Engineering project archive information management method and system

Abstract

The invention relates to the field of data processing, in particular to an engineering project archive information management method and system. The method comprises the following steps: obtaining an engineering project file, and carrying out Hash transformation to obtain a standard sequence; obtaining a coordinate point corresponding to the standard sequence; obtaining a descriptor of the standard sequence in the dimension; taking a set formed by the standard sequences as a father node, calculating the isolation degree of each dimension, dividing the standard sequences in the set corresponding to the father node into two classes based on the descriptor of the dimension with the maximum isolation degree, and taking a set formed by data in each class as each child node of the father node; performing classification cut-off control by using the maximum isolation degree to obtain a plurality of leaf nodes; and taking the engineering project archives corresponding to the data in the leaf nodes as one class for management. And 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 particularly to a method and system for managing engineering project archive information. Background Art

[0002] Engineering project archives contain digital materials of various stages of engineering projects. These archive materials are important bases for engineering project quality control, maintenance and operation, legal litigation, and historical research. Therefore, engineering project archives play an important role. However, the current management of engineering project archives is relatively chaotic, resulting in low search efficiency of engineering project archives. In order to improve the search efficiency of engineering project archives, classification management of engineering project archives is carried out.

[0003] Traditional classification methods of engineering project archives mostly classify by the similarity of archive information. However, for some engineering project archives of the same category, the similarity of their archive information is low, but they have strong relevance. For example, there are differences in engineering project data at the same stage of different projects. Using the similarity between data, it is easy to divide design documents into two categories, while there is a correlation between engineering project data at the same stage of different projects. Therefore, the correlation can be used to separate engineering project data of different projects at the same stage together. Thus, how to combine correlation information to improve the classification accuracy of engineering project archives has become the research focus of the present invention.

[0004] The patent application document with the publication number CN117332023A discloses an industrial data governance method based on data classification management. The classification method in this patent application document does not consider the correlation between data. Therefore, the technical problems of the present invention cannot be solved by using the classification method in this patent application document. Summary of the Invention

[0005] To solve the problem of how to combine correlation information to improve the classification accuracy of engineering project archives, the present invention provides a method and system for managing engineering project archive information.

[0006] In the first aspect, the present invention provides a method for managing engineering project archive information, adopting the following technical solution: A method for managing engineering project archive information includes the steps of: Obtain engineering project archives and perform hash transformation to obtain a standard sequence; Obtain the coordinate points corresponding to the standard sequence; obtain any one-dimensional value of the coordinate points of the standard sequence and the included angle between the line connecting the coordinate points and the origin and the coordinate axis corresponding to this dimension, and use the vector composed of the value and the included angle as the descriptor of the standard sequence in this dimension; Take the set composed of the standard sequence as the parent node and calculate the isolation degree of each dimension , sort the adjacent distances of all standard sequences in descending order to obtain a distance sequence, and use the standard sequence corresponding to the distance before the mutation point in the distance sequence as the boundary standard sequence. represents the adjacent distance of the th standard sequence, represents the number of standard sequences between two adjacent boundary standard sequences, represents the connectivity interruption degree of the broken line formed by all boundary standard sequences, represents the number of boundary standard sequences, represents the hyperbolic tangent function. Based on the descriptor of the dimension with the largest isolation degree, the standard sequences in the set corresponding to the parent node are divided into two categories, and the sets formed by the data in each category are used as the child nodes of the parent node; In response to the maximum isolation degree being greater than the preset threshold, use the child node as the new parent node, and classify the data in the set corresponding to the new parent node to obtain the child nodes of the new parent node; in response to the maximum isolation degree not being greater than the preset threshold, end the loop to obtain several leaf nodes; manage the project archives corresponding to the data in the leaf nodes as one category. The present invention improves the management efficiency of project archives by classifying project archives; further, when classifying project archives, use the descriptor of each dimension as the classification benchmark, so that when classifying, not only the values of each dimension are considered, but also the correlation between dimensions is considered; further, in the process of classification, analyze the category isolation situation of data in each dimension, and select the dimension with a greater isolation degree for classification processing, so as to improve the accuracy of classification; further, in the process of classification, use the category isolation degree to realize the adaptive control of classification, so as to prevent over-classification or under-classification; further, when calculating the isolation degree, accurately reflect the situation where the data is separated by the interval points of the larger distance by introducing the connectivity interruption degree, and improve the accuracy of measuring the category isolation situation; further, when calculating the isolation degree, reflect the continuity of the interval points of the larger distance by introducing the number of standard sequences between two adjacent boundary standard sequences, and improve the accuracy of measuring the category isolation situation.

