A method for predicting the spread and scope of Bluetooth viruses based on infection rate decay.

Abstract

This invention relates to a method for predicting the spread trend and range of Bluetooth viruses based on infection rate decay. The method first integrates multivariate data. Then, based on this data, the system is initialized to generate a road network, nodes, and their movement trajectories. Next, a model for predicting Bluetooth virus spread is constructed based on the initialized system and parameters. This model traverses nodes at discrete time points, acquiring the node's location and its neighbors within its signal radiation range, and calculating the node's current state. As nodes move and time progresses, the system repeats this process until the predicted end time is reached. Finally, based on the node's state at each discrete time point, the system outputs the changing patterns of the number of nodes in various states over time, thereby predicting the trend and range of Bluetooth virus spread, and presenting the results to the user in the form of a graph. This method can quickly understand the spread of viruses in a specific area, providing strong support for prevention and control decisions.

Description

Technical Field

[0001] This invention belongs to the field of malware technology and relates to a method for predicting the spread trend and scope of Bluetooth viruses based on infection rate decay. Background Technology

[0002] With the rapid development of information technology, smart devices have become an indispensable part of people's lives, such as smartphones, tablets, smart home devices, and IoT devices. However, along with the convenience of technology, smart devices also face increasingly serious security threats, the most significant of which is the spread of viruses. These viruses spread through various means such as Bluetooth, Wi-Fi, SMS, and email, causing huge economic losses to users and potentially even leaking personal privacy information.

[0003] Bluetooth, as a short-range wireless transmission technology, has been widely used in recent years. Its standard, IEEE 802.15.1, was researched and developed by the IEEE. With its convenience, low power consumption, and wide device compatibility, Bluetooth technology has become an important means of data exchange between smart devices. However, this has also made Bluetooth a significant route for virus propagation. Bluetooth viruses, which use Bluetooth as a propagation medium to multiply in networks and control hosts, have become rampant in various smart devices. Due to their rapid spread and high degree of stealth, Bluetooth viruses pose a significant challenge to network security.

[0004] To address this challenge, both academia and industry are actively researching methods for preventing and controlling computer viruses. Currently, research on computer viruses primarily focuses on two approaches: the development of antivirus software and macroscopic modeling. However, both methods have limitations in combating the spread of Bluetooth viruses.

[0005] Antivirus software research is generally based on virus signature databases. This involves analyzing and identifying known viruses, extracting their signatures, and then building a virus database for detection and removal. This method is inherently time-consuming, as sufficient virus characteristics can only be collected for development after a virus has emerged and caused significant damage. Furthermore, with the continuous development of virus technology and the rapid rate of virus mutation, antivirus software struggles to effectively combat newly emerging viruses.

[0006] Macroscopic modeling, on the other hand, involves constructing network models to study the dynamics of virus propagation within networks. This approach is typically based on theoretical models such as small-world networks or scale-free networks. However, real-world networks often do not perfectly conform to the characteristics of these theoretical models, so the results of macroscopic modeling may deviate significantly from reality. Furthermore, macroscopic modeling requires ignoring details such as network node characteristics and terminal characteristics, which makes macroscopic modeling research prone to becoming detached from reality and possessing only theoretical significance.

[0007] Therefore, there is an urgent need in the field for a method that can effectively predict the spread trend and scope of Bluetooth viruses. Summary of the Invention

[0008] In view of this, the purpose of this invention is to provide a method for predicting the spread trend and range of Bluetooth viruses based on infection rate decay. By having the user input system parameters representing the characteristics of the Bluetooth virus, along with data such as the map of the virus's spread and the end time, the method predicts the spread trend and final spread range of the virus on the map. It is expected to help individual users, enterprises or organizations, and security service providers formulate virus spread suppression strategies.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A method for predicting the spread trend and scope of Bluetooth viruses based on infection rate decay, the method includes the following steps:

[0011] S1: Receive and integrate the multivariate data required for the input prediction, including: .osm map file, number of nodes N, initial ratio of infected nodes Ratio, and prediction end time T. end And other parameters related to predicting the spread of Bluetooth viruses;

[0012] S2: Based on the multi-data integrated in S1, perform system initialization, including generating a road network according to the map file, generating nodes according to the number of nodes and the initial ratio, and generating the movement trajectory of specified nodes on the road network according to the input or random generation.

