A data information processing method, device and medium based on cluster map

Abstract

The embodiment of the present invention discloses a data class information processing method, device and medium based on a cluster map. The method includes: obtaining multiple data classes arranged in order, and multiple areas displayed as the multiple data classes in the cluster map, wherein the same area is filled with the same color, and the multiple areas are arranged in order according to the following conditions: at least one area of ​​the same color is arranged continuously, at least one area corresponding to the same data class has the same color and is arranged continuously, and the order of each two data classes is the same as the order of the areas corresponding to the two data classes; at least one area with the same color is respectively regarded as a group; in the arranged order, it is judged in turn whether there is a corresponding relationship between each type of data and each group of areas, and whether the area with a corresponding relationship is a normal area or an abnormal area of ​​the data class. This embodiment extracts the normal area and abnormal area corresponding to each data class in the cluster map.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of data visualization technology, and in particular to a data type information processing method, device and medium based on a cluster map. Background Art

[0002] With the rapid development of Internet technology and the advent of the big data era, it is often difficult to directly obtain important information through text or data due to its large scale and complex structure. Therefore, data visualization has become an important means of data analysis.

[0003] Cluster visualization is a data visualization technology that aims to display the results of data cluster analysis through graphics and colors. Cluster map is a way to display cluster data, aiming to display the data of the same category in a continuous area. In the prior art, most cluster visualizations simply display the cluster map obtained by the visualization software directly, without clarifying the local information of each data class in the cluster map. Summary of the invention

[0004] The embodiment of the present invention provides a data class information processing method, device and medium based on a cluster map, which processes the cluster map directly obtained by the visualization software, clarifies the area corresponding to each data class in the cluster map, and identifies the normal area and abnormal area therein.

[0005] In a first aspect, an embodiment of the present invention provides a data class information processing method based on a cluster map, comprising:

[0006] Acquire multiple data classes arranged in sequence, and multiple regions displayed as the multiple data classes in a cluster map, wherein the same regions are filled with the same color, and the multiple regions are arranged in sequence according to the following conditions: at least one region of the same color is arranged continuously, at least one region corresponding to the same data class has the same color and is arranged continuously, and the sequence of every two data classes is the same as the sequence of the regions corresponding to the every two data classes;

[0007] At least one area with the same color is grouped together;

[0008] According to the arranged order, it is determined whether there is a corresponding relationship between each type of data and each group of regions, and whether the region with a corresponding relationship is a normal region or an abnormal region of the data class.

[0009] In a second aspect, an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0010] one or more processors;

[0011] a memory for storing one or more programs,

[0012] When the one or more programs are executed by the one or more processors, the one or more processors implement the data class information processing method based on the cluster map described in any embodiment.

[0013] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data class information processing method based on a cluster map as described in any embodiment.

[0014] Embodiments of the Invention In summary, embodiments of the present invention provide a data class information processing method based on a cluster map, which processes the cluster map directly displayed by the visualization software according to the characteristics of the visualization file, determines the correspondence between each area in the cluster map and the input data class, and identifies the normal area and abnormal area of ​​each type of data. Based on the recognition result, the local boundary of a certain data class can be obtained, which is conducive to the local acquisition, display and operation of the local map; the identified abnormal area can be used as an object for optimized display, which provides a basic guarantee for further optimizing the quality of the map and map visualization.

[0015] In particular, when judging whether each area belongs to a certain type of data, this embodiment does not directly use the coordinate points of the data class and the boundaries of each area to judge one by one, but groups the areas with the help of color attributes. After grouping, for each type of data, the number of loop traversals is less than directly traversing the boundaries of all areas, making the execution efficiency of the entire method higher; at the same time, after grouping the areas with the help of color attributes, the final detection accuracy of normal areas and abnormal areas is also higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a cluster map directly displayed by a visualization software provided by an embodiment of the present invention;

[0018] Figure 2 is a flow chart of a data class information processing method based on a cluster map provided by an embodiment of the present invention;

[0019] Figure 3 is a parsing result of an svg file provided by an embodiment of the present invention;

[0020] Figure 4 It is a partial schematic diagram of various data types provided by an embodiment of the present invention;

[0021] Figure 5 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. 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 belong to the scope of protection of the present invention.

