A network data asset space asset detection method based on discrete random numbers

Abstract

The present invention provides a network data asset spatial asset detection method and device based on discrete random numbers, which relates to the field of network data asset detection technology. The method includes: extracting the attribution weight and association value of the node based on the attribute value and connection strength of the node in the network data asset, generating a hierarchical interval using discrete random numbers, hierarchically calculating the attribution weight and node association value, and generating a node hierarchical weight distribution table. In the present invention, by combining discrete random numbers to generate hierarchical intervals, optimizing the hierarchy and accuracy of the node weight distribution, normalizing the weights of high-priority nodes and optimizing sparse connections, strengthening the clarity of the relationship between nodes, extracting trajectory changes and classifying intensity trends, and realizing accurate analysis of dynamic node characteristics. The distribution optimization of the target area improves the organizational efficiency of data assets, and the adjustment of the dynamic detection path enhances the coverage capability and accuracy of the detection, thereby improving the detection efficiency and dynamic adaptability as a whole.

Description

Technical Field

[0001] The present invention relates to the technical field of network data asset detection, and in particular to a method and device for detecting network data asset space assets based on discrete random numbers. Background Art

[0002] The field of network data asset detection encompasses methods and technologies for identifying, analyzing, classifying, and locating data resources within networks. The core of this technology involves analyzing data flows, packets, and relationships within network environments to obtain information about hidden or unclearly identified data assets and to implement structured management of these assets. This overall technical area primarily involves feature extraction and modeling of discrete data, parsing and reorganizing data relationships within cyberspace, and visually locating and analyzing the distribution patterns of data assets. It has widespread applications in information security, data asset management, and optimal network resource allocation.

[0003] The network data asset spatial asset detection method based on discrete random numbers refers to a method for detecting data assets and their distribution status in cyberspace by utilizing the generation characteristics and calculation rules of discrete random numbers. The technical matters targeted by this patent subject include constructing a data distribution model using discrete random numbers, parsing the mapping relationship between discrete random numbers and data distribution characteristics using statistical analysis methods, and detecting and locating the spatial distribution of data assets in combination with specific numerical iteration methods. Specifically, through the calculation process of optimizing the data distribution rules using discrete random numbers, the structured relationship of data assets is deconstructed and reorganized to achieve accurate identification of asset information and spatial detection.

[0004] Existing technologies for network data asset management lack an effective hierarchical weight distribution model, resulting in a flat management of data asset weights and relationships, making it difficult to adapt to the multi-level requirements of complex network environments. Regarding spatial distribution optimization, the processing of redundant connections often relies primarily on simple statistical analysis, lacking a comprehensive consideration of spatial density. This can easily lead to the inability to completely eliminate redundant connections in low-priority nodes, wasting network resources. In the processing of high-priority nodes, existing methods struggle to fine-tune weight values, and the optimization of sparse connections is insufficient, complicating node relationships in high-priority areas and reducing data operability. Regarding dynamic change management, there are limited analytical methods for node time series and trajectory changes, and a lack of detailed classification capabilities for node dynamic characteristics and connection strength trends, making it impossible to fully understand the dynamic changes of nodes. In path detection, there is a lack of the ability to dynamically adjust paths and optimize coverage, resulting in insufficient coverage or excessive redundancy in detection results, limiting adaptability and accuracy in dynamic network environments. These shortcomings make it difficult for existing technologies to meet the comprehensive needs of data asset detection and optimization in complex network environments. Summary of the Invention

[0005] To address the technical problems of the existing technologies, such as the lack of an effective hierarchical weight distribution model, the lack of comprehensive consideration of spatial density, the difficulty in fine-tuning weight values, the limited means of analyzing node time series and trajectory changes, and the problems of insufficient coverage or excessive redundancy in detection results, the present invention provides a method and device for spatial asset detection of network data assets based on discrete random numbers. The technical solution is as follows:

[0006] In one aspect, a method for detecting assets in a network data asset space based on discrete random numbers is provided. The method is implemented by an asset detection device and includes:

[0007] S1: Based on the attribute values ​​and connection strengths of nodes in network data assets, extract the node's attribution weight and association value, use discrete random numbers to generate hierarchical intervals, calculate the attribution weight and node association value hierarchically, and generate a node hierarchical weight distribution table;

[0008] S2: Based on the node hierarchical weight distribution table, set the node priority threshold, extract low-priority nodes, calculate the node spatial location density and the number of connections, remove redundant connections for low-density nodes and adjust the belonging range to generate a compressed node spatial distribution map;

[0009] S3: Based on the compressed node spatial distribution graph, extract the weight distribution values ​​and spatial coordinates of high-priority nodes, normalize the weight values, adjust the sparse connection values ​​between nodes, and generate a sparse relationship graph of high-priority areas;

[0010] S4: Based on the sparse relationship graph of the high-priority area, extract the time series data of the node, calculate the node trajectory change value, and identify the connection strength change trend. According to the relationship between the trajectory change range and strength change, classify and summarize the node data to generate a set of node dynamic change trajectories;

[0011] S5: Based on the node dynamic change trajectory set, set the change range threshold, extract the spatial distribution area of ​​the nodes with a large trajectory change range, calculate the regional node association strength, adjust the distribution range, classify the node position and connection characteristics of the target area, and generate the node distribution map of the target area;

[0012] S6: Based on the node distribution map of the target area, calculate the connection coverage and density values ​​between nodes, generate the initial detection path, dynamically adjust the path coverage and connection relationship, and record the node relationship to generate the target area node detection path map.

[0013] On the other hand, a network data asset space asset detection device based on discrete random numbers is provided, which is applied to a network data asset space asset detection method based on discrete random numbers. The device includes:

[0014] The weight distribution table generation module is used to extract the node's attribution weight and association value based on the attribute value and connection strength of the node in the network data asset, generate the layered interval using discrete random numbers, calculate the attribution weight and node association value in a hierarchical manner, and generate a node layered weight distribution table;

[0015] The node space distribution generation module is used to set the node priority threshold based on the node hierarchical weight distribution table, extract low-priority nodes, calculate the node spatial location density and the number of connections, remove redundant connections for low-density nodes and adjust the belonging range, and generate a compressed node space distribution map;

[0016] The sparse relationship graph generation module is used to extract the weight distribution value and spatial coordinates of high-priority nodes based on the compressed node spatial distribution graph, normalize the weight value, adjust the sparse connection value between nodes, and generate a sparse relationship graph of high-priority areas;

[0017] The change trajectory generation module is used to extract the time series data of nodes based on the sparse relationship graph of high-priority areas, calculate the node trajectory change value, and identify the trend of connection strength change. It classifies and summarizes the node data according to the relationship between trajectory change range and strength change, and generates a set of node dynamic change trajectories;

[0018] The target node distribution generation module is used to set the change range threshold based on the node dynamic change trajectory set, extract the spatial distribution area of ​​nodes with a large trajectory change range, calculate the regional node association strength, adjust the distribution range, classify the node position and connection characteristics of the target area, and generate the target area node distribution map;

[0019] The detection path map generation module is used to calculate the connection coverage and density values ​​between nodes based on the node distribution map of the target area, generate the initial detection path, dynamically adjust the path coverage and connection relationship, and record the node relationship to generate the target area node detection path map.

