Intelligent analysis method of data assets

Abstract

The present invention provides a data asset intelligent analysis method, which involves data processing technology, receives screening information from a user terminal, screens a data asset library according to the screening information, and obtains a first data set; when the first data set meets an interactive screening condition, retrieves an interactive screening interface and sends it to the user terminal, the interactive screening interface includes an interactive area corresponding to the data asset specification; determines an element area based on segmentation information of the interactive area by the user terminal, and determines a corresponding element type according to filling information of the element area by the user terminal; generates a data sequence according to position comparison information of the element area, the element type and the first data in the first data set, and sends the data sequence to the user terminal.

Description

Technical Field

[0001] The present invention relates to data processing technology, and in particular to a data asset intelligent analysis method. Background Art

[0002] Data assets refer to data resources owned or controlled by individuals or enterprises that can bring future economic benefits. These resources can be structured data, such as tables in a database, or unstructured data, such as text, images, and videos.

[0003] In the prior art, due to the increasing number of digital assets, when people select and view data assets of a large magnitude, they need to spend a lot of time to check them one by one from numerous data assets in order to determine the data required by the user, which takes a lot of time. At the same time, due to the large amount of data, data omissions are likely to occur during the manual screening process, which will greatly increase the screening time. For example, when a person wants to find photos of a trip to a certain place in an album, he needs to look through the photos in the album one by one, which takes a lot of time and lacks the process of interacting with the user's memory, which affects the accuracy and efficiency of screening data assets.

[0004] Therefore, how to customize image design based on users' inherent memory impressions, provide data reference for data asset screening, and thus improve the accuracy and efficiency of data screening has become an urgent problem to be solved. Summary of the invention

[0005] The embodiment of the present invention provides a data asset intelligent analysis method, which can perform image customization design according to the user's inherent memory impression, provide data reference for data asset screening, and thus improve the accuracy and efficiency of data screening.

[0006] A first aspect of an embodiment of the present invention provides a data asset intelligent analysis method, comprising:

[0007] Receiving screening information from a user terminal, and screening the data asset library according to the screening information to obtain a first data set;

[0008] When the first data set meets the interactive screening condition, calling an interactive screening interface and sending it to the user terminal, the interactive screening interface includes an interactive area corresponding to the data asset specification;

[0009] Determine an element area based on segmentation information of the interactive area by the user terminal, and determine a corresponding element type according to filling information of the element area by the user terminal;

[0010] A data sequence is generated according to the element region, the element type and the position comparison information of the first data in the first data set, and the data sequence is sent to the user terminal.

[0011] Optionally, in a possible implementation of the first aspect, when the first data set meets the interactive screening condition, an interactive screening interface is retrieved and sent to the user terminal, and the interactive screening interface includes an interactive area corresponding to the data asset specification, including:

[0012] Obtaining a first quantity of first data in the first data set;

[0013] When it is determined that the first number is greater than or equal to a preset number, an interactive screening interface is retrieved and sent to the user terminal, and the interactive screening interface includes an interactive area corresponding to the data asset specification of the first data.

[0014] Optionally, in a possible implementation manner of the first aspect, determining the element area based on the segmentation information of the interaction area by the user terminal, and determining the corresponding element type according to the filling information of the element area by the user terminal includes:

[0015] Determine an element area based on the segmentation information of the interactive area by the user terminal, and receive filling information of the element area by the user terminal;

[0016] The element type of each of the element regions is determined based on the filling information.

[0017] Optionally, in a possible implementation manner of the first aspect, generating a data sequence according to the element region, the element type, and position comparison information of the first data in the first data set, and sending the data sequence to the user terminal includes:

[0018] Obtaining a preliminary screening set according to the element region, the element type and the longitudinal position comparison information of the first data in the first data set;

[0019] Based on the element region, the element type and the lateral position comparison information of the second data in the primary screening set, obtaining a lateral similarity value of the second data;

[0020] Calculate the similarity coefficient based on the longitudinal similarity value and the transverse similarity value of the second data in the primary screening set;

[0021] The second data is sorted in descending order based on the similarity coefficient to generate a data sequence, and the data sequence is sent to the user terminal.

[0022] Optionally, in a possible implementation manner of the first aspect, obtaining a preliminary screening set according to the element region, the element type and the longitudinal position comparison information of the first data in the first data set includes:

[0023] Taking the element type corresponding to each element area in the interactive area as the reference type, and sequentially counting the reference types in the interactive area based on a preset longitudinal direction to generate an interactive element sequence;

[0024] Acquire the element type that is the same as the reference type in each first data in the first data set as the first type of each first data;

[0025] sequentially counting the first types of the first data based on a preset longitudinal direction to obtain a first longitudinal sequence corresponding to the first data;

[0026] sequentially selecting the first types in the interactive element sequence to obtain a reference longitudinal sequence corresponding to the first data;

[0027] The first data whose first longitudinal sequence is consistent with the reference longitudinal sequence is selected as the second data, and the second data is counted to obtain a preliminary screening set.

[0028] Optionally, in a possible implementation manner of the first aspect, obtaining a lateral similarity value of the second data based on the element region, the element type and the lateral position comparison information of the second data in the primary screening set includes:

[0029] Acquire the center point of the interaction area as the interaction midpoint, perform coordinate processing on the interaction area based on the interaction midpoint, acquire the center point of the second data as the comparison midpoint, and perform coordinate processing on the second data based on the comparison midpoint;

[0030] Retrieving the first longitudinal sequence corresponding to the second data in the primary screening set, selecting the first element type in the first longitudinal sequence as a reference type, and taking the remaining element types as combination types;

[0031] Select any one of the combination types and combine it with the reference type to obtain multiple comparison combinations;

[0032] Using the area in the second data corresponding to the reference type as a data base area, and using the area in the second data corresponding to the combination type as a data comparison area;

[0033] Acquire the coordinates of the center point of the data reference area as the data reference coordinates, and acquire the coordinates of the center point of the data comparison area as the data comparison coordinates;

[0034] Calculating according to the data reference coordinates and the data comparison coordinates to obtain a data slope corresponding to the comparison combination;

[0035] Using the element region in the interactive area corresponding to the reference type as an interactive reference region, and using the element region in the interactive area corresponding to the combination type as an interactive comparison region;

[0036] Acquire the coordinates of the center point of the interactive reference area as the interactive reference coordinates, and acquire the coordinates of the center point of the interactive comparison area as the interactive comparison coordinates;

[0037] Calculating according to the interactive reference coordinates and the interactive comparison coordinates to obtain an interactive slope corresponding to the comparison combination;

[0038] When it is determined that the data slope corresponding to the comparison combination is consistent with the positive and negative relationship of the corresponding interaction slope, a preset consistent matching value is retrieved and configured to the comparison combination corresponding to the second data;

[0039] When it is determined that the data slope corresponding to the comparison combination is inconsistent with the positive and negative relationship of the corresponding interaction slope, a preset difference matching value is retrieved and configured to the comparison combination corresponding to the second data;

[0040] Obtaining a preset matching value corresponding to the comparison combination according to the preset consistent matching value and the preset different matching value of the comparison combination corresponding to the second data;

[0041] The preset matching values ​​of the comparison combinations corresponding to the second data are counted to obtain the horizontal similarity value of the second data.

