Network data asset and space asset detection method based on discrete random numbers

Abstract

The invention provides a network data asset space asset detection method and device based on discrete random numbers, and relates to the technical field of network data asset detection. The method comprises the following steps: based on attribute values and connection strength of nodes in network data assets, extracting affiliation weights and association values of the nodes, generating hierarchical intervals by using discrete random numbers, calculating the affiliation weights and the node association values in a hierarchical manner, and generating a node hierarchical weight distribution table. According to the method, hierarchical intervals are generated by combining discrete random numbers, the hierarchy and accuracy of node weight distribution are optimized, and the weight normalization and sparse connection optimization of high-priority nodes are carried out, so that the definiteness of the relationship between the nodes, track change extraction and strength trend classification are enhanced, and the accurate analysis of dynamic node characteristics is realized; the distribution optimization of the target area improves the organization efficiency of data assets, the adjustment of the dynamic detection path enhances the coverage capability and precision of detection, and the overall detection efficiency and dynamic adaptability are improved.

Description

Technical Field

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

[0002] The technical field of network data asset detection includes relevant methods and technologies for identifying, analyzing, classifying, and locating data resources in a network. The core content of this technical field is to analyze data streams, data packets, and data relationships in a network environment to obtain hidden or unexplicitly identified data asset information and perform structured management on these assets. The overall technical field mainly involves feature extraction and modeling of discrete data, analysis and recombination of data relationships in the network space, and visualization positioning and distribution law analysis of data assets, and is widely applied in information security, data asset management, and network resource optimization allocation.

[0003] Among them, the method for detecting network data asset spatial assets based on discrete random numbers refers to a method for detecting data assets and their distribution states in the network space by using the generation characteristics and calculation rules of discrete random numbers. The technical matters targeted by this patent theme include constructing a data distribution model through discrete random numbers, analyzing the mapping relationship between discrete random numbers and data distribution characteristics by means of statistical analysis, and detecting and locating the spatial distribution of data assets in combination with specific numerical iteration methods. Specifically, the calculation process of the data distribution law is optimized through discrete random numbers, the structured relationship of data assets is deconstructed and recombined to complete the accurate identification and spatial detection of asset information.

[0004] In the existing technology for network data asset management, there is a lack of an effective hierarchical weight distribution model, resulting in a flat management of data asset weights and association relationships, making it difficult to meet the multi-level requirements of complex network environments. In terms of spatial distribution optimization, the processing of redundant connections often mainly relies on simple statistical analysis, lacking comprehensive consideration of spatial density, which easily leads to the inability to completely eliminate redundant connections of low-priority nodes, wasting network resources. In the process of processing high-priority nodes, it is difficult for the existing methods to finely adjust the weight values, and the optimization of sparse connections is insufficient, making the node relationships in high-priority areas complex and reducing the operability of data. In terms of dynamic change management, the analysis means for node time series and trajectory changes are limited, lacking the ability to refine the classification of node dynamic characteristics and connection strength change trends, resulting in the inability to comprehensively master the dynamic change rules of nodes. In path detection, there is a lack of the ability to dynamically adjust paths and optimize coverage, and the detection results have problems of insufficient coverage or excessive redundancy, restricting the adaptability and accuracy in a dynamic network environment. These deficiencies make it difficult for the existing technology to meet the comprehensive requirements of data asset detection and optimization in complex network environments. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, 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, and the limited analysis means for node time series and trajectory changes, resulting in problems such as insufficient coverage or excessive redundancy in detection results, embodiments of the present invention provide a method and device for detecting spatial assets of network data assets based on discrete random numbers. The technical solutions are as follows:

[0006] On the one hand, a method for detecting spatial assets of network data assets based on discrete random numbers is provided. This 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 ownership weights and association values of nodes, use discrete random numbers to generate hierarchical intervals, calculate the ownership weights and node association values hierarchically, and generate a node hierarchical weight distribution table;

[0008] S2: Based on the node hierarchical weight distribution table, set node priority thresholds, extract low-priority nodes, calculate the spatial position density and connection quantity of nodes, remove redundant connections from nodes with lower density and adjust the ownership scope, and generate a compressed node spatial distribution map;

[0009] S3: Based on the compressed node spatial distribution map, extract the weight distribution values and spatial coordinates of high-priority nodes, perform normalized calculation on the weight values, adjust the sparse connection values between nodes, and generate a sparse relationship map of high-priority regions;

[0010] S4: Based on the sparse relationship map of high-priority regions, extract the time series data of nodes, calculate the node trajectory change values, and identify the change trend of connection strength. Classify and summarize the node data according to the trajectory change range and strength change relationship, and generate a set of node dynamic change trajectories;

[0011] S5: Based on the set of node dynamic change trajectories, set a change range threshold, extract the spatial distribution regions of nodes with larger trajectory change ranges, calculate the association strength of regional nodes, and adjust the distribution range. Classify the node positions and connection characteristics of the target region, and generate a node distribution map of the target region;

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

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

[0014] A weight distribution table generation module, which is used to extract the ownership weight and association value of nodes based on the attribute values and connection strengths of nodes in network data assets, generate hierarchical intervals using discrete random numbers, calculate the ownership weight and node association value hierarchically, and generate a node hierarchical weight distribution table;

[0015] A node spatial distribution generation module, which is used to set a node priority threshold based on the node hierarchical weight distribution table, extract low-priority nodes, calculate the node spatial position density and the number of connections, remove redundant connections from nodes with lower density and adjust the ownership range, and generate a compressed node spatial distribution map;