[0020] On the other hand, an asset detection device is provided, which includes: a processor; a memory, wherein computer-readable instructions are stored in the memory, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned network data asset space asset detection methods based on discrete random numbers is implemented.

[0021] On the other hand, a computer-readable storage medium is provided, in which at least one instruction is stored. The at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned network data asset space asset detection methods based on discrete random numbers.

[0022] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0023] The present invention proposes a network data asset spatial asset detection method based on discrete random numbers. By combining discrete random numbers to generate hierarchical intervals, the hierarchy and accuracy of node weight distribution are optimized, the weights of high-priority nodes are normalized and sparse connections are optimized, the clarity of the relationship between nodes is enhanced, trajectory change extraction and intensity trend classification are carried out, and accurate analysis of dynamic node characteristics is achieved. The distribution optimization of target areas improves the organizational efficiency of data assets, and the adjustment of dynamic detection paths enhances the coverage capability and accuracy of detection, thereby improving the overall detection efficiency and dynamic adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 This is a flow chart of a network data asset space asset detection method based on discrete random numbers provided by an embodiment of the present invention;

[0026] Figure 2 This is a block diagram of a network data asset space asset detection device based on discrete random numbers provided by an embodiment of the present invention;

[0027] Figure 3 It is a structural diagram of an asset detection device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0029] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0030] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0031] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0032] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0033] The embodiment of the present invention provides a network data asset space asset detection method based on discrete random numbers. The method can be implemented by an asset detection device, which can be a terminal or a server. Figure 1 The flowchart of the network data asset space asset detection method based on discrete random numbers is shown. The processing flow of this method may include the following steps:

[0034] S1: Based on the attribute values ​​and connection strengths of nodes in network data assets, the attribution weights and association values ​​of nodes are extracted, and the hierarchical intervals are generated using discrete random numbers. The attribution weights and node association values ​​are calculated hierarchically to generate a node hierarchical weight distribution table.

[0035] Optionally, the node hierarchical weight distribution table includes attribution weights, node association values, and hierarchical interval values;

[0036] The compressed node spatial distribution graph includes the node spatial location density, the number of node connections and the node belonging range;

[0037] The sparse relationship graph of the high priority area includes node weight distribution values, node spatial coordinates and node sparse connection values;

[0038] The node dynamic change trajectory set includes trajectory change range, connection strength change trend and node classification data;

[0039] The node distribution map of the target area includes the spatial distribution of regional nodes, node association strength and node connection characteristics;

[0040] The target area node detection path diagram includes path coverage, path connection relationship and node dynamic adjustment record.

[0041] Optionally, based on the attribute values ​​and connection strengths of the nodes in the network data assets, the attribution weights and association values ​​of the nodes are extracted, the hierarchical intervals are generated using discrete random numbers, the attribution weights and node association values ​​are calculated hierarchically, and a node hierarchical weight distribution table is generated, including:

[0042] S101: Based on the attribute values ​​and connection strengths of the nodes in the network data assets, extract the attribute data of each node, and combine the connection strength values ​​of its adjacent nodes to calculate the association value between the node and the surrounding nodes, and generate a node attribute and connection strength data table.

[0043] In a feasible implementation, the attribute data of each node and the connection strength values ​​of its adjacent nodes are obtained from the network. First, a matrix structure containing node attributes and connection strengths is established, the attribute data of each node is extracted one by one, and the connection strength values ​​between nodes are statistically calculated. The connection relationship between nodes is set as a two-dimensional array, and the node attribute values ​​are introduced into the matrix as vectors. The connection strength values ​​of each pair of adjacent nodes are obtained by mapping their positions in the matrix. The correlation values ​​between nodes and adjacent nodes are calculated by statistical methods. The correlation values ​​can be calculated using a weighted average method, where the weight value is determined by the connection strength, and finally a node attribute and connection strength data table is formed.

[0044] S102: Based on the node attribute and connection strength data table, generate interval stratification rules through discrete random numbers, analyze the numerical distribution of node attributes and associated values, define stratification intervals according to the distribution range, filter data groups one by one according to the stratification rules, mark the classification information of weights and associated values, and generate a node stratification weight interval table.

[0045] In a feasible implementation, the rule of interval layering is generated by discrete random numbers, the numerical distribution of node attributes and associated values ​​is analyzed, and the hierarchical associated values ​​of the nodes are calculated. The process is as follows (1):

[0046]

[0047] Among them, R ij represents the association value of node i in layer interval j, a ik represents the value of the attribute value of node i in interval k, S kj is the connection strength from interval k to interval j, and n is the total number of nodes.

[0048] Where A ik Indicates the node attribute data generated by the hierarchical rules of node attribute values. It can be directly attributed to different hierarchical intervals and then statistically obtained. For example, if a node attribute value is 12 and the hierarchical interval is [0-10], [10-20], [20-30], it belongs to the second interval and is counted as 1; S kjis the connection strength value of adjacent nodes, which is realized through the statistics of the adjacency matrix. For example, the connection strength between nodes k and j is 3, which is directly introduced into the calculation as the weight value; R ij For the final goal, the weighted average correlation value of node attribute value and connection strength is calculated by formula.

[0049] Specific example: Assume that the total number of nodes is 4, the attribute value matrix is ​​A = [5, 12, 25, 18]; the layer interval is [0-10], [10-20], [20-30], and the connection strength matrix is

[0050] Calculate the hierarchical correlation value of node 1 in the second interval and substitute it into the above formula.

[0051] The result shows that the correlation value between node 1 and interval 2 is 13.33, which indicates the weight degree of the node in this hierarchical interval, and finally generates a node hierarchical weight interval table.

[0052] S103: Based on the node hierarchical weight interval table, the node attribution weight of each level is cumulatively calculated layer by layer, and the correlation values ​​between nodes are summarized and processed according to the hierarchical distribution, and the distribution results and hierarchical relationships of the nodes are recorded to generate a node hierarchical weight distribution table.

[0053] In a feasible implementation method, the node association values ​​of each level are extracted according to the hierarchical weight interval table, the node association values ​​are weighted and accumulated according to the hierarchical order, the node association values ​​of each level are grouped and summarized, the attribution weights of the hierarchical nodes are calculated according to the hierarchical distribution using the association values ​​between nodes, and the hierarchical classification is performed by weighted mean method, the weight attribution levels of nodes at different levels are recorded in the weight distribution table, the distribution results and hierarchical relationships of the nodes are recorded, and finally a node hierarchical weight distribution table is generated.

[0054] S2: Based on the node hierarchical weight distribution table, set the node priority threshold, extract low-priority nodes, calculate the node spatial position density and the number of connections, remove redundant connections for low-density nodes and adjust the belonging range to generate a compressed node space distribution map.

