Method and device for processing wireless network coverage vulnerabilities, and computing equipment

Abstract

The embodiment of the invention provides a wireless network coverage vulnerability processing method and device and computing equipment, and relates to the technical field of communication, and the method comprises the steps: obtaining weak fingerprint data of each terminal in a coverage area collected by each wireless access device AP in a preset time period; the weak fingerprint data of any terminal is signal data of each AP collected by the terminal under a weak signal condition; determining a suspected coverage vulnerability according to the weak fingerprint data of each terminal; for each suspected coverage vulnerability, determining whether the suspected coverage vulnerability meets a filtering condition or not; and if the suspected coverage vulnerability meets the filtering condition, determining that the suspected coverage vulnerability does not belong to the wireless network coverage vulnerability. Through the method, the coverage comprehensiveness of the vulnerabilities can be effectively improved, and the accuracy of coverage vulnerability recognition is improved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method and apparatus for processing wireless network coverage vulnerabilities, a computing device, and a storage medium. Background Art

[0002] In modern society, people are increasingly dependent on wireless networks. The stability and transmission speed of wireless networks directly affect people's quality of life and work efficiency. However, due to various reasons, wireless network coverage loopholes still exist. Wireless network coverage loopholes refer to the situation in which the wireless network signal strength is insufficient in a certain area, resulting in the inability to provide normal wireless network services. This not only affects people's network experience, but may also affect some important network services, such as telemedicine and online education.

[0003] At present, wireless access devices AP are generally used to obtain the signal strength of each terminal device. If the signal strength of many terminals in a certain area is lower than a preset threshold, it is determined that there is a wireless network coverage loophole. However, misjudgment may occur during the detection process. Therefore, it is necessary to provide a method for handling wireless network coverage loopholes with better accuracy.

[0004] Based on this, the present application provides a method for processing wireless network coverage holes, which is used to improve the accuracy of coverage hole processing. Summary of the invention

[0005] The embodiment of the present application provides a method for processing a wireless network coverage hole, which is used to improve the accuracy of coverage hole processing.

[0006] In a first aspect, an embodiment of the present application provides a method for processing a wireless network coverage vulnerability, including: obtaining weak fingerprint data of each terminal within the coverage range collected by each wireless access device AP; the weak fingerprint data of any terminal is the signal data of each AP collected by the terminal under weak signal conditions; based on the weak fingerprint data of each terminal, determining a suspected coverage vulnerability; for each suspected coverage vulnerability, determining whether the suspected coverage vulnerability meets a filtering condition, if the suspected coverage vulnerability meets the filtering condition, determining that the suspected coverage vulnerability does not belong to a wireless network coverage vulnerability.

[0007] In the above implementation, the signal data of each AP collected under weak signal conditions within the coverage range, i.e., the weak fingerprint data of each terminal, is obtained; suspected coverage holes are determined based on the weak fingerprint data, and the suspected coverage holes are judged to determine whether they meet the filtering conditions, thereby eliminating the misjudgment of wireless network coverage holes caused by the filtering conditions. Compared with the current implementation, the suspected coverage holes are considered in time and space, and the factors that are prone to misjudgment are taken into account, which effectively ensures the comprehensiveness and accuracy of the coverage of the coverage holes.

[0008] In a possible implementation, it also includes obtaining weak fingerprint data of each terminal within the coverage range collected by each wireless access device AP; the weak fingerprint data of any terminal is the signal data of each AP collected by the terminal under weak signal conditions; according to the weak fingerprint data of each terminal and the problematic AP, a suspected coverage vulnerability is determined; the problematic AP is an AP whose signal strength is lower than a threshold among the APs associated with each terminal obtained through the cloud;

[0009] For each suspected coverage vulnerability, determine whether the suspected coverage vulnerability meets the filtering condition. If the suspected coverage vulnerability meets the filtering condition, determine that the suspected coverage vulnerability does not belong to the wireless network coverage vulnerability.

[0010] In the above implementation method, the signal data of each AP collected under weak signal conditions within the coverage range, that is, the weak fingerprint data of each terminal, is obtained; the suspected coverage loophole is determined based on the weak fingerprint data and the AP obtained from the cloud, and then it is judged whether the suspected coverage loophole meets the filtering conditions, so as to determine that the suspected coverage loophole does not belong to the wireless network coverage loophole. In other words, when determining the suspected coverage loophole, the suspected coverage loophole is determined based on the weak fingerprint data and the connected problematic AP, which is more accurate.

[0011] In a possible implementation manner, the filtering condition includes that collection times corresponding to the first coverage hole in at least two collection cycles belong to the same time range, and / or that the AP corresponding to the first coverage hole is an edge AP.

[0012] In the above implementation, the filtering condition includes two aspects, one is the time influencing factor, that is, judging the tidal phenomenon, that is, judging whether the corresponding collection moments in at least two collection cycles belong to the same time range, that is, the coverage loopholes that appear at the same time range belong to the tidal phenomenon, which is one of the factors that may cause the misjudgment of wireless network loopholes; the other is the spatial influencing factor, that is, judging whether the corresponding AP of the coverage loophole belongs to the edge AP. If so, the suspected coverage loophole does not belong to the wireless network coverage loophole. That is to say, if the suspected coverage loophole meets the filtering conditions, it does not belong to the wireless network coverage loophole. Taking the reasons that can affect the misjudgment into consideration, the wireless network coverage loophole can be effectively handled and the accuracy of its handling can be improved.

[0013] In one possible implementation, determining whether a suspected coverage vulnerability meets a filtering condition includes: determining suspected coverage vulnerabilities corresponding to different times based on weak fingerprint data of each terminal within the coverage range of each AP obtained at different times; if the difference between the collection times corresponding to any suspected coverage vulnerability in at least two collection cycles is greater than a preset time difference threshold, then the suspected coverage vulnerability meets the filtering condition.

[0014] In the above implementation method, weak fingerprint data of each terminal within the coverage of each AP at different times is obtained, and suspected coverage holes corresponding to different time periods are determined according to the obtained weak fingerprint data of each terminal. That is to say, if the difference between at least two acquisition times corresponding to the suspected coverage hole is greater than the preset time difference threshold, then the suspected coverage hole meets the filtering conditions and is a normal tidal phenomenon. That is to say, the terminal is in a weak signal state due to similar reasons, such as a large number of users using the network during rush hour, causing suspected coverage holes, thereby avoiding misjudgment and improving the accuracy of coverage hole processing.

[0015] In a possible implementation manner, whether the suspected coverage hole meets the filtering condition includes: if it is determined that the AP corresponding to the suspected coverage hole is an edge AP, then the suspected coverage hole meets the filtering condition.

[0016] According to the above implementation, it can be seen that when the corresponding AP of the suspected coverage vulnerability is an edge AP, the filtering condition is met, that is, the suspected coverage vulnerability occurs because the connected AP is an edge AP, which causes a misjudgment, thereby improving the accuracy of determining the wireless network vulnerability.

[0017] In one possible implementation, if the collection moments corresponding to the suspected coverage hole in at least two collection cycles do not belong to the same time range, determine whether the corresponding AP of the suspected coverage hole is an edge AP; if the corresponding AP of the suspected coverage hole is an edge AP, determine that the suspected coverage hole meets the filtering conditions; or, if the corresponding AP of the suspected coverage hole is not an edge AP, determine whether the collection moments corresponding to the suspected coverage hole in at least two collection cycles belong to the same time range; if the collection moments corresponding to the suspected coverage hole in at least two collection cycles belong to the same time range, determine that the suspected coverage hole meets the filtering conditions.