[0016] A sparse relationship graph generation module, which is used to extract the weight distribution values and spatial coordinates of high-priority nodes based on the compressed node spatial distribution map, perform normalized calculation on the weight values, adjust the sparse connection values between nodes, and generate a sparse relationship graph of the high-priority area;

[0017] A change trajectory generation module, which is used to extract the time series data of nodes based on the sparse relationship graph of the high-priority area, calculate the node trajectory change value, identify the change trend of the connection strength, classify and summarize the node data according to the trajectory change range and the relationship between the strength changes, and generate a set of node dynamic change trajectories;

[0018] A target node distribution generation module, which is used to set a change range threshold based on the set of node dynamic change trajectories, extract the spatial distribution areas of nodes with larger trajectory change ranges, calculate the association strength of the regional nodes, adjust the distribution range, classify the positions and connection characteristics of the nodes in the target area, and generate a target area node distribution map;

[0019] A detection path map generation module, which is used to calculate the connection coverage range and density value between nodes based on the target area node distribution map, generate an initial detection path, dynamically adjust the path coverage range and connection relationship, and record the node relationship, and generate a target area node detection path map.

[0020] On the other hand, an asset detection device is provided. The asset detection device includes: a processor; a memory, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor, any one of the methods in the above-mentioned network data asset spatial asset detection method 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, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned methods for detecting network data asset spatial assets based on discrete random numbers.

[0022] The beneficial effects brought by the technical solutions provided in the embodiments of the present invention at least include:

[0023] The present invention proposes a method for detecting network data asset spatial assets 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 weight normalization and sparse connection optimization of high-priority nodes strengthen the clarity of the relationship between nodes. Trajectory change extraction and intensity trend classification are carried out to achieve precise analysis of dynamic node characteristics. The distribution optimization of the target area improves the organization efficiency of data assets, and the adjustment of the dynamic detection path enhances the coverage ability and accuracy of detection, thus overall improving the 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 will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 is a flowchart of a method for detecting network data asset spatial assets based on discrete random numbers provided by an embodiment of the present invention;

[0026] Figure 2 is a block diagram of a device for detecting network data asset spatial assets based on discrete random numbers provided by an embodiment of the present invention;

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

[0028] The following describes the technical solutions in the present invention with reference to the drawings.

[0029] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0030] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "of", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

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

[0032] To make the technical problems to be solved, technical solutions, and advantages of the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0033] The embodiments of the present invention provide a method for detecting spatial assets of network data assets based on discrete random numbers. This method can be implemented by an asset detection device, which can be a terminal or a server. As Figure 1 shown in the flowchart of the method for detecting spatial assets of network data assets based on discrete random numbers, the processing flow of this method can include the following steps:

[0034] S1: Based on the attribute values and connection strengths of nodes in network data assets, extract the ownership weights and association values of nodes, use discrete random numbers to generate hierarchical intervals, calculate the ownership weights and node association values hierarchically, and generate a node hierarchical weight distribution table.

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

[0036] The compressed node space distribution map includes node space position density, the number of node connections, and the node ownership range;

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

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

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

[0040] The target area node detection path map includes the path coverage range, path connection relationship, and node dynamic adjustment records.

[0041] Optionally, based on the attribute values and connection strengths of the nodes in the network data asset, extract the attribution weights and association values of the nodes, generate hierarchical intervals using discrete random numbers, calculate the attribution weights and node association values hierarchically, and generate a node hierarchical weight distribution table, including:

[0042] S101: Based on the attribute values and connection strengths of the nodes in the network data asset, 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 its surrounding nodes, and generate a node attribute and connection strength data table.

[0043] In a feasible implementation, obtain the attribute data of each node and the connection strength values of its adjacent nodes from the network. First, establish a matrix structure containing node attributes and connection strengths. Extract the attribute data of each node one by one, statistically calculate the connection strength values between nodes, set the connection relationship between nodes as a two-dimensional array, introduce the node attribute values as vectors into the matrix, and obtain the connection strength values of each pair of adjacent nodes through their positions in the matrix. Calculate the association value between the node and its adjacent nodes through statistical methods. The association value can be calculated by the weighted average method, where the weight value is determined by the connection strength, and finally form a node attribute and connection strength data table.

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

[0045] In a feasible implementation, generate rules for interval stratification through discrete random numbers, analyze the numerical distribution of node attributes and association values, and calculate the hierarchical association value of the node. The process is as follows in formula (1):

[0046]

[0047] where, R ij represents the association value of node i in stratification 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] In the formula, A ik represents the node attribute data generated by the stratification rule of the node attribute value, which can be obtained by directly attributing the node attribute value to different stratification intervals and then counting. For example, if a certain attribute value of a node is 12 and the stratification intervals are [0 - 10], [10 - 20], [20 - 30], then 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, if the connection strength between node k and j is 3, it is directly introduced as a weight value into the calculation; R ij is the final goal, and the weighted average correlation value between the node attribute value and the connection strength is calculated through a formula.

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

[0050] Calculate the stratification correlation value of node 1 in the second interval. Substituting into the above formula, we can know

[0051] This result shows that the correlation value between node 1 and interval 2 is 13.33, indicating the weight degree of the node in this stratification interval, and finally generating a node stratification weight interval table.

[0052] S103: Based on the node stratification weight interval table, calculate the node attribution weights of each level layer by layer, summarize and process the connection values between nodes according to the hierarchical distribution, record the distribution results and hierarchical relationships of the nodes, and generate a node stratification weight distribution table.