[0055] Optionally, based on the node hierarchical weight distribution table, a node priority threshold is set, low-priority nodes are extracted, the node spatial location density and the number of connections are calculated, redundant connections are removed for low-density nodes, and the attribution range is adjusted to generate a compressed node spatial distribution map, including:

[0056] S201: Based on the node hierarchical weight distribution table, set the priority threshold range according to the numerical distribution of the weight values, compare the weight values ​​of the nodes one by one, and filter out the nodes that meet the conditions, group and mark the nodes below the threshold, and record their spatial positions and connection relationships to generate a low-priority node information table.

[0057] In a feasible implementation method, the node weight value is compared with the set threshold one by one, the nodes below the threshold are marked by a mapping algorithm, a structured record table containing the node number, spatial position and connection relationship is established, the connection relationship matrix of the node is calculated, and it is grouped according to the weight value and spatial position relationship, the connection relationship between low-weight nodes is counted and associated with their adjacent nodes, and their spatial distribution matrix is ​​recorded at the same time, and finally a low-priority node information table is generated.

[0058] S202: Based on the low-priority node information table, calculate the spatial position density value of each node according to its spatial distribution record, and identify the number of direct connections. Classify and mark the nodes whose density values ​​are lower than the target benchmark, and select the connections with lower connection strength by parsing the connection relationship of the marked nodes, and perform a removal operation to generate a redundant node connection adjustment table.

[0059] In a feasible implementation, the spatial position density value is calculated based on the spatial distribution records of the nodes, and the spatial position density value of each node is calculated according to formula (2):

[0060]

[0061] Among them, D i is the spatial density value of node i, C ij is the number of connections between node i and its neighboring node j, A i is the area of ​​the space occupied by node i, and n is the number of neighboring nodes connected to node i.

[0062] Where C ij Indicates the number of connections between two nodes, which can be obtained by the adjacency matrix statistics of the node connection relationship. For example, if there are three connection paths between node 1 and node 2, then C 12 =3;A i is the spatial area recorded by node i in the distribution table, which can be calculated based on the geometric characteristics of the node space division. For example, if the space of node 1 occupies 25 square units, then A1 = 25.

[0063] Specific example: Assume that node 1 is connected to node 2 and node 3, and the number of connections between nodes is C 12 =2, C 13 =1, the area of ​​node 1 is 25 square units, calculate the spatial position density value of node 1, and substitute it into the formula, we can know

[0064] The result shows that the spatial density value of node 1 is 0.12, indicating that the connection density of nodes in the region is relatively low. Based on this result, low-density nodes can be further screened and classified and marked, and finally a redundant node connection adjustment table can be generated.

[0065] S203: Based on the redundant node connection adjustment table, re-compare the node spatial distribution data after adjustment, check the node belonging ranges one by one, and correct the conflicts caused by overlapping belonging ranges, summarize and update the corrected spatial distribution, and generate a compressed node spatial distribution map.

[0066] In a feasible implementation method, the spatial distribution errors caused by adjusting the connection relationship are eliminated. By checking the node's belonging range and the influence relationship of the adjacent nodes one by one, the nodes with overlapping belonging ranges are corrected, and the adjusted node belonging range and spatial position relationship are recorded. The connection relationship between the nodes is re-counted through the adjacency matrix, and the node spatial data after the area division is corrected, and finally a compressed node spatial distribution map is generated.

[0067] S3: Based on the compressed node spatial distribution map, extract the weight distribution values ​​and spatial coordinates of high-priority nodes, normalize the weight values, adjust the sparse connection values ​​between nodes, and generate a sparse relationship map of high-priority areas.

[0068] Optionally, based on the compressed node spatial distribution graph, the weight distribution values ​​and spatial coordinates of the high-priority nodes are extracted, the weight values ​​are normalized, and the sparse connection values ​​between the nodes are adjusted to generate a sparse relationship graph of the high-priority area, including:

[0069] S301: Based on the compressed node spatial distribution map, analyze the spatial position and weight value of the node, compare and filter the nodes with higher priority one by one, record the weight distribution value and corresponding spatial coordinates of the high-priority nodes as associated data, and generate a high-priority node weight and coordinate table.

[0070] In a feasible implementation method, the weight value and spatial coordinate of the node are read row by row from the compressed spatial data table. By setting a high-priority threshold standard, the nodes whose weight values ​​meet the priority requirements are screened in turn, and the spatial coordinates of the high-priority nodes are recorded in the associated data. At the same time, the weight distribution of the node is paired and mapped with the spatial data, and finally a matching table of the node weight value and the spatial coordinate is formed, generating a high-priority node weight and coordinate table.

[0071] S302: Based on the high-priority node weights and coordinate table, calculate the proportional factor according to the weight distribution range, normalize the weight value of each node, correct the node sparse connection value corresponding to the normalized weight value one by one, update the connection relationship weights between nodes according to the correction results, and generate a normalized sparse connection adjustment table.

[0072] The calculation formula of the normalized correction weight value is as follows (3):

[0073]

[0074] Among them, W i ′ represents the normalized modified weight value of node i, W i represents the original weight value of node i, represents the average weight value of all nodes, n represents the total number of nodes, C i represents the sparse connection value of node i, m represents the total number of sparsely connected nodes, represents the sum of the squared differences between the weight values ​​of all nodes and the average value, Indicates the absolute difference between the node weight value and the average value.

[0075] In one feasible implementation, is the sum of the squared differences between the weight values ​​of all nodes and the average value, n is the total number of nodes in the network, obtained from the actual number of node statistics, C i is the sparse connection value of node i, which is obtained by counting the number of direct connections of the node. m is the total number of sparsely connected nodes, and the total number of nodes with sparse connection attributes is recorded. is the sum of the sparse connection values ​​of all sparsely connected nodes.

[0076] Input data: There are 5 nodes, the original weight values ​​of the nodes are W1=20, W2=25, W3=15, W4=30, W5=10, there are 3 sparsely connected nodes, corresponding to nodes 1, 3 and 5 respectively, and the sparse connection values ​​are C1=3, C3=2, C5=4 respectively.

[0077] Calculation process: Calculate the average weight value

[0078]

[0079] Calculate the sum of the squared differences between the weight values ​​of all nodes and the average value

[0080] The sum of squared differences is: Calculate the standard deviation Calculate the sum of sparse connection values C1+C3+C5=3+2+4=9; calculate the normalized corrected weight value W of each sparse node one by one i ′.

[0081] For node 1:

[0082] Substituting the values:

[0083] For node 3:

[0084] Substituting the values:

[0085] For node 5:

[0086] Substituting the values:

[0087] Results: The normalized corrected weight value of node 1 is 0, that of node 3 is 0.157, and that of node 5 is 0.628.

[0088] The results show that the normalized correction value of the node weight can reflect the degree of difference from the average weight value. At the same time, the weight value corrected by the sparse connection value is used to update the connection relationship weight between nodes, and finally generate a normalized sparse connection adjustment table.

[0089] S303: Based on the normalized sparse connection adjustment table, analyze the distribution of sparse connections of high-priority nodes, verify and map the sparse connection status between nodes in the network one by one, convert the adjusted connection relationship into network structure visualization data, and generate a sparse relationship graph of high-priority areas.