[0023] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0024] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] The embodiment of the present invention provides a data class information processing method based on a cluster map, using visualization software to display multiple data classes as a cluster map, and determining the corresponding relationship between each area in the cluster map and each data class, so as to identify the local boundary of each data class, and at the same time judge the normal area and abnormal area of ​​each data class, so that the cluster map can more accurately display the data characteristics. In order to illustrate the method, the cluster map directly displayed by the visualization software is first described. For visualization software such as Graphviz and tulip, the user can input multiple data classes into the software, and the software automatically generates a cluster map according to the data points in each data class. The cluster map includes multiple areas, the same type of data is closer, and different types of data are farther. Ideally, the user expects the cluster map to be able to display the data in each data class in the same continuous area, and each area corresponds to each data class one by one. However, in the cluster map directly output or displayed by the visualization software, each data class input by the user may correspond to one area, or may correspond to multiple areas, and it is unknown which area corresponds to which data class.

[0026] For example, Graphviz visualization software is an open source graph visualization tool set. The output formats of the graph include svg, png, etc. The most commonly used format is svg, which is a vector image format that can be enlarged or reduced without loss of quality and is suitable for display devices and output devices of various resolutions. Assuming that the user inputs 4 data classes into Graphviz, Graphviz can automatically generate the following: Figure 1 The cluster map shown in Figure 1. Ideally, the user expects the cluster map to include four polygonal areas, each corresponding to an input data class. Figure 1 It can be seen that the polygonal areas in the cluster map directly displayed by Graphviz are greater than 4, and there is no one-to-one correspondence, and it is also unknown which data class each polygonal area corresponds to. Graphviz does not further clarify the correspondence between each area and each data class for the cluster map in svg format, so it is impossible to display the local boundaries of each data class in the cluster map, and it is even more impossible to perform other operations related to local boundaries.

[0027] In view of the above situation, Figure 2 The flowchart of a data information processing method based on a cluster map provided by an embodiment of the present invention is applicable to the case where a cluster map directly generated by visualization software such as Graphviz and tulip is processed and executed by an electronic device. Figure 2 As shown, the method specifically includes:

[0028] S110, obtaining a plurality of data classes arranged in order, and a plurality of regions displayed as the plurality of data classes in the cluster map. The same regions are filled with the same color, and the plurality of regions are arranged in order according to the following conditions: at least one region of the same color is arranged continuously, at least one region corresponding to the same data class has the same color and is arranged continuously, and the order of every two data classes is the same as the order of the regions corresponding to the two data classes. For the convenience of description, this condition is hereinafter referred to as the order condition.

[0029] The multiple data classes obtained in this step refer to multiple types of data that have been pre-clustered and can be directly input by the user. Each data class can include a class number and specific data. The content of the specific data is not limited and can be an ID number or a logical number of the data. Exemplarily, in the field of network security, the specific data can be an ASN number, and each data class includes a class number and multiple ASN numbers. Optionally, each specific data can be regarded as a data point, and the data point name can be used to distinguish each data item. Then each data class can include a class number and multiple data point names (such as Node1, Node2, etc.).

[0030] The multiple regions refer to the multiple regions included in the map after the visualization software displays the multiple data classes as a cluster map, which can be parsed from the visualization file output by the visualization software. This embodiment obtains multiple data classes and their arrangement order, as well as the multiple regions displayed as cluster maps of these data classes and the arrangement order of their region information, as the data source of the entire method.

