Information Security Transmission Method and System for Large-Scale Devices in the Internet of Things

Abstract

The present invention relates to the technical field of big data transmission, and particularly relates to an information security transmission method and system for a large number of Internet of Things devices, including: obtaining the historical transmission data and location information data of each device, and constructing a device graph structure; obtaining the data association degree between every two devices according to the difference between the significant data of the historical transmission data of every two devices and the aggregation situation of the significant features of the historical transmission data; obtaining the trend consistency degree between every two devices according to the result of spatial distribution analysis of the data volume of the historical transmission data of each device and the location information data, and combining the change trend of the data volume within the spatial range; determining the edge weights between nodes in the device graph structure by using the data association degree and the trend consistency degree, and determining the data encryption transmission method in the network according to the device graph structure. The network division accuracy of the present invention is relatively high, and the possibility of network congestion is reduced during data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of big data transmission, and particularly to an information security transmission method and system for a large number of Internet of Things (IoT) devices. Background Art

[0002] When transmitting data for a large number of IoT devices, the surge in device data brings a huge demand for data transmission. The traditional method of directly uploading all data to a single data center for transmission is more likely to cause network congestion. Therefore, a network is constructed to optimize the data transmission process, and the data within the network is encrypted identically, which can effectively improve the information security of data transmission. Currently, the method of constructing a local network is to establish network connections between devices that are geographically close or have similar data characteristics to form a small-scale network cluster. However, only dividing the local network based on the distance between devices has a relatively single consideration factor, resulting in low accuracy in constructing the local network, which in turn affects the data transmission efficiency. Summary of the Invention

[0003] In order to solve the technical problem that the construction accuracy of the local network in the existing method is relatively low, resulting in low data transmission efficiency, the purpose of the present invention is to provide an information security transmission method and system for a large number of IoT devices. The specific technical solutions adopted are as follows:

[0004] In a first aspect, the present invention provides an information security transmission method for a large number of IoT devices, including:

[0005] Obtain the historical transmission data and location information data of each device, and construct a device graph structure according to the distribution of the location information data between each device and other devices;

[0006] Based on the difference between the significant data of the historical transmission data between each device and other devices, and the aggregation of the significant features of the historical transmission data, obtain the data association degree between every two devices;

[0007] Based on the result of the spatial distribution analysis of the data volume and location information data of the historical transmission data of each device, and combined with the change trend direction of the data volume between every two devices within the spatial range, obtain the trend consistency degree between every two devices;

[0008] Use the data association degree and trend consistency degree to determine the edge weights between the nodes in the device graph structure, and perform network division according to the device graph structure to determine the data encryption transmission method in the network.

[0009] Preferably, obtaining the data association degree between every two devices according to the difference situation between the significant data of the historical transmission data of each device and other devices, and the aggregation situation between the significant features of the historical transmission data specifically includes:

[0010] Based on the time sequence of the historical transmission data of each device, determining the transmission data sequence of each device; extracting the significant features of each transmission data sequence to obtain the local significant subsequence corresponding to each significant feature of each device;

[0011] According to the data overlap situation between the local significant subsequences of each corresponding significant feature between every two devices, and the difference situation between the time features of the local significant subsequences, obtaining the significant feature aggregation index of each corresponding significant feature between every two devices;

[0012] Based on the negative correlation coefficient of the difference distance between the local significant subsequences of each corresponding significant feature between every two devices, determining the feature similarity coefficient of each corresponding significant feature between every two devices;

[0013] Taking the sum of the products of the significant feature aggregation index and the feature similarity coefficient of each corresponding significant feature between every two devices as the data association degree between every two devices.

[0014] Preferably, obtaining the significant feature aggregation index of each corresponding significant feature between every two devices according to the data overlap situation between the local significant subsequences of each corresponding significant feature between every two devices, and the difference situation between the time features of the local significant subsequences specifically includes:

[0015] Denoting any two devices corresponding to the nodes with edge connections in the device graph structure as the first device and the second device respectively;

[0016] Denoting the significant feature corresponding to any serial number as the target significant feature;

[0017] Obtaining the first length of the time union between the local significant subsequence of the first device under the target significant feature and the local significant subsequence of the second device under the target significant feature; obtaining the second length of the time intersection between the local significant subsequence of the first device under the target significant feature and the local significant subsequence of the second device under the target significant feature;

[0018] Based on the difference between the first length and the second length, determining the first difference coefficient; based on the difference between the time length of the local significant subsequence of the first device under the target significant feature and the time length of the local significant subsequence of the second device under the target significant feature, determining the second difference coefficient;

[0019] Take the negative correlation coefficient of the product between the first coefficient of variation and the second coefficient of variation as the significant feature aggregation index of the first device and the second device under the target significant feature.

[0020] Preferably, based on the results of the spatial distribution analysis of the data volume and location information data of the historical transmission data of each device, combined with the change trend direction of the data volume between every two devices within the spatial range, obtain the degree of trend consistency between every two devices, specifically including:

[0021] Based on the position coordinates in the location information data of each device and the data volume of all historical transmission data of each device, determine the three-dimensional coordinate values of each device, map the device into a three-dimensional coordinate system, and determine the three-dimensional data points of each device;

[0022] According to the distribution of the three-dimensional data points of all devices, analyze the local data change trend, and determine the comprehensive change trend vector;

[0023] According to the difference between the change trend distribution between the three-dimensional data points corresponding to every two devices and the comprehensive change trend vector, determine the degree of trend consistency between every two devices.