[0053] In a feasible implementation, extract the node correlation values of each level according to the stratification weight interval table, perform weighted accumulation on the correlation values of the nodes in the order of levels, group and summarize the correlation values of the nodes at each level, calculate the attribution weights of the hierarchical nodes using the connection values between nodes according to the hierarchical distribution, and perform hierarchical classification by the weighted mean method. Record the weight attribution levels of the nodes at different levels in the weight distribution table, record the distribution results and hierarchical relationships of the nodes, and finally generate a node stratification weight distribution table.

[0054] S2: Based on the node stratification weight distribution table, set the node priority threshold, extract the low-priority nodes, calculate the node spatial position density and the number of connections, remove redundant connections from the nodes with lower density and adjust the attribution range, and generate a compressed node spatial distribution map.

[0055] Optionally, based on the node stratification weight distribution table, set the node priority threshold, extract the low-priority nodes, calculate the node spatial position density and the number of connections, remove redundant connections from the nodes with lower density and adjust the attribution range, and 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 for each node one by one, and screen out the qualified nodes. Group and label the nodes with weights lower than the threshold, record their spatial positions and connection relationships, and generate a low-priority node information table.

[0057] In a feasible implementation, compare the node weight values with the set threshold one by one. Label the nodes with weights lower than the threshold through a mapping algorithm, establish a structured record table including node numbers, spatial positions, and connection relationships, calculate the connection relationship matrix of the nodes, group them according to the weight values and spatial position relationships, count the connection relationships between low-weight nodes and associate their adjacent nodes, and record their spatial distribution matrix at the same time. Finally, generate a low-priority node information table.

[0058] S202: Based on the low-priority node information table, calculate the spatial position density value according to the spatial distribution record of each node, identify the direct connection quantity, classify and mark the nodes with density values lower than the target benchmark, select the connections with lower connection strength by analyzing the connection relationships of the marked nodes, and perform elimination operation processing to generate a redundant node connection adjustment table.

[0059] In a feasible implementation, calculate the spatial position density value according to the spatial distribution record of the nodes, and calculate the spatial position density value of each node according to formula (2):

[0060]

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

[0062] In the formula, C ij represents the number of connections between two nodes, which can be statistically obtained through the adjacency matrix of the node connection relationship. For example, if there are 3 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 through the geometric characteristics of node space division. For example, if the spatial occupation of node 1 is 25 square units, then A1 = 25.

[0063] Specific example: Suppose node 1 is connected to node 2 and node 3, the number of connections between nodes is C 12 = 2, C 13 = 1, the area of the region of node 1 is 25 square units. Calculate the spatial position density value of node 1 and substitute it into the formula to get

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

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

[0066] In a feasible implementation, eliminate the spatial distribution errors caused by adjusting the connection relationship. By checking the attribution ranges of the nodes and the influence relationships of adjacent nodes one by one, correct the nodes with overlapping attribution ranges, record the attribution ranges and spatial position relationships of the nodes after adjustment, re-statistical the connection relationships between the nodes through the adjacency matrix, and correct the node spatial data after regional division. Finally, generate a compressed node spatial distribution map.

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

[0068] Optionally, based on the compressed node spatial distribution map, extract the weight distribution values and spatial coordinates of high-priority nodes, perform normalization calculation on the weight values, adjust the sparse connection values between the nodes, and generate a sparse relationship map of high-priority regions, including:

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

[0070] In a feasible implementation, read the weight values and spatial coordinates of the nodes row by row from the compressed spatial data table. By setting a threshold standard for high priorities, sequentially screen the nodes whose weight values meet the priority requirements, record the spatial coordinates of the high-priority nodes into the associated data, and at the same time pair and map the weight distribution and spatial data of the nodes. Finally, form a matching table of node weight values and spatial coordinates, and generate a high-priority node weight and coordinate table.

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

[0072] Among them, the formula for the corrected weight value of the normalization process is as follows in Equation (3):

[0073]

[0074] 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 sparse connection nodes, represents the sum of the squared differences between all node weight values and the average value, represents the absolute difference between the node weight value and the average value.

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

[0076] Input data: Suppose there are 5 nodes, and the original weight values of the nodes are W1 = 20, W2 = 25, W3 = 15, W4 = 30, W5 = 10 respectively. There are 3 sparse connection 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 all node weight values and the average value

[0080] The sum of the squared differences is: Calculate the standard deviation term Calculate the sum of the 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] Substitute the values:

[0083] For node 3:

[0084] Substitute the values:

[0085] For node 5:

[0086] Substitute the values:

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

[0088] This result indicates that the normalized corrected value of the node weight can reflect the degree of difference from the average weight value. At the same time, the weight value corrected in combination with the sparse connection value is used to update the connection relationship weight between nodes, and finally a normalized sparse connection adjustment table is generated.

[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, and convert the adjusted connection relationship into network structure visualization data to generate a sparse relationship graph for the high-priority area.

[0090] In a feasible implementation, verify the sparse connection distribution of nodes item by item, map the normalized weight value of each node to the adjusted connection relationship, analyze the sparse connection status between nodes in the network, read the connection relationship and sparsity degree row by row through the node connection matrix, convert the adjusted connection relationship into network topology data, record the sparse connection value of each node and the corresponding spatial coordinate data, and draw a visual topology graph of the network structure according to the mapping result, and finally generate a sparse relationship graph for the high-priority area.

