresponding to the parent node are divided into two categories, and the set consisting of the data in each category is used as each child node of the parent node.

[0109] Optionally, as an example, based on the descriptor of the dimension with the greatest degree of isolation, the standard sequences in the set corresponding to the parent node are divided into two categories, and the sets consisting of data in each category are used as the child nodes of the parent node, including:

[0110] Based on the descriptor of the dimension with the greatest degree of isolation, the K-means clustering method is used to divide the standard sequences in the set corresponding to the parent node into two categories, and the set composed of data in each category is used as the child nodes of the parent node.

[0111] It is understandable that classification using this method requires other classification algorithms for classification processing, which will increase unnecessary computational effort.

[0112] Preferably, as an example, based on the descriptor of the dimension with the greatest degree of isolation, the standard sequences in the set corresponding to the parent node are divided into two categories, and the set consisting of data in each category is used as each child node of the parent node, including:

[0113] If both end points of the dividing line are on the outer envelope, the dividing line is taken as the final dividing line, and the standard sequence on one side of the final dividing line is taken as one class to obtain the classification result;

[0114] If any endpoint of the dividing line is not on the outer envelope line, the analysis point and the fitting straight line in the extension direction are recorded as the extended straight line. The area between the analysis point and the outer envelope line is spliced together with the dividing line to obtain the final dividing line. The standard sequence on one side of the final dividing line is taken as one class to obtain the classification result.

[0115] The set of data in each category in the classification result is used as the child nodes of the parent node.

[0116] It can be understood that by using the dividing line for classification, there is no need to introduce other classification algorithms, thus reducing unnecessary calculations.

[0117] S4: In response to the maximum isolation degree being greater than a preset threshold, the child node is used as a new parent node, and the data in the set corresponding to the new parent node is classified and processed to obtain the child nodes of the new parent node; in response to the maximum isolation degree being not greater than the preset threshold, the loop ends and several leaf nodes are obtained; the engineering project files corresponding to the data in the leaf nodes are managed as a category.

[0118] It should be noted that the degree of isolation reflects the interval between categories. If the category interval is small, it means that the data does not need to be further classified, so the classification cutoff can be controlled by the degree of isolation.

[0119] Preferably, as an example, in response to the maximum isolation degree being greater than a preset threshold, the child node is used as a new parent node, and the data in the set corresponding to the new parent node is classified and processed to obtain the child nodes of the new parent node; in response to the maximum isolation degree being not greater than the preset threshold, the loop ends to obtain a plurality of leaf nodes; and the engineering project files corresponding to the data in the leaf nodes are managed as a category, including:

[0120] The maximum isolation degree is selected from the isolation degrees of all dimensions. In response to the maximum isolation degree being greater than a preset threshold, the child node is used as the new parent node. The data in the set corresponding to the new parent node is classified and processed according to the method in step S2-step S3. The set composed of data in each category in the classification result is used as the child nodes of the new parent node. In response to the maximum isolation degree not being greater than the preset threshold, the loop ends, and several leaf nodes are obtained. The engineering project files in the leaf nodes are managed as a category.

[0121] In this embodiment, a node without any child nodes is regarded as a leaf node.

[0122] This embodiment is described by taking the preset threshold value of 0.35 as an example. Other embodiments may take other values, and this embodiment does not impose any specific limitation.

[0123] An embodiment of the present invention further discloses an engineering project archive information management system, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an engineering project archive information management method according to the present invention is implemented.

[0124] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0125] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory, dynamic random access memory, static random access memory, enhanced dynamic random access memory, high bandwidth memory, hybrid memory cube, etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium may be part of, accessible to, or connectable to the device.

[0126] While several embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous modifications, variations, and alternatives will occur to those skilled in the art without departing from the concept and spirit of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

[0127] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for managing project archive information, characterized in that: Including steps: Obtain the project files and perform hash transformation to obtain the standard sequence; Obtaining coordinate points corresponding to the standard sequence; obtaining the value of any dimension of the coordinate points of the standard sequence and the angle between the line connecting the coordinate point and the origin and the coordinate axis corresponding to the dimension, and using the vector formed by the value and the angle as the descriptor of the standard sequence in the dimension; Take the set of standard sequences as the parent node and calculate the isolation degree of each dimension , arrange the adjacent distances of all standard sequences in descending order to obtain a distance sequence, and take the standard sequence corresponding to the distance before the mutation point in the distance sequence as the boundary standard sequence, Indicates the The adjacent distance of a standard sequence, Indicates the The number of standard sequences between two adjacent boundary standard sequences is: Indicates the degree of connectivity and interruption of the polyline formed by all boundary standard sequences, represents the number of boundary criteria sequences, represents the variance of all data in the set, Represents the hyperbolic tangent function. Based on the descriptor of the dimension with the largest degree of isolation, the standard sequences in the set corresponding to the parent node are divided into two categories, and the sets composed of data in each category are used as the child nodes of the parent node. In response to the maximum isolation degree being greater than a preset threshold, the child node is used as a new parent node, and the data in the set corresponding to the new parent node is classified and processed to obtain the child nodes of the new parent node; in response to the maximum isolation degree being not greater than the preset threshold, the loop ends and several leaf nodes are obtained; the engineering project files corresponding to the data in the leaf nodes are managed as a category.