[0090] In a feasible implementation method, the sparse connection distribution of the nodes is verified item by item, the normalized weight value of each node is mapped to the adjusted connection relationship, the sparse connection status between the nodes in the network is analyzed, the connection relationship and sparsity are read row by row through the node connection matrix, the adjusted connection relationship is converted into network topology data, the sparse connection value and corresponding spatial coordinate data of each node are recorded, and a visual topology map of the network structure is drawn based on the mapping results, and finally a sparse relationship map of the high-priority area is generated.

[0091] S4: Based on the sparse relationship graph of high-priority areas, extract the time series data of the nodes, calculate the node trajectory change value, and identify the trend of connection strength change. Classify and summarize the node data according to the relationship between trajectory change range and strength change, and generate a set of node dynamic change trajectories.

[0092] Optionally, based on the sparse relationship graph of the high-priority area, extract the time series data of the node, calculate the node trajectory change value, and identify the connection strength change trend. Classify and summarize the node data according to the relationship between the trajectory change range and the strength change, and generate a node dynamic change trajectory set, including:

[0093] S401: Based on the high-priority area sparse relationship graph, parse the spatial location data and weight value of each node, extract the connection weight and status of the node at different time points one by one, organize the data in chronological order and construct the time series attribute information of the node, and generate a node time series data table.

[0094] In a feasible implementation, the spatial position data and corresponding weight value of each node are extracted, the connection weight and connection status of the node at differentiated time points are read, the time points are regarded as one-dimensional sequences, a weight time series matrix is ​​constructed, and time series statistical analysis is performed on the connection weight of each node at different time points. The connection weights and node status of differentiated time points are sorted and classified into different columns in the matrix in chronological order. Finally, the spatial coordinate data of the node and the time series matrix are combined to generate a node time series data table.

[0095] S402: Based on the node time series data table, calculate the trajectory offset value of the node between the differentiated time points in the time series one by one, classify and analyze the offset direction and amplitude, calculate the intensity change value based on the change of connection weight between time points, integrate the trajectory and connection change data, and generate the node trajectory and intensity change table.

[0096] The calculation formula of the intensity change value is as follows (4):

[0097]

[0098] in, Represents the node at time t i At time t j The intensity change between and W j (t) Represents the node at time point t i and time point t j The connection weight value of and Represents the node at time point t i and time point t j The horizontal coordinate position of and Represents the node at time point t i and time point t j The vertical coordinate position, t i and t jRepresent the timestamps of the node at time point i and time point j respectively, represents the absolute change in the node connection weight, Represents the spatial distance between nodes at a time point, t j -t i Indicates a time interval.

[0099] In a feasible implementation, the calculation example is as follows: Assume that the data of nodes A and B at time points t1=1 and t2=3 are as follows:

[0100] The weight of node A The weight of node B The coordinates of node A are The coordinates of node B are

[0101] Time point t1=1, time point t2=3.

[0102] The calculation process is as follows: 1. Calculate the absolute change in connection weight:

[0103]

[0104] Calculate the spatial distance between nodes at a point in time:

[0105]

[0106] Calculate the time interval:

[0107] t2-t1=3-1=2;

[0108] Calculate the intensity change:

[0109] Enter specific values:

[0110] Results: This result shows that the strength change between nodes A and B between time points t1 = 1 and t2 = 3 is 1. This value represents the dynamic characteristics of the change in connection weight between nodes in time and space. Combined with trajectory data, it can further analyze the dynamic change trend between nodes and provide key data support for subsequent node trajectory and strength change tables.

[0111] S403: Based on the node trajectory and strength change table, analyze the relationship between trajectory change values ​​and connection strength changes, compare the node trajectory offset amplitude and connection change trend, classify the node into a trajectory change pattern group, and record the classification information. Integrate the classification results into the aggregated data of the dynamic change trajectory to generate the node dynamic change trajectory set.

[0112] In a feasible implementation, the trajectory deviation amplitude and connection change trend of each node are classified one by one, and the trajectory pattern features are extracted based on the deviation amplitude and connection change data. The nodes are divided into different trajectory change pattern groups, and the node classification information of each pattern group is recorded item by item. At the same time, all the classified trajectory change pattern data are integrated into a collective form, and the collective data is further processed to obtain the spatial distribution characteristics of the node dynamic change trajectory, and finally a node dynamic change trajectory set is generated.

[0113] S5: Based on the node dynamic change trajectory set, set the change range threshold, extract the spatial distribution area of ​​nodes with a large trajectory change range, calculate the regional node association strength, adjust the distribution range, classify the node position and connection characteristics of the target area, and generate the target area node distribution map.

[0114] Optionally, based on the node dynamic change trajectory set, a change range threshold is set, the spatial distribution area of ​​nodes with a large trajectory change range is extracted, the regional node association strength is calculated, and the distribution range is adjusted. The node positions and connection characteristics of the target area are classified to generate a node distribution map of the target area, including:

[0115] S501: Based on the node dynamic change trajectory set, analyze the trajectory change range value of each node one by one, set the change range threshold, compare and filter according to the change range, and extract the nodes with larger change ranges. Record the spatial distribution coordinates of the nodes and the connection relationship between adjacent nodes, and generate a spatial distribution table of nodes with larger trajectory changes.

[0116] In one feasible implementation, the minimum and maximum values ​​of the nodes in the trajectory change data are extracted, the trajectory change range value is calculated and compared with the set change range threshold, the nodes with larger change ranges are screened out, the distribution statistics of the screened nodes are performed based on the spatial coordinate information, and the connection relationship matrix is ​​generated by combining the connection relationship data of adjacent nodes. At the same time, the connections with lower weights in the matrix are eliminated, and finally a spatial distribution table of nodes with larger trajectory changes is generated.

[0117] S502: Based on the spatial distribution table of nodes with large trajectory changes, analyze the connection relationship between nodes and the spatial coordinate data of adjacent nodes, calculate the association strength between the node and its adjacent nodes, compare and analyze the association strength values, classify the nodes into corresponding strength range areas, correct the node ranges with overlapping boundary distributions, and generate a target area node association strength table.

[0118] In a feasible implementation, the association strength between a node and its neighboring nodes is calculated based on the spatial distribution of nodes with large trajectory changes, and the association strength value between nodes is calculated according to formula (5):

[0119]

[0120] Among them, I ij represents the association strength value between node i and node j, C ij represents the connection weight between node i and node j, d ij is the spatial distance between two nodes, and n is the total number of neighboring nodes of node i.

[0121] Where C ij is the connection weight between nodes i and j, which can be extracted from the connection relationship matrix in the spatial distribution table of nodes with large trajectory changes; d ij is the spatial distance between two nodes, which is calculated by the coordinate difference of the nodes. The calculation formula is as follows (6):

[0122]

[0123] Among them, x i ,y i and x j ,y j Represent the spatial coordinates of nodes i and j respectively.