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

[0092] Optionally, based on the high-priority area sparse relationship graph, extract the time series data of the nodes, 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 set of node dynamic change trajectories, including:

[0093] S401: Based on the high-priority area sparse relationship graph, analyze the spatial position data and weight values of each node, extract the connection weights and states of the nodes at different time points one by one, organize the data in chronological order, and construct the time series attribute information of the nodes to generate a node time series data table.

[0094] In a feasible implementation, extract the spatial position data and corresponding weight values of each node, read the connection weights and connection states of the nodes at different time points, regard the time points as a one-dimensional sequence, construct a weight time series matrix, perform time series statistical analysis on the connection weights of each node at different time points, and organize and classify the connection weights and node states at different time points into different columns in the matrix. Finally, combine the spatial coordinate data of the nodes and the time series matrix to generate a node time series data table.

[0095] S402: Based on the node time series data table, calculate the trajectory offset values between different time points in the time series of each node one by one, classify and analyze the offset directions and amplitudes, calculate the strength change value according to the change of the connection weights between time points, and integrate the trajectory and connection change data to generate a node trajectory and strength change table.

[0096] Among them, the calculation formula of the strength change value is as follows in formula (4):

[0097]

[0098] Among them, represents the strength change value between the node at time point t i and time point t j , and W j (t) respectively represent the connection weight values of the node at time point t i and time point t j , and respectively represent the abscissa positions of the node at time point t i and time point t j , and respectively represent the ordinate positions of the node at time point t i and time point t j , t i and t jRepresent the timestamps of the node at time point i and time point j respectively. Indicates the absolute change in the node connection weight. Indicates the spatial distance between nodes at different time points, t j -t i Indicates the time interval.

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

[0100] The weight value of node A The weight value 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 the connection weight:

[0103]

[0104] Calculate the spatial distance between nodes at different time points:

[0105]

[0106] Calculate the time interval:

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

[0108] Calculate the intensity change value:

[0109] Substitute specific values:

[0110] Result description: This result shows that the intensity change value between nodes A and B at time points t1 = 1 and t2 = 3 is 1. This value represents the dynamic characteristics of the change in the connection weight between nodes in terms of time and space. Combining with the trajectory data, the dynamic change trend between nodes can be further analyzed, providing key data support for the subsequent node trajectory and intensity change table.

[0111] S403: Based on the node trajectory and intensity change table, analyze the relationship between the trajectory change value and the connection intensity change, compare the trajectory deviation amplitude and connection change trend of the nodes, classify the nodes into the trajectory change mode group, record the classification information, integrate the classification results into the aggregated data of the dynamic change trajectory, and generate the node dynamic change trajectory set.

[0112] In a feasible implementation manner, by classifying the trajectory offset amplitude and connection change trend of each node one by one, extracting the trajectory pattern features based on the offset amplitude and connection change data, dividing the nodes into different trajectory change pattern groups, recording the node classification information of each pattern group item by item, integrating all the classified trajectory change pattern data into a collective form, and further processing the collective data to obtain the spatial distribution features of the node dynamic change trajectory, finally generating a set of node dynamic change trajectories.

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

[0114] Optionally, based on the set of node dynamic change trajectories, set a change range threshold, extract the spatial distribution area of the nodes with a larger trajectory change range, calculate the regional node association strength, adjust the distribution range, classify the node positions and connection characteristics in the target area, and generate a node distribution map of the target area, including:

[0115] S501: Based on the set of node dynamic change trajectories, analyze the trajectory change range values of each node one by one, set a change range threshold, compare and screen according to the change range, extract the nodes with a larger change range, record the spatial distribution coordinates of the nodes and the connection relationships with adjacent nodes, and generate a spatial distribution table of the nodes with a larger trajectory change.

[0116] In a feasible implementation manner, extract the minimum value and the maximum value in the trajectory change data of the nodes, calculate the trajectory change range value and compare it with the set change range threshold, screen out the nodes with a larger change range, perform distribution statistics on the screened nodes according to the spatial coordinate information, generate a connection relationship matrix in combination with the adjacent node connection relationship data, and at the same time perform an elimination operation on the connections with lower weights in the matrix, finally generating a spatial distribution table of the nodes with a larger trajectory change.

[0117] S502: Based on the spatial distribution table of the nodes with a larger trajectory change, analyze the connection relationships between the nodes and the spatial coordinate data of the neighboring nodes, calculate the association strength between the nodes and their neighboring nodes, conduct a comparative analysis on the association strength values, assign the nodes to the corresponding strength range areas, and correct the node ranges with overlapping boundary distributions to generate a target area node association strength table.

[0118] In a feasible implementation manner, according to the spatial distribution of the nodes with a larger trajectory change, calculate the association strength between the nodes and their neighboring nodes, and calculate the association strength value between the nodes according to formula (5):

[0119]

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

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

[0122]

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

[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, and 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 is used as the basis for subsequent division of the strength range area.

[0132] S503: Based on the target area node association strength table, analyze the spatial position distribution and connection characteristics of the target area nodes one by one. Classify and integrate the target area according to the node distribution law, visually organize the positions and connection characteristics of the nodes within the area, and generate a target area node distribution map.

[0133] In a feasible implementation, extract the strength change data in the node connection relationship, divide the nodes into different spatial area groups according to the spatial distribution characteristics, and at the same time correct the range of the boundary distribution overlapping area. Integrate the position and connection characteristic data of the nodes to generate the topological structure relationship between the nodes, and visually present the structure relationship. Draw the connection distribution map of the nodes through a graphical tool, and finally form a target area node distribution map.