[0042] Optionally, in a possible implementation manner of the first aspect, the calculating according to the longitudinal similarity value and the transverse similarity value of the second data in the primary screening set to obtain the similarity coefficient includes:

[0043] Obtaining the number of the first category in the first longitudinal sequence corresponding to the second data as the longitudinal comparison number of the second data;

[0044] Acquire the number of reference types in the interactive element sequence as the vertical reference number, and obtain the vertical similarity according to the ratio of the vertical comparison number to the vertical reference number;

[0045] A longitudinal coefficient is obtained according to the product of the longitudinal similarity value and the longitudinal similarity weight, and a transverse coefficient is obtained according to the product of the transverse similarity value and the transverse similarity weight;

[0046] Based on the longitudinal coefficient and the transverse coefficient and value, a similarity coefficient is obtained.

[0047] Optionally, in a possible implementation manner of the first aspect, obtaining a similarity coefficient based on the longitudinal coefficient and the transverse coefficient and value includes:

[0048] The similarity coefficient is obtained by the following formula:

[0049] in, is the similarity coefficient, is the number of vertical comparisons, is the longitudinal reference quantity, is the vertical similarity weight, is the upper limit of the number of comparison combinations corresponding to the second data, For the second data The preset matching value of the comparison combination, is the horizontal similarity weight.

[0050] Optionally, in a possible implementation of the first aspect, the method further includes:

[0051] When it is determined that the data slope corresponding to the comparison combination is consistent with the positive and negative relationship of the corresponding interaction slope, the corresponding comparison combination is regarded as a same-direction combination;

[0052] Acquire the horizontal coordinate of the interaction reference coordinate corresponding to the same orientation combination as the interaction reference horizontal coordinate, and the horizontal coordinate of the interaction comparison coordinate as the interaction comparison horizontal coordinate;

[0053] Obtaining an interactive lateral distance corresponding to the same-orientation combination according to an absolute value of a difference between the interactive reference horizontal coordinate and the interactive comparison horizontal coordinate;

[0054] Acquire the abscissa of the data reference coordinate corresponding to the same orientation combination as the data reference abscissa, and the abscissa of the data comparison coordinate as the data comparison abscissa;

[0055] Obtaining the data lateral distance corresponding to the same-orientation combination according to the absolute value of the difference between the data reference lateral coordinate and the data comparison lateral coordinate;

[0056] Based on the absolute value of the difference between the interactive lateral distance and the data lateral distance, an azimuth difference distance is obtained, and the preset consistent matching value is reduced and adjusted according to the azimuth difference distance to obtain an actual matching value.

[0057] Optionally, in a possible implementation manner of the first aspect, the reducing and adjusting the preset consistent matching value according to the azimuth difference distance to obtain the actual matching value includes:

[0058] Obtaining an azimuth adjustment coefficient according to a ratio of the azimuth difference distance to a reference adjustment distance, and retrieving an adjustment constant value corresponding to the reference adjustment distance;

[0059] Obtaining a matching adjustment value according to a product of the orientation adjustment coefficient and the adjustment constant value, and obtaining an actual matching value based on a difference between the preset consistent matching value and the matching adjustment value;

[0060] The actual matching value is obtained by the following formula:

[0061] in, is the actual matching value, To preset consistent matching value, is the interaction lateral distance, is the horizontal distance of the data, Adjust the distance to the benchmark, To adjust the constant value.

[0062] According to a third aspect of the present invention, there is provided an electronic device, comprising: a memory, a processor and a computer program, wherein the computer program is stored in the memory, and the processor runs the computer program to execute the first aspect of the present invention and various methods that may be involved in the first aspect.

[0063] According to a fourth aspect of the present invention, a storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the first aspect of the present invention and various methods that may be involved in the first aspect.

[0064] The beneficial effects of the present invention are as follows:

[0065] 1. The present invention can customize the interactive area according to the user's inherent memory impression, and provide data reference when screening a large number of data assets, so as to improve the accuracy and efficiency of data screening. First, the present invention can perform preliminary screening in the data asset library according to the screening information input by the user to obtain the first data set, reduce the amount of data selection, and save the user's sliding search time. Secondly, the present invention can also call out the interactive screening interface when the amount of data is large, so that the user can mark in the interactive area according to the memory, so that different element areas and element types can be automatically identified and compared with the first data in the future, reducing the search time, so that the user does not need to check all data assets one by one, and greatly improving the efficiency of data screening.

[0066] 2. The present invention can compare the position of the first data in the first data set through the element area and element type marked by the user, so as to obtain a data sequence, thereby ensuring that the user can quickly obtain data assets that meet their needs. Among them, the present invention can preliminarily screen the first data through the vertical position comparison information, determine the second data, obtain the preliminary screening set, and preliminarily reduce the amount of subsequent data comparison. In addition, the present invention can also identify the element type and the position of the corresponding type in the second data, compare the horizontal position, and obtain the horizontal similarity value, which is convenient for subsequent calculation of the vertical and horizontal similarities to generate the similarity coefficient. The larger the similarity coefficient, the more similar the corresponding data is to the interactive area. This process combines the user's interactive memory and the actual characteristics of the data, so that the system can accurately evaluate the similarity between each data and the user's memory. By introducing horizontal position comparison and vertical position comparison, the accuracy of data matching is further improved, and finally the data with high similarity is displayed to the user first, effectively saving the user's search time and improving work efficiency.

[0067] 3. The present invention can also adjust the matching value of the element combination in each data to improve the accuracy of the similarity coefficient corresponding to each data. Among them, the present invention can obtain the horizontal regional spacing distance of different element types in the corresponding second data by the consistent positive and negative relationship between the data slope and the corresponding interaction slope, so as to compare and calculate the distance between the corresponding element areas in the interaction area, and then adjust the preset consistent matching value according to the corresponding horizontal distance difference to obtain the actual matching value, improve the accuracy of the similarity coefficient corresponding to the second data, so as to finally prioritize the data with high similarity for users to view, provide users with a more efficient data screening experience, and effectively save users' search time. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A flow chart of a data asset intelligent analysis method provided by the present invention;

[0069] Figure 2 A schematic diagram of an interactive area provided by the present invention;

[0070] Figure 3 A schematic diagram determined as second data provided by the present invention;

[0071] Figure 4 A schematic diagram of the hardware structure of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein.

[0074] It should be understood that in various embodiments of the present invention, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0075] It should be understood that in the present invention, "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0076] It should be understood that in the present invention, "plurality" refers to two or more than two. "And / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "Contains A, B and C", "Contains A, B, C" means that A, B, and C are all included, "Contains A, B or C" means that one of A, B, and C is included, and "Contains A, B and / or C" means that any one, any two, or any three of A, B, and C are included.

[0077] It should be understood that in the present invention, "B corresponding to A", "B corresponding to A", "A corresponds to B" or "B corresponds to A" means that B is associated with A and B can be determined based on A. Determining B based on A does not mean determining B based only on A, but B can also be determined based on A and / or other information. A and B match when the similarity between A and B is greater than or equal to a preset threshold.

[0078] Depending on the context, "if" as used herein may be interpreted as "when" or "when" or "in response to determining" or "in response to detecting."

[0079] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0080] like Figure 1 As shown, the present invention provides a data asset intelligent analysis method, comprising:

[0081] S1, receiving screening information from a user terminal, screening a data asset library according to the screening information, and obtaining a first data set.

[0082] It should be noted that with the development of smart devices, the number of data assets stored in data asset libraries is increasing. Therefore, when users want to select the data they need from many data asset libraries, they need to perform preliminary screening based on the characteristics of the data they need in order to reduce the interference of redundant data and improve selection efficiency.