[0007] Preferably, the obtaining of the coordinate points corresponding to the standard sequence includes: Take each dimension in the standard sequence as an axis to construct a coordinate system, and locate the coordinate points of the standard sequence in the coordinate system according to the values of all dimensions in the standard sequence.

[0008] Preferably, the method for obtaining the adjacent distance includes: The position of the standard sequence located based on the descriptor is denoted as the one-dimensional position of the standard sequence; A preset angle sliding window is used to rotate and slide around the one-dimensional position of the standard sequence with a preset angle step as the step size; The adjacent standard sequences of the standard sequence within the angle sliding window are obtained and denoted as reference standard sequences. The mean value of the distances between the descriptors of the standard sequence and all reference standard sequences is denoted as the optional adjacent distance of the standard sequence, and the maximum value of all optional adjacent distances of the standard sequence is used as the adjacent distance of the standard sequence.

[0009] In the present invention, the adjacent distance is obtained by means of an angle sliding window. Compared with the method of calculating the adjacent distance using all adjacent data, it can accurately measure the interval distance on one side and prevent the phenomenon of the distance value being equalized caused by introducing too much adjacent data.

[0010] Preferably, the method for obtaining the mutation point includes: Denote 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 denote it as the adjacency difference of the target adjacent distance; Denote the absolute value of the difference between the mean value of the variances of the adjacent distances before the target adjacent distance and the variances of the adjacent distances after the target adjacent distance and the variance in the distance sequence as the consistency truncation degree of the target adjacent distance; Take the product of the adjacency difference of the target adjacent distance and the consistency truncation degree as the mutation degree; Take the maximum value of the mutation degree as the mutation point.

[0011] When obtaining the mutation point in the present invention, not only the consistency of the data changes before or after the data point is considered, but also the mutation situation of the data point is considered, and the mutation point is screened out more comprehensively and accurately.

[0012] Preferably, the method for obtaining the number of standard sequences between two adjacent boundary standard sequences includes: Obtain the one-dimensional positions of the two adjacent boundary standard sequences, project the one-dimensional positions of all standard sequences onto the line connecting the one-dimensional positions of the two adjacent boundary standard sequences, and obtain the number of projection points between the two boundary standard sequences as the number of standard sequences between the two adjacent boundary standard sequences.

[0013] In the present invention, the number of standard sequences between two adjacent boundary standard sequences is obtained by projection, and this method can more accurately reflect the continuous situation of the two adjacent boundary standard sequences.

[0014] Preferably, the method for obtaining the connection interruption degree includes: The envelope line of the one-dimensional positions of all standard sequences; connect the one-dimensional positions of every two adjacent boundary standard sequences to obtain a demarcation broken line, and determine whether both endpoints of the demarcation broken line are on the envelope line. If both endpoints of the demarcation broken line are on the envelope line, the degree of connectivity interruption is 1; If any endpoint of the demarcation broken line is not on the envelope line, mark the endpoint not on the envelope line as the analysis point, perform a linear fitting on the one-dimensional positions of all boundary standard sequences, and obtain the truncated length of the fitted line within the envelope line; obtain the average value of the nearest distances from the analysis point to the envelope line in the extension direction, and subtract the ratio of the average value of the nearest distances to the truncated length from 1 to obtain the degree of connectivity interruption.