[0018] According to the above implementation, it can be known that the suspected coverage loophole meets the filtering condition when the corresponding collection moments in at least two collection cycles belong to the same time range, or the corresponding problematic AP of the suspected coverage loophole is an edge AP. That is to say, as long as one of them meets the judgment condition, it meets the filtering condition; on the contrary, if the corresponding collection moments in at least two collection cycles of the suspected coverage loophole do not belong to the same time range, it is further determined whether the corresponding AP of the suspected coverage loophole belongs to an edge AP. If it does not belong to an edge AP, the suspected coverage loophole does not meet the filtering condition, that is, the suspected coverage loophole neither belongs to an edge AP nor belongs to the same time range.

[0019] In a possible implementation, whether the AP corresponding to the suspected coverage hole meets the filtering conditions is determined in the following manner, including: determining the node characteristics of the AP corresponding to the suspected coverage hole and other APs; the node characteristics of any AP are used to represent the wireless network status of the AP; determining the node-to-node characteristics between the AP corresponding to the suspected coverage hole and other APs; wherein the node-to-node characteristics are used to represent the correlation between AP nodes based on terminal roaming data; determining that the AP corresponding to the suspected coverage hole is an edge AP based on the node characteristics and node-to-node characteristics of the AP corresponding to the suspected coverage hole and other APs.

[0020] In one possible implementation, the AP corresponding to the suspected coverage hole is determined to be an edge AP based on the node characteristics of the AP corresponding to the suspected coverage hole and the inter-node characteristics, including: constructing AP graph structure data based on the node characteristics of the AP corresponding to the suspected coverage hole and the inter-node characteristics; and determining that the suspected coverage hole corresponds to an edge AP through the AP graph structure data.

[0021] In the above implementation, when determining whether it is an edge AP, it is mainly determined based on the node characteristics of the corresponding AP and the node characteristics between other APs. Specifically, first determine the node characteristics of each AP used to represent the wireless network status of the AP, then determine the node characteristics between the corresponding AP and others based on the correlation between the AP nodes based on the terminal roaming data, construct the node characteristics of each AP and the node characteristics between other APs to construct AP graph structure data, and determine the edge AP in each AP based on the AP graph structure data. In other words, the above method is adopted to eliminate the phenomenon that affects the judgment of coverage holes in the spatial dimension, and improve the accuracy of coverage hole processing.

[0022] In a possible implementation, the attention coefficient between the AP corresponding to the suspected coverage hole and other APs is determined based on the node characteristics of the AP corresponding to the suspected coverage hole and the node characteristics of other APs, the node characteristics of the AP corresponding to the suspected coverage hole and the node characteristics of other APs; the fusion node characteristics of the AP corresponding to the suspected coverage hole are determined based on the attention coefficients of each adjacent AP; and the AP corresponding to the suspected coverage hole is determined to be an edge AP based on the fusion node characteristics.

[0023] In the above implementation, for the AP graph structure, the attention coefficient between the corresponding AP and other APs is determined by the node features of each AP and the node features between nodes, which is more helpful to capture the interaction relationship between AP nodes. After obtaining the attention coefficient, the fusion node features of the AP are determined according to the attention coefficient, that is, the original AP node features are updated to the new features of the AP node, that is, the fusion node features of the AP. Finally, the corresponding AP of the suspected coverage hole is determined as an edge AP according to the fusion node features of the AP, which avoids the weak signal introduced by the edge AP, causing the misjudgment of the coverage hole, and improves the accuracy of the coverage hole processing.

[0024] In a possible implementation, edge APs among APs are determined through AP graph structure data, including: inputting the AP graph structure data into a graph structure model to obtain a node feature vector of each AP; inputting the node feature vector of each AP into a classification model to obtain the corresponding AP of suspected coverage hole as an edge AP.

[0025] In the above implementation, when determining the edge APs in each AP based on the AP graph structure data, it is obtained based on the graph structure model and the classification model. That is, the AP graph structure data is first input into the graph structure model to obtain the node feature vectors of each AP, and then the obtained node feature vectors of each AP are input into the classification model to obtain the corresponding APs of each AP with suspected coverage holes as edge APs. The edge APs are determined using the model because the parameters in the model are obtained through training, which means that the edge APs obtained in the end are more accurate.

[0026] In a possible implementation, the node characteristics include AP mutual scanning data, roaming data and online and offline data of each terminal under the AP coverage; the inter-node characteristics are the roaming times of the terminals between nodes and / or the roaming directions of the terminals.

[0027] In the above implementation, the node characteristics of the AP are determined based on the AP mutual scanning data, the roaming data of each terminal under the AP coverage, and the online and offline data. The inter-node characteristics of the AP are determined based on the number of roaming times of the terminal between nodes and / or the roaming direction of the terminal. By determining the node characteristics and inter-node characteristics through the above data, the real characteristic relationship of the AP can be obtained more effectively, so that the subsequent calculation of whether the coverage gap is determined is more accurate.

[0028] In a possible implementation, suspected coverage loopholes are determined based on the weak fingerprint data of each terminal, including: clustering the weak fingerprint data of each terminal based on a density parameter to obtain first clusters and first outliers; each first outlier is weak fingerprint data that does not belong to any first cluster; based on the adjusted density parameter, clustering the first clusters and the first outliers to obtain second clusters and second outliers until a clustering termination condition is met; determining each cluster that meets the clustering termination condition as a suspected coverage loophole; wherein the density parameter includes a cluster domain size and a minimum number of points in a cluster; and the clustering process is based on the cosine distance of the weak fingerprint data.

[0029] In the above implementation, the weak fingerprint data of each terminal is clustered by density parameters to determine suspected coverage loopholes. That is, the weak fingerprint data of each terminal is clustered based on the density parameters determined by the cluster domain size and the minimum number of points in the cluster to obtain each first cluster and each first abnormal point, and then clustered again according to the adjusted density parameters to determine whether each second cluster and each second abnormal point after clustering meets the clustering termination condition. If not, the clustering process continues. If it does, each cluster that meets the clustering termination condition is determined as a suspected coverage loophole, which can achieve efficient and accurate processing of coverage loophole areas with high accuracy and low false alarm rate. Among them, the clustering process is based on the cosine distance of the weak fingerprint data, which makes it more robust.

[0030] In a possible implementation, the clustering termination condition is determined based on the comparison of outliers in at least two clustering results. If the number of outliers is equal, the clustering termination condition is met, otherwise, clustering continues.

[0031] In the above implementation, whether clustering is terminated is judged by calculating each abnormal point in the results of at least two clustering results, that is, the termination condition is determined, and the final cluster is determined according to the termination condition, and then the suspected coverage loophole is obtained, thereby improving the accuracy of determining the suspected coverage loophole and reducing the probability of misjudgment.

[0032] In a possible implementation, before determining that the suspected coverage vulnerability is a wireless network coverage vulnerability, it also includes: determining whether the number of terminals meets a threshold; if so, finally determining that the suspected coverage vulnerability is a wireless network coverage vulnerability.

[0033] In the above implementation method, before determining whether it is a wireless network coverage vulnerability, it is also necessary to confirm the number of terminals in the suspected coverage vulnerability at this time. If the number meets the threshold, it is a wireless network coverage vulnerability. This can effectively avoid the misjudgment of wireless network coverage vulnerabilities caused by the terminal itself, and ensure the accuracy of the determination of wireless network coverage vulnerabilities.

[0034] In a second aspect, an embodiment of the present application provides a device for processing a wireless network coverage vulnerability, including:

[0035] An acquisition unit is used to acquire weak fingerprint data of each terminal within the coverage area collected by each wireless access device AP; the weak fingerprint data of any terminal is the signal data of each AP collected by the terminal under weak signal conditions;

[0036] A determination unit, configured to determine a suspected coverage vulnerability according to the weak fingerprint data of each terminal;

[0037] The determination unit is further configured to determine, for each suspected coverage vulnerability, whether the suspected coverage vulnerability meets a filtering condition; if the suspected coverage vulnerability meets the filtering condition, determine that the suspected coverage vulnerability does not belong to a wireless network coverage vulnerability.