[0083] Among them, the user end is the information terminal of the person who selects the data assets, such as a mobile phone, the filtering information is the characteristic information of the selected data assets, for example, when the user wants to view camping images, the corresponding filtering information may be camping, the data asset library is a database for storing data assets, such as a database for storing photos, and the first data set is a set of data that meets the filtering information. For example, when the filtering information is camping, photos related to camping can be selected from the photo library to form a data set.

[0084] It is not difficult to understand that when filtering the data asset library, the filtering information can be compared with the labels of the data classification in the data asset library. For example, when the filtering information is camping, the labels of camping and photos can be compared to determine the photos that meet the requirements of camping and integrate them into a set so that users can subsequently select from the first data set, thereby improving data selection efficiency.

[0085] S2: When the first data set meets the interactive screening condition, an interactive screening interface is retrieved and sent to the user terminal, wherein the interactive screening interface includes an interactive area corresponding to the data asset specification.

[0086] It should be noted that the amount of data in the first data set obtained by filtering information may be large or small. When the amount is small, the user can directly view the information in the first data set and quickly determine the data required by the user in a short time, but when the amount is large, the user still cannot find the data he needs in a short time. Therefore, it is possible to determine whether the first data set meets the interactive filtering conditions so as to call out the interactive filtering interface and send it to the user end, so that the user can subsequently mark in the interactive area of ​​the interactive filtering interface according to his inherent memory, so as to compare the data in the first data set, save the user's time to check one by one, and improve the data selection rate.

[0087] It can be understood that the interactive filtering condition is a judgment condition for whether to call up the interactive filtering interface. For example, the interactive filtering condition may be greater than a preset number. That is, when the number in the first data set is greater than the preset number, it means that the first data set satisfies the interactive filtering condition, and then the interactive filtering interface can be called up and sent to the user end. However, when the first data set does not meet the interactive filtering condition, the interactive filtering interface will not be called up.

[0088] Among them, the interactive filtering interface is a filtering interface for information interaction with the data asset library, including an interactive area corresponding to the data asset specifications. The data asset specifications are data specifications corresponding to the data assets. For example, when the data asset is a picture, the specifications of the interactive area are consistent with the picture size. The interactive area is the area for information customization in the interactive filtering interface.

[0089] In some embodiments, the specific implementation steps of step S2 (when the first data set meets the interactive screening condition, calling the interactive screening interface and sending it to the user terminal, the interactive screening interface including an interactive area corresponding to the data asset specification) include:

[0090] S21: Obtain a first quantity of first data in the first data set.

[0091] It can be understood that the first data is the information data in the first data set, such as pictures, videos, etc., and the first quantity is the number of first data in the first data set. For example, when the number of photos in the first data set is 3,000, the corresponding first quantity is 3,000.

[0092] Through the above implementation, the present invention can obtain the first quantity, so as to subsequently determine whether the interactive screening conditions are met, and then determine the corresponding implementation steps to improve the data selection efficiency.

[0093] S22, when it is determined that the first number is greater than or equal to a preset number, an interactive screening interface is retrieved and sent to the user terminal, wherein the interactive screening interface includes an interactive area corresponding to a data asset specification of the first data.

[0094] It is understandable that when it is determined that the first number is greater than or equal to the preset number, it means that the amount of data in the current first data set is too large and the user cannot determine the required data in a short time. Therefore, it is necessary to call up the interactive screening interface and send it to the user end for subsequent customized processing based on user memory.

[0095] The preset number is a pre-set number, which may be manually preset.

[0096] S3, determining an element area based on the segmentation information of the interactive area by the user terminal, and determining a corresponding element type according to the filling information of the element area by the user terminal.

[0097] It is understandable that the user can perform area segmentation in the interactive area based on the memory of the data to be found, thereby determining the element areas corresponding to different element types, and filling the corresponding element types in each element area, so as to facilitate the subsequent analysis of the second data and compare it with the interactive area, thereby improving the accuracy and speed of data screening.

[0098] In some embodiments, the specific implementation steps of step S3 (determining the element area based on the segmentation information of the interactive area by the user terminal, and determining the corresponding element type according to the filling information of the element area by the user terminal) include:

[0099] S31, determining an element area based on the segmentation information of the interactive area by the user terminal, and receiving filling information of the element area by the user terminal.

[0100] It can be understood that the segmentation information is the operation information of the user end to segment the interactive area. For example, the interactive area can be divided into four areas, and the corresponding positions of each area can be obtained, so as to obtain the element area, wherein the element area is the area where the element corresponding to the data asset is located, for example, Figure 2 As shown, when the camping photo required by the user contains mountains, water, tents, and people, the user can determine the approximate position of each element in the corresponding interactive area according to the relevant memory impression, circle it, obtain each element area, and fill in the corresponding element information in the element area.

[0101] Among them, the filling information is the information filled in the element area. For example, from top to bottom, it is determined that the first element area above the interactive area is filled with "mountain", the second area is filled with "water", the third area is filled with "tent", and the fourth area is filled with "person".

[0102] Through the above implementation, the present invention can determine the element area in the interactive area and the filling information on the user input, so as to subsequently determine the corresponding element type according to the filling information, and facilitate subsequent data screening according to the element type to speed up data acquisition efficiency.

[0103] S32: Determine the element type of each of the element regions based on the filling information.

[0104] It can be understood that the element type is the type corresponding to the element area, that is, the element type corresponding to the information filled in by the user. For example, when the filled information is a mountain, the corresponding element type is a mountain; when the filled information is a person, the corresponding element type is a person, etc., so that the first data in the first data set can be compared with the element type in the subsequent information to determine the data that meets the user's needs.

[0105] S4, generating a data sequence according to the element region, the element type and the position comparison information of the first data in the first data set, and sending the data sequence to the user terminal.

[0106] It should be noted that due to the long time, people's impressions may have certain errors. Therefore, the first data in the first data set can be compared according to the element area and element type, so as to screen the data information in the set, reduce the amount of data, and narrow the scope of data selection. At the same time, the position of the relevant element information in the first data can be compared according to the relative position corresponding to the element area and element type, so as to improve the accuracy of data selection, further realize data screening, generate data sequences, and arrange the data with high similarity to the user's impression in front, so as to reduce the time for users to view and select data and improve data selection efficiency.

[0107] It can be understood that the data sequence is a sequence obtained by arranging the first data, for example, arranging and displaying a plurality of pictures to obtain an image sequence for user viewing.

[0108] In some embodiments, the specific implementation steps of step S4 (generating a data sequence according to the element region, the element type and the position comparison information of the first data in the first data set, and sending the data sequence to the user terminal) include:

[0109] S41, obtaining a preliminary screening set according to the element region, the element type and the longitudinal position comparison information of the first data in the first data set.

[0110] It can be understood that by determining the element area and element type through user impression, a preliminary information comparison can be performed on the first data that matches the user's memory. For example, the element type in the user's memory is compared with the first data in the first data set to determine the number of elements of the same type or the same type, and a preliminary screening set is obtained, excluding some data that are significantly different from the user's memory, so as to increase the data screening speed.

[0111] The longitudinal position comparison information is information obtained by longitudinally comparing the first data according to the element region and the element type, and the preliminary screening set is a data set obtained by preliminary screening.

[0112] In some embodiments, the specific implementation steps of step S41 (obtaining the preliminary screening set according to the element region, the element type and the longitudinal position comparison information of the first data in the first data set) include:

[0113] S411, taking the element type corresponding to each element area in the interactive area as a reference type, and sequentially counting the reference types in the interactive area based on a preset longitudinal direction to generate an interactive element sequence.