[0031] In a specific implementation, the user can arrange multiple data classes and input them into the visualization software together. The visualization software can automatically generate a cluster map and a visualization file based on the input data points. The regional information in the visualization file will be arranged according to the above order conditions; the regional information is parsed in sequence to obtain the regional boundaries arranged in order. Specifically, in the visualization file, each region corresponds to a piece of attribute information, which includes the region name, region color, and the two-dimensional coordinates of the regional boundary point in the cluster map. In summary, the arrangement order of the attribute information of each region in the visualization file satisfies: the attribute information of all regions with the same color is arranged continuously, at least one region corresponding to the same data class has the same color and is arranged continuously, and the order of each two data classes in the input data (or input file) is the same as the order of the regional attribute information corresponding to each two data classes. In other words, all regions corresponding to the same data class have the same color, and the attribute information of these regions is arranged continuously in the visualization file; but the region with the same color does not correspond to only one data class, and there may be multiple data classes corresponding to the same color; at the same time, the attribute information of each region is arranged in sequence according to the order of the data classes input into the visualization software. For example, assuming that the data classes input into the visualization software are in the order of class 1, class 2, class 3, and class 4, then in the output visualization file, the attribute information of each area will be arranged according to the area corresponding to class 1, the area corresponding to class 2, the area corresponding to class 3, and the area corresponding to class 4.

[0032] For example, taking Graphviz visualization software as an example, the information of the cluster map is stored in an svg visualization file. By parsing the svg file, three main attributes of the cluster map can be obtained: polygon, polyline and point. Figure 3 An example is shown Figure 2 The parsed information of the svg files corresponding to the four data classes in the cluster map, where polygon represents the attribute information of the polygon area in the cluster map, and each polygon attribute corresponds to a polygon area; in polygon, fill represents the name of the polygon area fill, stroke represents the color of the polygon area fill, and points represents the coordinates of the border filling points of the polygon area in the cluster map; polyline represents the coordinates of the points on the border line in the cluster map (non-filled points), and each polyline attribute represents a non-closed border line; Figure 3Except for polygon and polyline, the rest of the attributes are point attributes, where g id represents the data point name, and x and y in each data point attribute represent the two-dimensional coordinates of the data point in the cluster map. It can be seen that the number of polygon attributes is not the same as the number of categories. There will be several consecutive areas of the same color. These areas may correspond to one data class or multiple data classes. Among them, except stroke = 'transparent', which is a transparent background polygon, the polygon attributes of all other polygons are arranged in sequence according to the above order conditions. By parsing each polygon attribute in turn, the color of each polygon area and the coordinates of the boundary filling point in the cluster map can be obtained in turn.

[0033] Furthermore, the content format of the svg file can be parsed using ElementTree in xml. Now, the entire document is obtained according to the parse function, and then the entire attribute content of getroot is obtained. Finally, all polygon boundaries and color information are obtained according to the polygon attribute.

[0034] S120, group at least one region with the same color.

[0035] Since the areas of the same data class will be filled with the same color, this embodiment groups the areas by color as a basic unit for subsequent judgment of the corresponding relationship with the data. Optionally, still taking the Graphviz visualization software as an example, multiple polygon attributes with the same color and continuous position in all the arranged polygon attributes can be divided into a group, and each group of area information is stored in the same list. After grouping, the arrangement order of the areas within and between groups remains unchanged, and the above order condition is still met.

[0036] In a specific implementation, a global variable may be set to record the color information in the previous polygon attribute, and then the current color information is obtained and compared with the color information in the global variable. If the two are the same, they are added to the same list; otherwise, they are added to the next list, and the global variable is replaced with the current color information, so that the polygon attributes are traversed sequentially and continuously, and the polygon attribute information with the same color is stored in the same list. The final result is a two-dimensional list, in which each list stores the polygon boundaries of the same color, recorded as polygon_group. Combined with Figure 3, a total of 8 polygon attributes are displayed, which are recorded as polygon0-polygon7. Except for the first polygon with stroke = 'transparent', which is a transparent background polygon, the remaining polygons are grouped in the following order:

[0037] {group1:[polygon1,polygon2],

[0038] group2: [polygon3],

[0039] group3:[polygon4,polygon5,polygon6],

[0040] group2: [polygon7]}

[0041] S130. According to the coordinates of the data points, determine in turn in the above arrangement order whether each type of data has any intersection with each group of areas; if there is an intersection, there is a full or partial correspondence between the two; if there is no intersection, there is no correspondence between the two; thereby determining in turn which data class each area belongs to, and whether it is a normal area or an abnormal area belonging to the data class.

[0042] Among them, the normal area is the area containing the most data points in a data class, and the abnormal area is the area containing the remaining data points in the same data class. Figure 1 In this figure, the four largest areas are the normal areas of the four data classes, and the smaller polygonal areas are the abnormal areas of some data classes.