[0015] The present invention accurately measures the interval between data categories by analyzing the situation where the connection line of interval points with a large distance divides the data into two isolated regions.

[0016] Preferably, the descriptor based on the dimension with the maximum degree of isolation divides the standard sequences in the set corresponding to the parent node into two categories, including: If both endpoints of the demarcation broken line are on the envelope line, use the demarcation broken line as the final demarcation line, and use the standard sequences on one side of the final demarcation line as one category to obtain the classification result; If any endpoint of the demarcation broken line is not on the envelope line, denote the analysis point and the fitted line in the extension direction as the extension line, splice the region between the analysis point and the envelope line of the extension line with the demarcation broken line to obtain the final demarcation line, and use the standard sequences on one side of the final demarcation line as one category to obtain the classification result.

[0017] Preferably, the obtaining of the standard sequence by performing a hash transformation includes: Extract the hash sequence of the engineering project archives as the standard sequence.

[0018] In a second aspect, the present invention provides an engineering project archives information management system, adopting the following technical solution: An engineering project archives information management system includes: a processor and a memory, and the memory stores computer program instructions. When the computer program instructions are executed by the processor, the above-mentioned engineering project archives information management method is implemented.

[0019] By adopting the above technical solution, generate a computer program for the above-mentioned engineering project archives information management method and store it in the memory to be loaded and executed by the processor, so as to manufacture a terminal device according to the memory and the processor, which is convenient to use.

[0020] The present invention has the following technical effects: The present invention improves the management efficiency of engineering project archives by classifying the engineering project archives; Further, when classifying project archives, descriptors of each dimension are used as the classification basis, so that when classifying, not only the values of each dimension are taken into account, but also the correlation between dimensions can be considered; Further, during the classification process, analyze the category isolation of data in each dimension, and select the dimension with a greater degree of isolation for classification processing, thereby improving the accuracy of classification; Further, during the classification process, use the degree of category isolation to achieve adaptive control of classification, thereby preventing over-classification or under-classification; Further, when calculating the degree of isolation, by introducing the degree of connection interruption to accurately reflect the situation where data is separated by interval points at a large distance, the accuracy of measuring the category isolation is improved; Further, when calculating the degree of isolation, by introducing the number of standard sequences between adjacent two boundary standard sequences to reflect the continuity of interval points at a large distance, the accuracy of measuring the category isolation is improved. Description of the Drawings

[0021] By referring to the following detailed description with reference to the drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0022] Figure 1 is a flowchart of a method in a method for managing project archive information according to an embodiment of the present invention; Figure 2 is a schematic diagram of data point description provided by an embodiment of the present invention; Figure 3 is a schematic diagram of the continuous distribution of interval regions with a large adjacent distance provided by an embodiment of the present invention. Detailed Embodiments

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0024] It should be understood that when terms such as "first" and "second" are used in the claims, the description and the drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "comprising" and "including" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0025] An embodiment of the present invention discloses a method for managing engineering project archive information. Referring to Figure 1 , it includes steps S1 - S4: S1: Obtain the engineering project archive and perform a hash transformation to obtain a standard sequence.

[0026] Specifically, obtain the engineering project archive and perform a hash transformation on the engineering project archive using a hash algorithm to obtain a standard sequence.

[0027] It should be noted that through the hash algorithm, the main information of the engineering project archive can be extracted. In this way, not only can data dimensionality reduction be achieved, but also the dimensionality of all data can be adjusted to be the same.

[0028] It should be noted that the engineering project archive contains some text, and these texts can be converted into numbers through the ASCALL table. The present invention performs a hash transformation on the converted data.

[0029] 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 included angle between the line connecting the coordinate points and the origin and the coordinate axis corresponding to this dimension, and use the vector composed of the value and the included angle as the descriptor of the standard sequence in this dimension.