[0013] S3: Based on the multi-data parameters integrated in S1 and the system initialized in S2, construct a model to predict the propagation of Bluetooth viruses;

[0014] S4: The Bluetooth virus propagation prediction model traverses the nodes generated in S2 at discrete time points, obtains the location of the node and its neighbors within the signal radiation range, and calculates the current state of the node.

[0015] S5: The current node changes position according to the movement trajectory generated in S2, enters the next discrete time point, and repeats S4 until the predicted time T input in S1. end Finish;

[0016] S6: Based on the state of each node in the system at each discrete time point calculated in S4, output the changing pattern of the number of nodes in various states over time, predict the trend and scope of Bluetooth virus propagation, and return the prediction result graph to the user.

[0017] Furthermore, in S1, the .osm map file is a user-defined area where the spread of the Bluetooth virus needs to be predicted. The input is a .osm file representing the map outline, and the .osm file is used to create a recognizable .xml road network file. The road network file consists of points and edges, where points represent intersections of roads and edges represent actual roads. The roads include sidewalks, railways, and highways.

[0018] Other parameters related to predicting the spread of Bluetooth viruses include: the Bluetooth signal radiation range (Radius) of the smart device, the immune cycle (Cycle) of the smart device, the ideal infectivity (β) of infected nodes, and the click-through rate (P) when a smart device user receives a suspicious connection. click 1. The probability of a latent node becoming an outbreak node α2. The recovery rate of an outbreak node α3. The probability of a node returning to a susceptible state after losing its immunity α4. The number of points in the generated road network Map_s and the number of edges in the generated road network Map_p.

[0019] Furthermore, step S2, based on the multi-data integrated in step S1, performs system initialization, specifically including the following steps:

[0020] S21: The system calls the netgenerate function to generate an .xml file representing the road network based on the .osm map file imported by the user. If the user chooses to generate a custom road network, the user can input the road network type, which includes grid, regular and random. The number of points Map_s and the number of edges Map_p are used to generate the road network.

[0021] S22: The system initializes the nodes according to the input number of nodes N and the initial ratio Ratio of infected nodes. The total number of nodes at any time t satisfies:

[0022] N t =S t +E t +I t +R t (1)

[0023] Where S represents a node in a susceptible state, E represents a node in a latent state, I represents a node in an infected state, and R represents a node in an immune state.

[0024] S23: Based on the road network generated in S21 and the nodes generated in S22, the system randomly generates the position of each node on the road network at any time t, and generates a pedestrian flow file of the specified node movement trajectory, represented by a .rou.xml file; wherein, the time unit for the node position change is 1 step, and the distance moved per unit time is a value calculated based on the speed.

[0025] Furthermore, in step S3, a model for predicting the spread of Bluetooth viruses is constructed, specifically including the following steps:

[0026] Model assumptions: 1) E nodes and I nodes have the same infection capability; 2) The infection capability of a virus node decays proportionally to the Bluetooth signal decay; 3) The total number of nodes N in the entire network remains constant at any given time; 4) The virus will not kill nodes; 5) Nodes in the R state will probabilistically return to the S state outside the immune cycle.

[0027] S31: Calculate the actual signal power that can be received based on the distance between any two communicable nodes. The calculation formula is as follows:

[0028]

[0029] Where distance represents the distance between the two devices, A represents the power received when they are 1 meter apart, and n represents the environmental attenuation factor, which is affected by obstacles, temperature, and humidity; the empirical values ​​of A and n are 59 and 2.0, respectively, and users can adjust them according to the actual scenario; RSSI is the received signal strength indicator, which is implemented after the reverse channel baseband receiving filter; the formula (2) is converted to obtain:

[0030]

[0031] Among them, the power limit is 0, and the range of [-60, -70] is considered good. The wireless signal is mostly at the mW (megawatt) level. Polarization operation is performed, which does not mean that the signal is negative.