[0124] Specific example: Assume that the connection weight between node i and node j is 5, and the coordinates of the two nodes are (x i ,y i )=(3,4) and (x j ,y j )=(7,1), the total number of neighboring nodes is 3, the connection weights of other neighboring nodes are 4 and 6 respectively, and the coordinates are (x k1 ,y k1 )=(2,2) and (x k2 ,y k2 )=(6,5), the calculation process is as follows:

[0125] Calculate the spatial distance between node i and each neighboring node:

[0126]

[0127] Calculate the association strength between node i and node j:

[0128]

[0129] Expand the denominator:

[0130] Substitute the result into the numerator and denominator, and finally calculate

[0131] This result shows the association strength value between nodes, which serves as the basis for subsequent division of intensity range areas.

[0132] S503: Based on the target area node association strength table, the spatial position distribution and connection characteristics of the target area nodes are analyzed one by one, the target area is classified and integrated according to the node distribution law, the position and connection characteristics of the nodes in the area are visualized, and a target area node distribution map is generated.

[0133] In a feasible implementation method, the intensity change data in the node connection relationship is extracted, and the nodes are divided into different spatial area groups according to the spatial distribution characteristics. At the same time, the range of the overlapping area of ​​the boundary distribution is corrected, and the node position and connection characteristic data are integrated to generate the topological structure relationship between the nodes. The structural relationship is visualized and the connection distribution diagram of the nodes is drawn through a graphical tool to finally form a node distribution diagram of the target area.

[0134] S6: Based on the node distribution map of the target area, calculate the connection coverage and density values ​​between nodes, generate the initial detection path, dynamically adjust the path coverage and connection relationship, and record the node relationship to generate the target area node detection path map.

[0135] Optionally, based on the target area node distribution map, the connection coverage and density values ​​between nodes are calculated to generate an initial detection path, the path coverage and connection relationship are dynamically adjusted, and the node relationship is recorded to generate a target area node detection path map, including:

[0136] S601: Based on the node distribution map of the target area, extract the spatial coordinates and connection relationship data of the nodes, analyze the connection range between the nodes one by one, identify the coverage area of ​​the node and its neighboring nodes, calculate the node connection density value based on the node coverage area and the number of connections, organize and form a node connection and coverage characteristic data table, and generate a node connection coverage and density table.

[0137] In a feasible implementation method, the intersection of the spatial coverage area of ​​a node and its neighboring nodes is calculated to identify the coverage relationship between the nodes, the geometric data of the node coverage area and its connection quantity data are integrated, the connection density value of the node is calculated and recorded as the attribute data of the corresponding node, and the overlapping matrix of the coverage area between the nodes is sorted out. The spatial coverage and connection characteristics are integrated into a data table to generate a node connection coverage and density table.

[0138] S602: Based on the node connection coverage and density table, compare the coverage overlap between nodes one by one, sort the node connection paths according to the coverage area overlap ratio and connection density value, filter the priority of the connection paths and gradually arrange the path connection order to generate an initial detection path set.

[0139] In a feasible implementation, the priority of the connection path is calculated based on the overlap ratio of node coverage and the connection density value, and the connection priority value between node i and node j is calculated according to formula (7):

[0140]

[0141] Among them, P ij is the priority value, C ij is the connection density between node i and node j, O ij is the overlapping ratio of the coverage area of ​​node i and node j, and n is the total number of neighboring nodes of node i.

[0142] Where C ij is the connection density value between nodes i and j, extracted from the node connection coverage and density table; ij is the overlap ratio of the coverage area, and the calculation formula is as follows (8):

[0143]

[0144] Among them, A i ∩A j is the intersection area of ​​the two node coverage areas, A i ∪A j is the union area of ​​the areas covered by the two nodes.

[0145] Specific example: Assume that the connection density between node i and node j is 8, the intersection area of ​​the two nodes' coverage areas is 15, the union area is 40, the total number of adjacent nodes is 3, the connection density values ​​of other adjacent nodes are 6 and 10 respectively, and the coverage overlap ratios are 0.5 and 0.4 respectively. The calculation process is as follows:

[0146] Calculate the coverage area overlap ratio:

[0147] Calculate the connection priority value:

[0148] Expanding the denominator: (8 0.375) + (6 0.5) + (10 0.4) = 3 + 3 + 4 = 10;

[0149] Calculate the priority value:

[0150] The result shows that the connection priority between node i and node j is 0.3, which indicates its priority compared with other connection paths, and finally generates the initial detection path set.

[0151] S603: Based on the initial detection path set, the path coverage and connection relationship are analyzed, the coverage area between the paths is gradually adjusted, the connection characteristics of each node in the adjusted path are re-verified, the corrected path connection information is integrated and processed, and a target area node detection path map is generated.

[0152] In a feasible implementation method, the path coverage and connection relationship are gradually analyzed, the spatial coverage area between the paths is adjusted, the node connection attributes of adjacent paths are realigned with the coverage data, and the areas where the coverage overlaps in the paths are corrected. By verifying the connection relationship and coverage characteristics of the adjusted paths, the independence and integrity of each path are ensured. At the same time, the node connection matrix in the corrected path is recorded, and visual topology data is generated based on the adjusted coverage, and finally a node detection path map for the target area is generated.

[0153] The present invention proposes a network data asset spatial asset detection method based on discrete random numbers. By combining discrete random numbers to generate hierarchical intervals, the hierarchy and accuracy of node weight distribution are optimized, the weights of high-priority nodes are normalized and sparse connections are optimized, the clarity of the relationship between nodes is enhanced, trajectory change extraction and intensity trend classification are carried out, and accurate analysis of dynamic node characteristics is achieved. The distribution optimization of target areas improves the organizational efficiency of data assets, and the adjustment of dynamic detection paths enhances the coverage capability and accuracy of detection, thereby improving the overall detection efficiency and dynamic adaptability.

[0154] Figure 2 This is a block diagram of a network data asset space asset detection device based on discrete random numbers according to an exemplary embodiment. The device is used in a network data asset space asset detection method based on discrete random numbers. Figure 2 The device includes a weight distribution table generation module 210, a node space distribution generation module 220, a sparse relationship graph generation module 230, a change trajectory generation module 240, a target node distribution generation module 250, and a detection path graph generation module 260. Among them:

[0155] The weight distribution table generation module 210 is used to extract the attribution weights and association values ​​of the nodes based on the attribute values ​​and connection strengths of the nodes in the network data assets, generate the hierarchical intervals using discrete random numbers, calculate the attribution weights and node association values ​​in a hierarchical manner, and generate a node hierarchical weight distribution table;

[0156] The node spatial distribution generation module 220 is used to set the node priority threshold based on the node hierarchical weight distribution table, extract low-priority nodes, calculate the node spatial location density and the number of connections, remove redundant connections for low-density nodes and adjust the belonging range, and generate a compressed node spatial distribution map;

[0157] The sparse relationship graph generation module 230 is used to extract the weight distribution values ​​and spatial coordinates of high-priority nodes based on the compressed node spatial distribution graph, normalize the weight values, adjust the sparse connection values ​​between nodes, and generate a sparse relationship graph of the high-priority area;

[0158] The change trajectory generation module 240 is used to extract the time series data of the nodes based on the sparse relationship graph of the high-priority region, calculate the node trajectory change value, identify the connection strength change trend, classify and summarize the node data according to the relationship between the trajectory change range and the strength change, and generate a node dynamic change trajectory set;

[0159] The target node distribution generation module 250 is used to set a change range threshold based on the node dynamic change trajectory set, extract the spatial distribution area of ​​nodes with a large trajectory change range, calculate the regional node association strength, adjust the distribution range, classify the node positions and connection characteristics of the target area, and generate a node distribution map of the target area;

[0160] The detection path map generation module 260 is used to calculate the connection coverage and density values ​​between nodes based on the target area node distribution map, generate an initial detection path, dynamically adjust the path coverage and connection relationship, and record the node relationship to generate a target area node detection path map.