[0038] In a possible implementation, the acquisition unit is further used to acquire weak fingerprint data of each terminal within the coverage range collected by each wireless access device AP; the weak fingerprint data of any terminal is the signal data of each AP collected by the terminal under weak signal conditions; based on the weak fingerprint data of each terminal and the problem AP, a suspected coverage vulnerability is determined; the problem AP is an AP whose signal strength is lower than a threshold among the APs associated with each terminal obtained from the cloud;

[0039] The determination unit is further configured to determine, for each suspected coverage vulnerability, whether the suspected coverage vulnerability meets a filtering condition, and if the suspected coverage vulnerability meets the filtering condition, determine that the suspected coverage vulnerability does not belong to a wireless network coverage vulnerability.

[0040] In a possible implementation manner, the filtering condition includes that collection times corresponding to the first coverage hole in at least two collection cycles belong to the same time range, and / or that the AP corresponding to the first coverage hole is an edge AP.

[0041] In a possible implementation, the determining unit is specifically configured to determine suspected coverage holes corresponding to different times based on weak fingerprint data of each terminal within the coverage range of each AP acquired at different times;

[0042] The determining unit is specifically configured to determine that if any suspected coverage hole meets the filtering condition, the suspected coverage hole meets the filtering condition if the difference between the collection moments corresponding to at least two collection cycles is implemented in a manner that meets the preset time difference threshold.

[0043] In a possible implementation manner, the determining unit is further specifically configured to: if it is determined that the AP corresponding to the suspected coverage hole is an edge AP, then the suspected coverage hole meets the filtering condition.

[0044] In one possible implementation, the determination unit is specifically used to determine whether the AP corresponding to the suspected coverage vulnerability is an edge AP if the collection moments corresponding to the suspected coverage vulnerability in at least two collection cycles do not belong to the same time range; if the AP corresponding to the suspected coverage vulnerability is an edge AP, determine that the suspected coverage vulnerability meets the filtering conditions; or, if the AP corresponding to the suspected coverage vulnerability is not an edge AP, determine whether the collection moments corresponding to the suspected coverage vulnerability in at least two collection cycles belong to the same time range; if the collection moments corresponding to the suspected coverage vulnerability in at least two collection cycles belong to the same time range, determine that the suspected coverage vulnerability meets the filtering conditions.

[0045] The determining unit is specifically used to determine the node characteristics of the AP corresponding to the suspected coverage hole and other APs; the node characteristics of any AP are used to represent the wireless network status of the AP;

[0046] The determining unit is specifically configured to determine the inter-node characteristics between the AP corresponding to the suspected coverage hole and the other APs; wherein the inter-node characteristics are used to represent the correlation between AP nodes based on terminal roaming data;

[0047] The determining unit is specifically configured to determine that the AP corresponding to the suspected coverage hole is an edge AP according to the node characteristics of the AP corresponding to the suspected coverage hole and the other APs and the inter-node characteristics.

[0048] In a possible implementation, the determination unit is specifically configured to construct AP graph structure data according to node characteristics of the AP corresponding to the suspected coverage hole and the inter-node characteristics; and determine that the suspected coverage hole corresponds to an edge AP through the AP graph structure data.

[0049] In a possible implementation, the determining unit is specifically configured to determine the attention coefficient between the AP corresponding to the suspected coverage hole and the other APs according to the node characteristics of the AP corresponding to the suspected coverage hole and the node characteristics of the other APs, the node characteristics of the AP corresponding to the suspected coverage hole and the node characteristics of the other APs; determine the fusion node characteristics of the AP corresponding to the suspected coverage hole according to the attention coefficients of each adjacent AP;

[0050] The determining unit is specifically configured to determine, according to the fusion node feature, that the AP corresponding to the suspected coverage hole is an edge AP.

[0051] In a possible implementation, the determining unit is further specifically configured to input the AP graph structure data into a graph structure model to obtain a node feature vector of each AP;

[0052] The determining unit is further specifically configured to input the node feature vector of each AP into a classification model to obtain that the AP corresponding to the suspected coverage hole is an edge AP.

[0053] In a possible implementation, the node characteristics include AP mutual scanning data, roaming data and online and offline data of each terminal under the coverage of the AP;

[0054] The inter-node feature is the roaming times of the terminal between the nodes and / or the roaming direction of the terminal.

[0055] In a possible implementation, the determining unit is specifically configured to perform clustering processing on the weak fingerprint data of each terminal based on a density parameter to obtain each first cluster and each first abnormal point; each first abnormal point is weak fingerprint data that does not belong to any first cluster;

[0056] The determining unit is specifically configured to perform clustering processing on the first clusters and the first outliers based on the adjusted density parameter to obtain the second clusters and the second outliers until a clustering termination condition is met;

[0057] The determination unit is specifically used to determine each cluster that meets the clustering termination condition as a suspected coverage hole; wherein the density parameter includes the cluster domain size and the minimum number of points in the cluster; and the clustering process is based on the cosine distance of the weak fingerprint data.

[0058] In a possible implementation, the determination unit is specifically used to determine the clustering termination condition based on the comparison of abnormal points in at least two clustering results. If the number of abnormal points is equal, the clustering termination condition is met, otherwise, clustering continues.

[0059] In a possible implementation, the determining unit is further configured to determine whether the number of terminals meets a threshold value, and if the number of terminals meets the threshold value, it is ultimately determined that the suspected coverage loophole is a wireless network coverage loophole.

[0060] In a third aspect, the present application further provides a computing device, including:

[0061] A memory for storing program instructions;

[0062] The processor is used to call the program instructions stored in the memory and execute the method for handling the wireless network coverage vulnerability according to the obtained program.

[0063] In a fourth aspect, the present application also provides a computer-readable non-volatile storage medium, comprising computer-readable instructions, which, when a computer reads and executes the computer-readable instructions, enables the computer to execute the above-mentioned method for processing wireless network coverage vulnerabilities.

[0064] The technical effects that can be achieved in any of the second to fourth aspects mentioned above can refer to the description of the beneficial effects in the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0066] Figure 1 A framework diagram of a wireless network coverage vulnerability processing system provided in an embodiment of the present application;

[0067] Figure 2 A flowchart of a method for processing a wireless network coverage vulnerability provided in an embodiment of the present application;

[0068] Figure 3 Provide a trigger flow chart for obtaining weak fingerprint data for the embodiment of the present application;

[0069] Figure 4 A schematic diagram of a process for determining a suspected coverage vulnerability is provided for an embodiment of the present application;

[0070] Figure 5 Provide a schematic diagram of an AP graph structure for an embodiment of the present application;

[0071] Figure 6 A schematic diagram of determining edge APs among APs is provided for an embodiment of the present application;

[0072] Figure 7 A schematic diagram of the overall process of determining coverage vulnerabilities in an embodiment of the present application;

[0073] Figure 8 A schematic diagram of a device for processing a wireless network coverage vulnerability provided in an embodiment of the present application;

[0074] Fig. 9 A computing device is also provided for the embodiment of the present application. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] The application scenarios described in the embodiments of the present invention are intended to more clearly illustrate the technical solutions of the embodiments of the present invention, and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention. It is known to those skilled in the art that with the emergence of new application scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0077] With the development of society, wireless networks have become an indispensable part of people's daily life and work. Whether it is online learning, remote office, social media, or online entertainment, they all rely on the support of wireless networks. However, due to various reasons, the problem of wireless network coverage loopholes still exists in many places. This not only affects people's network experience, but may also affect some important network services, such as telemedicine and online education. Wireless network coverage loopholes refer to the situation where the wireless network signal strength is insufficient in a certain area, resulting in the inability to use wireless network services normally. This situation may cause slow network connection speed, unstable data transmission, or even inability to connect to the network. The current processing method is often to obtain the signal strength of the client through the AP device, compare the signal strength of each AP position and the client with the preset signal strength threshold to determine the suspected coverage loophole, and determine the number of clients at the corresponding position of the suspected coverage loophole, and determine the coverage loophole according to the determination result. However, in the process of processing coverage loopholes, it is not considered that it is processed into a coverage loophole due to misjudgment, such as tidal phenomenon or edge AP. The tidal phenomenon refers to the increase in user demand for wireless network use during rush hour, resulting in a slower network speed. For example, during rush hour after get off work, when users leave the workplace, their mobile phones will continue to associate with the company or home AP, resulting in a continuously low signal. These weak signal events are not caused by coverage loopholes but by user habits. Therefore, if there is a lack of analysis of the tidal effect, it will lead to misjudgment of coverage loopholes. The edge AP refers to a wireless access point located at the edge of the network coverage. It is usually the outermost device in the network or the device farthest away from the core AP, such as near the edge of the building wall or the entrance. When the user has left the work or living area, but is close to the building wall such as the parking lot or the door, it will still be associated with the edge AP, which will also introduce weak signal events and cause misjudgment of coverage loopholes.