[0030] It should be noted that for different types of engineering projects, the correlation between data in different dimensions is different. For example, the correlation between the size and strength of a concrete engineering project is different from that of a steel structure engineering project. Therefore, the engineering project archives can be classified according to the correlation between data in different dimensions.

[0031] S20: Obtain the coordinate points corresponding to the standard sequence.

[0032] Preferably, as an example, obtaining the coordinate points corresponding to the standard sequence includes: Take each dimension in the standard sequence as a coordinate axis to construct a coordinate system, and locate the coordinate points of the standard sequence in the coordinate system according to the values of all dimensions in the standard sequence.

[0033] S21: Obtain the value of any dimension of the coordinate points of the standard sequence and the angle between the line connecting the coordinate points and the origin and the corresponding coordinate axis of this dimension, and use the vector composed of the value and the angle as the descriptor of the standard sequence in this dimension.

[0034] It can be understood that, as Figure 2 shown in the schematic diagram of data point description, Figure 2 which contains two dimensions, the values of the data point in the two dimensions are 3 and 4 respectively. According to the principle of obtaining the tangent value, the angle between the data point and the coordinate axis can reflect the proportional relationship of the values in the two dimensions. Therefore, in this embodiment, the angle between the data point and the coordinate axis is used to reflect the correlation of different dimensions. The descriptor not only contains the angle value that can describe the correlation of different dimensions, but also contains the data value of the dimension. Therefore, the descriptor can provide a data basis for accurate segmentation.

[0035] S3: Use the set composed 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 use the sets composed of the data in each category as the child nodes of the parent node.

[0036] S30: Use the set composed of standard sequences as the parent node, and calculate the isolation degree of each dimension.

[0037] It should be noted that the standard sequence contains data of multiple dimensions. There are some dimensions that not only do not increase the classification effect, but even interfere with the classification effect. To prevent the interference of some dimensions, this embodiment uses a hierarchical classification method for classification processing, so that the dimensions with better classification ability can play a greater classification role.

[0038] It should be further noted that, in order to achieve a better classification effect, it is necessary to select the dimension with stronger classification ability. When the classification ability of a dimension is strong, the inter-class interval degree of the data points in this dimension is large. The classification ability of the dimension is judged by analyzing the inter-class interval degree of the dimension.

[0039] Preferably, as an example, using the set composed of standard sequences as the parent node, calculating the isolation degree of each dimension includes:

[0040] Among them, arrange the adjacent distances of all standard sequences in descending order to obtain a distance sequence, and use the standard sequences corresponding to the distances before the mutation points in the distance sequence as the boundary standard sequences. represents the adjacent distance of the th standard sequence, represents the set composed of adjacent distances, represents the variance of all data in the set, Indicates the number of standard sequences existing between two adjacent boundary standard sequences, indicates the number of boundary standard sequences, indicates the connected interruption degree of the broken line formed by all boundary standard sequences, indicates the hyperbolic tangent function, which is used for normalization processing, indicates the isolation degree of each dimension.

[0041] It can be understood that when the inter-class interval degree 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, generally speaking, the variance of the distance between adjacent data should be large, that is, is large. When the inter-class interval is large, there is an obvious classification boundary between categories, and at the same time, different categories can be separated through the classification boundary. The obvious classification boundary is mainly manifested in that the interval areas with a large adjacent distance can be continuously distributed, reflects the continuous distribution of the interval areas with a large adjacent distance, such as Figure 3 shows a schematic diagram of the continuous distribution of the interval areas with a large adjacent distance; in addition, whether the classification boundary can separate different categories is mainly manifested in whether the classification boundary can interrupt the connectivity of different categories, reflects the situation where the classification boundary interrupts the connectivity of different categories.

[0042] It should be noted that the above isolation degree calculation method can better evaluate the classification ability without classifying the data, saving the classification process and improving the classification efficiency.

[0043] The above embodiments involve adjacent distance, mutation points, the number of standard sequences existing between two adjacent boundary standard sequences, and the connected interruption degree index. Next, the determination methods of these indexes need to be described.