[0161] The node layer weight distribution table includes the attribution weight, node association value and layer interval value;

[0162] The compressed node spatial distribution graph includes the node spatial location density, the number of node connections and the node belonging range;

[0163] The sparse relationship graph of the high priority area includes node weight distribution values, node spatial coordinates and node sparse connection values;

[0164] The node dynamic change trajectory set includes trajectory change range, connection strength change trend and node classification data;

[0165] The node distribution map of the target area includes the spatial distribution of regional nodes, node association strength and node connection characteristics;

[0166] The target area node detection path diagram includes path coverage, path connection relationship and node dynamic adjustment record.

[0167] Optionally, the weight distribution table generating module 210 is further configured to:

[0168] S101: Based on the attribute values ​​and connection strengths of the nodes in the network data assets, extract the attribute data of each node, and combine the connection strength values ​​of its adjacent nodes to calculate the association value between the node and the surrounding nodes, and generate a node attribute and connection strength data table;

[0169] S102: Based on the node attribute and connection strength data table, generate interval stratification rules using discrete random numbers, analyze the numerical distribution of node attributes and associated values, delineate stratification intervals based on the distribution range, filter data groups one by one according to the stratification rules, annotate the classification information of weights and associated values, and generate a node stratification weight interval table;

[0170] S103: Based on the node hierarchical weight interval table, the node attribution weight of each level is cumulatively calculated layer by layer, and the correlation values ​​between nodes are summarized and processed according to the hierarchical distribution, and the distribution results and hierarchical relationships of the nodes are recorded to generate a node hierarchical weight distribution table.

[0171] Optionally, the node space distribution generating module 220 is further configured to:

[0172] S201: Based on the node hierarchical weight distribution table, a priority threshold range is set according to the numerical distribution of the weight values, the weight values ​​are compared for each node, and the nodes that meet the conditions are screened. The nodes below the threshold are grouped and labeled, and their spatial positions and connection relationships are recorded to generate a low-priority node information table;

[0173] S202: Based on the low-priority node information table and the spatial distribution records of each node, calculate its spatial location density value and identify the number of direct connections. Nodes with density values ​​lower than the target benchmark are classified and marked. Connections with lower connection strengths are selected by analyzing the connection relationships of the marked nodes and removed, thereby generating a redundant node connection adjustment table.

[0174] S203: Based on the redundant node connection adjustment table, re-compare the node spatial distribution data after adjustment, check the node belonging ranges one by one, and correct the conflicts caused by overlapping belonging ranges, summarize and update the corrected spatial distribution, and generate a compressed node spatial distribution map.

[0175] Optionally, the sparse relationship graph generating module 230 is further configured to:

[0176] S301: Based on the compressed node spatial distribution graph, the spatial positions and weight values ​​of the nodes are analyzed, and nodes with higher priorities are compared and screened one by one. The weight distribution values ​​and corresponding spatial coordinates of the high-priority nodes are recorded as associated data, and a high-priority node weight and coordinate table is generated.

[0177] S302: Based on the high-priority node weights and coordinate table, a scaling factor is calculated according to the weight distribution range, and the weight value of each node is normalized. The sparse connection value of the node corresponding to the normalized weight value is corrected one by one. The connection relationship weights between the nodes are updated based on the correction results to generate a normalized sparse connection adjustment table;

[0178] S303: Based on the normalized sparse connection adjustment table, analyze the distribution of sparse connections of high-priority nodes, verify and map the sparse connection status between nodes in the network one by one, convert the adjusted connection relationship into network structure visualization data, and generate a sparse relationship graph of high-priority areas.

[0179] The calculation formula of the normalized correction weight value is as follows (1):

[0180]

[0181] Among them, W i ′ represents the normalized modified weight value of node i, W i represents the original weight value of node i, represents the average weight value of all nodes, n represents the total number of nodes, C i represents the sparse connection value of node i, m represents the total number of sparsely connected nodes, represents the sum of the squared differences between the weight values ​​of all nodes and the average value, Indicates the absolute difference between the node weight value and the average value.

[0182] Optionally, the change trajectory generating module 240 is further configured to:

[0183] S401: Based on the sparse relationship graph of the high-priority region, the spatial location data and weight value of each node are parsed, the connection weight and status of the node at different time points are extracted one by one, the data is sorted in chronological order, and the time series attribute information of the node is constructed to generate a node time series data table;

[0184] S402: Based on the node time series data table, calculate the trajectory offset value of each node between different time points in the time series one by one, classify and analyze the offset direction and amplitude, calculate the intensity change value based on the change of connection weight between time points, integrate the trajectory and connection change data, and generate a node trajectory and intensity change table;

[0185] S403: Based on the node trajectory and strength change table, analyze the relationship between trajectory change values ​​and connection strength changes, compare the node trajectory offset amplitude and connection change trend, classify the node into a trajectory change pattern group, and record the classification information. Integrate the classification results into the aggregated data of the dynamic change trajectory to generate the node dynamic change trajectory set.

[0186] The calculation formula of the intensity change value is as follows (2):

[0187]

[0188] in, Represents the node at time t i At time tj The intensity change between and W j (t) Represents the node at time point t i and time point t j The connection weight value of and Represents the node at time point t i and time point t j The horizontal coordinate position of and Represents the node at time point t i and time point t j The vertical coordinate position, t i and t j Represent the timestamps of the node at time point i and time point j respectively, represents the absolute change in the node connection weight, Represents the spatial distance between nodes at a time point, t j -t i Indicates a time interval.

[0189] Optionally, the target node distribution generating module 250 is further configured to:

[0190] S501: Based on the node dynamic change trajectory set, analyze the trajectory change range value of each node one by one, set the change range threshold, compare and filter according to the change range, and extract the nodes with larger change ranges. Record the spatial distribution coordinates of the nodes and the connection relationship between adjacent nodes, and generate a spatial distribution table of nodes with larger trajectory changes;

[0191] S502: Based on the spatial distribution table of nodes with significant trajectory changes, the connection relationships between nodes and the spatial coordinate data of adjacent nodes are analyzed, the association strength between the node and its adjacent nodes is calculated, the association strength values ​​are compared and analyzed, and the nodes are assigned to corresponding strength ranges. The node ranges with overlapping boundary distributions are corrected to generate a target area node association strength table;

[0192] S503: Based on the target area node association strength table, the spatial position distribution and connection characteristics of the target area nodes are analyzed one by one, the target area is classified and integrated according to the node distribution law, the position and connection characteristics of the nodes in the area are visualized, and a target area node distribution map is generated.