[0032] S32: Based on S31, calculate the actual power signal rate received between two devices at any distance that can communicate, and further calculate the effective infection rate γ. The calculation formula is as follows:

[0033]

[0034] Wherein, k and p are two constants that maintain a linear relationship between infection rate decay and power decay. They are calculated based on the fact that the effective infection rate is 0 when the distance between devices reaches the limit of the signal radiation range, which is the ideal infection capability of the β infected node.

[0035] Then, based on user security awareness and γ, the probability α1 of a node being infected is calculated using the following formula:

[0036]

[0037] Among them, P click Click-through rate when smart device users receive suspicious connections;

[0038] S33: Based on the infection rate decay algorithm proposed in S32, a Bluetooth virus propagation prediction model is constructed. The model can predict the state of each node in the network at time t+1 based on the node's own state, the state of its neighbors, and the distance at any time t.

[0039] If the state of a node at time t is represented by δ t This means that if δ t Given a susceptible state, where random represents a generated random number and random∈[0,1), then δ t+1 The specific calculation steps are as follows:

[0040] S331: Obtain the neighbors of states E and I within the radius, denoted by the set G(n):

[0041] G(n)={E1,E2,...,I1,I2,...} (6)

[0042] S332: Traverse G(n), for each E x with I y The infection rate is calculated based on S32. If α1 >= random, the current node is infected, the traversal ends, and a state switch δ is performed. t+1 =E;

[0043] If δ t If the state is latent and α2 >= random, then δ t+1 =I;

[0044] If δ t If the state is infected and α3>=random, then δ t+1 =R;

[0045] If δ t In an immune state, the immune cycle has ended and α4 >= random, then δ t+1 =S;

[0046] S34: Use S t The number of intelligent devices in state S at time t is represented by the following formula. The calculation process for the number of nodes in the system at time t+1 is as follows:

[0047]

[0048] Furthermore, in S4, the model predicting the spread of Bluetooth viruses traverses the nodes generated in S2 at discrete time points, obtains the location of the node and its neighbors within its signal radiation range, and calculates the current state of the node, specifically including the following steps:

[0049] S41: Traverse the four types of nodes to obtain each node object, and obtain the set of diseased neighbors G(n) based on the location and signal radiation range Radius of each node;

[0050] S42: Calculate the current state of the node based on the model for predicting the spread of Bluetooth viruses in step S3.

[0051] Furthermore, in step S6, based on the state of each node in the system at each discrete time point calculated in S4, the variation law of the number of nodes in various states over time is output to predict the trend and scope of Bluetooth virus propagation, and the prediction result graph is returned to the user. Specifically, this includes the following steps:

[0052] S61: Obtain the number of nodes in the system at each discrete time point, output the changing pattern of the number of nodes in various states over time, and predict the trend and scope of Bluetooth virus propagation.

[0053] S62: Extract the prediction data when only the values ​​of the comparison parameters differ based on the input comparison parameters, and perform comparative analysis;

[0054] S63: Output predicted data.

[0055] The beneficial effects of this invention are as follows:

[0056] First, by integrating multi-source data and constructing a prediction model based on infection rate decay, this invention can more accurately predict the spread range of Bluetooth viruses in actual space.

[0057] Secondly, this invention not only considers traditional infectious disease model parameters, such as the number of nodes and the initial proportion of infected nodes, but also introduces parameters related to Bluetooth signal propagation, such as signal radiation range and immune cycle. This makes the prediction results closer to reality and improves the accuracy of the prediction.

[0058] Third, this invention allows users to specify the areas to be predicted and provides various road network generation methods, such as grid, rule-based, and random methods. This makes the technical solution highly flexible and scalable, capable of adapting to prediction needs in different scenarios.

[0059] Fourth, this invention not only predicts the trend and scope of Bluetooth virus propagation, but also outputs the changing patterns of the number of nodes in various states over time. This provides users with a wealth of information.