[0044] First, the method for obtaining the adjacent distance is introduced.

[0045] Optionally, as an example, the method for obtaining the adjacent distance includes: Locate the position of the standard sequence based on the value in the descriptor, which is recorded as the one-dimensional position of the standard sequence. Preset the neighborhood radius, and obtain the standard sequences within the preset neighborhood radius of a standard sequence based on the one-dimensional position as the neighborhood standard sequences. Take the mean of the distances between this standard sequence and the neighborhood standard sequences as the adjacent distance of this standard sequence. In this embodiment, the preset neighborhood radius is taken as 10 for description. Other values can be taken in other embodiments, and this embodiment does not make specific limitations.

[0046] It should be noted that for the data points on the category boundary, only the distances to some adjacent data points are large. The adjacent distance calculated by the above method can easily reduce the distance value through averaging.

[0047] Preferably, as an example, the method for obtaining the adjacent distance includes: Preset an angular sliding window, and use the angular sliding window to rotate and slide around the one-dimensional position of the standard sequence with a preset angular step as the step length; Obtain the adjacent standard sequences of the standard sequence within the angular sliding window and record them as reference standard sequences. Calculate the average value of the distances between the descriptors of the standard sequence and all reference standard sequences, which is recorded as the optional adjacent distance of the standard sequence. Take the maximum value of all optional adjacent distances of the standard sequence as the adjacent distance of the standard sequence.

[0048] In this embodiment, the 180-degree region enclosed by two rays is used as the preset angular sliding window. Other embodiments can set the angular sliding window in other ways, and this embodiment does not make specific limitations. In this embodiment, the preset angular step is taken as 1. Other embodiments can take other values, and this embodiment does not make specific limitations. In this embodiment, the standard sequences within the preset radius of the one-dimensional position of the standard sequence are used as the adjacent standard sequences of the standard sequence.

[0049] It can be understood that the adjacent distance calculated through the adjacent standard sequences within the angular sliding window can prevent the problem that the distance value is reduced by averaging.

[0050] Then, the method for obtaining the mutation point is introduced.

[0051] Optionally, as an example, 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, which is recorded as the adjacency difference of the target adjacent distance, and take the adjacent distance with the largest adjacency difference as the mutation point.

[0052] It should be noted that since the adjacent distances of the data at the category boundary are relatively large and the distances of the adjacent data within the category are relatively small, there will be a mutation from the adjacent distance corresponding to the category boundary to the adjacent distance of the adjacent data within the category. And the mutation point can distinguish the distance at the category boundary from the distance within the category. The mutation point should be a point where a sudden change occurs, that is, the fluctuations before and after the data points before and after this point should be relatively small. Therefore, the mutation point cannot be accurately obtained only through data differences.

[0053] Preferably, as an example, 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, which is recorded as the adjacency difference of the target adjacent distance; 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 absolute value of the difference from the variance in the distance sequence is denoted as the consistency truncation degree of the target adjacent distance; The product of the adjacency difference of the target adjacent distance and the consistency truncation degree is used as the mutation degree; The maximum value of the mutation degree is used as the mutation point.

[0054] It can be understood that by combining the consistency of the fluctuations of the front and back data points of the data and the severity of the data fluctuations, the mutation point can be accurately extracted.

[0055] After that, the method for obtaining the number of standard sequences existing between two adjacent boundary standard sequences is introduced.

[0056] Optionally, as an example, the method for obtaining the number of standard sequences existing between two adjacent boundary standard sequences includes: Connect the one-dimensional positions of the two adjacent boundary standard sequences, and take the number of the one-dimensional positions of the standard sequences passed by the connection line as the number of standard sequences existing between the two adjacent boundary standard sequences.

[0057] It should be noted that for some two adjacent boundary standard sequences, there are many standard sequences, but their connection lines do not pass through, so this method cannot accurately reflect the number of standard sequences existing between the two adjacent boundary standard sequences.

