Information security transmission method and system for large-scale equipment of Internet of Things

Abstract

The invention relates to the technical field of big data transmission, in particular to an information security transmission method and system for large-scale devices of the Internet of Things, and the method comprises the steps: obtaining historical transmission data and position 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 condition between the significant features of the historical transmission data; according to the data volume of the historical transmission data of each device and the result of spatial distribution analysis of the position information data, the trend consistency degree between every two devices is obtained by combining the change trend of the data volume in the spatial range; and determining an edge weight between the nodes in the equipment graph structure by using the data association degree and the trend consistency degree, and carrying out networking division according to the equipment graph structure to determine a data encryption transmission method in networking. According to the invention, the networking division precision is 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 in particular to a method and system for secure information transmission for large-scale devices in the Internet of Things. Background Art

[0002] When facing the data transmission of large-scale IoT devices, the surge in device data brings about the demand for the transmission of massive data. The traditional method of directly uploading all data to a single data center for transmission is more likely to cause network congestion. Therefore, by building a network to optimize the data transmission process and encrypting the data within the network for identity, the information security of data transmission can be effectively improved. At present, the method of building a local network is to establish a network connection between devices that are geographically close or have similar data characteristics to form a small-scale network cluster. However, the division of local networks only by the distance between devices takes into account relatively single factors, resulting in low accuracy in the construction of local networks, which in turn affects the efficiency of data transmission. Summary of the invention

[0003] In order to solve the technical problem that the construction accuracy of local networking in the existing method is low, which leads to low data transmission efficiency, the purpose of the present invention is to provide an information security transmission method and system for large-scale devices of the Internet of Things. The technical solution adopted is as follows:

[0004] In a first aspect, the present invention provides a method for secure information transmission for large-scale IoT devices, comprising:

[0005] 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;

[0006] 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;

[0007] 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;

[0008] The data association degree and trend consistency degree are used to determine the edge weights between nodes in the device graph structure, network division is performed according to the device graph structure, and the data encryption transmission method in the network is determined.

[0009] Preferably, the step of obtaining the data association degree between each 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, specifically includes:

[0010] 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;

[0011] 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;

[0012] 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;

[0013] 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.

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

[0015] 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;

[0016] The salient feature corresponding to any sequence number is recorded as the target salient feature;

[0017] 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;

[0018] 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;

[0019] 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.

[0020] Preferably, 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 every two devices within the spatial range to obtain the trend consistency between every two devices, specifically including:

[0021] 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;

[0022] 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;

[0023] 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.

[0024] Preferably, 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] 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.

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

[0027] 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;

[0028] 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;

[0029] 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;

[0030] 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.

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

[0032] 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.

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

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

[0035] 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;

[0036] 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.

[0037] Preferably, the network division according to the device graph structure specifically includes:

[0038] The cabddcg clustering algorithm is used to split the device graph structure and obtain the network division result.

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

[0040] 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;

[0041] 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;

[0042] 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;

[0043] 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.

[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 device, and uses the distribution of the location information data of the device to preliminarily construct a graph structure, which provides a data basis for the subsequent further analysis of the weight distribution of the edge in the graph structure. Then, the differences and aggregation of the historical transmission data of each two devices in terms of significant features are analyzed for the first aspect, and the time overlap correlation and similarity between the two devices are characterized by the degree of data association. Further, the spatial change trend of the historical transmission data of each two devices in terms of data volume and location information is analyzed for the second aspect, and the consistency of the trend is used to characterize the consistency of the data change trend between the two devices. Finally, combining the overall characteristics of the feature analysis of these two aspects, the weight of the corresponding edge of the device in the graph structure, that is, the edge weight, can be determined, so as to split and cluster the device graph structure to realize adaptive networking division. The division process takes into account the performance of data features in multiple dimensions, so that the networking division is more conducive to data transmission, and the accuracy is higher. Network transmission is performed based on the networking division result, reducing the possibility of network congestion, and improving the security of information transmission. BRIEF 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 drawings required for use in the embodiments or the prior art descriptions are 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.

[0047] Figure 1 It is a flowchart of the steps of a method for secure information transmission for large-scale IoT devices provided by the present invention;

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

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

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

[0051] Figure 5 It is a structural schematic 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 structural schematic diagram of a computer device provided by the present invention. DETAILED DESCRIPTION

[0053] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the information security transmission method and system for large-scale IoT devices proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0054] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0055] The following is a detailed description of a specific scheme of a method and system for secure information transmission for large-scale IoT devices provided by the present invention in conjunction with the accompanying drawings.