[0060] Fifth, this invention considers the impact of signal attenuation in wireless environments on the spread of Bluetooth viruses. It can create road networks based on the map structure of the area requiring prediction and supports input based on the actual situation of virus spread. Therefore, this method has high scalability and accuracy. This method can realistically predict the spread range and trend of virus types represented by parameters on the map represented by the road network. Compared with traditional mathematical modeling research, the prediction results consider parameters that are more consistent with reality. The prediction data provided by this method is of great significance for the formulation of anti-virus solutions.

[0061] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0062] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0063] Figure 1 This is a flowchart of the present invention;

[0064] Figure 2 This is a road network map of Deyuan Street in Chengdu.

[0065] Figure 3 A random map automatically generated based on the input number of edges and vertices;

[0066] Figure 4 This is a node state switching diagram in the prediction model of the present invention;

[0067] Figure 5 The prediction results are shown for Pclick = 0.3 and α3 = 0.05.

[0068] Figure 6 The prediction results are shown for Pclick = 0.5 and α3 = 0.03.

[0069] Figure 7 The prediction results are shown for Pclick = 0.7 and α3 = 0.01.

[0070] Figure 8 A comparison chart of data with Pclick=0.3 and Pclick=0.5;

[0071] Figure 9 A comparison chart of data with Pclick=0.3 and Pclick=0.7;

[0072] Figure 10A comparison chart of data with Pclick=0.5 and Pclick=0.7;

[0073] Figure 11 For P click ~(0.3, 0.3) 2 The prediction results for α3 = 0.05 are shown in the figure.

[0074] Figure 12 For P click ~(0.5, 0.3) 2 The prediction results for α3 = 0.03 are shown in the figure.

[0075] Figure 13 For P click ~(0.7, 0.3) 2 The prediction results for α3 = 0.01 are shown in the figure.

[0076] Figure 14 For P click ~(0.3, 0.3) 2 ) and P click ~(0.7, 0.3) 2 (Data comparison chart)

[0077] Figure 15 For P click ~(0.5, 0.3) 2 ) and P click ~(0.7, 0.3) 2 (Data comparison chart)

[0078] Figure 16 For P click ~(0.3, 0.3) 2 ) and P click ~(0.5, 0.3) 2 (Data comparison chart) Detailed Implementation

[0079] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0080] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0081] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0082] Please see Figures 1 to 16 This is a method for predicting the spread trend and scope of Bluetooth viruses based on infection rate decay.

[0083] Example 1

[0084] The following will describe, with reference to the accompanying drawings and specific examples, a method for predicting the spread of Bluetooth viruses based on infection rate attenuation, as described in this invention. Figure 1 As shown, further explanation is provided:

[0085] The Deyuan area of ​​Chengdu was selected as the location of the Bluetooth virus transmission. According to the Seventh National Population Census Bulletin of Pidu District, as of November 1, 2020, the total population of Deyuan Subdistrict, Chengdu was 65,184, and the total area was 30.72 square kilometers. This data is in the middle range among urban statistics nationwide. Furthermore, the road network topology of this subdistrict is complex and varied, exhibiting typical characteristics in both statistical data and overall layout compared to other cities across the country. Therefore, studying the virus transmission in this area has significant reference value. The road network of Deyuan area is as follows: Figure 2 As shown, it includes 939 nodes and 1971 roads, of which there are 34 types of roads.

[0086] According to the investigation, the radiation range of a typical Bluetooth device is approximately 15m. Therefore, the Bluetooth radiation range in this prediction is set to Radius = 15m. The latency conversion rate of this Bluetooth virus is set to α2 = 0.15, the immunization inactivation rate to α4 = 0.5, the immunization cycle to Cycle = 5, the ideal infection rate to β = 0.9, the total number of nodes to N = 1000, the initial proportion of infected nodes to Ratio = 0.1, and the prediction end time to T. end=3000. Simultaneously, the comparison parameter Pclick was set to 0.3 / 0.5 / 0.7, and α3 was set to 0.05 / 0.03 / 0.01 accordingly. This set of comparison parameters mainly aims to explore the impact of security awareness on the virus's spreadability. If users have high security awareness, their click-through rate P... click The recovery rate α3 after infection is low, which is reasonable.