[0114] It can be understood that the reference type is the type of information element in the data selected by the user, that is, the element type corresponding to each element area in the interactive area, the preset longitudinal direction is a preset longitudinal direction, for example, it can be from top to bottom or from bottom to top, and the interactive element sequence is the element sequence after arranging the reference types. For example, when the preset longitudinal direction is from top to bottom, the reference types in the interactive area can be determined in sequence from top to bottom as mountain, water, tent, and person, and the interactive element sequence is arranged as (mountain, water, tent, person).

[0115] Through the above implementation, the present invention can determine the interactive element sequence, so as to subsequently compare the elements and element arrangement positions with the first data in the first data set, thereby improving the accuracy of screening data.

[0116] S412: Acquire the element type that is the same as the reference type in each first data in the first data set as the first type of each first data.

[0117] It can be understood that the first category is the element type in the first data that is the same as the baseline category. For example, when the baseline category includes mountains, water, tents, and people, and the element types in the first data include mountains, water, and people, it can be obtained that the first category in the first data includes mountains, water, and people.

[0118] S413: Count the first types of the first data in sequence based on a preset longitudinal direction to obtain a first longitudinal sequence corresponding to the first data.

[0119] It can be understood that the first vertical sequence is a category sequence after arranging the first category in the first data according to a preset vertical direction. For example, when the preset vertical direction is from top to bottom, the first category in the picture is identified as mountain, water, and person in sequence according to the preset vertical direction, and then the first vertical sequence can be obtained as (mountain, water, person).

[0120] Through the above-mentioned implementation mode, the present invention can obtain the first vertical sequence corresponding to the first data, so as to subsequently count the benchmark vertical sequence in the interactive element sequence according to the first category, and then compare the benchmark vertical sequence with the first vertical sequence, so as to determine the first data in which the relative positions of the elements arranged in the first data are relatively the same as the elements in the user's memory, thereby facilitating the subsequent acquisition of the preliminary screening set and improving the data screening efficiency.

[0121] S414, selecting first categories in the interactive element sequence in sequence to obtain a reference vertical sequence corresponding to the first data.

[0122] It should be noted that the user's memory may be different from the actually selected picture. Therefore, the element types determined in the interactive area may have a certain error with the number of first types in the first data in the first data set. However, when the relative positions of the first types are the same, they may also be the data required by the user. Therefore, the first types in the interactive element sequence can be selected in turn to obtain a benchmark vertical sequence corresponding to the first data, which is convenient for subsequent position comparison with the first vertical sequence, thereby obtaining a preliminary screening set that initially meets the conditions and reducing the number of screenings.

[0123] The reference vertical sequence is a reference sequence for determining the first vertical sequence, that is, a sequence obtained by sequentially selecting the first category from the interactive element sequence and arranging them.

[0124] For example, when the interactive element sequence is (mountain, water, tent, person), and the first category corresponding to the first data is mountain, water, and person, the same element types in the interactive element sequence can be arranged in sequence according to the first category to obtain a benchmark vertical sequence of (mountain, water, person), which is convenient for subsequent comparison with the first vertical element sequence, thereby determining the first data in the initial screening set.

[0125] S415, selecting first data whose first longitudinal sequence is consistent with the reference longitudinal sequence as second data, and counting the second data to obtain a preliminary screening set.

[0126] It can be understood that when the first vertical sequence is consistent with the reference vertical sequence, it can be explained that the arrangement position of the elements in the current first data is consistent with the element position in the user's memory, which can indicate that the first data may be the data required by the user, and therefore, the corresponding first data can be used as the second data.

[0127] For example, Figure 3 As shown, when the vertical relative positions of the elements mountain, water, and person in the compared first data picture are consistent with the vertical positions of the elements mountain, water, and person in the user-customized interactive area, the picture may be what the user is looking for. Therefore, the picture can be used as the second data. However, when the number of the first categories in the first data picture is inconsistent with the reference category and the relative positions of the first categories are also inconsistent, it means that the picture deviates greatly from the picture required by the user, so the corresponding first data is not used as the second data, which can reduce the interference of some data, facilitate the selection of the data required by the user, and speed up the selection.

[0128] Among them, the second data is the first data selected from the first data set, that is, the first data whose first longitudinal sequence is consistent with the benchmark longitudinal sequence, and the initial screening set is a set formed by statistically combining the second data, for example, a picture set obtained by combining multiple pictures selected after longitudinal position comparison of elements.

[0129] Through the above implementation, the present invention can obtain a preliminary screening set, so that only the lateral position judgment is performed on the data in the preliminary screening set later, thereby improving the data screening speed.

[0130] S42, obtaining a lateral similarity value of the second data based on the element region, the element type and the lateral position comparison information of the second data in the primary screening set.

[0131] It can be understood that multiple element areas are customized in the interaction area according to user memory, so in order to select data that meets user needs, the second data in the initial screening set can be screened again, that is, the horizontal position corresponding to each element area in the interaction area can be compared with the horizontal position corresponding to each type of element in the second data to obtain a horizontal similarity value, so that the second data in the initial screening set can be arranged according to the horizontal similarity value, so that the data with high similarity to the interaction area customized by the user according to memory is arranged in front, so that the user can quickly find the required data.

[0132] The horizontal position comparison information is information obtained by comparing the horizontal positions of the element regions, and the horizontal similarity value is a numerical value of the similarity degree obtained by performing horizontal judgment.

[0133] In some embodiments, the specific implementation steps of step S42 (obtaining the lateral similarity value of the second data based on the lateral position comparison information of the element region, the element type and the second data in the primary screening set) include:

[0134] S4201, obtaining the center point of the interaction area as the interaction midpoint, performing coordinate processing on the interaction area based on the interaction midpoint, and obtaining the center point of the second data as the comparison midpoint, and performing coordinate processing on the second data based on the comparison midpoint.

[0135] It can be understood that since the interactive area and the second data have the same specifications, the center point of the interactive area and the center point of the second data can be obtained respectively, and then a coordinate system can be constructed for the interactive area and the second data, and coordinate processing can be performed so that the corresponding position of the element area can be determined based on the coordinate position to improve the accuracy of data screening.

[0136] The interaction midpoint is the center point of the interaction area, and the comparison midpoint is the center point of the second data.

[0137] It is not difficult to understand that the center point of the interaction area and the second data can be determined through existing image processing technology such as OpenCV.

[0138] S4202, retrieve the first longitudinal sequence corresponding to the second data in the primary screening set, select the first element type in the first longitudinal sequence as a reference type, and use the remaining element types as combination types.

[0139] It is understandable that when determining the relative positions corresponding to element types, one element type needs to be preferentially selected as a reference type so that the relative positions of other element regions can be determined according to the position of the reference type.

[0140] The reference type is the first element type in the first vertical sequence, and the combination type is the remaining element types.

[0141] For example, when the first vertical sequence is (mountain, water, person), "mountain" can be used as a reference type, and then "water" and "person" are combination types, so that they can be combined with "mountain" to obtain the corresponding comparison combination, thereby obtaining the corresponding position interval, so as to compare with the position of the elements of the corresponding type in the interaction area to obtain the horizontal similarity value.

[0142] S4203, selecting any one of the combination types and combining it with the reference type to obtain a plurality of comparison combinations.

[0143] It can be understood that the comparison combination is a combination of element types for position comparison, such as combining the combination type "water" and the reference type "mountain" to obtain a comparison combination "mountain and water", and combining the combination type "person" and the reference type "mountain" to obtain a comparison combination "mountain person", so that the area corresponding to the elements in the corresponding comparison combination can be selected for position confirmation, and then compared with the position of the same element area in the interactive area, so as to obtain the similarity between each picture and the corresponding comparison combination in the interactive area.