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

[0057] See also Figure 1, which shows a flowchart of a method for secure information transmission for large-scale IoT devices provided by an embodiment of the present invention, the method comprising the following steps:

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

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

[0060] Specifically, in this embodiment, the time series data transmitted by each device is obtained through the Internet of Things platform and recorded as historical transmission data, and the location information data of each device is obtained at the same time. In this embodiment, the location coordinates of each device are used as 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 historical transmission data of different devices may also be different. Subsequently, the connection relationship between devices can be divided according to the association relationship and consistency relationship 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 that are close to each other within the spatial range, a connection relationship is generally not set when the spatial distance between devices is large. In this embodiment, any device is taken as an example for explanation, that is, any device is recorded as the target device. And the difference distance of the location information data between each two devices is calculated to obtain the spatial distance between each two devices. Based on the location information data of each device, the spatial distance between each two devices can be obtained. Specifically, the Euclidean distance of the location information data between each two devices can be used to calculate the spatial distance between each two devices.

[0062] Furthermore, a preset number of other devices that are closest to the target device in space are obtained as reference devices of the target device, and the average of the distances between the target device and each reference device is used as the characteristic distance data of the target device. The reference device represents the devices that are closest to the target device in space. The characteristic distance data represents the nearest neighbor distance parameter of the target data, which avoids 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 between two devices in terms of significant characteristics 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 time sequence of 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 a local significant subsequence corresponding to each significant feature of each device.

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

[0071] Taking into account that if the amount of data collected is large, there is often data redundancy in the transmission data sequence of each device. In order to be able to more significantly and prominently analyze the similarities of the characteristic parts in the transmission data sequence, we can first use the feature extraction operation to obtain the degree of significant feature expression of the transmission data sequence, which can more intuitively characterize the characteristic degree of the data to a certain extent.

[0072] Specifically, this embodiment uses the Shapelet algorithm to extract the significant features of each transmission data sequence, and obtains the local significant subsequence 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. The algorithm can be used to directly obtain the local significant data segments in the time series. This embodiment regards each data segment 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, obtaining a salient feature aggregation index for each corresponding salient feature between every two devices according to the data overlap of the local salient subsequences of each corresponding salient feature between every two devices and the difference between the time features of the local salient subsequences.

[0074] For the local significant subsequences corresponding to the significant features of each device, if significant features appear in the same positions of different devices, and the characteristic expressions between the significant features are relatively similar, it means that the overall data volume between the two devices is relatively similar, and the data changes are also relatively similar. More specifically, if the time length difference corresponding to the local significant subsequences between different devices is small, and the corresponding time intervals of appearance are also relatively consistent, it means that the reasons for the data change characteristics corresponding to the significant features between different devices may be relatively consistent, which further indicates that the correlation relationship and similarity between the two devices are relatively large. It is beneficial to data transmission to classify devices with large correlations into the same local networking structure.

[0075] Based on this, the significant feature aggregation index between each two devices is quantified by analyzing the data overlap and time difference between the significant features corresponding to each two devices. In this embodiment, any two devices are used as an example for explanation. Considering that the weight of the edge is characterized later, this implementation specifically analyzes the devices with edge connection relationships in the device graph structure, that is, any two devices corresponding to the nodes with edge connection in the device graph structure are recorded as the first device and the second device respectively.

[0076] At the same time, taking any significant feature as an example, that is, recording the significant feature corresponding to any serial number 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 is represented as the significant feature corresponding to the first local significant subsequence in the transmission data sequence corresponding to each device.

[0077] Furthermore, 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 is obtained; and 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 is obtained.

[0078] The first length represents the time overlap length 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 interval of the significant feature. The second length represents the total time length of the local significant subsequences of the first device and the second device under the target significant feature.

[0079] Based on the difference between the first length and the second length, a first difference coefficient is determined; 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, a second difference coefficient is determined; and the negative correlation coefficient of the product of the first difference coefficient and the second difference coefficient is used as a significant feature aggregation indicator of the first device and the second device under the target significant feature.

[0080] As a specific example, taking the uth device as the first device, the vth device as the second device, and the ith salient feature as the target salient feature, the calculation formula of the salient feature aggregation index can be specifically expressed as:

[0081]

[0082] Among them, R u,v(i) represents the salient feature aggregation index of the u-th device and the v-th device under the i-th salient feature, that is, the salient feature aggregation index of the first device and the second device under the target salient feature, u represents the u-th device, v represents the v-th device, and i represents the i-th salient feature; X v (i) represents the time period corresponding to the local significant subsequence of the vth device under the ith 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 vth 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, and exp() represents the exponential function with e as the base.