[0193] Optionally, the detection path map generating module 260 is further configured to:

[0194] S601: Based on the node distribution map of the target area, extract the spatial coordinates and connection relationship data of the nodes, analyze the connection ranges between the nodes one by one, identify the coverage areas of the nodes and their neighboring nodes, calculate the node connection density value based on the node coverage area and the number of connections, organize the data into a node connection and coverage characteristic data table, and generate a node connection coverage and density table;

[0195] S602: Based on the node connection coverage and density table, the coverage overlap between nodes is compared one by one, and the node connection paths are sorted according to the coverage area overlap ratio and connection density value. The connection paths are prioritized and the path connection order is gradually arranged to generate an initial detection path set;

[0196] S603: Based on the initial detection path set, the path coverage and connection relationship are analyzed, the coverage area between the paths is gradually adjusted, the connection characteristics of each node in the adjusted path are re-verified, the corrected path connection information is integrated and processed, and a target area node detection path map is generated.

[0197] The present invention proposes a network data asset spatial asset detection method based on discrete random numbers. By combining discrete random numbers to generate hierarchical intervals, the hierarchy and accuracy of node weight distribution are optimized, the weights of high-priority nodes are normalized and sparse connections are optimized, the clarity of the relationship between nodes is enhanced, trajectory change extraction and intensity trend classification are carried out, and accurate analysis of dynamic node characteristics is achieved. The distribution optimization of target areas improves the organizational efficiency of data assets, and the adjustment of dynamic detection paths enhances the coverage capability and accuracy of detection, thereby improving the overall detection efficiency and dynamic adaptability.

[0198] Figure 3 FIG. 1 is a structural diagram of an asset detection device provided by an embodiment of the present invention. Figure 3 As shown, the asset detection equipment may include the above Figure 2 Optionally, the asset detection device 310 may include a first processor 2001 .

[0199] Optionally, the asset detection device 310 may further include a memory 2002 and a transceiver 2003 .

[0200] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0201] The following combination Figure 3 The following describes the components of the asset detection device 310:

[0202] The first processor 2001 is the control center of the asset detection device 310 and can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0203] Optionally, the first processor 2001 may execute various functions of the asset detection device 310 by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0204] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 3 CPU0 and CPU1 are shown in FIG.

[0205] In a specific implementation, as an embodiment, the asset detection device 310 may also include multiple processors, such as Figure 3 1 and 2. The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0206] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0207] Alternatively, the memory 2002 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001 or exist independently and accessed through the interface circuit ( Figure 3 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0208] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0209] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 3 (not shown separately in the figure). The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0210] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently and communicate with the asset detection device 310 through an interface circuit ( Figure 3 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0211] It should be noted that Figure 3 The structure of the asset detection device 310 shown in the figure does not constitute a limitation on the router. The actual knowledge structure identification device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0212] In addition, the technical effects of the asset detection device 310 can refer to the technical effects of the network data asset space asset detection method based on discrete random numbers described in the above method embodiment, and will not be repeated here.

[0213] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0214] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0215] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0216] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0217] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0218] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes 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.

[0219] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0220] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0221] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0222] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0223] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0224] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0225] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A network data asset space asset detection method based on discrete random numbers, characterized in that: The method comprises: S1: Based on the attribute values ​​and connection strengths of nodes in network data assets, extract the node's attribution weight and association value, use discrete random numbers to generate hierarchical intervals, calculate the attribution weight and node association value hierarchically, and generate a node hierarchical weight distribution table; S2: Based on the node hierarchical weight distribution table, set the node priority threshold, extract low-priority nodes, calculate the node spatial location density and the number of connections, remove redundant connections for low-density nodes and adjust the belonging range to generate a compressed node spatial distribution map; S3: Based on the compressed node spatial distribution graph, extract the weight distribution values ​​and spatial coordinates of high-priority nodes, normalize the weight values, adjust the sparse connection values ​​between nodes, and generate a sparse relationship graph of high-priority areas; S4: Based on the sparse relationship graph of the high-priority area, extract the time series data of the node, calculate the node trajectory change value, and identify the connection strength change trend. According to the relationship between the trajectory change range and strength change, classify and summarize the node data to generate a set of node dynamic change trajectories; S5: Based on the node dynamic change trajectory set, set the change range threshold, extract the spatial distribution area of ​​the nodes with a large trajectory change range, calculate the regional node association strength, adjust the distribution range, classify the node position and connection characteristics of the target area, and generate the node distribution map of the target area; S6: Based on the node distribution map of the target area, calculate the connection coverage and density values ​​between nodes, generate the initial detection path, dynamically adjust the path coverage and connection relationship, and record the node relationship to generate the target area node detection path map.

2. The network data asset space asset detection method based on discrete random numbers according to claim 1 is characterized in that: The node hierarchical weight distribution table includes attribution weight, node association value and hierarchical interval value; The compressed node space distribution graph includes node space location density, node connection quantity and node belonging range; The high priority area sparse relationship graph includes node weight distribution values, node spatial coordinates and node sparse connection values; The node dynamic change trajectory set includes trajectory change range, connection strength change trend and node classification data; The target area node distribution graph includes regional node spatial distribution, node association strength and node connection characteristics; The target area node detection path diagram includes path coverage, path connection relationship and node dynamic adjustment record.

3. The network data asset space asset detection method based on discrete random numbers according to claim 1 is characterized in that: The method of extracting the node's attribution weight and association value based on the attribute value and connection strength of the node in the network data asset, generating a hierarchical interval using discrete random numbers, calculating the attribution weight and node association value in a hierarchical manner, and generating a node hierarchical weight distribution table includes: S101: Based on the attribute values ​​and connection strengths of the nodes in the network data assets, extract the attribute data of each node, and combine the connection strength values ​​of its adjacent nodes to calculate the association value between the node and the surrounding nodes, and generate a node attribute and connection strength data table; S102: Based on the node attribute and connection strength data table, generate interval stratification rules using discrete random numbers, analyze the numerical distribution of node attributes and associated values, delineate stratification intervals based on the distribution range, filter data groups one by one according to the stratification rules, annotate the classification information of weights and associated values, and generate a node stratification weight interval table; S103: Based on the node hierarchical weight interval table, the node attribution weight of each level is cumulatively calculated layer by layer, and the correlation values ​​between nodes are summarized and processed according to the hierarchical distribution, and the distribution results and hierarchical relationships of the nodes are recorded to generate a node hierarchical weight distribution table.