[0024] Preferably, the step of analyzing the local data change trend and determining the comprehensive change trend vector according to the distribution of the three-dimensional data points of all devices specifically includes:

[0025] Perform function fitting on the three-dimensional data points of all devices to obtain a characteristic function, obtain each maximum point and minimum point based on the characteristic function, and starting from the minimum point corresponding to the minimum data volume, sequentially record the vector from each minimum point to the nearest maximum point as each comprehensive change trend vector.

[0026] Preferably, the step of determining the degree of trend consistency between every two devices according to the difference between the change trend distribution between the three-dimensional data points corresponding to every two devices and the comprehensive change trend vector specifically includes:

[0027] For any two devices corresponding to the nodes with edge connections in the device graph structure, record the device corresponding to the maximum data volume as the first characteristic device, and record the minimum data volume as the second characteristic device;

[0028] In the three-dimensional coordinate system, take the vector from the second characteristic device to the first characteristic device as the data change vector of the first characteristic device and the second characteristic device;

[0029] Obtain the comprehensive change trend vector corresponding to the minimum point closest to the first characteristic device and the second characteristic device and record it as the characteristic vector;

[0030] Take the cosine value of the angle between the data change vector and the feature vector as the degree of trend consistency between the first feature device and the second feature device.

[0031] Preferably, the method for determining the edge weights between nodes in the device graph structure by using the data correlation degree and the trend consistency degree specifically includes:

[0032] For any two devices corresponding to nodes with an edge connection relationship in the device graph structure, calculate the product of the Euclidean distance between the position information data corresponding to the two devices and the trend consistency degree, and determine an adjustment coefficient based on this product. The value range of the adjustment coefficient is [1, 2]; take the product of the adjustment coefficient and the data correlation degree between the two devices as the edge weight between the nodes corresponding to the two devices.

[0033] Preferably, the method for constructing the device graph structure according to the distribution of the position information data between each device and other devices specifically includes:

[0034] Calculate the difference distance of the position information data between each two devices to obtain the spatial distance between each two devices;

[0035] Denote any one device as the target device, obtain a preset number of other devices with the closest spatial distance to the target device as the reference devices of the target device, and take the average value of the distances between the target device and each reference device as the characteristic distance data of the target device;

[0036] Take the average value of the characteristic distance data of all devices as the distance threshold, construct an edge connection relationship between two devices with a spatial distance less than the distance threshold, and take each device as a node to construct a device graph structure.

[0037] Preferably, the method for network grouping according to the device graph structure specifically includes:

[0038] Use the cabddcg clustering algorithm to split the device graph structure to obtain the network grouping result.

[0039] In a second aspect, the present invention provides an information security transmission system for large-scale IoT devices, which is used to implement the steps of an information security transmission method for large-scale IoT devices. The information security transmission system for large-scale IoT devices includes:

[0040] A data preprocessing module, configured to obtain the historical transmission data and position information data of each device, and construct a device graph structure according to the distribution of the position information data between each device and other devices;

[0041] The relevance analysis module is used to obtain the data association degree between every two devices according to the difference situation between the significant data of the historical transmission data of each device and other devices, and the aggregation situation between the significant features of the historical transmission data;

[0042] The consistency analysis module is used to obtain the trend consistency degree between every two devices according to the result of the spatial distribution analysis of the data volume and location information data of the historical transmission data of each device, and combining the change trend direction of the data volume between every two devices within the spatial range;

[0043] The data transmission module is used to determine the edge weights between the nodes in the device graph structure by using the data association degree and the trend consistency degree, perform network grouping according to the device graph structure, and determine the data encryption transmission method in the network.

[0044] The embodiments of the present invention have at least the following beneficial effects:

[0045] The present invention first collects the transmission data information and location information of the devices, and uses the distribution of the location information data of the devices to initially construct a graph structure, which provides a data basis for further analyzing the weight distribution of the edges in the graph structure. Then, for the first aspect, analyze the difference situation and aggregation situation of the historical transmission data of every two devices in terms of significant features, and use the data association degree to characterize the time overlap relevance and similarity between the two devices. Further, for the second aspect, analyze the spatial change trend of the historical transmission data of every two devices in terms of data volume and location information, and use the trend consistency degree to characterize the data change trend consistency situation between the two devices. Finally, combining the overall characteristics of the feature analysis of these two aspects, the weight size of the corresponding edge of the device in the graph structure, that is, the edge weight, can be determined, and the device graph structure is split and clustered to achieve adaptive network grouping. This grouping process considers the data feature performance in multiple dimensions, making the network grouping more conducive to data transmission, with high accuracy. Based on the network grouping result for network transmission, the possibility of network congestion is reduced, and at the same time, the security level of information transmission is improved. Description of the Drawings

[0046] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0047] Figure 1 It is a step flowchart of an information security transmission method for a large number of Internet of Things devices provided by the present invention;

[0048] Figure 2 It is a flowchart of the steps of the method for obtaining the data correlation degree between every two devices provided by the present invention;

[0049] Figure 3 It is a flowchart of a step of the method for obtaining the degree of trend consistency between every two devices provided by the present invention;

[0050] Figure 4 It is another flowchart of the steps of the method for obtaining the degree of trend consistency between every two devices provided by the present invention;

[0051] Figure 5 It is a schematic structural diagram of an information security transmission system for large-scale Internet of Things devices provided by the present invention;

[0052] Figure 6 It is a schematic structural diagram of a computer device provided by the present invention. Detailed implementation manners

[0053] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following combines the accompanying drawings and preferred embodiments to detail the specific implementation manners, structures, features and effects of an information security transmission method and system for large-scale Internet of Things devices proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0055] The following specifically describes the specific solutions of an information security transmission method and system for large-scale Internet of Things devices provided by the present invention with reference to the accompanying drawings.