2. A method for managing project archive information according to claim 1, characterized in that: The obtaining of the coordinate points corresponding to the standard sequence includes: Each dimension in the standard sequence is used as a coordinate axis to construct a coordinate system, and the coordinate points of the standard sequence in the coordinate system are located according to the values of all dimensions in the standard sequence.

3. A method for managing project archive information according to claim 1, characterized in that: The method for obtaining the adjacent distance includes: The position of the standard sequence located based on the descriptor is recorded as the single-dimensional position of the standard sequence; Preset angle sliding window, use the angle sliding window with the preset angle step size as the step size to rotate and slide around the single-dimensional position of the standard sequence; Obtain the adjacent standard sequences of the standard sequence within the angle sliding window and record them as the reference standard sequence. Calculate the mean distance between the descriptors of the standard sequence and all reference standard sequences and record it as the optional adjacent distance of the standard sequence. The maximum value of all optional adjacent distances of the standard sequence is taken as the adjacent distance of the standard sequence.

4. A method for managing project archive information according to claim 1, characterized in that: The method for obtaining the mutation point includes: Record any adjacent distance in the distance sequence as the target adjacent distance; calculate the difference between the target adjacent distance and the previous adjacent distance and record it as the adjacency difference of the target adjacent distance; The absolute value of the difference between the mean of the variance of the adjacent distances before the target adjacent distance and the variance of the adjacent distances after the target adjacent distance and the variance in the distance sequence is recorded as the consistency cutoff degree of the target adjacent distance; The product of the adjacency difference of the target adjacent distance and the consistency cutoff degree is taken as the mutation degree; The maximum mutation degree is taken as the mutation point.

5. A method for managing project archive information according to claim 3, characterized in that: The method for obtaining the number of standard sequences between two adjacent boundary standard sequences includes: The one-dimensional positions of two adjacent boundary standard sequences are obtained, and the one-dimensional positions of all standard sequences are projected onto the line connecting the one-dimensional positions of the two adjacent boundary standard sequences. The number of projection points between the two boundary standard sequences is obtained as the number of standard sequences existing between the two adjacent boundary standard sequences.

6. A method for managing project archive information according to claim 3, characterized in that: The method for obtaining the degree of connectivity interruption includes: The outer envelope of the single-dimensional position of all standard sequences; connect the single-dimensional positions of every two adjacent boundary standard sequences to obtain a dividing line, and determine whether the two end points of the dividing line are both on the outer envelope. If both end points of the dividing line are on the outer envelope, the degree of connectivity interruption is 1; If any endpoint of the demarcation line is not on the outer envelope line, the endpoint not on the envelope line is recorded as the analysis point, and the one-dimensional position fitting straight line of all boundary standard sequences is processed to obtain the truncation length of the fitting straight line within the outer envelope line; the average of the closest distances from the analysis point to the outer envelope line in the extension direction is obtained, and the ratio of 1 minus the average of the closest distances to the truncation length is obtained to obtain the degree of connectivity interruption.

7. A method for managing project archive information according to claim 6, characterized in that: The descriptor based on the dimension with the greatest degree of isolation divides the standard sequences in the set corresponding to the parent node into two categories, including: If both end points of the dividing line are on the outer envelope, the dividing line is taken as the final dividing line, and the standard sequence on one side of the final dividing line is taken as one class to obtain the classification result; If any endpoint of the dividing line is not on the outer envelope line, the analysis point and the fitting straight line in the extension direction are recorded as the extended straight line. The area between the analysis point and the outer envelope line is spliced together with the dividing line to obtain the final dividing line. The standard sequence on one side of the final dividing line is taken as one class to obtain the classification result.

8. A method for managing project archive information according to claim 1, characterized in that: The performing of hash transformation to obtain a standard sequence includes: Extract the hash sequence of the project file as the standard sequence.

9. A project archive information management system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for managing engineering project archive information according to any one of claims 1 to 8 is implemented.

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