[0144] S4204: Use the area in the second data corresponding to the reference type as a data base area, and use the area in the second data corresponding to the combination type as a data comparison area.

[0145] It can be understood that the data reference area is the area corresponding to the reference type in the second data, for example, the area displayed as the reference type "mountain" in the camping picture, and the data comparison area is the area corresponding to the combined type in the second data, for example, the area corresponding to the uncombined types "water" and "people" displayed in the camping picture.

[0146] Through the above-mentioned implementation mode, the present invention can determine the data reference area and data comparison area corresponding to the reference type and the combination type in the second data respectively, so as to subsequently calculate the relative position between the areas corresponding to the reference type and the combination type, so as to compare with the area position of the element type corresponding to the interaction area.

[0147] S4205, obtaining the coordinates of the area center point of the data reference area as the data reference coordinates, and obtaining the coordinates of the area center point of the data comparison area as the data comparison coordinates.

[0148] It can be understood that the data reference coordinates are the coordinates of the regional center point of the data reference area. For example, the coordinates of the regional center point can be obtained by determining the maximum and minimum values ​​of the horizontal coordinate corresponding to the data reference area, and the maximum and minimum values ​​corresponding to the vertical coordinate, and performing mean calculations respectively. Among them, the regional center point coordinates are the position coordinates corresponding to the regional center point, and the data comparison coordinates are the coordinates of the regional center point of the data comparison area. The acquisition method can be consistent with the acquisition method of the data reference coordinates, which will not be repeated here.

[0149] S4206: Calculate according to the data reference coordinates and the data comparison coordinates to obtain a data slope corresponding to the comparison combination.

[0150] It should be noted that in order to quickly and accurately screen out images with high similarity to user memory from more second data, the slopes corresponding to different element types can be compared with the position slopes of the corresponding element types in the user-customized interaction area, and then the lateral position distances can be compared. By comparing in multiple dimensions, the similarity between each second data and the interaction area can be calculated, so that personnel can find the required data more quickly and save user search time.

[0151] It can be understood that the data slope is the position slope corresponding to the positions of different element types in the comparison combination, that is, the data slope corresponding to the comparison combination can be obtained by substituting the data reference coordinates and the data comparison coordinates into the slope formula.

[0152] S4207: Use the element region in the interactive area corresponding to the reference type as an interactive benchmark region, and use the element region in the interactive area corresponding to the combination type as an interactive comparison region.

[0153] It can be understood that the interaction reference area is the element area corresponding to the reference type in the interaction area. For example, the element area corresponding to the reference element "mountain" in the interaction area is the interaction reference area, and the interaction comparison area is the element area corresponding to the combined type in the interaction area, such as the element area corresponding to the combined elements "water" and "people" in the interaction area customized by the user.

[0154] Through the above-mentioned implementation mode, the present invention can determine the corresponding interaction reference area and interaction comparison area in the interaction area, so as to subsequently determine the interaction slope through the center point coordinates corresponding to the interaction reference area and the interaction comparison area, and then compare it with the data slope corresponding to the second data to determine the corresponding degree of similarity.

[0155] S4208, obtaining the coordinates of the center point of the interactive reference area as the interactive reference coordinates, and obtaining the coordinates of the center point of the interactive comparison area as the interactive comparison coordinates.

[0156] It can be understood that the interactive reference coordinates are the coordinates of the center point of the interactive reference area, and the interactive comparison coordinates are the coordinates of the center point of the interactive comparison area.

[0157] It is not difficult to understand that the coordinates of the center point of the region can be obtained by obtaining the maximum and minimum values ​​of the corresponding horizontal coordinates of the region, and performing mean calculation to obtain the central horizontal coordinate, and by obtaining the maximum and minimum values ​​of the corresponding vertical coordinates of the region, and performing mean calculation to obtain the central vertical coordinate, and then the coordinates of the center point of the region can be obtained according to the central horizontal coordinate and the central vertical coordinate.

[0158] S4209: Calculate according to the interactive reference coordinates and the interactive comparison coordinates to obtain an interactive slope corresponding to the comparison combination.

[0159] It can be understood that the interaction slope is the position slope corresponding to the comparison combination in the interaction area, that is, the slope value obtained by substituting the interaction reference coordinates and the interaction comparison coordinates into the slope calculation formula.

[0160] S4210: When it is determined that the data slope corresponding to the comparison combination is consistent with the positive and negative relationship of the corresponding interaction slope, a preset consistent matching value is retrieved and configured to the comparison combination corresponding to the second data.

[0161] It can be understood that when the relative positions of the corresponding reference types and combination types in the comparison combinations in the second data and the interaction area are consistent, the positive and negative relationships between the data slope and the interaction slope are also the same. For example, when the area corresponding to the element "mountain" in the second data is located at the upper left of the element "water", the data slope is positive. At the same time, when it is determined that the element area corresponding to the element "mountain" in the interaction area is also at the upper left of the element area of ​​the element "water", the interaction slope is positive. This means that the relative positions of the elements in the current second data and the interaction area customized by the user through memory are roughly the same. The preset consistent matching value can be configured to the comparison combination corresponding to the second data so that the similarity corresponding to the second data can be calculated subsequently.

[0162] The positive and negative relationship refers to the positive and negative conditions of the slope values ​​corresponding to the data slope and the interaction slope, and the preset consistent matching value is the matching similarity value corresponding to the preset positive and negative relationships, for example, it may be 1.

[0163] S4211, when it is determined that the data slope corresponding to the comparison combination is inconsistent with the positive and negative relationship of the corresponding interaction slope, a preset difference matching value is retrieved and configured to the comparison combination corresponding to the second data.

[0164] It can be understood that when the relative positions of the corresponding reference types and combination types in the comparison combinations in the second data and the interaction area are inconsistent, the positive and negative relationships between the data slope and the interaction slope are also different. For example, when the area corresponding to the element "mountain" in the second data is located to the upper right of the element "water", the data slope is negative. At the same time, when it is determined that the element area corresponding to the element "mountain" in the interaction area is also to the upper left of the element area of ​​the element "water", the interaction slope is positive. This indicates that the relative positions of the elements in the current second data and the interaction area customized by the user through memory are different, so that the preset difference matching value can be configured to the comparison combination corresponding to the corresponding second data, so as to subsequently calculate the lateral similarity value of the second data.

[0165] The preset difference matching value is a matching similarity value corresponding to when the preset positive and negative relationships are inconsistent, for example, it may be -1.

[0166] S4212: Obtain a preset matching value corresponding to the comparison combination according to the preset consistent matching value of the comparison combination corresponding to the second data and the preset different matching value.

[0167] It can be understood that the preset matching value is a preset matching degree value between the second data and the interactive area, including a preset consistent matching value and a preset different matching value.

[0168] Through the above implementation, the present invention can determine the preset matching value corresponding to each comparison combination in the second data, so as to subsequently calculate the horizontal similarity value between the corresponding second data and the interaction area according to the preset matching value of each comparison combination.

[0169] S4213: Count preset matching values ​​of the comparison combination corresponding to the second data to obtain a horizontal similarity value of the second data.

[0170] It can be understood that by statistically summing up the preset matching values ​​corresponding to all comparison combinations in the second data, a horizontal similarity value for judging the horizontal position of the second data can be obtained, so that the similarity coefficient corresponding to the second data can be subsequently calculated in conjunction with the vertical similarity value, and the second data can be judged and arranged to reduce the time for users to slide and search, thereby improving the user's search speed.