[0087] Generate an .xml road network file from the imported .osm map file, such as... Figure 3 As shown; then initialize 1000 node objects based on the input N and Ratio, including 900 S-state nodes and 100 I-state nodes, satisfying N0=S0+E0+I0+R0; then generate the position of each node on the road network at any discrete time t based on random movement, represented by a .rou.xml file, where the distance that a node can move at each time is determined by a default fixed speed.

[0088] Based on the input and the products of the two steps above, a Bluetooth virus propagation prediction model is constructed. This model is then used to calculate the current discrete-time state of each node. The node state switching rules and probabilities of the model are as follows: Figure 4 As shown. At time t, the nodes in the four states are traversed, using δ... t Let represent the current node's state, and ...

[0089] G(n)={E1,E2,…,I1,I2,…} (1)

[0090] For a susceptible node (S), first traverse the set of infected neighbors G(n), and calculate the signal power attenuation between any two neighbors in the set. The calculation formula is as follows:

[0091]

[0092] Where distance represents the distance between the two points, calculated from their current positions on the map; A represents the power received when they are 1 meter apart; and n represents the environmental attenuation factor (affected by obstacles, temperature, humidity, etc.). Based on experience, n is set to 59 and A to 2.0, but these values ​​can be adjusted according to actual conditions. The effective infection rate γ, after attenuating the ideal infection rate β from the current neighbor's power, is calculated using the following formula:

[0093]

[0094] With β set to 0.9 as input, and k and p being constants maintaining the proportionality between infection rate decay and power decay, their calculated values ​​are 4.25 and 250.75, respectively. The probability α1 of a node being infected is then calculated based on user security awareness and γ, using the following formula:

[0095]

[0096] Among them, P click The values ​​were 0.3 / 0.5 / 0.7 in the three comparisons, for each E in the set of diseased neighbors G(n). x with I y Calculate α1. If α1 >= random, the current node is infected, and a state switch δ is performed. t+1 =E.

[0097] For a latent node (E), if α2 >= random, then δ t+1 =I, where α2 = 0.15 is substituted.

[0098] For infected node (I), if α3>=random, then δ t+1 =R, where α3 is the input value.

[0099] For an immune node (R), if the node has been in state R for longer than the immune cycle, and α4 >= random, then δ t+1 =E, where α4 = 0.4.

[0100] The state of each node is calculated based on the formula set for node state switching described above, and the calculation results for each discrete moment are compiled into a file. The next moment position of each object in the flow file .rou.xml is obtained, with the discrete moment advanced by 1 step.

[0101] When the system time t > the input T end Once the prediction is complete, read the statistical data for each discrete time point and plot the number of nodes versus time. The prediction result for this input data is as follows: Figures 5-7 As shown. Among them, Figure 5 , Figure 6 and Figure 7 The prediction results correspond to Pclick values ​​of 0.3 / 0.5 / 0.7 and α3 values ​​of 0.05 / 0.03 / 0.01, respectively. The relationship between the prediction results and the comparison parameters is shown in Table 1.

[0102] Table 1

[0103] picture <![CDATA[P click ]]> <![CDATA[α3]]> Figure 5 0.3 0.05 Figure 6 0.5 0.03 Figure 7 0.7 0.01

[0104] like Figure 8 , Figure 9 and Figure 10Comparative data shows that for every level of security awareness improvement, the number of stable S nodes increases significantly. Figure 10 The data shows that when Pclick decreased from 0.7 to 0.5, the number of S nodes increased by nearly 400, while when its value was as follows... Figure 8 As shown, when the value is reduced from 0.5 to 0.3, the number of S nodes only increases by about 150 (α3 also follows P). click (Taking different values). Large incremental differences indicate that the relationship between security awareness and the number of S-nodes is not a simple proportional linear relationship, but rather a logarithmic function. Specifically, in Figure 10 In the middle, the number of S1 at the viral equilibrium point is almost twice the number of S2, which also positively proves P click And the enormous influence of α3 on viral transmission.

[0105] The prediction results show that user security awareness has a significant impact on the scope and speed of virus transmission. To address this, strategies can be developed, such as increasing anti-virus publicity to raise the average user's security awareness, thereby reducing the risk of virus transmission. click At the same time, α3 is added, which can be implemented by the manufacturer, but the final effect depends on the user.