[0056] The main purpose of the present invention is specifically: when large-scale Internet of Things devices perform data transmission, a network can be constructed to optimize the data transmission process, and the data in the network can be encrypted uniformly to improve information security. A graph structure of a spatial range is formed according to the location information of the devices, and further, according to the temporal similarity of the devices and the data change situation in space, the graph structure is split and clustered to determine the network structure of the devices, and then different levels of security encryption are performed according to the data in the network to reduce network congestion and improve the information security of data transmission at the same time.

[0057] Please refer to Figure 1, which shows a step flowchart of an information security transmission method for a large number of Internet of Things devices provided by an embodiment of the present invention. The method includes the following steps:

[0058] Step S100, obtain the historical transmission data and location information data of each device, and construct a device graph structure according to the distribution of the location information data between each device and other devices.

[0059] In the Internet of Things platform, each device is connected to the platform, and the information data will be saved to the terminal data center of the platform. At the same time, the platform can monitor the location information of each device in real time. Based on this, the graph structure can be initially constructed by combining the location information of the devices with the connection relationship between the devices, providing a data basis for subsequent construction of the local network structure through split clustering.

[0060] Specifically, in this embodiment, the time-series data of each device's transmission is obtained through the Internet of Things platform, denoted as historical transmission data. At the same time, the location information data of each device is obtained. In this embodiment, the location coordinates of each device are used as the location information data. It can be understood that the historical transmission data of each device is time-series data, but for different devices, the time interval between two adjacent historical transmission data may be different, and the volume of the historical transmission data of different devices may also be different. Subsequently, the connection relationship between devices can be divided according to the correlation and consistency relationships of various aspects of the data, so as to effectively improve the accuracy of network construction.

[0061] Then, considering that there is a certain connection relationship between devices with relatively close distances in the spatial range, and generally no connection relationship is set when the spatial distance between devices is large. In this embodiment, any one device is taken as an example for illustration, that is, any one device is denoted as the target device. And the difference distance of the location information data between every two devices is calculated to obtain the spatial distance between every two devices. Based on the location information data of each device, the spatial distance between every two devices can be obtained. Specifically, the Euclidean distance between the location information data of every two devices can be used to calculate the spatial distance between every two devices.

[0062] Furthermore, obtain a preset number of other devices with the closest spatial distance to the target device as the reference devices of the target device, and take the average value of the distances between the target device and each reference device as the characteristic distance data of the target device. The reference devices represent several devices with the closest distance to the target device within the spatial range. The characteristic distance data represents the nearest neighbor distance parameter of the target data, avoiding the situation where some devices are too discrete to establish a connection relationship with other devices.

[0063] In this embodiment, the spatial distances between other devices and the target device are sorted in ascending order, and a preset number of other devices, which are reference devices of the target device, are obtained in the arrangement order. The average of the preset number of spatial distances in the arrangement order is the characteristic distance data of the target device, where the preset number is 3, and the implementer can set it according to the specific implementation scenario.

[0064] The characteristic distance data of each device can be obtained in the same way as the characteristic distance data of the target device, and then the average of the characteristic distance data of all devices is used as the distance threshold, and the distance threshold is used as the screening condition to establish a connection relationship between devices with a closer distance, and not to establish a connection relationship between devices with a farther distance.

[0065] Specifically, an edge connection relationship is established between two devices whose spatial distance is less than the distance threshold, and each device is regarded as a node to construct a device graph structure. At this point, the device graph structure preliminarily represents the connection relationship between devices.

[0066] Step S200, obtaining the data association degree between every two devices according to the difference between the significant data of the historical transmission data of each device and other devices, and the aggregation between the significant features of the historical transmission data.

[0067] The device graph structure is an initial connection preliminarily constructed through the spatial structural relationship between devices. In order to make the subsequent split clustering more accurate, further, on the first hand, the significant change characteristics of the historical transmission data of each device in time series are considered, and the clustering of the significant change characteristics between different devices is analyzed, so that the similarity of the data in time series between devices with edge connection relationships can be quantitatively analyzed. On the second hand, considering the different data transmission volumes of different devices, when performing data transmission, the historical transmission data corresponding to the device with a smaller data transmission volume is transmitted to other devices with a larger data transmission volume as much as possible, and the similarity adjustment parameters are constructed based on this feature, so that the weights of the edges between devices in the device graph structure are finally more accurate.

[0068] Specifically, we first analyze the characteristics of the first aspect. The data transmission volume between devices has uniform data changes in the same time series interval. We can determine the similarity of the data transmission situation in terms of significant characteristics between two devices based on the significant change characteristics of the data in the time series and the aggregation between the time series intervals. Figure 2 As shown, the method for acquiring the data association degree between every two devices can be implemented by steps S201 to S204.

[0069] Step S201: Based on the chronological order of the historical transmission data of each device, determine the transmission data sequence of each device; extract the significant features of each transmission data sequence to obtain the local significant subsequences corresponding to each significant feature of each device.