[0171] S43, calculating according to the longitudinal similarity value and the transverse similarity value of the second data in the primary screening set to obtain a similarity coefficient.

[0172] It can be understood that the similarity coefficient is a similarity value between the second data and the data corresponding to the interaction area, that is, a uniform calculation of the longitudinal similarity value and the lateral similarity value of the same specification.

[0173] It is not difficult to understand that the larger the similarity coefficient is, the more similar the corresponding second data is to the data required by the user, which can assist the user in data screening, reduce the user's viewing time, and improve search efficiency.

[0174] In some embodiments, the specific implementation steps in step S43 (the calculation based on the longitudinal similarity value and the transverse similarity value of the second data in the primary screening set to obtain the similarity coefficient) include:

[0175] S431, obtaining the number of the first category in the first longitudinal sequence corresponding to the second data as the longitudinal comparison number of the second data.

[0176] It can be understood that the longitudinal comparison quantity is the number of element types in the first longitudinal sequence corresponding to the second data, that is, the number of the first type in the first longitudinal sequence corresponding to the second data.

[0177] Through the above implementation, the present invention can determine the number of longitudinal comparisons corresponding to the second data, so as to subsequently perform a comparison calculation with the number of elements in the interactive area to obtain the longitudinal similarity.

[0178] S432, obtaining the number of reference types in the interactive element sequence as the vertical reference number, and obtaining the vertical similarity according to the ratio of the vertical comparison number to the vertical reference number.

[0179] It can be understood that the vertical benchmark number is the benchmark number of element types corresponding to the vertical judgment, that is, the number of benchmark types in the interactive element sequence, and the vertical similarity is the similarity obtained by comparing the vertical quantities, that is, the ratio of the vertical comparison quantity to the vertical benchmark quantity.

[0180] It is not difficult to understand that the interaction area is customized by the user according to memory, and a large number of second data are compared and judged with the interaction area in order to determine the second data similar to the interaction area. Therefore, the number of element types in the interaction area can be used as the vertical benchmark number to improve the accuracy of the vertical similarity corresponding to the second data.

[0181] S433: Obtain a longitudinal coefficient according to the product of the longitudinal similarity value and the longitudinal similarity weight, and obtain a transverse coefficient according to the product of the transverse similarity value and the transverse similarity weight.

[0182] It can be understood that the vertical similarity weight is the proportional weight of the vertical similarity value in the similarity coefficient, which can be artificially preset, and the vertical coefficient is the product of the vertical similarity value and the vertical similarity weight. The horizontal similarity weight is the proportional weight of the horizontal similarity value in the similarity coefficient, which can be artificially preset, and the horizontal coefficient is the product of the horizontal similarity value and the horizontal similarity weight.

[0183] S434, obtaining a similarity coefficient based on the longitudinal coefficient and the transverse coefficient and value.

[0184] It is understandable that the similarity coefficient corresponding to the second data can be obtained by summing up the vertical coefficient and the horizontal coefficient, so that the second data can be subsequently arranged in descending order according to the similarity coefficient to generate a data sequence for easy viewing by users.

[0185] In some embodiments, the specific implementation steps of step S434 (obtaining the similarity coefficient based on the longitudinal coefficient and the transverse coefficient and value) include:

[0186] S4341, the similarity coefficient is obtained by the following formula,

[0187] in, is the similarity coefficient, is the number of vertical comparisons, is the longitudinal reference quantity, is the vertical similarity weight, is the upper limit of the number of comparison combinations corresponding to the second data, For the second data The preset matching value of the comparison combination, is the horizontal similarity weight.

[0188] It can be understood that the longitudinal comparison quantity is the quantity of the first category in the first longitudinal sequence, and the first category is the same element category as the element category in the interactive area. Therefore, the longitudinal comparison quantity is less than or equal to the longitudinal reference quantity, so when the longitudinal comparison quantity The larger the value, the closer the number of the same elements in the second data and the interaction area is. Therefore, the vertical similarity value The larger the vertical weight value The longitudinal coefficient obtained after multiplication The larger the value is, the higher the similarity between the second data and the interactive area in the vertical direction is. is the horizontal similarity value, which is obtained by statistically accumulating the preset matching values ​​corresponding to all comparison combinations in the second data. Therefore, when The larger the value is, the larger the preset matching value of the comparison combination in the second data is, which further indicates that the horizontal relative position of the corresponding comparison combination in the second data and the interaction area is more similar. Therefore, the horizontal coefficient obtained by multiplying the horizontal similarity value by the horizontal similarity weight is The larger the similarity coefficient is, the higher the final similarity coefficient will be. The larger the value is, the higher the similarity between the second data and the interactive area is, so the second data can be arranged in front to facilitate the user to determine the data, thereby reducing the user's viewing time.

[0189] On the contrary, when the vertical comparison quantity The smaller the value, the smaller the number of elements in the second data that are identical to those in the interaction area. The smaller the vertical weight value The longitudinal coefficient obtained after multiplication The smaller the value, the lower the similarity between the second data and the interactive area in the vertical direction. The smaller the value is, the smaller the preset matching value of the comparison combination in the second data is, which further indicates that the horizontal relative position of the corresponding comparison combination in the second data and the interaction area is less similar. Therefore, the horizontal coefficient after multiplying the horizontal similarity value by the horizontal similarity weight is The smaller the similarity coefficient is, the The smaller it is, the lower the similarity between the second data and the interactive area is, and the second data can be arranged at the end subsequently.

[0190] S44, sorting the second data in descending order based on the similarity coefficient to generate a data sequence, and sending the data sequence to the user terminal.

[0191] It can be understood that the corresponding second data is arranged in descending order according to the similarity coefficient to obtain a data sequence. For example, the pictures in the user gallery can be arranged in descending order according to the similarity coefficient with the interactive area to obtain a data sequence, so that the data sequence can be sent to the user end, which is convenient for the user to view according to the arranged data, reducing the searching time due to the disordered arrangement of data and improving the user's search efficiency.

[0192] In some embodiments, it also includes:

[0193] A1, when it is determined that the data slope corresponding to the comparison combination is consistent with the positive and negative relationship of the corresponding interaction slope, the corresponding comparison combination is regarded as a same-direction combination.

[0194] It should be noted that in order to improve the accuracy of the lateral similarity value, the horizontal regional spacing distance of different element types in the second data can be obtained for comparison with the distance between corresponding element areas in the interaction area. That is, the smaller the difference compared with the interaction area, the more similar the second data is to the interaction area. For example, when the second data corresponds to a picture, the smaller the difference between the distance between the corresponding areas of "mountain" and "water" in the second data picture and the distance between the corresponding element areas of "mountain" and "water" in the interaction area, the more similar the picture is to the picture the user is looking for.

[0195] It can be understood that when it is determined that the data slope corresponding to the comparison combination is consistent with the positive and negative relationship of the corresponding interaction slope, the corresponding comparison combination can be used as a same-orientation combination to subsequently determine the lateral spacing distance between the corresponding element areas based on the same-orientation combination, which is convenient for subsequent comparison with the interaction area.

[0196] A2, obtaining the horizontal coordinate of the interaction reference coordinate corresponding to the same orientation combination as the interaction reference horizontal coordinate, and the horizontal coordinate of the interaction comparison coordinate as the interaction comparison horizontal coordinate.

[0197] It can be understood that the interaction reference abscissa is the abscissa of the interaction reference coordinate corresponding to the same orientation combination, and the interaction comparison abscissa is the abscissa of the interaction comparison coordinate.