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

[0135] Optionally, based on the target area node distribution map, calculate the connection coverage range and density value between the nodes, generate an initial detection path, dynamically adjust the path coverage range and connection relationship, and record the node relationship to generate a target area node detection path map, including:

[0136] S601: Based on the target area node distribution map, extract the spatial coordinates and connection relationship data of the nodes, analyze the connection range between the nodes one by one, identify the coverage areas of the nodes and their neighboring nodes, calculate the node connection density value according to the node coverage area and connection quantity, 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, identify the coverage relationship between the nodes by calculating the intersection of the spatial coverage area of the nodes and their neighboring nodes, integrate the geometric data of the node coverage area and its connection quantity data, calculate the connection density value of the nodes and record it as the attribute data of the corresponding nodes. At the same time, sort out the overlapping matrix of the coverage areas between the nodes, integrate the spatial coverage and connection characteristics into a data table, and generate a node connection coverage and density table.

[0138] S602: Based on the node connection coverage and density table, compare the overlapping situation of the coverage ranges between the nodes one by one, sort the node connection paths according to the coverage area overlapping ratio and connection density value, screen 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 manner, according to the overlapping ratio of node coverage ranges and the connection density value, calculate the priority of the connection path, and calculate the connection priority value between node i and node j according to formula (7):

[0140]

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

[0142] In the formula, C ij is the connection density value between node i and j, which is extracted from the node connection coverage and density table; O ij is the overlapping ratio of the coverage areas, and the calculation formula is the following formula (8):

[0143]

[0144] Wherein, A i ∩A j is the intersection area of the coverage areas of the two nodes, and A i ∪A j is the union area of the coverage areas of the two nodes.

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

[0146] Calculate the coverage area overlapping ratio:

[0147] Calculate the connection priority value:

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

[0149] Calculate the priority value:

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

[0151] S603: Based on the initial set of detection paths, analyze the path coverage and connection relationships, gradually adjust the coverage areas between paths, re-verify the connection characteristics of each node in the adjusted paths, and integrate and process the corrected path connection information to generate a node detection path map for the target area.

[0152] In a feasible implementation, gradually analyze the path coverage and connection relationships, adjust the spatial coverage areas between paths, realign the node connection attributes and coverage data of adjacent paths, correct the overlapping areas in the path coverage, and ensure the independence and integrity of each path by verifying the connection relationships and coverage characteristics of the adjusted paths. At the same time, record the node connection matrix in the corrected path and generate visual topology data based on the adjusted coverage to finally generate a node detection path map for the target area.

[0153] The present invention proposes a method for detecting spatial assets of network data assets based on discrete random numbers. By combining discrete random numbers to generate hierarchical intervals, it optimizes the hierarchy and accuracy of node weight distribution, normalizes the weights of high-priority nodes and optimizes sparse connections, strengthens the clarity of the relationships between nodes, extracts trajectory changes and classifies intensity trends, realizes accurate analysis of dynamic node characteristics, optimizes the distribution of the target area to improve the organization efficiency of data assets, and adjusts dynamic detection paths to enhance the coverage ability and accuracy of detection, thus overall improving the detection efficiency and dynamic adaptability.

[0154] Figure 2 It is a block diagram of a device for detecting spatial assets of network data assets based on discrete random numbers shown according to an exemplary embodiment. This device is used for the method of detecting spatial assets of network data assets based on discrete random numbers. Refer to Figure 2 , this device includes a weight distribution table generation module 210, a node spatial 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 map generation module 260. Among them:

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

[0156] The node spatial distribution generation module 220 is used to set node priority thresholds based on the node hierarchical weight distribution table, extract low-priority nodes, calculate the node spatial position density and connection quantity, remove redundant connections from nodes with lower density and adjust the attribution range to 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 map, perform normalized calculation on the weight values, adjust the sparse connection values between nodes, and generate a sparse relationship graph for the high-priority area;

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

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

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

[0161] Among them, the node hierarchical weight distribution table includes the attribution weight, node association value, and hierarchical interval value;

[0162] The compressed node spatial distribution map includes the node spatial position density, the number of node connections, and the node attribution range;

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

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

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

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

[0167] Optionally, the weight distribution table generation module 210 is further used for:

[0168] S101: Based on the attribute values and connection strength of nodes in the network data asset, 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 its surrounding nodes, and generate a node attribute and connection strength data table;

[0169] S102: Based on the data table of node attributes and connection strengths, generate rules for interval stratification through discrete random numbers, analyze the numerical distributions of node attributes and correlation values, delimit stratification intervals according to the distribution ranges, screen data groups one by one according to the stratification rules, mark the classification information of weights and correlation values, and generate a node stratification weight interval table;

[0170] S103: Based on the node stratification weight interval table, perform cumulative calculations layer by layer on the node attribution weights of each level, conduct summary and induction processing on the hierarchical distribution of correlation values between nodes, record the distribution results and hierarchical relationships of the nodes, and generate a node stratification weight distribution table.