[0106] Example 2

[0107] Considering the heterogeneity of nodes, any node may have different security awareness; therefore, the comparison parameter P in Instance 1 will be changed. click Adjusted to follow a normal distribution, where P click ~(0.3, 0.3) 2 ) / (0.5, 0.3) 2 ) / (0.7, 0.3) 2 The corresponding recovery rates α3 were set to 0.05 / 0.03 / 0.01, as shown below. Figure 11 , Figure 12 and Figure 13 As shown. Human cybersecurity awareness must comprehensively consider differences in biological, psychological, and cultural factors. Although the level of awareness varies greatly from person to person, it follows a normal distribution from a macroscopic perspective. Therefore, this example is more realistic than Example 1.

[0108] Similarly, the prediction results obtained according to the steps shown in this method are as follows: Figures 11-13 As shown, different P click For example, the predicted data for the value. Figure 14-16 As shown in Table 2, the relationship between the prediction results and the comparison parameters is shown in Table 2.

[0109] Table 2

[0110] picture <![CDATA[P click ]]> <![CDATA[α3]]> Figure 11 <![CDATA[(0.3,0.3 2 )]]> 0.05 Figure 12 <![CDATA[(0.5,0.3 2 )]]> 0.03 Figure 13 <![CDATA[(0.7,0.3 2 )]]> 0.01

[0111] like Figures 11-16 The comparison between the predicted results and the data shows that the spread trend and scope of the virus in this example are similar to those in Example 1.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for predicting the trend and range of Bluetooth virus propagation based on the decay of infection rate, characterized in that: The method comprises the following steps: S1: receiving and integrating multiple data required for input prediction, including:.osm map file, node number N, initial ratio of infected nodes Ratio, prediction end time T end and other parameters related to Bluetooth virus propagation range prediction, including: Bluetooth signal radiation range Radius of smart devices, immune cycle Cycle of smart devices, ideal infection capacity β of nodes with virus, click rate Pclick of smart device users when receiving suspicious connections, probability α2 of latent nodes becoming outbreak nodes, recovery rate α3 of outbreak nodes, probability α4 of nodes losing immunity and returning to susceptible state, point number Map_s of generated road network, and edge number Map_p of generated road network; S2: based on the integrated multi-element data in S1, system initialization is performed, including generating a road network according to a map file, generating nodes according to the number of nodes and an initial scale, and generating a movement trajectory of a specified node on the road network according to input or randomness; S2, based on the integrated multi-element data in S1, performs system initialization, specifically comprising the following steps: S21: the system calls a function netgenerate to generate an.xml file representing a road network according to an.osm map file imported by a user, and if a custom road network is selected, a road network type is inputted by the user, wherein the road network type comprises a grid, a rule and randomness, and the number of points Map_s and the number of edges Map_p generate a road network; S22: the system initializes nodes according to the number of nodes N and the initial proportion of infected nodes Ratio, and the total number of nodes at any time t satisfies: N = S + E + I + R wherein S represents a node in a susceptible state, E represents a node in a latent state, I represents a node in an infected state, and R represents a node in an immune state; S23: the system randomly generates the position of each node on the road network at any time t according to the road network generated in S21 and the nodes generated in S22, and generates a people flow file representing a movement trajectory of a specified node in a.rou.xml file; wherein the time unit of node position change is 1 step, and the distance moved in a unit time is a value calculated according to the speed; S3: based on the integrated multi-element data parameters in S1 and the system completed in S2, a prediction Bluetooth virus propagation model is constructed; in S3, a model for predicting the propagation of a Bluetooth virus is constructed, specifically comprising the following steps: Model assumptions: 1) E nodes and I nodes have the same infection ability; 2) the infection ability of a virus node is proportional to the attenuation of a Bluetooth signal; 3) the total number of nodes N in the entire network at any time is constant; 4) the virus does not cause node death; 5) a node in the R state will probabilistically return to the S state outside the immune cycle; S31: the actual signal power received between any two communicable nodes is calculated according to the distance therebetween, and the calculation formula is: wherein distance represents the distance between two devices, A represents the power received when the distance is 1 meter, n represents an environmental attenuation factor, the environmental attenuation factor includes obstacles, temperature and humidity, the empirical values of A and n are 59 and 2.0, RSSI is the strength indication of a received signal, and formula (2) is converted to obtain: wherein the upper limit of the power is 0, and the value in [-60, -70] is good; S32: the actual signal power received between any two devices at a communicable distance is calculated according to S31, and the effective infection rate γ is further calculated, and the calculation formula is: wherein k and p are two constants maintaining a linear relationship between the infection rate attenuation and the power attenuation, the effective infection rate is calculated based on the fact that the effective infection rate is 0 when the distance between devices reaches the limit of signal radiation, and β represents the ideal infection ability of a virus-carrying node. ​ According to the user's safety awareness and The probability of infection of the node α1 is calculated, and the calculation formula is: where P click is the click rate for the smart device user when receiving a suspicious connection; S33: constructing a Bluetooth virus propagation prediction model based on the infection rate decay algorithm proposed in S32, which can predict the state of each node at t+1 time according to the state of the node itself, the state and distance of the neighbor at any t time; If the state of a node at time t is denoted by δ t , if δ t is the susceptible state, random represents a generated random number, and , then the calculation step of δ t+1 is: S331: obtaining the neighbors with state E and I within the radius range, denoted by set G(n); S332: Traverse G(n), for each E x with I y Infection rate calculated according to S32, if If the current node is infected, the traversal ends and a state transition δ is performed. t+1 =E; if δ t is in a latent state and then I; if δ t is an infection state and then ; if δ t is the immune status, the immune cycle has passed and then S; S34: use S t The number of intelligent devices in state S at time t is represented as N (S, t). The number of nodes in the system at time t+1 is calculated as follows: S4: the prediction model of Bluetooth virus propagation traverses the nodes generated in S2 at discrete time points, obtains the position of the node and its neighbors within the signal radiation range, and calculates the state of the node at the current time; S5: the current node changes the position according to the moving trajectory generated in S2, enters the next discrete time point, and repeats S4 until the prediction time T input in S1 end End; S6: according to the state of each node in the system at each discrete time point calculated in S4, outputting the change rule of the number of nodes in various states with time, predicting the trend and range of Bluetooth virus propagation, and returning the prediction result graph to the user.