[0043] In a specific implementation, under the constraints of the above sequence conditions, S130 may include the following steps:

[0044] Step 1: Extract the data class ranked first as the current data class, and the group of regions ranked first as the current group of regions. After S110 and S120, all data and all regional groups are arranged according to certain data, and the data class ranked first in the data class sorting is extracted as the current data class to be processed, and the group of regions ranked first in the regional group sorting is extracted as the current group of regions to be processed.

[0045] Step 2: Calculate the proportion of data points in the current data class that fall into each area of ​​the current group in the cluster map, and the proportion represents the probability that each area belongs to the current data class.

[0046] Optionally, the coordinates of the data points in each data class in the cluster map can be determined first. Still taking Graphviz visualization software as an example, the correspondence between the point name and the coordinates can be extracted from the svg file {node1: [995.82, 75.84], node2: [1293.41, 380.5]....}, and the correspondence between the point name and the data class can be extracted from the input data {cluster1: [node1, node2,...], cluster2: [...],.....}; then, the coordinates and data corresponding to the same point name are matched to obtain the coordinates of each data point in each data class in the cluster map {cluster1: [[995.82, 75.84], [1293.41, 380.5]], cluster2: [......],.......}, and the coordinate information is recorded as cluster_pos.

[0047] Then, according to the coordinates of each data point in the current data class in the cluster map and the coordinates of the boundary points of each area in the current group area, it is judged in turn whether each data point falls within each area; and then the number of data points falling within each area is divided by the total amount of data in the current data class to obtain the proportion of data points falling in each area.

[0048] Step 3: According to the corresponding proportions of each area in the current group area, determine whether each area belongs to the current data class. If it belongs to the current data class, after determining the normal area and the abnormal area, update the current data class and keep the current group area unchanged; otherwise, update the current group area and keep the current data class unchanged; return to step 2 and recalculate the proportion of points of the data class falling in each area in the area group in the next cycle; repeat this cycle until all types of data and groups of areas are traversed.

[0049] Specifically, in each loop, the proportion of the current data class that falls in each area of ​​the current group area is calculated respectively, and the maximum value of all proportions is taken; depending on the maximum proportion, the following two processing methods can be included:

[0050] Method 1: If the maximum ratio is not 0, it means that the current data class has the most points in the area corresponding to the ratio, then it can be judged that the area belongs to the current data class and is a normal area in the current data class. Then, according to the principle of uniqueness of the cluster map (one data class corresponds to only one normal area), the areas corresponding to the remaining non-0 ratios can be judged as abnormal areas in the current data class. After the normal area and the abnormal area are judged, the current group area is kept unchanged, and the next data class of the current data class is extracted in the arranged order as the new current data class, and then return to step 2 to calculate the proportion of the data in the new current data class that falls in each area of ​​the current group area, and perform step 3 again according to each proportion; this cycle is repeated until all data classes are traversed.

[0051] Method 2: If the maximum ratio is 0, indicating that all regions in the current group of regions do not have an intersection with the current data class, then the next group of regions of the current group of regions is extracted in the above arrangement order as the new current group of regions, and the current data class remains unchanged. Then return to step 2, calculate the ratio of the data in the current data class that falls in each region of the new current group of regions, and perform step 3 again based on each ratio; repeat this cycle until all groups of regions are traversed.

[0052] The above two processing methods can cover various situations such as a group of areas with the same color corresponding to multiple data classes (one-to-many) and a group of areas with the same color corresponding to one data class (one-to-one), so that each situation can be processed in an orderly manner.

[0053] For example, after all non-zero ratios are judged in method 1, if there are still other zero ratios, it means that the area corresponding to the zero ratio does not belong to the current data class, and the data to which it actually belongs is still undetermined. This belongs to the above-mentioned "one-to-many" situation. At this time, according to method 1, the current group area is kept unchanged, and the next data class of the current data class is extracted in the arranged order as the new current data class. Then return to step 2, calculate the ratio of the data in the new current data class to each area in the current group area, and perform step 3 again according to each ratio to determine whether each area in the current group area belongs to the new current data class. Finally, all areas in the group are matched successfully, so that the above-mentioned "one-to-many" situation is handled in an orderly manner.