[0070] In this embodiment, the number of historical transmission data of each device is different and all belong to time series data. Therefore, the historical transmission data of each device is arranged in chronological order to construct a data sequence, and the transmission data sequence of each device is obtained.

[0071] Considering that when the data acquisition volume is large, there is often data redundancy in the transmission data sequence of each device. In order to more significantly and prominently analyze the similarity of the feature parts in the transmission data sequence, the feature extraction operation can be used first to obtain the degree of significant feature representation of the transmission data sequence, which can more intuitively characterize the degree of data features to a certain extent.

[0072] Specifically, in this embodiment, the Shapelet algorithm is used to extract the significant features of each transmission data sequence to obtain the local significant subsequences corresponding to each significant feature of each device. It should be noted that the Shapelet algorithm is a well-known technology and will not be introduced in detail here. Using this algorithm, the locally significant data segments in the time series can be directly obtained. In this embodiment, each data segment is regarded as a significant feature, that is, each device may extract multiple local significant subsequences, and each local significant subsequence corresponds to a significant feature.

[0073] Step S202: According to the data overlap situation of the local significant subsequences corresponding to each corresponding significant feature between every two devices, and the difference situation between the time features of the local significant subsequences, obtain the significant feature aggregation index corresponding to each corresponding significant feature between every two devices.

[0074] For the local significant subsequences corresponding to the significant features of each device, if significant features appear at the same position in different devices and the feature manifestations between the significant features are also relatively similar, it indicates that the overall data volume between the two devices is relatively similar and the data change situation is also relatively similar. More specifically, if the difference in the time lengths corresponding to the local significant subsequences between different devices is small and the time intervals corresponding to the occurrences are also relatively consistent, it indicates that the reasons for the data change features corresponding to the significant features between different devices may be relatively consistent, and further indicates that the correlation between the two devices is large and the similarity is large. Dividing the devices with a large correlation into the same local networking structure is beneficial to data transmission.

[0075] Based on this, by analyzing the data overlap and time difference between the significant features corresponding to every two devices, the significant feature aggregation index between every two devices is quantified. In this embodiment, any two devices are taken as examples for illustration. Considering that the subsequent characterization is for the edge weights, in this implementation, the analysis is specifically performed on the devices with edge connection relationships in the device graph structure, that is, any two devices corresponding to the nodes with edge connections in the device graph structure are respectively denoted as the first device and the second device.

[0076] Meanwhile, taking any one significant feature as an example for illustration, that is, the significant feature corresponding to any one serial number is denoted as the target significant feature. It can be understood that the serial number represents the position order of the local significant subsequence corresponding to the significant feature in the transmission data sequence corresponding to each device. For example, the significant feature with serial number 1 represents the significant feature corresponding to the first local significant subsequence in the transmission data sequence corresponding to each device.

[0077] Furthermore, obtain the first length of the time union between the local significant subsequence of the first device under the target significant feature and the local significant subsequence of the second device under the target significant feature; obtain the second length of the time intersection between the local significant subsequence of the first device under the target significant feature and the local significant subsequence of the second device under the target significant feature.

[0078] The first length characterizes the length of time overlap between the local significant subsequences of the first device and the second device under the target significant feature. The larger the value of the first length, the greater the data overlap between the first device and the second device under the target significant feature, and the higher the degree of consistency of the time intervals corresponding to the significant features. The second length characterizes the total time length for the local significant subsequences of the first device and the second device to appear traversally under the target significant feature.

[0079] Based on the difference between the first length and the second length, determine the first difference coefficient; based on the difference between the time length of the local significant subsequence of the first device under the target significant feature and the time length of the local significant subsequence of the second device under the target significant feature, determine the second difference coefficient; take the negative correlation coefficient of the product between the first difference coefficient and the second difference coefficient as the significant feature aggregation index between the first device and the second device under the target significant feature.

[0080] As a specific example, taking the u-th device as the first device, the v-th device as the second device, and the i-th significant feature as the target significant feature, the calculation formula for the significant feature aggregation index can be specifically expressed as:

[0081]

[0082] where, R u,v(i) represents the significant feature aggregation index of the $u$-th device and the $v$-th device under the $i$-th significant feature, that is, the significant feature aggregation index of the first device and the second device under the target significant feature. $u$ represents the $u$-th device, $v$ represents the $v$-th device, and $i$ represents the $i$-th significant feature; X v (i) represents the time period corresponding to the local significant subsequence of the $v$-th device under the $i$-th significant feature, X u (i) represents the time period corresponding to the local significant subsequence of the $u$-th device under the $i$-th significant feature, L[X v (i) ∩ X u (i)] represents the length of the time union between the time periods corresponding to the local significant subsequences of the first device and the second device under the $i$-th significant feature, L[X v (i) ∪ X u (i)] represents the length of the time intersection between the time periods corresponding to the local significant subsequences of the first device and the second device under the $i$-th significant feature; T v (i) represents the time length corresponding to the local significant subsequence of the $v$-th device under the $i$-th significant feature, T u (i) represents the time length corresponding to the local significant subsequence of the $u$-th device under the $i$-th significant feature, exp() represents the exponential function with base $e$.

[0083] is the first difference coefficient, and the ratio represents the overlap situation of the time intervals corresponding to the data under the current significant feature. The closer this ratio is to 1, the higher the overlap of the significant features of the first device and the second device in the time dimension, the smaller the value of the corresponding first difference coefficient, and further indicates that the difference between the first device and the second device under the current significant feature is smaller.