2. The method for predicting Bluetooth virus propagation trend and range based on infection rate decay according to claim 1, characterized in that: In S1, the.osm map file is an area set by the user for which the Bluetooth virus propagation range needs to be predicted. The.osm file representing the map outline is input, and the.osm file is made into a recognizable.xml road network file according to the.osm file. The road network file is composed of points and edges, where the point represents the intersection of the road, and the edge represents an actual road, including sidewalks, railways and highways.

3. The method for predicting Bluetooth virus propagation trend and range based on infection rate decay as claimed in claim 1, wherein: In S4, the model for predicting the propagation of Bluetooth viruses traverses the nodes generated in S2 at discrete time points, obtains the position of the node and its neighbors within the signal radiation range, and calculates the state of the node at the current time, which specifically includes the following steps: S41: traversing four types of nodes to obtain each node object, and obtaining the infected neighbor set G(n) according to the position and signal radiation range of each node; S42: calculating the current state of the node according to the model for predicting the propagation of Bluetooth viruses in step S3.

4. The method for predicting the trend and range of Bluetooth virus propagation based on the infection rate decay according to claim 3, characterized in that: In S6, according to the state of each node in the system at each discrete time point calculated in S4, outputting the change rule of the number of nodes in various states with time, predicting the trend and range of Bluetooth virus propagation, and returning the prediction result graph to the user, which specifically includes the following steps: S61: obtaining the number of nodes in the system at each discrete time point calculated, outputting the change rule of the number of nodes in various states with time, and predicting the trend and range of Bluetooth virus propagation; S62: extracting only the prediction data when the value of the comparison parameter is different according to the input comparison parameter, and making a comparative analysis; S63: outputting the prediction data.

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