[0171] Optionally, the node spatial distribution generation module 220 is further configured to:

[0172] S201: Based on the node stratification weight distribution table, set a priority threshold range according to the numerical distribution of weight values, compare the weight values for each node one by one, screen the nodes that meet the conditions, group and mark the nodes with weight values lower than the threshold, and record their spatial positions and connection relationships to generate a low-priority node information table;

[0173] S202: Based on the low-priority node information table, calculate the spatial position density value according to the spatial distribution records of each node, identify the direct connection quantity, classify and mark the nodes with density values lower than the target benchmark, select the connections with lower connection strengths by analyzing the connection relationships of the marked nodes, and perform elimination operation processing to generate a redundant node connection adjustment table;

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

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

[0176] S301: Based on the compressed node spatial distribution map, analyze the spatial positions and weight values of the nodes, compare and screen the nodes with higher priorities one by one, record the weight distribution values and corresponding spatial coordinates of the high-priority nodes as associated data, and generate a high-priority node weight and coordinate table;

[0177] S302: Based on the high-priority node weight and coordinate table, calculate the scale factor according to the weight distribution range, perform normalization processing on the weight values of each node, correct the sparse connection values of the nodes corresponding to the normalized weight values one by one, and update the connection relationship weights between nodes according to the correction results to generate a normalized sparse connection adjustment table;

[0178] S303: Analyze the distribution of sparse connections of high-priority nodes based on the normalized sparse connection adjustment table, 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 regions.

[0179] Among them, the calculation formula for the corrected weight value after normalization is as follows in Equation (1):

[0180]

[0181] Among them, W i ' represents the normalized corrected weight value of node i, and 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 sparse connection nodes, represents the sum of the squared differences between the weight values of all nodes and the average value, represents the absolute difference between the node weight value and the average value.

[0182] Optionally, the change trajectory generation module 240 is further used for:

[0183] S401: Based on the sparse relationship graph of high-priority regions, analyze the spatial position data and weight values of each node, extract the connection weights and status of the nodes at different time points one by one, organize the data in chronological order and construct the time series attribute information of the nodes, and generate a node time series data table;

[0184] S402: Based on the node time series data table, calculate the trajectory offset values between different time points in the time series of the nodes one by one, classify and analyze the offset directions and amplitudes, calculate the intensity change values according to the change of the connection weights 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 intensity change table, analyze the relationship between the trajectory change value and the connection intensity change, compare the trajectory offset amplitude and the connection change trend of the nodes, classify the nodes into the trajectory change mode group, record the classification information, and integrate the classification results into the aggregated data of the dynamic change trajectory to generate a node dynamic change trajectory set.

[0186] Among them, the calculation formula for the intensity change value is as follows in Equation (2):

[0187]

[0188] Among them, represents the node at time point t i and time point tj The intensity change value between and W j (t) respectively represent the connection weight values of the node at time points t i and time point t j ; and respectively represent the abscissa positions of the node at time points t i and time point t j ; and respectively represent the ordinate positions of the node at time points t i and time point t j ; t i and t j respectively represent the timestamps of the node at time points i and j. represents the absolute change amount of the node connection weight, represents the spatial distance between nodes at time points, t j -t i represents the time interval.

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

[0190] S501: Based on the node dynamic change trajectory set, parse 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 relationships of 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 larger trajectory changes, parse the connection relationships between nodes and the spatial coordinate data of neighboring nodes, calculate the association strength between the node and its neighboring nodes, conduct a comparative analysis of the association strength values, classify the nodes into corresponding strength range regions, and correct the overlapping node ranges at the boundaries to generate a target area node association strength table;

[0192] S503: Based on the target area node association strength table, parse the spatial position distribution and connection characteristics of the target area nodes one by one, classify and integrate the target area according to the node distribution law, and visually organize the positions and connection characteristics of the nodes in the area to generate a target area node distribution map.

[0193] Optionally, the detection path map generation 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 range between nodes one by one, identify the coverage areas of nodes and their neighboring nodes, calculate the node connection density value according to the node coverage area and the number of connections, organize and form a data table of node connection and coverage characteristics, and generate a node connection coverage and density table;

[0195] S602: Based on the node connection coverage and density table, compare the overlapping situation of the coverage ranges between nodes one by one, sort the node connection paths according to the overlapping ratio of the coverage area and the connection density value, screen the priority of the connection paths and gradually arrange the path connection order to generate an initial detection path set;

[0196] S603: Based on the initial detection path set, analyze the path coverage range and connection relationship, gradually adjust the coverage areas between paths, re-verify the connection characteristics of each node in the adjusted path, and integrate and process the corrected path connection information to generate a node detection path map of the target area.

[0197] The present invention proposes a method for detecting spatial assets of network data assets based on discrete random numbers. By combining discrete random numbers to generate hierarchical intervals, optimizing the hierarchy and accuracy of node weight distribution, normalizing the weights of high-priority nodes and optimizing sparse connections, the clarity of the relationship between nodes is strengthened. Through the extraction of trajectory changes and intensity trend classification, accurate analysis of dynamic node characteristics is realized. The distribution optimization of the target area improves the organization efficiency of data assets, and the adjustment of dynamic detection paths enhances the coverage ability and accuracy of detection, thus overall improving the detection efficiency and dynamic adaptability.

[0198] Figure 3 It is a schematic structural diagram of an asset detection device provided by an embodiment of the present invention. As Figure 3 shown, the asset detection device may include the above-mentioned Figure 2 spatial asset detection device of network data assets based on discrete random numbers. 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] Among them, the first processor 2001, the memory 2002, and the transceiver 2003 may be connected through a communication bus.

[0201] Next, in combination with Figure 3 specific introductions will be made to the various components of the asset detection device 310:

[0202] Among them, the first processor 2001 is the control center of the asset detection device 310, which can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or can be an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the 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 can execute various functions of the asset detection device 310 by running or executing software programs 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 can include one or more CPUs, such as Figure 3 the CPU0 and CPU1 shown in

[0205] In a specific implementation, as an embodiment, the asset detection device 310 can also include multiple processors, such as Figure 3 the first processor 2001 and the second processor 2004 shown in

[0206] Among them, the memory 2002 is used to store software programs 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 embodiments and will not be elaborated here.