[0084] is the second difference coefficient, and the ratio represents the comparison of the time lengths corresponding to the data under the current significant feature. The closer this ratio is to 1, the smaller the difference in the aggregation degree of the current significant feature, and the smaller the value of the corresponding second difference coefficient.

[0085] When the value of the first difference coefficient is smaller and the value of the second difference coefficient is also smaller, it indicates that the time performance lengths of the first device and the second device under the current significant feature are relatively consistent, and the data aggregation degrees are relatively consistent. The value of the corresponding significant feature aggregation index is larger. The significant feature aggregation index reflects the similarity and correlation of the aggregation feature performance of the historical transmission data of the two devices under each significant feature.

[0086] Step S203: Determine the feature similarity coefficient for each corresponding significant feature between every two devices based on the difference distance of the local significant subsequences of each corresponding significant feature between every two devices.

[0087] In this embodiment, taking the first device and the second device as an example, the negative correlation coefficient of the DTW distance between the local significant subsequences of the first device and the second device under the target significant feature is used as the feature similarity coefficient of the first device and the second device under the target significant feature. The feature similarity coefficient reflects the difference situation between the time series data of two devices under the corresponding significant feature.

[0088] Step S204: Take the sum of the products of the significant feature aggregation index and the feature similarity coefficient for each corresponding significant feature between every two devices as the data association degree between every two devices.

[0089] When analyzing the relevance and similarity between every two devices, on the one hand, the data overlap situation and the time length difference situation in the time distribution of the two devices under the corresponding significant feature are analyzed to quantitatively characterize the similarity of the data aggregation degree of the two devices. On the other hand, the feature similarity situation of the historical transmission data of the two devices under the corresponding significant feature is analyzed. Further combining the similarity performance and difference performance of these two aspects, comprehensively analyze the data association situation and data similarity situation between the two devices.

[0090] Specifically, taking the first device and the second device as an example, the products of the significant feature aggregation index and the feature similarity coefficient of the first device and the second device under all significant features are accumulated to obtain the corresponding data association degree of the first device and the second device. As a specific example, the calculation formula of the data association degree can be expressed as:

[0091]

[0092] where Q u,v represents the data association degree corresponding to the u-th device and the v-th device, that is, the data association degree corresponding to the first device and the second device, S u (i) represents the local significant subsequence of the u-th device under the i-th significant feature, S v (i) represents the local significant subsequence of the v-th device under the i-th significant feature, R u,v (i) represents the significant feature aggregation index of the u-th device and the v-th device under the i-th significant feature, N0 represents the minimum value of the total number of local significant subsequences in the first device and the second device. DTW[S v (i), S u (i)] represents the DTW distance between the local significant subsequences of the u-th device and the v-th device under the i-th significant feature. It represents the feature similarity coefficient between the u-th device and the v-th device under the i-th significant feature.

[0093] The significant feature aggregation index characterizes the feature analysis result of the first aspect, and the feature similarity coefficient characterizes the feature analysis result of the second aspect. The larger the values of both, the greater the similarity between the corresponding two devices, the greater the degree of association, and the larger the value of the corresponding data association degree.

[0094] Step S300: Based on the result of the spatial distribution analysis of the data volume and location information data of the historical transmission data of each device, and combining the change trend direction of the data volume between every two devices within the spatial range, obtain the degree of trend consistency between every two devices.

[0095] In the above steps, the similarity and correlation between two devices in terms of data information and time information in the first aspect are analyzed. Further considering that the data volumes of the historical transmission data of different devices are different, and the distance distributions between different devices are also different. When constructing a local network, it is more desirable to send devices with smaller data volumes to devices with larger data volumes to reduce the situation of data volume transmission in the network. Based on this, the direction of the change trend between different devices can be analyzed through the spatial distribution of the devices in terms of data volume and location information data, and the degree of trend consistency is used to quantitatively characterize the degree of preference for data transmission between two devices.

[0096] In this embodiment, as Figure 3 shown, the method for obtaining the degree of trend consistency between every two devices can be implemented by steps S301 to S303.

[0097] Step S301: Based on the position coordinates in the location information data of each device and the data volume of all the historical transmission data of each device, determine the three-dimensional coordinate value of each device, map the device into a three-dimensional coordinate system, and determine the three-dimensional data point of each device.

[0098] First, construct an information feature descriptor for each device, mainly including location information and data volume information, to provide a data basis for subsequent analysis of the data change trends in two dimensions of location and data volume. Specifically, the total number of all the historical transmission data of each device is the data volume corresponding to each device. By combining the location coordinates in the location information data of each device with the data volume, a three-dimensional coordinate value can be obtained. For example, the three-dimensional coordinate value of the u-th device can be expressed as (x u , y u , z u ), where (x u , y u ) represents the location coordinates of the u-th device, such as longitude and latitude, and z uRepresents the data volume of the u-th device.

[0099] Furthermore, each device corresponds to a point. By mapping all devices into a three-dimensional coordinate system, three-dimensional data points corresponding to each device can be obtained.

[0100] Step S302: According to the distribution of the three-dimensional data points of all devices, analyze the local data change trend and determine the comprehensive change trend vector.