[0054] For another example, after all non-zero ratios are determined in method 1, if there are no other zero ratios, it indicates that all regions in the current group of regions are successfully matched, which belongs to the above-mentioned "one-to-one" situation. At this time, according to method 1, the current group of regions is kept unchanged, and the next data class of the current data class is extracted in the arranged order as the new current data class. Then return to step 2 for the next cycle. In the next cycle, the maximum value of the ratios corresponding to each region will be 0, and then turn to method 2. In method 2, the next group of regions will be used as the new current group of regions, and the next group of regions will be matched, so that the above-mentioned "one-to-one" situation can also be processed in an orderly manner.

[0055] It is precisely because in the visualization software, the input data class and the output area attribute meet a specific arrangement order that the above loop will eventually traverse each area and each data class, and determine the data class described in each area, and whether the area is a normal area or an abnormal area in the data class.

[0056] Specifically for Graphviz visualization software, since there is a correspondence between the class and the polygon attribute in an increasing order, under the previous parameter settings, a set of polygon information can be taken out from polygon_group in order each time, recorded as polygon_info. polygon_info contains a set of polygon boundary information of the same color, and cluster_pos contains the point coordinates corresponding to each class. Get the coordinate points of each class in cluster_pos in order, and for the point coordinates of the first class, compare them with polygon_info to determine whether the point is in the polygon boundary, and the probability (i.e., proportion) of the first class in each polygon boundary. If the maximum probability is not 0, it means that the class and the polygon boundary are in a corresponding relationship; at this time, in addition to the maximum probability, other probabilities have non-zero probabilities, which means that there is an abnormal area in this class, otherwise there is no abnormal area in this class; the polygon boundary corresponding to the maximum probability will be saved as a normal boundary, and the polygon boundary corresponding to other non-zero probabilities will be saved as an abnormal boundary; the next step is to obtain the point coordinates of the next class, and the current polygon_info remains unchanged, and continue to judge. If the maximum probability is 0, it means that there is no corresponding relationship between the class and polygon_info, and the next set of polygon_info information is obtained, while the current class information remains unchanged, and the judgment continues. The above process is iterated until all polygon_group information is traversed.

[0057] Furthermore, after the processing of S130, for each data class, the normal area and its boundary, as well as the abnormal area and its boundary (if any) of the data class can be obtained, and the boundaries of the two types of areas together constitute the local boundary of the data class in the cluster map. The local boundary of each data class can be further processed, such as displaying the local map of each data class separately, such as Figure 4 As shown in the figure, the detected abnormal areas can be optimized accordingly to improve the visualization quality of the cluster map.

[0058] In summary, the embodiment of the present invention provides a data class information processing method based on a cluster map. According to the characteristics of the visualization file, the cluster map directly displayed by the visualization software is processed to determine the correspondence between each area in the cluster map and the input data class, and to identify the normal area and abnormal area of ​​each type of data. Based on the recognition result, the local boundary of a certain data class can be obtained, which is conducive to the local acquisition, display and operation of the local map; the identified abnormal area can be used as an object for optimized display, which provides a basic guarantee for further optimizing the quality of the map and map visualization.

[0059] In particular, when judging whether each area belongs to a certain type of data, this embodiment does not directly use the coordinate points of the data class and the boundaries of each area to judge one by one, but groups the areas with the help of color attributes. After grouping, for each type of data, the number of loop traversals is less than directly traversing the boundaries of all areas, making the execution efficiency of the entire method higher; at the same time, after grouping the areas with the help of color attributes, the final detection accuracy of normal areas and abnormal areas is also higher.

[0060] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 5 As shown, the device includes a processor 60, a memory 61, an input device 62 and an output device 63; the number of processors 60 in the device can be one or more. Figure 5 A processor 60 is taken as an example; the processor 60, the memory 61, the input device 62 and the output device 63 in the device can be connected by a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0061] The memory 61 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the data class information processing method based on the cluster map in the embodiment of the present invention. The processor 60 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 61, that is, realizing the above-mentioned data class information processing method based on the cluster map.