[0058] Preferably, as an example, the method for obtaining the number of standard sequences existing between two adjacent boundary standard sequences includes: Obtain the one-dimensional positions of the two adjacent boundary standard sequences, project the one-dimensional positions of all the standard sequences onto the connection line of the one-dimensional positions of the two adjacent boundary standard sequences, and take the number of the projection points between the two boundary standard sequences as the number of standard sequences existing between the two adjacent boundary standard sequences.

[0059] It can be understood that through the projection method, the standard sequences between the two adjacent boundary standard sequences can be more accurately screened out, and this method for determining the number of standard sequences existing between the two adjacent boundary standard sequences is more accurate.

[0060] Finally, the method for obtaining the connection and penetration degree is introduced.

[0061] Preferably, as an example, the method for obtaining the connection and penetration degree includes: Obtain the outer envelope line of the one-dimensional positions of all the standard sequences; connect the one-dimensional positions of every two adjacent boundary standard sequences to obtain a demarcation broken line, and judge whether both endpoints of the demarcation broken line are on the outer envelope line. If both endpoints of the demarcation broken line are on the outer envelope line, the connection and penetration degree of the broken line formed by all the boundary standard sequences is 1; If any endpoint of the demarcation polyline is not on the outer envelope line, mark the endpoints that are not on the envelope line as analysis points, perform a straight-line fitting process on the one-dimensional positions of all boundary standard sequences, and obtain the truncated length of the fitted straight line within the outer envelope line; obtain the closest distance from each analysis point to the outer envelope line in the extension direction, obtain the average value of the closest distances from all analysis points to the outer envelope line in the extension direction, and subtract the ratio of the average value of the closest distances from 1 to obtain the connection interruption degree.

[0062] In this embodiment, the distance between the two intersection points of the fitted straight line and the outer envelope line is used as the truncated length.

[0063] It can be understood that by analyzing the situation where the demarcation polyline divides the connected domain formed by all standard sequences into two isolated connected domains, the connection interruption degree can be more accurately reflected.

[0064] S31: Based on the descriptors of the dimension with the largest isolation degree, divide the standard sequences in the set corresponding to the parent node into two categories, and use the sets formed by the data in each category as the child nodes of the parent node.

[0065] Optionally, as an example, based on the descriptors of the dimension with the largest isolation degree, divide the standard sequences in the set corresponding to the parent node into two categories, and use the sets formed by the data in each category as the child nodes of the parent node, including: Based on the descriptors of the dimension with the largest isolation degree, use the K-means clustering method to divide the standard sequences in the set corresponding to the parent node into two categories, and use the sets formed by the data in each category as the child nodes of the parent node.

[0066] It can be understood that by classifying in this way, other classification algorithms are also required for classification processing, which will increase unnecessary computational complexity.

[0067] Preferably, as an example, based on the descriptors of the dimension with the largest isolation degree, divide the standard sequences in the set corresponding to the parent node into two categories, and use the sets formed by the data in each category as the child nodes of the parent node, including: If both endpoints of the demarcation polyline are on the outer envelope line, use the demarcation polyline as the final demarcation line, and use the standard sequences on one side of the final demarcation line as one category to obtain the classification result; If any endpoint of the demarcation polyline is not on the outer envelope line, record the analysis point and the extension straight line in the extension direction as the extension straight line, splice the area between the analysis point and the outer envelope line of the extension straight line with the demarcation polyline to obtain the final demarcation line, and use the standard sequences on one side of the final demarcation line as one category to obtain the classification result.

[0068] Use the sets formed by the data in each category in the classification result as the child nodes of the parent node.

[0069] It can be understood that by classifying through the demarcation broken line, there is no need to introduce other classification algorithms, reducing unnecessary computational complexity.