[0207] Optionally, 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 may also be 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 discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the 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 may exist independently and be coupled to the first processor 2001 through an interface circuit ( Figure 3 not shown) of the asset detection device 310. The embodiments of the present invention do not make specific limitations on this.

[0208] A transceiver 2003 is configured to communicate with a network device or with a terminal device.

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

[0210] Optionally, the transceiver 2003 may be integrated with the first processor 2001 or may exist independently and be coupled to the first processor 2001 through an interface circuit ( Figure 3 not shown) of the asset detection device 310. The embodiments of the present invention do not make specific limitations on this.

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

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

[0213] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be 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 ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (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 but not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0215] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any combination thereof. 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 programs are loaded or executed on a computer, the processes or functions described in 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 devices. 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 by wired (such as infrared, wireless, microwave, etc.) means. 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 one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0216] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context.

[0217] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0218] It should be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

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

[0220] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0221] In 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 only a logical function division, and there may be other division methods in actual implementation. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

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

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

[0224] When the above-mentioned 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, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0225] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for detecting spatial assets of network data assets based on discrete random numbers, characterized in that, The method includes: S1: Based on the attribute values and connection strengths of nodes in the network data asset, extract the attribution weights and correlation values of the nodes, generate hierarchical intervals using discrete random numbers, calculate the attribution weights and node correlation values hierarchically, and generate a node hierarchical weight distribution table; S2: Based on the node hierarchical weight distribution table, set a node priority threshold, extract low-priority nodes, calculate the node spatial position density and connection quantity, remove redundant connections from nodes with lower density and adjust the attribution scope, and generate a compressed node spatial distribution map; S3: Based on the compressed node spatial distribution map, extract the weight distribution values and spatial coordinates of high-priority nodes, perform normalization calculation on the weight values, adjust the sparse connection values between nodes, and generate a sparse relationship map of the high-priority area; S4: Based on the sparse relationship map of the high-priority area, extract the time series data of the nodes, calculate the node trajectory change values, and identify the change trend of the connection strength. Classify and summarize the node data according to the trajectory change range and the relationship between the strength changes, and generate a set of node dynamic change trajectories; S5: Based on the set of node dynamic change trajectories, set a change range threshold, extract the spatial distribution areas of nodes with larger trajectory change ranges, calculate the association strength of the regional nodes, and 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; S6: Based on the node distribution map of the target area, calculate the connection coverage range and density value between nodes, generate an initial detection path, dynamically adjust the path coverage range and connection relationship, and record the node relationship, and generate a node detection path map of the target area.

2. The method for detecting spatial assets of network data assets based on discrete random numbers according to claim 1, wherein The node hierarchical weight distribution table includes attribution weights, node correlation values, and hierarchical interval values; The compressed node spatial distribution map includes node spatial position density, node connection quantity, and node attribution scope; The sparse relationship map of the high-priority area includes node weight distribution values, node spatial coordinates, and node sparse connection values; The set of node dynamic change trajectories includes trajectory change ranges, connection strength change trends, and node classification data; The node distribution map of the target area includes regional node spatial distribution, node association strength, and node connection characteristics; The node detection path map of the target area includes path coverage range, path connection relationship, and node dynamic adjustment record.

3. The method for detecting spatial assets of network data assets based on discrete random numbers according to claim 1, wherein The step of, based on the attribute values and connection strengths of nodes in the network data asset, extracting the attribution weights and correlation values of the nodes, generating hierarchical intervals using discrete random numbers, calculating the attribution weights and node correlation values hierarchically, and generating a node hierarchical weight distribution table, includes: S101: Based on the attribute values and connection strengths of nodes in the network data asset, extract the attribute data of each node, and combine the connection strength values of its adjacent nodes to calculate the correlation 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 rules for interval stratification through discrete random numbers, analyze the numerical distribution of node attributes and correlation values, delimit stratification intervals according to the distribution range, screen data groups one by one according to the stratification rules, and mark the classification information of weights and correlation values, and generate a node hierarchical weight interval table; S103: Based on the node hierarchical weight interval table, cumulatively calculate the node attribution weights layer by layer for each level of nodes. Through summarizing and generalizing the correlation values among nodes according to the hierarchical distribution, record the distribution results and hierarchical relationships of the nodes, and generate a node hierarchical weight distribution table.

4. The method for detecting network data asset spatial assets based on discrete random numbers according to claim 1, wherein 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 from nodes with lower density and adjust the attribution range, and generate a compressed node spatial distribution map, including: 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 for each node one by one, and screen out the nodes that meet the conditions. Group and label the nodes with weights lower than the threshold, and record their spatial positions and connection relationships to generate a low-priority node information table; S202: Based on the low-priority node information table, calculate the spatial position density value according to the spatial distribution record of each node, and identify the number of direct connections. Classify and mark the nodes with density values lower than the target benchmark. Select the connections with lower connection strength by analyzing the connection relationships of the marked nodes, and perform removal operation processing to generate a redundant node connection adjustment table; S203: Based on the redundant node connection adjustment table, re-compare the spatial distribution data of the nodes after adjustment, check the node attribution range one by one, and correct the conflicts caused by overlapping attribution ranges. Summarize and update the corrected spatial distribution to generate a compressed node spatial distribution map.