4. The network data asset space asset detection method based on discrete random numbers according to claim 1 is characterized in that: The method of setting a node priority threshold based on the node hierarchical weight distribution table, extracting low-priority nodes, calculating the node spatial position density and the number of connections, removing redundant connections for low-density nodes and adjusting the attribution range, and generating a compressed node spatial distribution map includes: S201: Based on the node hierarchical weight distribution table, a priority threshold range is set according to the numerical distribution of the weight values, the weight values ​​are compared for each node, and the nodes that meet the conditions are screened. The nodes below the threshold are grouped and labeled, and their spatial positions and connection relationships are recorded to generate a low-priority node information table; S202: Based on the low-priority node information table and the spatial distribution records of each node, calculate its spatial location density value and identify the number of direct connections. Nodes with density values ​​lower than the target benchmark are classified and marked. Connections with lower connection strengths are selected by analyzing the connection relationships of the marked nodes and removed, thereby generating a redundant node connection adjustment table. S203: Based on the redundant node connection adjustment table, re-compare the node spatial distribution data after adjustment, check the node belonging ranges one by one, and correct the conflicts caused by overlapping belonging ranges, summarize and update the corrected spatial distribution, and generate a compressed node spatial distribution map.

5. The network data asset space asset detection method based on discrete random numbers according to claim 1 is characterized in that: The method extracts the weight distribution values ​​and spatial coordinates of high-priority nodes based on the compressed node spatial distribution graph, normalizes the weight values, adjusts the sparse connection values ​​between nodes, and generates a high-priority area sparse relationship graph, including: S301: Based on the compressed node spatial distribution graph, the spatial positions and weight values ​​of the nodes are analyzed, and nodes with higher priorities are compared and screened one by one. The weight distribution values ​​and corresponding spatial coordinates of the high-priority nodes are recorded as associated data, and a high-priority node weight and coordinate table is generated. S302: Based on the high-priority node weights and coordinate table, a scaling factor is calculated according to the weight distribution range, and the weight value of each node is normalized. The sparse connection value of the node corresponding to the normalized weight value is corrected one by one. The connection relationship weights between the nodes are updated based on the correction results to generate a normalized sparse connection adjustment table; S303: Based on the normalized sparse connection adjustment table, analyze the distribution of sparse connections of high-priority nodes, verify and map the sparse connection status between nodes in the network one by one, convert the adjusted connection relationship into network structure visualization data, and generate a sparse relationship graph of high-priority areas.

6. The network data asset space asset detection method based on discrete random numbers according to claim 5 is characterized in that: The calculation formula of the modified weight value of the normalization process is as follows (1): Among them, W′ i Represents the normalized corrected weight value of node i, W i represents the original weight value of node i, represents the average weight value of all nodes, n represents the total number of nodes, C i represents the sparse connection value of node i, m represents the total number of sparsely connected nodes, represents the sum of the squared differences between the weight values ​​of all nodes and the average value, Indicates the absolute difference between the node weight value and the average value.

7. The network data asset space asset detection method based on discrete random numbers according to claim 1 is characterized in that: Based on the sparse relationship graph of the high-priority area, the time series data of the nodes are extracted, the node trajectory change value is calculated, and the connection strength change trend is identified. The node data is classified and summarized according to the relationship between the trajectory change range and the strength change, and a node dynamic change trajectory set is generated, including: S401: Based on the sparse relationship graph of the high-priority region, the spatial location data and weight value of each node are parsed, the connection weight and status of the node at different time points are extracted one by one, the data is sorted in chronological order, and the time series attribute information of the node is constructed to generate a node time series data table; S402: Based on the node time series data table, calculate the trajectory offset value of each node between different time points in the time series one by one, classify and analyze the offset direction and amplitude, calculate the intensity change value based on the change of connection weight between time points, integrate the trajectory and connection change data, and generate a node trajectory and intensity change table; S403: Based on the node trajectory and strength change table, analyze the relationship between trajectory change values ​​and connection strength changes, compare the node trajectory offset amplitude and connection change trend, classify the node into a trajectory change pattern group, and record the classification information. Integrate the classification results into the aggregated data of the dynamic change trajectory to generate the node dynamic change trajectory set.

8. The network data asset space asset detection method based on discrete random numbers according to claim 7 is characterized in that: The calculation formula of the intensity change value is as follows (2): in, Represents the node at time t i At time t j The intensity change between and Represents the node at time point t i and time point t j The connection weight value of and Represents the node at time point t i and time point t j The horizontal coordinate position of and Represents the node at time point t i and time point t j The vertical coordinate position, t i and t j Represent the timestamps of the node at time point i and time point j respectively, represents the absolute change in the node connection weight, Represents the spatial distance between nodes at a time point, t j -t i Indicates a time interval.

9. The network data asset space asset detection method based on discrete random numbers according to claim 1 is characterized in that: The method is based on the node dynamic change trajectory set, sets the change range threshold, extracts the spatial distribution area of ​​nodes with larger trajectory change range, calculates the regional node association strength, adjusts the distribution range, classifies the node position and connection characteristics of the target area, and generates the target area node distribution map, including: S501: Based on the node dynamic change trajectory set, analyze the trajectory change range value of each node one by one, set the change range threshold, compare and filter according to the change range, and extract the nodes with larger change ranges. Record the spatial distribution coordinates of the nodes and the connection relationship between adjacent nodes, and generate a spatial distribution table of nodes with larger trajectory changes; S502: Based on the spatial distribution table of nodes with significant trajectory changes, the connection relationships between nodes and the spatial coordinate data of adjacent nodes are analyzed, the association strength between the node and its adjacent nodes is calculated, the association strength values ​​are compared and analyzed, and the nodes are assigned to corresponding strength ranges. The node ranges with overlapping boundary distributions are corrected to generate a target area node association strength table; S503: Based on the target area node association strength table, the spatial position distribution and connection characteristics of the target area nodes are analyzed one by one, the target area is classified and integrated according to the node distribution law, the position and connection characteristics of the nodes in the area are visualized, and a target area node distribution map is generated.

10. The network data asset space asset detection method based on discrete random numbers according to claim 1 is characterized in that: The method of calculating the connection coverage and density values ​​between nodes based on the target area node distribution map, generating an initial detection path, dynamically adjusting the path coverage and connection relationship, and recording the node relationship to generate the target area node detection path map includes: S601: Based on the node distribution map of the target area, extract the spatial coordinates and connection relationship data of the nodes, analyze the connection ranges between the nodes one by one, identify the coverage areas of the nodes and their neighboring nodes, calculate the node connection density value based on the node coverage area and the number of connections, organize the data into a node connection and coverage characteristic data table, and generate a node connection coverage and density table; S602: Based on the node connection coverage and density table, the coverage overlap between nodes is compared one by one, and the node connection paths are sorted according to the coverage area overlap ratio and connection density value. The connection paths are prioritized and the path connection order is gradually arranged to generate an initial detection path set; S603: Based on the initial detection path set, the path coverage and connection relationship are analyzed, the coverage area between the paths is gradually adjusted, the connection characteristics of each node in the adjusted path are re-verified, the corrected path connection information is integrated and processed, and a target area node detection path map is generated.

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