[0101] First, based on the three-dimensional coordinate values of the three-dimensional data points of each device, all devices can be subjected to function fitting to obtain a characteristic function, which can characterize the changes in the position information and information volume information of the devices. Among them, the method of function fitting can choose the least squares method. In other embodiments, the implementer can set according to the specific implementation scenario.

[0102] Then, based on the characteristic function, each maximum point and minimum point are obtained. That is, the maximum point and minimum point in the characteristic function can be obtained by using the method of partial derivative. This process is a well-known technology and will not be introduced in detail here.

[0103] Furthermore, starting from the minimum point corresponding to the minimum data volume, the direction from each minimum point to the nearest maximum point is recorded as each comprehensive change trend vector in turn. The direction corresponding to each comprehensive change trend vector represents the direction from the device with a smaller data volume to the device with a larger data volume in the local space range, that is, the change trend direction of the local data volume increase.

[0104] Step S303: Determine the degree of trend consistency between every two devices according to the difference between the change trend distribution of the three-dimensional data points corresponding to every two devices and the comprehensive change trend vector.

[0105] By analyzing the consistency of the change trend direction of the data volume increase between two devices and the change trend direction of the data volume increase corresponding to the whole of all devices, the consistency and similarity of the data volume change between the two devices and the data volume change of the current space as a whole can be reflected. In this embodiment, any two devices corresponding to the nodes connected by edges in the device graph structure are taken as examples for illustration. For example, Figure 4 As shown, the degree of trend consistency can be realized by steps S3031 to S3034.

[0106] Step S3031: For any two devices corresponding to the nodes connected by edges in the device graph structure, the device corresponding to the maximum data volume is recorded as the first characteristic device, and the minimum data volume is recorded as the second characteristic device.

[0107] Assume that among the nth device and the mth device with edge connections, the data volume of the nth device is the largest and the data volume of the mth device is the smallest. Then, the nth device can be denoted as the first characteristic device, and the mth device can be denoted as the second characteristic device.

[0108] Step S3032: In a three-dimensional coordinate system, use the vector from the second characteristic device to the first characteristic device as the data change vector of the first characteristic device and the second characteristic device.

[0109] The data change vector characterizes the change trend direction from the device with a smaller data volume to the device with a larger data volume between two devices, that is, the change trend direction of the increasing data volume between two devices.

[0110] Step S3033: Obtain the comprehensive change trend vector corresponding to the minimum value point closest to the first characteristic device and the second characteristic device, and denote it as the characteristic vector.

[0111] In a three-dimensional coordinate system, the distance between every two devices can be obtained by calculating the Euclidean distance between three-dimensional data points. Then, the distance between each minimum value point and the first characteristic device, and the distance between each minimum value point and the second characteristic device can be obtained respectively. Among all the distances, the comprehensive change trend vector of the minimum value point corresponding to the minimum distance is the characteristic vector. The direction corresponding to the characteristic vector can reflect the change trend direction of the increasing data volume within the local space range where the first characteristic device and the second characteristic device are located.

[0112] Step S3034: Use the cosine value of the angle between the data change vector and the characteristic vector as the degree of trend consistency between the first characteristic device and the second characteristic device.

[0113] The smaller the angle between the data change vector and the characteristic vector, the larger the value of the corresponding cosine value, indicating that the degree of consistency between the two vector directions is greater. Furthermore, the degree of trend consistency characterizes the consistency between the direction of the data change vector of the first characteristic device and the second characteristic device and the direction of the characteristic vector within the current local space range. The larger this value, the stronger the consistency between the change trend direction of the increasing data volume between the current two devices and the current local space range, and thus the better the effect of dividing them into the same local network.

[0114] Step S400: Use the data association degree and the degree of trend consistency to determine the edge weights between nodes in the device graph structure, perform network partitioning according to the device graph structure, and determine the data encryption transmission method in the network.

[0115] In the first aspect, the correlation between two devices is analyzed from the overlap of significant features between the two devices. In the second aspect, the correlation between the two devices is analyzed from the consistency between the changing trend direction of the increasing data volume between the two devices and the local space. Combining the correlation analysis results of the two aspects can comprehensively represent the weight size corresponding to the connection edge between every two devices, that is, the greater the correlation between every two devices, the greater the weight of the corresponding connection edge.

[0116] Specifically, for any two devices corresponding to the nodes with edge connection relationships in the device graph structure, calculate the product of the Euclidean distance and the trend consistency degree between the position information data corresponding to the two devices, and determine the adjustment coefficient based on this product. The value range of the adjustment coefficient is [1, 2]; use the product of the adjustment coefficient and the data correlation degree between the two devices as the edge weight value between the corresponding nodes of the two devices.

[0117] As a specific example, this embodiment takes the u-th device and the v-th device as an example for illustration. Then the calculation formula for the edge weight value between the corresponding nodes of the u-th device and the v-th device can be specifically expressed as: γ u,v = Q u,v ×[1 + softmax(W u,v ×D u,v )]; where γ u,v represents the edge weight value between the corresponding nodes of the u-th device and the v-th device, Q u,v represents the data correlation degree between the u-th device and the v-th device, W u,v represents the trend consistency degree between the u-th device and the v-th device, D u,v represents the Euclidean distance between the position information data of the u-th device and the position information data of the v-th device, and softmax represents the normalization function.