[0062] The memory 61 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 61 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 61 may further include a memory remotely arranged relative to the processor 60, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0063] The input device 62 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 63 may include a display device such as a display screen.

[0064] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the data class information processing method based on a cluster map of any embodiment is implemented.

[0065] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0066] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0067] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0068] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0069] 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 by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A data information processing method based on cluster map, characterized in that: include: Acquire multiple data classes arranged in sequence, and multiple regions displayed as the multiple data classes in a cluster map, wherein the same regions are filled with the same color, and the multiple regions are arranged in sequence according to the following conditions: at least one region of the same color is arranged continuously, at least one region corresponding to the same data class has the same color and is arranged continuously, and the sequence of every two data classes is the same as the sequence of the regions corresponding to the every two data classes; At least one area with the same color is grouped together; Extract the first-ranked data class as the current data class, and the first-ranked group of regions as the current group of regions; Calculate the proportion of data points in the current data class that fall in each area of ​​the current group in the cluster map; If the maximum value among the proportions corresponding to each area is not zero, it is determined that the area corresponding to the maximum value belongs to the current data class and is a normal area in the current data class; it is determined that the areas corresponding to the non-zero proportions among the remaining proportions belong to the current data class and are abnormal areas in the current data class; the next data class is extracted as the new current data class in the arranged order; If the maximum value of the ratios corresponding to the regions is 0, extract the next group of regions in the arranged order as the new current group of regions; The unupdated one of the current data class and the current group area is kept unchanged, and the operation of calculating the proportion of the data points in the current data class that fall in each area of ​​the current group area in the cluster map is returned until all data classes or group areas are traversed.

2. The method according to claim 1, characterized in that The step of obtaining a plurality of data classes arranged in sequence, and displaying the plurality of data classes as a plurality of regions in a cluster map, comprises: Inputting the plurality of data classes arranged in sequence into a visualization software to obtain a visualization file for displaying a cluster map; The visualization file is parsed to obtain boundaries of multiple areas in the cluster map.

3. The method according to claim 2, characterized in that The step of inputting the plurality of data classes arranged in sequence into a visualization software to obtain a visualization file for displaying a cluster map comprises: inputting the plurality of data classes arranged in sequence into a visualization software Graphviz to obtain an svg visualization file for displaying a cluster map; Correspondingly, the visualization file is parsed to obtain the boundaries of multiple areas in the cluster map, including: parsing multiple polygon attributes from the svg visualization file, wherein each polygon attribute includes the color of a polygonal area and the coordinates of each boundary point of the polygonal area.

4. The method according to claim 1, characterized in that: The calculating the proportion of the data points in the current data class that fall in each area of ​​the current group area in the cluster map includes: According to the coordinates of each data point in the current data class in the cluster map and the coordinates of each area boundary point in the current group area, it is judged in turn whether each data point falls within each area; The number of data points falling in each region is divided by the total number of data points of the current data class to obtain the proportion of data points falling in each region.

5. The method according to claim 4, characterized in that The obtaining of a plurality of data classes arranged in sequence comprises: obtaining a plurality of data classes arranged in sequence, each data point in a data class corresponding to a point name; Correspondingly, before judging whether each data point falls within each area in turn according to the coordinates of each data point in the current data class in the cluster map and the coordinates of each area boundary point in the current group area, it also includes: inputting the multiple data classes into visualization software to obtain a visualization file for displaying the cluster map; parsing the visualization file to obtain the coordinates of each point name in the cluster map; associating the coordinates corresponding to the same point name with the data point to obtain the coordinates of each data point in each data class in the cluster map.

6. The method according to claim 1, characterized in that The data in each data class includes an ASN number, and each data class includes a class number and multiple ASN numbers.

7. The method according to claim 1, characterized in that The correspondence between the plurality of data classes and the plurality of regions includes: one data class corresponds to at least one region; The correspondence between the plurality of data classes and a group of regions with the same color includes: a group of regions corresponds to at least one data class.

8. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the data class information processing method based on the cluster map as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the data class information processing method based on a cluster map as described in any one of claims 1-7 is implemented.

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