[0078] Based on this, the processing of coverage holes is bound to be inaccurate. The embodiment of the present application provides a method for processing wireless network coverage holes, which is used to improve the accuracy of processing coverage holes.

[0079] Figure 1A framework diagram of a wireless network coverage vulnerability processing system provided in an embodiment of the present application, the system includes a device AP 101 and a terminal 102. The device AP 101 is provided with an AP access point, i.e., an AP node, which is used to provide a network for the terminal 102 to achieve interaction. The terminal 102 is used to access the AP node to achieve network services. Figure 1 Only one device AP is shown as an example, and the specific number is not limited. The device AP101 and the terminal 102 are connected by wireless, and other connection methods can also be used without specific limitation.

[0080] The method for dealing with specific wireless network coverage vulnerabilities is shown in Question 2. Figure 2 A flowchart of a method for processing a wireless network coverage vulnerability provided in an embodiment of the present application includes:

[0081] S201. Acquire weak fingerprint data of each terminal within the coverage area collected by each wireless access device AP; the weak fingerprint data of any terminal is the signal data of each AP collected by the terminal under weak signal conditions.

[0082] In order to achieve the problem of no coverage loopholes within the coverage range of the wireless network, it is necessary to deal with the coverage loopholes. Because the criterion for judging the coverage loopholes is the signal strength, it is necessary to obtain the weak fingerprint data of each terminal within the coverage range collected by each wireless access device AP, that is, the signal data of each AP collected by the terminal under weak signal conditions. Among them, the weak signal situation means that the signal strength of each AP perceived by the terminal is less than the threshold, then the terminal is considered to be in a weak signal situation, and the threshold can be an empirical value set according to actual needs. Figure 3 As shown, Figure 3 A trigger flow chart for obtaining weak fingerprint data is provided for the embodiment of the present application, that is, the wireless access device AP finds that the uplink signal of the terminal is less than the threshold set according to actual needs, and sends a Request signal to the terminal based on the protocol. After the terminal receives the request signal, it will reply the downlink signal of each AP under the terminal's perspective to the wireless access device AP in the form of Report. In this process, because the signal is volatile, it is necessary to send the Request signal multiple times and reply in the form of Report to obtain the weak fingerprint data of each terminal.

[0083] In an example, suppose there are three APs, namely AP1, AP2, and AP3. At time t1, it is detected that the uplink signal of the terminal is less than the threshold, triggering the 802.11K protocol to send a request signal, and the terminal returns the 802.11K protocol Report, and the weak signal data at t1 is obtained: AP1: -78; AP2: -80; AP3: -76; where the values ​​(-78, -80, -76) represent the signal strength. Similarly, at t2 and t3, the interaction is still carried out, and the weak signal data at t2 is obtained: AP1: -82; AP2: -; AP3: -80; the weak signal data at t3 is obtained: AP1: -90; AP2: -; AP3: -83; and the final weak fingerprint data is obtained by superimposing them in chronological order: AP1: -83; AP2: -80; AP3: -79.

[0084] It should be noted that the above-mentioned superposition adopts addition and averaging, and other superposition methods can also be used, which is not the only limitation here; the above-mentioned order adopts time order, and other orders can also be used, which is not the only limitation here.

[0085] S202. Determine suspected coverage vulnerabilities based on the weak fingerprint data of each terminal.

[0086] After the weak fingerprint data of each terminal is determined in the above step S201, a suspected coverage vulnerability is determined based on the weak fingerprint data of each terminal. As the name implies, a suspected coverage vulnerability is initially determined to be a possible coverage vulnerability.

[0087] Specific as Figure 4 As shown, Figure 4 A schematic flow chart of a method for determining a suspected coverage vulnerability is provided for an embodiment of the present application, including:

[0088] S401. Clustering the weak fingerprint data of each terminal based on a density parameter to obtain first clusters and first outliers; each first outlier is weak fingerprint data that does not belong to any first cluster.

[0089] Clustering refers to clustering and merging adjacent similar classification areas using morphological operators. Cluster refers to the name of the area to be clustered and merged. In this application, the fingerprint data of each terminal is clustered according to the density parameter to obtain each first cluster and each first abnormal point of the weak fingerprint data that does not belong to any first cluster.

[0090] In an example, assuming that there are 100 weak fingerprint data, these 100 weak fingerprint data are clustered to obtain 15 clusters and 25 first abnormal points.

[0091] S402. Based on the adjusted density parameter, clustering is performed on each first cluster and each first outlier point to obtain each second cluster and each second outlier point, until a clustering termination condition is met.

[0092] After obtaining the first clusters and first outliers after the first clustering process, clustering needs to be performed again. The reason is that after the first clustering process, it needs to be determined according to the clustering termination condition, and the clustering termination condition requires comparison of the clustering results twice. Therefore, after obtaining the first clusters and first outliers, clustering is performed again based on the adjustment of density parameters to obtain the second clusters and second outliers. The second outliers are compared with the first outliers. If there is no change, the clustering termination condition is met. If not, clustering continues until the clustering termination condition is met.

[0093] Continuing with the above example, the 15 clusters obtained by the first clustering process are averaged to obtain the first average cluster. The first average cluster and the first outlier are clustered based on the adjusted density parameter to obtain 13 clusters and 25 second outliers after clustering. At this time, the first outlier is equal to the second outlier, and the clustering termination condition is met.

[0094] S403. Determine each cluster that meets the clustering termination condition as a suspected coverage hole; wherein the density parameters include the cluster domain size and the minimum number of points in the cluster; and the clustering process is based on the cosine distance of the weak fingerprint data.

[0095] Specifically, each cluster that meets the cluster termination condition in the above step S402 is determined as a suspected coverage vulnerability. Among them, the density parameter is determined based on the cluster domain size ε and the minimum number of points in the cluster MinPts, and the initial value is set according to the actual scenario, that is, the cluster domain size ε is set according to the artificial distribution of weak fingerprint data on the time axis, and the minimum number of points in the cluster MinPts is set as an empirical value. The adjusted density parameter is further re-set based on the density parameter according to the distribution of each first cluster and each first abnormal point on the time axis, which can also be understood as an optimization process. That is to say, by continuously updating each cluster and each abnormal point as the initial value setting of the density parameter. The weak fingerprint data to be acquired is set based on the cluster domain size ε and the minimum number of points in the cluster MinPts based on actual needs, that is, the principle idea of ​​clustering processing is to use the cluster domain size ε as the zoning range, that is, the zoning range reaches the value of the minimum number of points in the cluster MinPts to form a cluster. It should be noted that the cluster size ε is determined by the cosine value, that is, the smaller the angle, the more similar the features are, and the stronger the correlation of the weak fingerprint data.