[0118] According to the same method, the edge weight values corresponding to every two devices with edge connections can be obtained, and then the device graph structure can be updated. The updated device graph structure completely represents the connection relationship between the devices, so as to further perform split clustering on the current space graph structure, and the local networking structure corresponding to the local space range with a relatively dense connection relationship within the space range can be determined, and the connection relationship between different local networking structures is relatively sparse, which can reduce the transmission of data volume on the network to a certain extent and reduce the possibility of network congestion.

[0119] Specifically, in this embodiment, the cabddcg clustering algorithm is used to split the device graph structure to obtain the network partitioning result. It should be noted that the cabddcg algorithm (clustering algorithm based on dynamic division of connected graph) constructs a connected graph by calculating the similarity between data, and then splits the connected graph. The final splitting result is the final clustering result. This algorithm is a well-known technology and will not be introduced in detail here. In the clustering result, the devices included in each cluster correspond to form a network structure, which is convenient for subsequent data transmission.

[0120] More specifically, in each network structure, the device with the largest data volume among all the devices in the network structure is determined as the central node, and the historical transmission data of other devices in the network structure is sent to the central node. The central node collects and encrypts the data of each device and then uploads it to the data center of the Internet of Things uniformly. The data encryption method is a well-known technology and will not be introduced in detail here. Implementers can choose according to specific implementation scenarios.

[0121] In summary, when the present invention performs information security transmission on large-scale data, it adopts a network transmission method. When performing network partitioning and allocation of devices, it first considers the position information of devices within the spatial structure range to initially construct a graph structure, and then determines the temporal correlation and similarity of devices according to the significant change characteristics of the data of current devices in time series and the aggregation between time series intervals. At the same time, it considers the data volume change trend performance of neighboring devices in space, combines the overall characteristics of devices in time and space, constructs the weights of the edges between devices in the graph structure, and then performs split clustering on the graph structure to determine the specific performance of each local network structure. Finally, it performs secure encrypted transmission through different networks, reduces the possibility of network congestion, and improves the security level of information transmission.

[0122] As Figure 5 shown, the present invention also provides an information security transmission system for large-scale devices in the Internet of Things, including:

[0123] A data preprocessing module, configured to obtain the historical transmission data and location information data of each device, and construct a device graph structure according to the distribution of the location information data between each device and other devices;

[0124] A relevance analysis module, configured to obtain the data association degree between every two devices according to the difference between the significant data of the historical transmission data of each device and other devices, and the aggregation of the significant characteristics of the historical transmission data;

[0125] A consistency analysis module is configured to obtain the degree of trend consistency between every two devices according to the results of spatial distribution analysis based on the data volume and location information data of the historical transmission data of each device, in combination with the change trend direction of the data volume between every two devices within the spatial range.

[0126] A data transmission module is configured to determine the edge weights between nodes in the device graph structure by using the data association degree and the trend consistency degree, perform network partitioning according to the device graph structure, and determine the data encryption transmission method in the network.

[0127] An embodiment of the present application further provides a computer device. Please refer to Figure 6 Figure 6, which shows a schematic structural diagram of a computer device provided by an embodiment of the present invention. The computer device includes a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and running on the processor 602. When the processor 602 executes the computer program 603, the computer device can execute an information security transmission method for a large number of Internet of Things devices introduced above.

[0128] An embodiment of the present application further provides a computer program product. When the computer program product runs on a computer device, the computer device can execute an information security transmission method for a large number of Internet of Things devices introduced above.

[0129] An embodiment of the present application further provides a computer-readable storage medium. Computer program code is stored in the computer-readable storage medium. When the computer program code runs on a computer device, the computer device can execute an information security transmission method for a large number of Internet of Things devices introduced above.

[0130] In the embodiments provided in the present application, it should be understood that the provided computer device, computer program product, and computer-readable storage medium are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the methods provided above, and will not be elaborated here.

[0131] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for secure information transmission for large-scale IoT devices, characterized in that: The method comprises the following steps: Obtain the historical transmission data and location information data of each device, and build a device graph structure based on the distribution of location information data between each device and other devices; According to the difference between the significant data of each device and other devices' historical transmission data, and the aggregation between the significant features of the historical transmission data, the data correlation degree between every two devices is obtained; Based on the results of spatial distribution analysis of the data volume and location information of each device's historical transmission data, combined with the change trend direction of the data volume between every two devices within the spatial range, the degree of trend consistency between every two devices is obtained; Determine the edge weights between nodes in the device graph structure using the data association degree and trend consistency degree, divide the network according to the device graph structure, and determine the data encryption transmission method in the network; The data association degree between each two devices is obtained based on the difference between the significant data of the historical transmission data of each device and other devices, and the aggregation between the significant features of the historical transmission data, specifically including: Based on the time sequence of the historical transmission data of each device, determine the transmission data sequence of each device; extract the significant features of each transmission data sequence to obtain the local significant subsequence corresponding to each significant feature of each device; Obtaining a significant feature aggregation index for each corresponding significant feature between every two devices according to the data overlap of the local significant subsequences of each corresponding significant feature between every two devices and the difference between the time features of the local significant subsequences; Determine a feature similarity coefficient of each corresponding significant feature between each two devices based on a negative correlation coefficient of a difference distance of a local significant subsequence of each corresponding significant feature between each two devices; The cumulative sum of the products of the significant feature aggregation index and the feature similarity coefficient of each corresponding significant feature of each two devices is used as the data association degree between each two devices; Determining the edge weights between nodes in the device graph structure by using the data association degree and trend consistency degree specifically includes: For any two devices corresponding to nodes with an edge connection relationship in the device graph structure, the product of the Euclidean distance between the location information data corresponding to the two devices and the trend consistency degree is calculated, and an adjustment coefficient is determined based on the product. The value range of the adjustment coefficient is [1,2]; the product of the adjustment coefficient and the data association degree between the two devices is used as the edge weight between the nodes corresponding to the two devices.