[0198] Through the above implementation, the present invention can obtain the interaction reference horizontal coordinate and the interaction comparison horizontal coordinate, so as to subsequently calculate the horizontal spacing distances between corresponding element types in the interaction area.

[0199] A3, obtaining an interactive lateral distance corresponding to the same-direction combination according to the absolute value of the difference between the interactive reference horizontal coordinate and the interactive comparison horizontal coordinate.

[0200] It can be understood that the interactive transverse distance is the distance between element areas in the same orientation combination in the interactive area in the transverse direction, that is, the absolute value of the difference between the interactive reference transverse coordinate and the interactive comparison transverse coordinate.

[0201] A4, obtaining the abscissa of the data reference coordinate corresponding to the same orientation combination as the data reference abscissa, and obtaining the abscissa of the data comparison coordinate as the data comparison abscissa.

[0202] It can be understood that the data reference abscissa is the abscissa of the data reference coordinate corresponding to the same orientation combination, and the data comparison abscissa is the abscissa of the data comparison coordinate.

[0203] Through the above implementation, the present invention can obtain the data reference horizontal coordinate and the data comparison horizontal coordinate, so as to subsequently calculate the interval distance of the corresponding element types in the second data in the horizontal direction.

[0204] A5, obtaining the data lateral distance corresponding to the same orientation combination according to the absolute value of the difference between the data reference lateral coordinate and the data comparison lateral coordinate.

[0205] It can be understood that the data transverse distance is the distance in the transverse direction between element regions in the same orientation combination in the second data, that is, the absolute value of the difference between the data reference transverse coordinate and the data comparison transverse coordinate.

[0206] A6, based on the absolute value of the difference between the interactive lateral distance and the data lateral distance, obtain the azimuth difference distance, and reduce and adjust the preset consistent matching value according to the azimuth difference distance to obtain the actual matching value.

[0207] It can be understood that the azimuth difference distance is the absolute value of the difference between the interaction lateral distance and the data lateral distance, that is, the distance difference is calculated by comparing the lateral distances of the element type areas corresponding to the same azimuth combination in the second data and the interaction area. For example, when the data lateral distance corresponding to "mountain" and "water" in the second data is 3 cm, and the baseline lateral distance corresponding to "mountain" and "water" in the interaction area is 4 cm, then the azimuth difference distance can be obtained as 1 cm.

[0208] It is not difficult to understand that when the azimuth difference distance is greater than 0, it can be explained that there is a certain deviation between the position of the element image in the current picture and the element type in the user-customized interaction area, and the larger the azimuth difference distance is, the larger the similarity deviation between the second data and the interaction area is. Therefore, the preset consistent matching value can be reduced and adjusted according to the azimuth difference distance to obtain the actual matching value, thereby improving the accuracy of the lateral similarity value.

[0209] The actual matching value is the matching value actually corresponding to the same direction combination.

[0210] In some embodiments, the specific implementation steps of step A6 (the step of reducing and adjusting the preset consistent matching value according to the azimuth difference distance to obtain the actual matching value) include:

[0211] A61, obtaining an azimuth adjustment coefficient according to the ratio of the azimuth difference distance to the reference adjustment distance, and retrieving an adjustment constant value corresponding to the reference adjustment distance.

[0212] It can be understood that the benchmark adjustment distance is the benchmark distance for numerical adjustment, which can be manually preset, and the azimuth adjustment coefficient is the coefficient for adjusting the preset consistent matching value corresponding to the same azimuth combination, that is, the ratio of the azimuth difference distance and the benchmark adjustment distance. The adjustment constant value is a constant value corresponding to the benchmark adjustment distance, which can be manually preset.

[0213] A62, obtaining a matching adjustment value according to the product of the orientation adjustment coefficient and the adjustment constant value, and obtaining an actual matching value based on a difference between the preset consistent matching value and the matching adjustment value.

[0214] It can be understood that the matching adjustment value is the value of the adjusted matching value, that is, the product of the orientation adjustment coefficient and the adjustment constant value, and the actual matching value can be obtained by the difference between the preset consistent matching value and the matching adjustment value.

[0215] It is not difficult to understand that the preset consistent matching value is the data set consistently for comparison between the second data and the interaction area. When the second data and the interaction have a distance difference in the horizontal direction, it means that there is a deviation between the second data and the interaction area. Therefore, only the preset consistent matching value is reduced and adjusted.

[0216] A63, the actual matching value is obtained by the following formula,

[0217] in, is the actual matching value, To preset consistent matching value, is the interaction lateral distance, is the horizontal distance of the data, Adjust the distance to the benchmark, To adjust the constant value.

[0218] It is understandable that when the interaction lateral distance Horizontal distance from data The larger the absolute value of the difference, the greater the distance of the azimuth difference. The larger the value is, the greater the distance difference between the element position in the current second data and the same element in the interactive area is, which further indicates that the deviation between the second data and the interactive area is greater. Therefore, the orientation difference distance is similar to the reference adjustment distance. Ratio, get the azimuth adjustment coefficient The larger the value, the more it matches the adjustment value. The larger the value is, the greater the reduction in the preset consistent matching value is, so the actual matching value obtained is The smaller.

[0219] On the contrary, when the interactive lateral distance Horizontal distance from data The smaller the absolute value of the difference, that is, the smaller the azimuth difference distance The smaller the distance, the smaller the difference between the element position in the current second data and the same element in the interactive area. This indicates that the deviation between the second data and the interactive area is smaller and the similarity is higher. Therefore, the orientation difference distance is similar to the reference adjustment distance. Ratio, get the azimuth adjustment coefficient The smaller it is, the better it matches the adjustment value. The smaller the value is, the smaller the preset consistent matching value is reduced, so the actual matching value is Larger.

[0220] It is not difficult to understand that by comparing the difference of lateral distances, the accuracy of the matching values ​​corresponding to each same-direction combination can be improved, and then the accuracy of the lateral similarity value can be improved, so as to save more user search time and improve search efficiency.

[0221] See also Figure 4 , is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention, the electronic device 40 includes: a processor 41, a memory 42 and a computer program; wherein

[0222] The memory 42 is used to store the computer program, and the memory may also be a flash memory. The computer program is, for example, an application program, a functional module, etc. for implementing the above method.

[0223] The processor 41 is used to execute the computer program stored in the memory to implement each step performed by the device in the above method. For details, please refer to the relevant description in the above method embodiment.

[0224] Optionally, the memory 42 may be independent or integrated with the processor 41 .

[0225] When the memory 42 is a device independent of the processor 41, the device may further include:

[0226] The bus 43 is used to connect the memory 42 and the processor 41 .

[0227] The present invention also provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided by the various embodiments described above.

[0228] Among them, the readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application-specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the readable storage medium can also exist in a communication device as discrete components. The readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0229] The present invention also provides a program product, which includes an execution instruction, which is stored in a readable storage medium. At least one processor of a device can read the execution instruction from the readable storage medium, and at least one processor executes the execution instruction so that the device implements the methods provided in the above various embodiments.