[0096] In an example, ε is set to 0.2 based on the cosine distance, MinPts is set to 3, and the cosine distance value of each weak fingerprint data is determined. That is, if there are fingerprint data with a cosine distance less than or equal to the set threshold, and the minimum number of points in the cluster involved, MinPts, is greater than 3, then these fingerprint data with similar cosine distances can be clustered into one cluster. It should be noted that the initial value of the cosine distance set to 0.2 is an empirical value.

[0097] The present application also provides another method for determining suspected coverage loopholes in step S202, that is, determining suspected coverage loopholes based on weak fingerprint data and problem APs; the problem APs are APs whose signal strength is lower than a threshold value among the APs associated with each terminal obtained through the cloud. The method for determining the problem AP is to detect the network experience status of each terminal in the cloud, and obtain weak fingerprint data and problem APs based on the network experience status, that is, the signal strength of the corresponding AP connected to the terminal is poor, that is, the AP whose signal strength is lower than the threshold value among the APs, and determine suspected coverage loopholes based on weak fingerprint data and problem APs. In short, after the suspected coverage loophole is determined, it is possible to know which corresponding AP is connected at this time, so as to prepare for the subsequent judgment of whether it is a wireless network loophole.

[0098] S203. For each suspected coverage vulnerability, determine whether the suspected coverage vulnerability meets the filtering condition; if the suspected coverage vulnerability meets the filtering condition, determine that the suspected coverage vulnerability does not belong to the wireless network coverage vulnerability.

[0099] When it is determined to be a suspected coverage vulnerability, the suspected vulnerability is judged mainly by judging according to the filtering conditions. If the suspected coverage vulnerability meets the filtering conditions, it is determined that the suspected coverage vulnerability does not belong to the wireless network coverage vulnerability. The filtering conditions are further screened according to the time dimension and the space dimension. First, from the time dimension, it is judged whether the suspected coverage vulnerability belongs to a tidal phenomenon; second, from the space dimension, it is judged whether the corresponding AP of the suspected coverage vulnerability is an edge AP. If the suspected coverage vulnerability belongs to a tidal phenomenon, that is, the corresponding collection moments in at least two collection cycles belong to the same time range, and / or, the corresponding AP of the suspected coverage vulnerability belongs to an edge AP, that is, the filtering conditions are determined according to the above two factors, then the suspected coverage vulnerability obtained at this time is a wireless network coverage vulnerability, which effectively improves the accuracy of coverage vulnerability processing.

[0100] In one example, there is a suspected coverage vulnerability M, and the collection times are 12 o'clock and 13 o'clock respectively. At this time, 12 o'clock and 13 o'clock both belong to the time range of 11 o'clock-13 o'clock, that is, they belong to the same time range, so it can be determined that it meets one of the factors of the filtering condition.

[0101] In addition to the above considerations of misjudgment in the time and space dimensions, the number of terminals also needs to be taken into account in order to effectively avoid misjudgment caused by a terminal encountering a problem, that is, not due to network problems, but to the terminal itself. Therefore, in addition to this, it is also necessary to obtain whether the number of terminals meets the threshold. If it meets the threshold, it is finally determined that the suspected coverage vulnerability is a wireless network coverage vulnerability. Among them, the threshold is set as an empirical value.

[0102] In an example, it is assumed that there are suspected coverage holes A, B, and C. After judging the suspected coverage holes A, B, and C, it is found that the suspected coverage holes A and B are neither always-appearing suspected coverage holes nor belong to edge APs. Therefore, the suspected coverage holes A and B are wireless network coverage holes.

[0103] Among them, there are various implementation methods for determining whether a suspected coverage vulnerability meets the filtering conditions. The first implementation method is provided below, that is, after determining the suspected coverage vulnerability, it is determined whether it is a tidal phenomenon. If so, it meets the filtering conditions, and there is no need to judge other conditions, that is, based on the weak fingerprint data of each terminal within the coverage range of each AP obtained at different times, the suspected coverage vulnerabilities corresponding to different times are determined; if the difference in the collection time corresponding to any suspected coverage vulnerability in at least two collection cycles is greater than the preset time difference threshold, then the suspected coverage vulnerability meets the filtering conditions.

[0104] The preset time difference threshold is set according to the actual experience value, that is to say, the loopholes that appear within the preset time difference threshold are suspected loopholes caused by tidal phenomena. When defining the preset threshold specifically, it is generally in hours. Of course, other units can also be set, and there is no unique limitation here. For example, that is to say, if the difference between at least two acquisition moments corresponding to the first coverage loophole is greater than the preset time difference threshold, it does not belong to a tidal phenomenon. The so-called tidal phenomenon refers to the suspected coverage loophole obtained by subsequent calculation of weak fingerprint data that appears due to the increase in users' demand for using wireless networks, resulting in a slowdown in network speed, such as during rush hour. In other words, the weak fingerprint data contains time characteristics, so the time distribution of the weak fingerprint data can be known, thereby judging whether the coverage loophole is caused by a tidal phenomenon.

[0105] In one example, the preset time period is set to one week, one day is used as a collection cycle, and the time is set to 0 to 24 o'clock. Assuming that the preset time difference threshold is defined as 1h, if the collection time of the suspected coverage vulnerability is 8 o'clock on Monday and 8 o'clock on Tuesday, the difference between the two collection times is 8-8=0, which is less than the preset time difference threshold, it belongs to a tidal phenomenon, that is, the suspected coverage vulnerability meets the filtering conditions, and the suspected coverage vulnerability at this time does not belong to a wireless network coverage vulnerability. Similarly, if the two collection times of the suspected coverage vulnerability are 9 o'clock on Monday and 12 o'clock on Tuesday, the difference between the two collection times is 12-9=3, which is greater than the preset time difference threshold, it does not belong to a tidal phenomenon, that is, the suspected coverage vulnerability at this time belongs to a wireless network coverage vulnerability.

[0106] It should be noted that the above example is for the suspected coverage hole collection time and only takes two as an example. In order to reduce the error, three or more moments can also be selected. Examples are not given one by one here.

[0107] In the above-mentioned first embodiment, the difference between at least two acquisition moments corresponding to the first coverage hole is equal to the preset time difference threshold as a single filtering condition for single-aspect judgment. It is also possible to just judge whether the corresponding AP of the suspected coverage hole is an edge AP. The second implementation method is provided below, that is, if the corresponding AP of the suspected coverage hole is determined to be an edge AP, then the suspected coverage hole meets the filtering condition. When judging whether the corresponding AP of the suspected coverage hole is an edge AP as the only filtering condition, that is, after determining the suspected coverage hole, judging whether the corresponding AP of the suspected coverage hole is an edge AP, if it is an edge AP, it meets the filtering condition, and no other conditions need to be judged.

[0108] That is, determine whether the corresponding AP of the coverage hole is an edge AP, where the corresponding AP is a suspected coverage hole determined based on weak fingerprint data, or a suspected coverage hole determined based on weak fingerprint data and a problem AP, and the method for determining the problem AP is as shown above in this application.

[0109] That is to say, after obtaining the suspected coverage hole, determine whether the corresponding AP of the suspected coverage hole is an edge AP. If so, it meets the filtering conditions. There are also various ways to determine the edge AP. Taking one implementation as an example, the node characteristics of each AP are determined; the node characteristics of any AP are used to represent the wireless network status of the AP; the node characteristics between each AP are determined; wherein the node characteristics are used to represent the correlation between AP nodes based on terminal roaming data; AP graph structure data is constructed based on the node characteristics of each AP and the node characteristics between each AP; the edge AP in each AP is determined through the AP graph structure data.