5. The method for detecting network data asset space assets based on discrete random numbers according to claim 1, characterized in that, Based on the compressed node spatial distribution map, extract the weight distribution values and spatial coordinates of high-priority nodes, perform normalization calculation on the weight values, and adjust the sparse connection values among the nodes to generate a high-priority area sparse relationship map, including: S301: Based on the compressed node spatial distribution map, analyze the spatial positions and weight values of the nodes, compare and screen out the high-priority nodes one by one, and record the weight distribution values and corresponding spatial coordinates of the high-priority nodes as associated data to generate a high-priority node weight and coordinate table; S302: Based on the high-priority node weight and coordinate table, calculate the scale factor according to the weight distribution range, perform normalization processing on the weight values of each node, correct the sparse connection values of the nodes corresponding to the normalized weight values one by one, and update the connection relationship weights among the nodes according to the correction results to generate a normalized sparse connection adjustment table; S303: Based on the normalized sparse connection adjustment table, analyze the distribution of the sparse connections of high-priority nodes, verify and map the sparse connection states among the nodes in the network one by one, and convert the adjusted connection relationships into network structure visualization data to generate a high-priority area sparse relationship map.

6. The method for detecting network data asset space assets based on discrete random numbers according to claim 5, wherein The calculation formula for the corrected weight value after the normalization process is as follows in formula (1): Among them, W′ i represents the normalized correction 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 sparse connection nodes, represents the sum of the squared differences between all node weight values and the average value, represents the absolute difference between the node weight value and the average value.

7. The method for detecting spatial assets of network data assets based on discrete random numbers according to claim 1, wherein Based on the high-priority area sparse relationship map, extract the time series data of the nodes, calculate the node trajectory change values, and identify the change trend of the connection strength. Classify and summarize the node data according to the trajectory change range and the strength change relationship to generate a node dynamic change trajectory set, including: S401: Based on the sparse relationship graph of high-priority regions, parse the spatial position data and weight values of each node, extract the connection weights and states of the nodes at different time points one by one, organize the data in chronological order, and construct the time-series attribute information of the nodes to generate a node time-series data table; S402: Based on the node time-series data table, calculate the trajectory offset values between different time points of the nodes in the time series one by one, classify and analyze the offset directions and amplitudes, calculate the intensity change values according to the change of connection weights 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 intensity change table, analyze the relationship between the trajectory change value and the connection intensity change, compare the trajectory offset amplitude and the connection change trend of the nodes, classify the nodes into the trajectory change mode group, record the classification information, integrate the classification results into the aggregated data of dynamic change trajectories, and generate a set of node dynamic change trajectories.

8. The method for detecting spatial assets of network data assets based on discrete random numbers according to claim 7, wherein, The calculation formula for the intensity change value is as follows in Equation (2): Among them, represents the intensity change value of the node at time point t i and time point t j The strength change value between them, and respectively represent the connection weight values of the node at time point t i and time point t j The connection weight value of, and respectively represent the abscissa positions of the node at time point t i and time point t j The abscissa position of, and respectively represent the ordinate positions of the node at time point t i and time point t j The ordinate position of, t i and t j respectively represent the timestamps of the node at time points i and j, represents the absolute change in the connection weight of the node, represents the spatial distance between time points of the node, t j -t i represents the time interval.

9. The method for detecting network data asset spatial assets based on discrete random numbers according to claim 1, characterized in that Based on the set of node dynamic change trajectories, set a change range threshold, extract the spatial distribution areas of the nodes with larger trajectory change ranges, calculate the regional node association intensity, adjust the distribution range, classify the positions and connection characteristics of the nodes in the target area, and generate a node distribution map of the target area, including: S501: Based on the set of node dynamic change trajectories, parse the trajectory change range values of each node one by one, set a change range threshold, compare and screen according to the change range, extract the nodes with larger change ranges, record the spatial distribution coordinates of the nodes and the connection relationships with adjacent nodes, and generate a spatial distribution table of nodes with larger trajectory changes; S502: Based on the spatial distribution table of nodes with larger trajectory changes, analyze the connection relationships between nodes and the spatial coordinate data of neighboring nodes, calculate the association intensity between a node and its neighboring nodes, conduct a comparative analysis of the association intensity values, classify the nodes into the corresponding intensity range areas, and correct the node ranges with overlapping boundary distributions to generate a table of node association intensities in the target area; S503: Based on the table of node association intensities in the target area, parse the spatial position distributions and connection characteristics of the nodes in the target area one by one, classify and integrate the target area according to the node distribution rules, and visually organize the positions and connection characteristics of the nodes in the area to generate a node distribution map of the target area.

10. The method for detecting network data asset spatial assets based on discrete random numbers according to claim 1, wherein Based on the node distribution map of the target area, calculate the connection coverage range and density values between nodes, generate an initial detection path, dynamically adjust the path coverage range and connection relationships, and record the node relationships to generate a node detection path map of the target area, including: S601: Based on the node distribution map of the target area, extract the spatial coordinate and connection relationship data of the nodes, parse the connection ranges between nodes one by one, identify the coverage areas of the nodes and their neighboring nodes, calculate the node connection density values according to the node coverage areas and connection numbers, organize and form a data table of node connection and coverage characteristics, and generate a table of node connection coverage and density; S602: Based on the node connection coverage and density table, compare the overlapping coverage ranges between nodes one by one, sort the node connection paths according to the overlapping ratio of the coverage areas and the connection density values, screen the priorities of the connection paths and gradually arrange the path connection order to generate an initial detection path set; S603: Based on the initial detection path set, analyze the path coverage range and connection relationship, gradually adjust the coverage areas between paths, re-verify the connection characteristics of each node in the adjusted paths, and integrate and process the corrected path connection information to generate a node detection path map for the target area.

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