[0070] S4: In response to the maximum isolation degree being greater than the preset threshold, use the child node as the new parent node, and classify the data in the set corresponding to the new parent node to obtain the child nodes of the new parent node; in response to the maximum isolation degree not being greater than the preset threshold, end the loop to obtain several leaf nodes; manage the project files corresponding to the data in the leaf nodes as one category.

[0071] It should be noted that the isolation degree reflects the interval between categories. When the category interval is small, it means that the data does not need to be classified further. Therefore, classification cut-off control can be performed through the isolation degree.

[0072] Preferably, as an example, in response to the maximum isolation degree being greater than the preset threshold, use the child node as the new parent node, and classify the data in the set corresponding to the new parent node to obtain the child nodes of the new parent node; in response to the maximum isolation degree not being greater than the preset threshold, end the loop to obtain several leaf nodes; manage the project files corresponding to the data in the leaf nodes as one category, including: Select the maximum value of the isolation degree among the isolation degrees in all dimensions. In response to the maximum isolation degree being greater than the preset threshold, use the child node as the new parent node, and classify the data in the set corresponding to the new parent node according to the method in step S2 - step S3. Use the sets formed by the data in each category in the classification result as the child nodes of the new parent node; in response to the maximum isolation degree not being greater than the preset threshold, end the loop to obtain several leaf nodes, and manage the project files in the leaf nodes as one category.

[0073] Among them, in this embodiment, the node without child nodes is used as the leaf node.

[0074] This embodiment takes the preset threshold as 0.35 as an example for description. Other embodiments can take other values, and this embodiment does not make specific limitations.

[0075] The embodiment of the present invention also discloses a project file information management system, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a project file information management method according to the present invention is implemented.

[0076] The above system also includes other components well-known to those skilled in the art such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be described in detail here.

[0077] In the present invention, the aforementioned memory may be any tangible medium that contains or stores a program, which 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, a resistive random access memory, a dynamic random access memory, a static random access memory, an enhanced dynamic random access memory, a high bandwidth memory, a hybrid memory cube, etc., or any other medium that can be used to store the required information and can be accessed by an application program, a module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0078] Although this specification has shown and described multiple embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many variations, changes, and alternative ways will occur to those skilled in the art without departing from the spirit and scope 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.

[0079] The above are all preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention shall be covered within the protection scope of the present invention.

Claims

1. A method for managing project archive information, characterized in that: Includes steps: Obtain the project archive and perform hash transformation to obtain the standard sequence; 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; Take the set of standard sequences as the parent node and calculate the degree of isolation 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 adjacent distance of a standard sequence, Indicates The number of standard sequences between two adjacent boundary standard sequences is: Indicates the degree of connectivity interruption of all boundary standard sequences forming the polyline. 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 a number of 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 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 the angle sliding window, use the angle sliding window to rotate and slide around the single-dimensional position of the standard sequence with the preset angle step size; Obtain the adjacent standard sequence of the standard sequence within the angle sliding window and record it as the reference standard sequence. Calculate the mean value of the distance between the descriptor of the standard sequence and all reference standard sequences and record it as the optional adjacent distance of the standard sequence. Take the maximum value of all optional adjacent distances of the standard sequence 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 comprises: 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 adjacent 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 existing between two adjacent boundary standard sequences includes: The one-dimensional positions of two adjacent boundary standard sequences are obtained, 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, and 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 boundary line, and determine whether the two end points of the boundary line are both on the outer envelope line. If both end points of the boundary line are on the outer envelope line, 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 degree of connectivity interruption is obtained by subtracting the ratio of the average of the closest distances to the truncation length from 1.

7. A method for managing project archive information according to claim 6, characterized in that: The descriptor based on the dimension with the largest degree of isolation divides the standard sequences in the corresponding set of 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, the analysis point and the fitting straight line in the extension direction are recorded as the extended straight line, and the area between the analysis point and the outer envelope is spliced ​​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 step of performing a hash transformation to obtain a standard sequence includes: Extract the hash sequence of the project archive 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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