[0110] Roaming data refers to the relevant data of the terminal roaming between different APs in a wireless network. When determining whether any AP is an edge AP, it is determined based on the node characteristics of the AP and the node characteristics between the APs. The node characteristics of each AP and the node characteristics between the APs are taken into account, which effectively ensures the accuracy of the correlation. Specifically, first determine the wireless network status of each AP, that is, the node characteristics of the AP; and the node characteristics of the AP include AP mutual scanning data, roaming data of each terminal under the coverage of the AP, and online and offline data; after determining the node characteristics of the AP, determine the correlation of the terminal roaming data between the APs, that is, the node characteristics between the APs; and the node characteristics between the APs are the number of roaming times of the terminal between the nodes and / or the roaming direction of the terminal. After determining the node characteristics of the AP and the node characteristics between the APs, construct the AP graph structure data, and determine the edge AP in each AP according to the AP graph structure data. Among them, the node characteristics of the above-mentioned APs are not fixedly restricted, and can be the data in the above-mentioned embodiment or other data.

[0111] like Figure 5 As shown, Figure 5 A schematic diagram of an AP graph structure is provided for an embodiment of the present application. Figure 5 There are five APs in the wireless network, as well as the edge features between the APs and other APs, that is, roaming data. The larger the edge feature, the more frequent the roaming times between the two APs, and the closer the relationship between the APs. Figure 5 The node characteristics of an AP include AP mutual scanning data. The so-called AP mutual scanning data refers to the signal strength data scanned by APs, which is divided into 5G frequency band (5G_rssi) and 2.4G frequency band (2.4G_rssi), and also includes the number of times the AP is scanned (Scaned_times); in addition to AP mutual scanning data, it also involves roaming data, that is, the signal strength of the terminal roaming into the AP (Roam_in_rssi) and the signal strength of the terminal roaming out of the AP (Roam_out_rssi); and online and offline data, that is, the number of times the AP is the first AP associated with the terminal (First Online_count), the number of times the AP is the last AP associated with the terminal (Last Offline_count), and the number of terminals associated with the AP (Sta_count).

[0112] In the above, the determination of the edge APs in each AP based on the construction of AP graph structure data is mainly obtained based on the graph structure model and the classification model. Specifically, the AP graph structure data is first input into the graph structure model to obtain the node feature vector of each AP; secondly, the node feature vector of each AP is input into the classification model to obtain the edge APs in each AP. Among them, the output of the classification model is a two-dimensional vector (X, Y), X represents the probability that the node belongs to the first category, and Y represents the probability that it belongs to the second category. By selecting the larger option, the type of the node is determined, that is, the edge APs in each AP are obtained.

[0113] In an example, Figure 6 As shown, Figure 6 A schematic diagram of determining edge APs among APs is provided for an embodiment of the present application. AP graph structure data is input into a graph attention model, and calculation and output are performed in the graph attention model, and the output is a node feature vector of each AP; the node feature vector of each AP is input into a classification model to obtain a cross entropy loss function. Based on the cross entropy loss function, the parameters of the classification model are trained, that is, the final output is (X, Y), and it is pre-set that X represents an edge AP and Y represents a non-edge AP. Assuming that X is 0.6 and Y is 0.4 at this time, X>Y, then the final output is X, that is, the AP is an edge AP.

[0114] It should be noted that the graph structure model can be different types of models and the classification model can also be different types of models. In the embodiment of the present application, a binary classifier is used for example. The embodiment of the present application takes the graph attention model as an example, and other models can also be used, which are not specifically limited here.

[0115] There are also various processing principles based on the internal model. The following takes the principle of the graph attention model as an example. For any AP in the AP graph structure data, the attention coefficient between AP and adjacent APs is determined based on the node characteristics between AP and adjacent APs, the node characteristics of AP and the node characteristics of adjacent APs; the fusion node characteristics of AP are determined based on the attention coefficients of each adjacent AP; and the edge AP in each AP is determined based on the fusion node characteristics of each AP.

[0116] The AP graph structure data contains the node features between AP and adjacent APs, the node features of AP, and the node features of adjacent APs. These three features are used together to determine the attention coefficient between AP and adjacent APs. The attention coefficient can well represent the relationship between nodes. After obtaining the attention coefficient, the fusion node features of AP are determined according to the attention coefficients of each adjacent AP, that is, the node features of AP are updated to obtain the fusion node features of AP. According to the fusion node features of each AP, the edge AP in each AP is determined.

[0117] In an example, assuming that there are AP1, AP2, and AP3, when calculating the fused node feature of AP1, first obtain the attention coefficient 1 and attention coefficient 2 of AP1 and AP2 based on the node features between AP1 and AP2, the node features between AP1 and AP3, and the node features of AP1, AP2, and AP3, determine the fused node feature m of AP1 based on the attention coefficient 1 and the attention coefficient 2, and determine whether AP1 is an edge AP based on the fused node feature m.

[0118] In the above-mentioned first embodiment, whether the corresponding collection moments of the first coverage loophole in at least two collection cycles belong to the same time range is used as a single filtering condition for a single aspect judgment. The second implementation method is to judge whether the corresponding AP of the suspected coverage loophole is an edge AP. When both judgments are considered, in the first embodiment, when the corresponding collection moments of the first coverage loophole in at least two collection cycles do not belong to the same time range, that is, the suspected coverage loophole does not conform to the tidal phenomenon, it is necessary to continue to judge according to the second judgment condition, that is, to judge whether the corresponding AP of the suspected coverage loophole belongs to an edge AP. If it does not belong to an edge AP, it does not meet the filtering condition. If the corresponding collection moments in at least two collection cycles belong to the same time range, that is, it meets the filtering condition, it can also continue to judge whether the suspected coverage loophole is an edge AP, increasing the accuracy of its judgment, that is, the suspected coverage loophole neither conforms to the tidal phenomenon nor is it an edge AP, that is, a wireless coverage loophole, so that it can be determined more accurately.

[0119] It should be noted that after determining the suspected coverage hole, it is possible to simultaneously determine whether the corresponding collection moments in at least two collection cycles belong to the same time range and whether the suspected coverage hole is an edge AP. It is also possible to first determine whether the corresponding collection moments in at least two collection cycles belong to the same time range, and then determine whether they are all edge APs. It is also possible to first determine whether the suspected coverage hole is an edge AP, and then determine whether the corresponding collection moments of the suspected coverage hole in at least two collection cycles belong to the same time range. The order is not uniquely limited.

[0120] like Figure 7 As shown, Figure 7 The overall process diagram for determining coverage vulnerabilities in the embodiment of the present application is as follows:

[0121] The cloud periodically collects relevant data of the wireless network, obtains weak fingerprint data of each terminal within the coverage range collected by each wireless access device AP based on the relevant data, obtains suspected coverage loopholes based on clustering processing of the weak fingerprint data, and determines whether the suspected loopholes meet the filtering conditions. If the suspected coverage loopholes meet the filtering conditions, it is determined that the suspected coverage loopholes do not belong to wireless network coverage loopholes, that is, whether there is a tidal phenomenon, that is, the corresponding collection moments in at least two collection cycles belong to the same time range implementation mode. If so, it belongs to the tidal phenomenon, and then continues to determine whether the AP is an edge AP. If so, the number of terminals at this time is obtained to determine whether the number of terminals reaches a threshold. If it reaches the threshold, it is determined that the suspected coverage loophole does not belong to the wireless network coverage loophole, that is, the misjudgment caused by the tidal phenomenon and the edge AP is excluded. The judgment of the number of terminals is that when the tidal phenomenon and the loopholes that still appear under the edge AP are excluded, it is necessary to consider that multiple terminals under an AP have suspected coverage loopholes, then there is a wireless network coverage loophole, avoiding the existence of misjudgment. Among them, the judgment of the edge AP is to input the AP graph structure data constructed according to the node characteristics and inter-node characteristics of the AP into the graph structure model and the classification model to obtain the type of AP, that is, whether it is an edge AP.

