Safety rate maximization method and system, and medium

Abstract

The invention discloses a safety rate maximization method and system, and a medium. The method comprises the following steps: initializing flight parameters of an unmanned aerial vehicle; establishing a system channel model, an energy consumption model and a system safety rate maximization model according to the flight parameters of the unmanned aerial vehicle; and calculating a task allocation and scheduling factor, an unmanned aerial vehicle transmitting power and an unmanned aerial vehicle flight path through an alternate iterative optimization algorithm until the safety rate convergence based on the unmanned aerial vehicle assisted MEC system, and outputting an optimized safety rate maximization result. According to the method, the requirements of calculation-intensive or delay-sensitive tasks for low time delay and calculation are met, the safety of an unmanned aerial vehicle to-ground communication system is enhanced, and the performance of the communication system is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial equipment monitoring and control systems, and in particular to a safety rate maximization method, system and medium. Background Art

[0002] As a highly maneuverable and flexible communication device, Unmanned Aerial Vehicle (UAV) has been widely used in the fifth-generation wireless network (5G). With its good line-of-sight transmission advantage, UAVs can provide better channel conditions in communication, thereby improving communication quality. However, as application scenarios such as real-time video recognition, augmented reality, and virtual reality put forward higher requirements on computing power and data processing latency, traditional UAVs are limited by computing resources and energy, which poses a challenge to their computing performance.

[0003] The emergence of Mobile Edge Computing (MEC) technology provides a solution for 5G communications, allowing drones to offload computing tasks to MEC servers at the edge of the network through wireless links, thereby significantly improving the computing performance and communication efficiency of drones. However, due to the strong line-of-sight characteristics of drone communication links, the risk of eavesdropping increases, resulting in threats to the security of data transmission. Therefore, enhancing the security of drone-assisted communication systems has become an urgent issue to be addressed.

[0004] Existing physical layer security technology (PLS) has been applied in some scenarios, but there are still problems such as complex algorithms and low optimization efficiency. Especially in an environment where multi-task computing offloading and eavesdropping threats coexist, how to maximize the security rate under limited energy and computing resources remains a technical challenge. Summary of the invention

[0005] The purpose of the present invention is to provide a method, system, and storage medium for maximizing safety rate to meet the requirements of low latency and computing for computing-intensive or delay-sensitive tasks, enhance the security of UAV-to-ground communication systems, and effectively improve the performance of communication systems.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for maximizing safety rate comprises the following steps:

[0008] Initialize the UAV flight parameters;

[0009] Establishing a system channel model, an energy consumption model and a system safety rate maximization model according to the UAV flight parameters;

[0010] The task allocation, scheduling factor, UAV transmission power and UAV flight trajectory are solved through an alternating iterative optimization algorithm until the safety rate of the UAV-assisted MEC system converges, and the optimized safety rate maximization result is output.

[0011] Establishing the system channel model includes:

[0012] Determine the channel power gain between the drone and the ground AP, the channel power gain between the drone and the ground eavesdropper, and the channel power gain between the ground AP and the ground eavesdropper;

[0013] Define the scheduling factors and determine the constraints of the scheduling factors;

[0014] Determine the rate between the drone and the ground AP, the rate between the drone and the ground eavesdropper, and the confidentiality rate achieved by the drone in the nth time slot with the kth ground AP, and build a system channel model.

[0015] In the above technical solution, establishing a system energy consumption model includes:

[0016] Determine the mission energy consumption, wherein the mission energy consumption includes computing mission energy consumption, communication mission energy consumption, and flight propulsion energy consumption;

[0017] The energy consumption constraints of each task are determined according to the energy consumption of the task; and the system energy consumption model required by the UAV in the process of transmitting power, calculating task allocation, and trajectory optimization is constructed according to the constraint conditions.

[0018] In the above technical solution, establishing a safety rate maximization model includes:

[0019] Define scheduling factors, task allocation, UAV launch power, and UAV trajectory as block variables;

[0020] The system energy consumption model and the system channel model are combined, and under the constraints of the system energy consumption model, the maximum system safety rate is determined by optimizing the task offloading decision, the transmission power and the flight trajectory of the UAV.

[0021] In the above technical solution, task allocation, scheduling factors, UAV transmission power and UAV flight trajectory are solved by alternating iterative optimization algorithms, including:

[0022] Iteratively optimize the initialized UAV flight parameters, preset the UAV trajectory and transmission power, solve the first stage problem, and determine the scheduling factors and task allocation of the first stage.