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

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

[0085] The smaller the value of the first difference coefficient is, and the smaller the value of the second difference coefficient is, it means that the time performance length of the first device and the second device under the current significant feature is relatively consistent, and the degree of data aggregation is relatively consistent, and the corresponding significant feature aggregation index value 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: determining a feature similarity coefficient of each corresponding significant feature between every two devices based on the difference distance of the local significant subsequence of each corresponding significant feature between every two devices.

[0087] In this embodiment, the first device and the second device are used as an example to illustrate that 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 between the time series data of the two devices under the corresponding significant feature.

[0088] Step S204: taking 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 as the data association degree between each two devices.

[0089] When analyzing the correlation and similarity between each two devices, the first aspect is to analyze the data overlap and time length difference in the time distribution of the two devices under the corresponding significant features, and quantify the similarity of the data aggregation degree of the two devices. The second aspect is to analyze the feature similarity of the historical transmission data of the two devices under the corresponding significant features. Further combining the similarities and differences in these two aspects, the data correlation and data similarity between the two devices are comprehensively analyzed.

[0090] Specifically, taking the first device and the second device as an example, the product of the significant feature aggregation index and the feature similarity coefficient of the first device and the second device under all significant features is accumulated to obtain the data association degree corresponding to 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] Among them, Q u,v represents the data association degree between the uth device and the vth device, that is, the data association degree between 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 vth device under the ith significant feature, R u,v (i) represents the salient feature aggregation index of the u-th device and the v-th device under the ith salient feature, N 0 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 represents the feature analysis results of the first aspect, and the feature similarity coefficient represents the feature analysis results of the second direction. The larger the values ​​of the two are, 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 spatial distribution analysis of the data volume and location information data of each device's historical transmission data, combined with the direction of the change trend of the data volume between every two devices within the spatial range, obtain the trend consistency between every two devices.

[0095] In the above steps, the similarities and correlations between the data information and time information of the two devices in the first aspect are analyzed. Further, considering that the amount of historical transmission data of different devices is different, and the distance distribution between different devices is also different, when building a local network, it is more desirable for devices with smaller data volumes to send to devices with larger data volumes to reduce the amount of data transmitted in the network. Based on this, the direction of the change trend between different devices can be analyzed through the distribution of the devices in terms of data volume and location information data in space, and the degree of trend consistency can be used to quantify the degree of data transmission optimization between two devices.

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

[0097] Step S301, based on the location 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 value of each device, map the device into the three-dimensional coordinate system, and determine the three-dimensional data point of each device.

[0098] First, construct the information feature descriptor of each device, which mainly includes location information and data volume information, to provide a data basis for the subsequent data change trend analysis in the two dimensions of location and data volume. Specifically, the total amount of all historical transmission data of each device is the data volume corresponding to each device. Combining the location coordinates and data volume in the location information data of each device, 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 ,yu ) represents the location coordinates of the u-th device, such as longitude and latitude, z u Indicates the data volume of the u-th device.

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

[0100] Step S302: Analyze the local data change trend according to the distribution of the three-dimensional data points of all devices, 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, a function fitting can be performed on all devices to obtain a characteristic function, which can characterize the change of the location information and information volume information of the device. Among them, the least squares method can be selected as the method for function fitting. In other embodiments, the implementer can set it according to the specific implementation scenario.

[0102] Then, each maximum point and minimum point is obtained based on the characteristic function, that is, the maximum point and minimum point in the characteristic function can be obtained by using the method of calculating partial derivatives. 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 value of the data volume, the direction from each minimum point to the nearest maximum point is recorded as each comprehensive change trend vector. 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 within the local space, that is, the change trend direction of the increase in local data volume.

[0104] Step S303, determining the trend consistency degree between every two devices according to the change trend distribution between the three-dimensional data points corresponding to every two devices and the difference between the comprehensive change trend vectors.

[0105] By analyzing the consistency of the change trend direction of the data volume increase between the two devices and the change trend direction of the data volume increase corresponding to all devices as a whole, it can be reflected that 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. In this embodiment, any two devices corresponding to the nodes with edge connection in the device graph structure are used as an example for explanation. Figure 4 As shown, the degree of trend consistency can be achieved by steps S3031 to S3034.

[0106] Step S3031: 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.

[0107] Assuming that, between the nth device and the mth device connected by an edge, the data volume of the nth device is the largest and the data volume of the mth device is the smallest, the nth device can be recorded as the first characteristic device and the mth device can be recorded as the second characteristic device.