[0122] Another implementation method is also provided in the present application as follows: the cloud periodically collects relevant data of the wireless network, obtains weak fingerprint data of each terminal within the coverage range collected by each wireless access device AP based on the relevant data, determines whether the AP connected to each terminal is a problem AP, determines that the AP connected to each terminal is a problem AP, and obtains suspected coverage vulnerabilities based on the problem AP and the weak fingerprint data, wherein the problem AP is the network usage status of each terminal obtained by the cloud, and obtains an AP whose signal strength is lower than the signal threshold based on the network usage status. Check whether the suspected vulnerability meets the filtering conditions. If the suspected coverage vulnerability meets the filtering conditions, it is determined that the suspected coverage vulnerability does not belong to the wireless network coverage vulnerability, that is, whether there is a tidal phenomenon, that is, the corresponding collection time in at least two collection cycles belongs to the same time range implementation method. If so, it belongs to the tidal phenomenon, and then continue to judge whether the AP is an edge AP. If so, then obtain the number of terminals at this time to determine whether the number of terminals reaches the threshold. If it reaches the threshold, it is determined that the suspected coverage vulnerability does not belong to the wireless network coverage vulnerability, that is, the misjudgment caused by the tidal phenomenon and the edge AP is excluded. The judgment of the number of terminals is to exclude the tidal phenomenon and the vulnerability that still appears under the edge AP. It is necessary to consider that multiple terminals under an AP have suspected coverage vulnerabilities, then there is a wireless network coverage vulnerability, avoiding the existence of misjudgment. Among them, the judgment of the edge AP is to input the AP graph structure data constructed according to the node characteristics and inter-node characteristics of the AP into the graph structure model and the classification model to obtain the type of AP, that is, whether it is an edge AP.

[0123] Based on the same technical concept, an embodiment of the present invention provides a device for processing a wireless network coverage vulnerability, such as Figure 8 As shown, including:

[0124] The acquisition unit 801 is used to acquire weak fingerprint data of each terminal within the coverage area collected by each wireless access device AP; the weak fingerprint data of any terminal is the signal data of each AP collected by the terminal under weak signal conditions;

[0125] A determination unit 802, configured to determine a suspected coverage vulnerability according to the weak fingerprint data of each terminal;

[0126] The determining unit 802 is further configured to determine, for each suspected coverage vulnerability, whether the suspected coverage vulnerability meets a filtering condition; if the suspected coverage vulnerability meets the filtering condition, determine that the suspected coverage vulnerability does not belong to a wireless network coverage vulnerability implementation mode.

[0127] In an optional implementation, the acquisition unit 801 is further used to acquire weak fingerprint data of each terminal within the coverage range collected by each wireless access device AP; the weak fingerprint data of any terminal is the signal data of each AP collected by the terminal under weak signal conditions; according to the weak fingerprint data of each terminal and the problem AP, a suspected coverage vulnerability is determined; the problem AP is an AP whose signal strength is lower than a threshold among the APs associated with each terminal obtained from the cloud;

[0128] The determining unit 802 is further configured to determine, for each suspected coverage vulnerability, whether the suspected coverage vulnerability meets a filtering condition, and if the suspected coverage vulnerability meets the filtering condition, determine that the suspected coverage vulnerability does not belong to a wireless network coverage vulnerability.

[0129] In an optional implementation, the filtering condition includes that collection times corresponding to the first coverage hole in at least two collection cycles belong to the same time range, and / or that the AP corresponding to the first coverage hole is an edge AP.

[0130] In an optional implementation manner, the determining unit 802 is specifically configured to determine suspected coverage holes corresponding to different times based on weak fingerprint data of each terminal within the coverage range of each AP acquired at different times;

[0131] The determining unit 802 is specifically configured to determine that any suspected coverage hole meets the filtering condition if the difference between the acquisition moments corresponding to any suspected coverage hole in at least two acquisition cycles meets a preset time difference threshold.

[0132] In an optional implementation manner, the determining unit 802 is further specifically configured to determine that if the AP corresponding to the suspected coverage hole is an edge AP, then the suspected coverage hole meets the filtering condition.

[0133] In an optional implementation manner, the determining unit 802 is further specifically configured to determine whether the AP corresponding to the suspected coverage hole is an edge AP if the collection moments corresponding to the suspected coverage hole in at least two collection cycles do not belong to the same time range; if the AP corresponding to the suspected coverage hole is an edge AP, determine that the suspected coverage hole meets the filtering condition;

[0134] or,

[0135] If the AP corresponding to the suspected coverage hole is not an edge AP, determine whether the collection moments corresponding to the suspected coverage hole in at least two collection cycles belong to the same time range; if the collection moments corresponding to the suspected coverage hole in at least two collection cycles belong to the same time range, determine that the suspected coverage hole meets the filtering conditions.

[0136] In an optional implementation manner, the determining unit 802 is specifically used to determine the node characteristics of the AP corresponding to the suspected coverage hole and other APs; the node characteristics of any AP are used to represent the wireless network status of the AP;

[0137] The determining unit 802 is further specifically configured to determine an inter-node feature between the AP corresponding to the suspected coverage hole and the other APs; wherein the inter-node feature is used to indicate the correlation between AP nodes based on terminal roaming data;

[0138] The determining unit 802 is further specifically configured to determine that the AP corresponding to the suspected coverage hole is an edge AP according to the node characteristics of the AP corresponding to the suspected coverage hole and the other APs and the inter-node characteristics.

[0139] In an optional implementation manner, the determining unit 802 is specifically configured to construct AP graph structure data according to the node characteristics of the AP corresponding to the suspected coverage hole and the inter-node characteristics;

[0140] The determining unit 802 is specifically configured to determine, through the AP graph structure data, that the suspected coverage hole corresponds to an edge AP.

[0141] In an optional implementation, the determination unit 802 is specifically used to determine the attention coefficient between the AP corresponding to the suspected coverage hole and the other APs according to the node characteristics of the AP corresponding to the suspected coverage hole and the node characteristics of the other APs, the node characteristics of the AP corresponding to the suspected coverage hole and the node characteristics of the other APs; determine the fusion node characteristics of the AP corresponding to the suspected coverage hole according to the attention coefficients of each adjacent AP;

[0142] The determining unit 802 is specifically configured to determine, according to the fusion node feature, that the AP corresponding to the suspected coverage hole is an edge AP.

[0143] In an optional implementation manner, the determining unit 802 is further specifically configured to input the AP graph structure data into a graph structure model to obtain a node feature vector of each AP;

[0144] The determining unit 802 is further specifically configured to input the node feature vector of each AP into a classification model to obtain an edge AP among the APs.

[0145] In an optional implementation manner, the node characteristics include AP mutual scanning data, roaming data and online and offline data of each terminal under the coverage of the AP;

[0146] The inter-node feature is the roaming times of the terminal between the nodes and / or the roaming direction of the terminal.

[0147] In an optional implementation manner, the determining unit 802 is specifically configured to perform clustering processing on the weak fingerprint data of each terminal based on a density parameter to obtain each first cluster and each first abnormal point; each first abnormal point is weak fingerprint data that does not belong to any first cluster;

[0148] The determining unit 802 is specifically configured to perform clustering processing on the first clusters and the first outliers based on the adjusted density parameter to obtain the second clusters and the second outliers until a clustering termination condition is met;

[0149] The determination unit 802 is specifically used to determine each cluster that meets the clustering termination condition as a suspected coverage hole; wherein the density parameter includes the cluster domain size and the minimum number of points in the cluster; and the clustering process is based on the cosine distance of the weak fingerprint data.

[0150] In an optional implementation, the determination unit 802 is specifically used to determine the clustering termination condition based on the comparison of abnormal points in at least two clustering results. If the number of abnormal points is equal, the clustering termination condition is met, otherwise, clustering continues.