2. According to claim 1, a method for secure information transmission for large-scale IoT devices is characterized in that: The method of obtaining the salient feature aggregation index of each corresponding salient feature between each two devices according to the data overlap of each local salient subsequence of each corresponding salient feature between each two devices and the difference between the time features of the local salient subsequences specifically includes: Any two devices corresponding to the nodes connected by an edge in the device graph structure are respectively recorded as the first device and the second device; The salient feature corresponding to any sequence number is recorded as the target salient feature; Obtaining a first length of a time union between a local significant subsequence of the first device under the target significant feature and a local significant subsequence of the second device under the target significant feature; obtaining a second length of a time intersection between a local significant subsequence of the first device under the target significant feature and a local significant subsequence of the second device under the target significant feature; determining a first difference coefficient based on a difference between the first length and the second length; determining a second difference coefficient based on a difference between a time length of a local significant subsequence under a target significant feature of the first device and a time length of a local significant subsequence under a target significant feature of the second device; The negative correlation coefficient of the product of the first difference coefficient and the second difference coefficient is used as a significant feature aggregation index of the first device and the second device under the target significant feature.

3. According to claim 1, a method for secure information transmission for large-scale IoT devices is characterized in that: The result of the spatial distribution analysis based on the data volume and location information data of each device's historical transmission data is combined with the change trend direction of the data volume between each two devices within the spatial range to obtain the trend consistency between each two devices, specifically including: Determine the three-dimensional coordinate value of each device based on the position coordinates in the position information data of each device and the data volume of all historical transmission data of each device, map the device into the three-dimensional coordinate system, and determine the three-dimensional data point of each device; According to the distribution of 3D data points of all devices, analyze the change trend of local data and determine the comprehensive change trend vector; The degree of trend consistency between every two devices is determined based on the difference between the distribution of change trends between the three-dimensional data points corresponding to every two devices and the comprehensive change trend vectors.

4. The information security transmission method for large-scale IoT devices according to claim 3 is characterized in that: The method of analyzing the local data change trend and determining the comprehensive change trend vector according to the distribution of the three-dimensional data points of all devices specifically includes: Function fitting is performed on the three-dimensional data points of all devices to obtain a characteristic function, and each maximum point and minimum point is obtained based on the characteristic function. Starting from the minimum point corresponding to the minimum value of the data volume, the vector pointing from each minimum point to the nearest maximum point is recorded as each comprehensive change trend vector.

5. The information security transmission method for large-scale IoT devices according to claim 4 is characterized in that: Determining the trend consistency between each two devices according to the change trend distribution between the three-dimensional data points corresponding to each two devices and the difference between the comprehensive change trend vectors specifically includes: For any two devices corresponding to nodes connected by an edge in the device graph structure, the device corresponding to the maximum data volume is recorded as the first characteristic device, and the device corresponding to the minimum data volume is recorded as the second characteristic device; In the three-dimensional coordinate system, the vector pointing from the second feature device to the first feature device is used as the data change vector of the first feature device and the second feature device; Obtain a comprehensive change trend vector corresponding to the minimum point closest to the first characteristic device and the second characteristic device and record it as a characteristic vector; The cosine value of the angle between the data change vector and the feature vector is used as the trend consistency between the first feature device and the second feature device.

6. The information security transmission method for large-scale IoT devices according to claim 1 is characterized in that: The device graph structure is constructed according to the distribution of location information data between each device and other devices, specifically including: Calculate the difference distance of the location information data between every two devices to obtain the spatial distance between every two devices; Record any device as a target device, obtain a preset number of other devices that are closest to the target device in space as reference devices for the target device, and use the average of the distances between the target device and each reference device as characteristic distance data of the target device; The mean of the characteristic distance data of all devices is used as the distance threshold, an edge connection relationship is established between two devices whose spatial distance is less than the distance threshold, and each device is regarded as a node to construct a device graph structure.

7. The information security transmission method for large-scale IoT devices according to claim 1 is characterized in that: The network division according to the device graph structure specifically includes: The cabddcg clustering algorithm is used to split the device graph structure and obtain the network division result.

8. An information security transmission system for large-scale IoT devices, characterized in that: The system is used to implement the steps of a method for secure information transmission for large-scale IoT devices as described in any one of claims 1 to 7, wherein the secure information transmission system for large-scale IoT devices comprises: The data preprocessing module is used to obtain the historical transmission data and location information data of each device, and build a device graph structure according to the distribution of location information data between each device and other devices; A correlation analysis module is used to obtain the data correlation degree between each two devices based on the difference between the significant data of the historical transmission data of each device and other devices, and the aggregation between the significant features of the historical transmission data; The consistency analysis module is used to perform spatial distribution analysis based on the data volume and location information data of each device's historical transmission data, and to obtain the trend consistency between each two devices in combination with the change trend direction of the data volume between each two devices within the spatial range; The data transmission module is used to determine the edge weights between nodes in the device graph structure by using the data association degree and trend consistency degree, divide the network according to the device graph structure, and determine the data encryption transmission method in the network.

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