[0230] In the embodiments of the above-mentioned devices, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0231] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data asset intelligent analysis method, characterized in that: include: Receiving screening information from a user terminal, and screening the data asset library according to the screening information to obtain a first data set; When the first data set meets the interactive screening condition, calling an interactive screening interface and sending it to the user terminal, the interactive screening interface includes an interactive area corresponding to the data asset specification; Determine an element area based on segmentation information of the interactive area by the user terminal, and determine a corresponding element type according to filling information of the element area by the user terminal; Generate a data sequence according to the element region, the element type and the position comparison information of the first data in the first data set, and send the data sequence to the user terminal; Generating a data sequence according to the element region, the element type and the position comparison information of the first data in the first data set, and sending the data sequence to the user terminal, including: Taking the element type corresponding to each element area in the interactive area as the reference type, and sequentially counting the reference types in the interactive area based on a preset longitudinal direction to generate an interactive element sequence; Acquire the element type that is the same as the reference type in each first data in the first data set as the first type of each first data; sequentially counting the first types of the first data based on a preset longitudinal direction to obtain a first longitudinal sequence corresponding to the first data; sequentially selecting the first types in the interactive element sequence to obtain a reference longitudinal sequence corresponding to the first data; Selecting first data that is consistent with the first longitudinal sequence and the reference longitudinal sequence as second data, and collecting statistics on the second data to obtain a preliminary screening set; A similarity coefficient is obtained according to the primary screening set, a data sequence is generated based on the similarity coefficient, and the data sequence is sent to the user terminal.

2. The method according to claim 1, characterized in that When the first data set meets the interactive screening condition, an interactive screening interface is retrieved and sent to the user terminal, wherein the interactive screening interface includes an interactive area corresponding to the data asset specification, including: Acquire a first quantity of first data in the first data set; When it is determined that the first number is greater than or equal to a preset number, an interactive screening interface is retrieved and sent to the user terminal, and the interactive screening interface includes an interactive area corresponding to the data asset specification of the first data.

3. The method according to claim 2, characterized in that The determining of the element area based on the segmentation information of the interactive area by the user terminal, and determining the corresponding element type according to the filling information of the element area by the user terminal, includes: Determine an element area based on the segmentation information of the interactive area by the user terminal, and receive filling information of the element area by the user terminal; The element type of each of the element regions is determined based on the filling information.

4. The method according to claim 3, characterized in that The generating a data sequence according to the element region, the element type and the position comparison information of the first data in the first data set, and sending the data sequence to the user terminal also includes: Based on the element region, the element type and the lateral position comparison information of the second data in the primary screening set, obtaining a lateral similarity value of the second data; Calculate the similarity coefficient based on the longitudinal similarity value and the transverse similarity value of the second data in the primary screening set; The second data is sorted in descending order based on the similarity coefficient to generate a data sequence, and the data sequence is sent to the user terminal.

5. The method according to claim 4, characterized in that The obtaining the lateral similarity value of the second data based on the lateral position comparison information of the element region, the element type and the second data in the primary screening set comprises: Acquire the center point of the interaction area as the interaction midpoint, perform coordinate processing on the interaction area based on the interaction midpoint, acquire the center point of the second data as the comparison midpoint, and perform coordinate processing on the second data based on the comparison midpoint; Retrieving the first longitudinal sequence corresponding to the second data in the primary screening set, selecting the first element type in the first longitudinal sequence as a reference type, and taking the remaining element types as combination types; Select any one of the combination types and combine it with the reference type to obtain multiple comparison combinations; Using the area in the second data corresponding to the reference type as a data base area, and using the area in the second data corresponding to the combination type as a data comparison area; Acquire the coordinates of the center point of the data reference area as the data reference coordinates, and acquire the coordinates of the center point of the data comparison area as the data comparison coordinates; Calculating according to the data reference coordinates and the data comparison coordinates to obtain a data slope corresponding to the comparison combination; Using the element region in the interactive area corresponding to the reference type as an interactive reference region, and using the element region in the interactive area corresponding to the combination type as an interactive comparison region; Acquire the coordinates of the center point of the interactive reference area as the interactive reference coordinates, and acquire the coordinates of the center point of the interactive comparison area as the interactive comparison coordinates; Calculating according to the interactive reference coordinates and the interactive comparison coordinates to obtain an interactive slope corresponding to the comparison combination; When it is determined that the data slope corresponding to the comparison combination is consistent with the positive and negative relationship of the corresponding interaction slope, a preset consistent matching value is retrieved and configured to the comparison combination corresponding to the second data; When it is determined that the data slope corresponding to the comparison combination is inconsistent with the positive and negative relationship of the corresponding interaction slope, a preset difference matching value is retrieved and configured to the comparison combination corresponding to the second data; Obtaining a preset matching value corresponding to the comparison combination according to the preset consistent matching value and the preset different matching value of the comparison combination corresponding to the second data; The preset matching values ​​of the comparison combinations corresponding to the second data are counted to obtain the horizontal similarity value of the second data.

6. The method according to claim 5, characterized in that The calculating according to the longitudinal similarity value and the transverse similarity value of the second data in the primary screening set to obtain a similarity coefficient includes: Obtaining the number of the first category in the first longitudinal sequence corresponding to the second data as the longitudinal comparison number of the second data; Acquire the number of reference types in the interactive element sequence as the vertical reference number, and obtain the vertical similarity according to the ratio of the vertical comparison number to the vertical reference number; A longitudinal coefficient is obtained according to the product of the longitudinal similarity value and the longitudinal similarity weight, and a transverse coefficient is obtained according to the product of the transverse similarity value and the transverse similarity weight; Based on the sum of the longitudinal coefficient and the transverse coefficient, a similarity coefficient is obtained.

7. The method according to claim 6, characterized in that The obtaining of the similarity coefficient based on the sum of the longitudinal coefficient and the transverse coefficient comprises: The similarity coefficient is obtained by the following formula: in, is the similarity coefficient, is the number of vertical comparisons, is the longitudinal reference quantity, is the vertical similarity weight, is the upper limit of the number of comparison combinations corresponding to the second data, For the second data The preset matching value of the comparison combination, is the horizontal similarity weight.

8. The method according to claim 7, characterized in that Also includes: When it is determined that the data slope corresponding to the comparison combination is consistent with the positive and negative relationship of the corresponding interaction slope, the corresponding comparison combination is regarded as a same-direction combination; Acquire the horizontal coordinate of the interaction reference coordinate corresponding to the same orientation combination as the interaction reference horizontal coordinate, and the horizontal coordinate of the interaction comparison coordinate as the interaction comparison horizontal coordinate; Obtaining an interactive lateral distance corresponding to the same-orientation combination according to an absolute value of a difference between the interactive reference horizontal coordinate and the interactive comparison horizontal coordinate; Acquire the abscissa of the data reference coordinate corresponding to the same orientation combination as the data reference abscissa, and the abscissa of the data comparison coordinate as the data comparison abscissa; Obtaining the data lateral distance corresponding to the same-orientation combination according to the absolute value of the difference between the data reference lateral coordinate and the data comparison lateral coordinate; Based on the absolute value of the difference between the interactive lateral distance and the data lateral distance, an azimuth difference distance is obtained, and the preset consistent matching value is reduced and adjusted according to the azimuth difference distance to obtain an actual matching value.

9. The method according to claim 8, characterized in that The step of reducing and adjusting the preset consistent matching value according to the azimuth difference distance to obtain an actual matching value includes: Obtaining an azimuth adjustment coefficient according to a ratio of the azimuth difference distance to a reference adjustment distance, and retrieving an adjustment constant value corresponding to the reference adjustment distance; Obtaining a matching adjustment value according to a product of the orientation adjustment coefficient and the adjustment constant value, and obtaining an actual matching value based on a difference between the preset consistent matching value and the matching adjustment value; The actual matching value is obtained by the following formula: in, is the actual matching value, To preset consistent matching value, is the interaction lateral distance, is the horizontal distance of the data, Adjust the distance to the benchmark, To adjust the constant value.

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