[0151] In an optional implementation, the determining unit 802 is further configured to determine whether the number of terminals meets a threshold value. If the number of terminals meets the threshold value, it is finally determined that the suspected coverage loophole is a wireless network coverage loophole.

[0152] Based on the same technical concept, the embodiment of the present invention also provides a computing device, such as Fig. 9 As shown, it includes at least one processor 901 and a memory 902 connected to the at least one processor. The specific connection medium between the processor 901 and the memory 902 is not limited in the embodiment of the present invention. Fig. 9For example, the processor 901 and the memory 902 are connected via a bus. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0153] In the embodiment of the present invention, the memory 902 stores instructions that can be executed by at least one processor 901. By executing the instructions stored in the memory 902, the at least one processor 901 can execute the steps included in the aforementioned processing method based on wireless network coverage vulnerability.

[0154] Among them, the processor 901 is the control center of the computing device, and can use various interfaces and lines to connect various parts of the computing device, and realize data processing by running or executing instructions stored in the memory 902 and calling data stored in the memory 902. Optionally, the processor 901 may include one or more processing units, and the processor 901 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes the issuance of instructions. It is understandable that the above-mentioned modem processor may not be integrated into the processor 901. In some embodiments, the processor 901 and the memory 902 may be implemented on the same chip, and in some embodiments, they may also be implemented separately on independent chips.

[0155] The processor 901 may be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit (ASIC), a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the embodiment of the method for processing a wireless network coverage vulnerability may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0156] The memory 902 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 902 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 902 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 902 in the embodiment of the present invention can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0157] Based on the same technical concept, an embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program that can be executed by a computing device. When the program runs on the computing device, the computing device executes the steps of the above-mentioned processing method based on wireless network coverage vulnerability.

[0158] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0159] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0160] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0161] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0162] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for processing wireless network coverage vulnerabilities, characterized in that: include: Obtain weak fingerprint data of each terminal within the coverage area collected by each wireless access device AP within a preset time period; The weak fingerprint data of any terminal is the signal data of each AP collected by the terminal under weak signal conditions; Determine a suspected coverage vulnerability based on the weak fingerprint data of each terminal; For each suspected coverage hole, determining whether the suspected coverage hole meets the filtering condition; If the suspected coverage vulnerability meets the filtering condition, it is determined that the suspected coverage vulnerability does not belong to a wireless network coverage vulnerability.

2. The method according to claim 1, characterized in that The filtering condition includes that collection times corresponding to the first coverage hole in at least two collection cycles belong to the same time range, and / or that the AP corresponding to the first coverage hole is an edge AP.

3. The method according to claim 2, characterized in that Determine whether the suspected coverage vulnerability meets the filtering conditions, including: Based on the weak fingerprint data of each terminal within the coverage range of each AP obtained at different times, the suspected coverage loopholes corresponding to different times are determined; If the difference between the collection times corresponding to any suspected coverage hole in at least two collection cycles is greater than a preset time difference threshold, the suspected coverage hole meets the filtering condition.

4. The method according to claim 2, characterized in that Whether the suspected coverage vulnerability meets the filtering conditions, including: If it is determined that the AP corresponding to the suspected coverage hole is an edge AP, the suspected coverage hole meets the filtering condition.

5. The method according to claim 2, characterized in that Whether the suspected coverage vulnerability meets the filtering conditions, including: If the collection times corresponding to the suspected coverage hole in at least two collection cycles do not belong to the same time range, determine whether the AP corresponding to the suspected coverage hole is an edge AP; if the AP corresponding to the suspected coverage hole is an edge AP, determine that the suspected coverage hole meets the filtering condition; or, If the AP corresponding to the suspected coverage hole is not an edge AP, determine whether the collection moments corresponding to the suspected coverage hole in at least two collection cycles belong to the same time range; if the collection moments corresponding to the suspected coverage hole in at least two collection cycles belong to the same time range, determine that the suspected coverage hole meets the filtering conditions.

6. The method according to claim 2, characterized in that Determining whether the AP corresponding to the suspected coverage hole meets the filtering condition is performed in the following manner, including: Determine the node characteristics of the AP corresponding to the suspected coverage hole and other APs; the node characteristics of any AP are used to represent the wireless network status of the AP; Determine the node-to-node characteristics between the AP corresponding to the suspected coverage hole and the other APs; wherein the node-to-node characteristics are used to represent the correlation between AP nodes based on terminal roaming data; The AP corresponding to the suspected coverage hole is determined to be an edge AP according to the node characteristics of the AP corresponding to the suspected coverage hole and the other APs and the inter-node characteristics.

7. The method according to claim 6, characterized in that Determining, according to the node feature of the AP corresponding to the suspected coverage hole and the inter-node feature, that the AP corresponding to the suspected coverage hole is an edge AP includes: Constructing AP graph structure data according to the node characteristics of the AP corresponding to the suspected coverage hole and the inter-node characteristics; It is determined through the AP graph structure data that the suspected coverage hole corresponds to an edge AP.

8. The method according to claim 6, characterized in that Determining, according to the node feature of the AP corresponding to the suspected coverage hole and the inter-node feature, that the AP corresponding to the suspected coverage hole is an edge AP includes: Determine the attention coefficient between the AP corresponding to the suspected coverage hole and the other APs according to the node characteristics of the AP corresponding to the suspected coverage hole and the node characteristics of the other APs, the node characteristics of the AP corresponding to the suspected coverage hole and the node characteristics of the other APs; determine the fusion node characteristics of the AP corresponding to the suspected coverage hole according to the attention coefficients of each adjacent AP; According to the fusion node feature, it is determined that the AP corresponding to the suspected coverage hole is an edge AP.

9. The method according to claim 7, characterized in that Determining an edge AP among the APs by using the AP graph structure data includes: Inputting the AP graph structure data into a graph structure model to obtain a node feature vector of each AP; The node feature vectors of each AP are input into a classification model, and the corresponding AP of the suspected coverage hole is obtained as an edge AP.

10. The method according to claim 6, characterized in that The node characteristics include one or more of AP mutual scanning data, roaming data of each terminal under the coverage of the AP, and online and offline data; The inter-node feature is the roaming times of the terminal between the nodes and / or the roaming direction of the terminal.

11. The method according to any one of claims 1 to 10, characterized in that According to the weak fingerprint data of each terminal, the suspected coverage vulnerability is determined, including: Clustering the weak fingerprint data of each terminal based on the density parameter to obtain each first cluster and each first abnormal point; each first abnormal point is weak fingerprint data that does not belong to any first cluster; Based on the adjusted density parameter, clustering is performed on the first clusters and the first outliers to obtain second clusters and second outliers until a clustering termination condition is met; Each cluster that meets the clustering termination condition is determined as a suspected coverage hole; wherein, the density parameters include the cluster domain size and the minimum number of points in the cluster; the clustering process is based on the cosine distance of the weak fingerprint data; the adjusted density parameters are the cluster domain size and the minimum number of points in the cluster redefined according to each first cluster and each first abnormal point.

12. A device for identifying wireless network coverage loopholes, characterized in that: include: An acquisition unit, used to acquire weak fingerprint data of each terminal within the coverage area collected by each wireless access device AP within a preset time period; The weak fingerprint data of any terminal is the signal data of each AP collected by the terminal under weak signal conditions; A determination unit, configured to determine a suspected coverage vulnerability according to the weak fingerprint data of each terminal; The determination unit is further configured to determine, for each suspected coverage vulnerability, whether the suspected coverage vulnerability meets a filtering condition; if the suspected coverage vulnerability meets the filtering condition, determine that the suspected coverage vulnerability does not belong to a wireless network coverage vulnerability.

13. A computing device, characterized in that: include: A memory for storing program instructions; A processor, configured to call the program instructions stored in the memory, and execute the method according to any one of claims 1 to 10 according to the obtained program.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 10 is executed.

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