[0108] Step S3032: 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 between the first feature device and the second feature device.

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

[0110] Step S3033, obtaining a comprehensive change trend vector corresponding to the minimum point closest to the first characteristic device and the second characteristic device and recording it as a characteristic vector.

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

[0112] Step S3034: taking 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.

[0113] The smaller the angle between the data change vector and the feature vector, the larger the corresponding cosine value, indicating that the consistency of the directions of the two vectors is greater. The trend consistency thus characterizes the consistency between the directions of the data change vectors of the first feature device and the second feature device and the directions of the feature vectors within the current local spatial range. The larger the value, the stronger the consistency between the direction of the change trend of the increase in the amount of data between the two devices and the current local spatial range, indicating that the effect of dividing them into the same local network is better.

[0114] Step S400, using the data association degree and trend consistency degree to determine the edge weights between nodes in the device graph structure, performing network division according to the device graph structure, and determining the data encryption transmission method in the network.

[0115] First, the correlation between the two devices is analyzed from the overlap of the significant features between the two devices. Second, the correlation between the two devices is analyzed from the consistency of the changing trend of the increase in the amount of data between the two devices and the local space. Combining the correlation analysis results of the two aspects, we can comprehensively characterize the weight corresponding to the connection edge between each two devices. That is, the greater the correlation between each two devices, the greater the weight of the corresponding connection edge.

[0116] Specifically, 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, and 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.

[0117] As a specific example, this embodiment takes the u-th device and the v-th device as an example for explanation, and the calculation formula of the edge weight between the nodes corresponding to 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 between the nodes corresponding to the u-th device and the v-th device, Q u,v Indicates the degree of data association between the u-th device and the v-th device, W u,v Indicates the trend consistency between the u-th device and the v-th device, D u,v represents the Euclidean distance between the location information data of the u-th device and the location information data of the v-th device, and softmax represents a normalization function.

[0118] According to the same method, the edge weight corresponding to every two devices with edge connection 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 split and cluster the current spatial graph structure. The local networking structure corresponding to the local spatial range with denser connection relationship in the spatial range can be determined, and the connection relationship between different local networking structures is relatively sparse, which can reduce the amount of data transmission 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 division 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 split result is the final clustering result. The algorithm is a well-known technology and will not be introduced in detail here. In the clustering result, the devices contained in each cluster correspond to a networking structure to facilitate subsequent data transmission.

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

[0121] In summary, the present invention adopts a networking transmission method when performing information security transmission of large-scale data. When performing networking division and allocation of devices, the location information of the device within the spatial structure is first considered to preliminarily construct a graph structure, and then the time series correlation and similarity of the device are determined based on the significant change characteristics of the current device's time series data and the aggregation between the time series intervals. At the same time, the data volume change trend of the neighboring devices in space is considered. Combined with the overall characteristics of the device in time and space, the weights of the edges between the devices in the graph structure are constructed, and the graph structure is split and clustered. The specific performance of each local networking structure can be determined, and finally secure encrypted transmission is performed through different networks to reduce the possibility of network congestion and improve the security of information transmission.

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

[0123] 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;

[0124] 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;

[0125] 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;

[0126] 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.

[0127] The present application also provides a computer device. Figure 6 , which shows a schematic diagram of the structure of a computer device provided by an embodiment of the present invention, wherein 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, wherein when the processor 602 executes the computer program 603, the computer device can execute the aforementioned information security transmission method for large-scale IoT devices.

[0128] An embodiment of the present application also provides a computer program product. When the computer program product is run on a computer device, the computer device can execute the aforementioned information security transmission method for large-scale IoT devices.

[0129] An embodiment of the present application also provides a computer-readable storage medium, in which a computer program code is stored. When the computer program code runs on a computer device, the computer device can execute the aforementioned information security transmission method for large-scale IoT devices.

[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 repeated here.

[0131] The embodiments described above 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 aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions 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; The data association degree and trend consistency degree are used to determine the edge weights between nodes in the device graph structure, network division is performed according to the device graph structure, and the data encryption transmission method in the network is determined.

2. According to claim 1, a method for secure information transmission for large-scale IoT devices is characterized in that: 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.

3. The information security transmission method for large-scale IoT devices according to claim 2 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.

4. 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.

5. The information security transmission method for large-scale IoT devices according to claim 4 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.

6. The information security transmission method for large-scale IoT devices according to claim 5 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.

7. The information security transmission method for large-scale IoT devices according to claim 1 is characterized in that: 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, and 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.

8. 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.

9. 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.

10. 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 9, 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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