[0023] In the above technical solution, the task allocation and scheduling factors, the UAV transmission power, and the UAV flight trajectory are solved by an alternating iterative optimization algorithm, and also include:

[0024] According to the scheduling factors, task allocation and initialized UAV trajectory in the first stage, the second stage problem is solved by continuous convex approximation method to obtain the UAV transmission power in the second stage.

[0025] In the above technical solution, task allocation, scheduling factors, UAV transmission power and UAV flight trajectory are solved by alternating iterative optimization algorithm, and also include:

[0026] Through the scheduling factors and task allocation in the first stage and the UAV transmission power in the second stage, slack variables are introduced into the confidentiality rate and flight propulsion energy consumption achieved by the UAV in the nth time slot and the kth ground AP. The third stage problem is solved by the continuous convex approximation method to obtain the UAV aircraft trajectory and slack variables in the third stage.

[0027] In the above technical solution, based on drone-assisted MEC system safety rate convergence, it includes:

[0028] The difference between the maximum system safety rate of the current iteration and the maximum system safety rate of the previous iteration is less than the convergence value or the maximum number of iterations is reached.

[0029] The present invention provides a drone control system, comprising

[0030] Rotary wing drones;

[0031] at least one ground access point;

[0032] Mobile edge computing server;

[0033] A control unit is used to implement any of the safety rate maximization methods described above.

[0034] The present invention provides a storage medium having a computer program stored thereon, wherein when the computer program is executed, the safety rate maximization method as described in any one of the above items is implemented.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present application provides a method, system, and medium for maximizing a safe rate. The method solves task allocation, scheduling factors, UAV transmission power, and UAV flight trajectory through an alternating iterative optimization algorithm to make judgments until the safe rate of the UAV-assisted MEC system converges, thereby maximizing the system safe rate under the constraints of UAV energy consumption and system confidentiality rate, effectively improving the performance of the communication system and enhancing the security of the communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a system structure diagram provided by an embodiment of the present invention.

[0038] Figure 2The present invention is a flowchart of an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Embodiment 1:

[0041] This embodiment provides a method for maximizing security rate, which is aimed at resource-constrained UAVs that cannot meet the low-latency communication and computing requirements of applications, and in the presence of ground eavesdroppers, it is necessary to meet the computing-intensive and delay-sensitive task requirements and ensure the security of the communication system to complete the total computing task.

[0042] Based on drone-assisted MEC system Figure 1 As shown in the figure, one of the drones starts from a fixed starting point and flies through K ground APs to the end point. The drone can offload the computing task to the ground AP equipped with the MEC server. The entire computing task processing needs to be completed within the drone flight time T. The drone can calculate the task by itself, or offload part of the computing task to the ground AP during the flight. In addition, considering the potential eavesdroppers on the ground, in order to enhance the security of the communication system, the ground AP also imposes interference noise on the eavesdroppers.

[0043] like Figure 2 As shown, the method comprises the following steps:

[0044] Step S1, initializing the UAV flight parameters, which include the starting point, end point, flight time, flight speed, energy consumption parameters and computing task volume of the UAV. Specifically, the UAV flight parameters specifically include the input of the location w of each ground AP k ={w1,w2,…,w k}, k∈K={1,2,…,K}, the starting point of the drone is q I With the end point q F and the ground eavesdropper position w e .

[0045] Assume that the drone maintains a constant altitude H during the flight. During the entire flight of the drone, the flight time T is evenly divided into N (n∈N={1,2,…,N}) time slots, and the duration of each time slot is δ=T / N. The total amount of tasks that the drone needs to complete L, the channel power gain β0 at the reference distance d=1m, the communication bandwidth B, and the noise power σ at the receiver2 , the interference power P of AP j , the blade power P of the drone in hovering state o and induced power P i , U tip is the rotor blade tip speed, v o represents the average rotor induced speed in forward flight, d o and s represent the fuselage drag ratio and rotor solidity, respectively, and ε and M represent the air density and rotor disc area. The number of cycles C of the drone's central processing unit (CPU) used to calculate each task u , the energy consumption of each CPU cycle of the drone is expressed as P u , the CPU calculation frequency f of the drone u , the total energy of the drone is E u , UAV peak power P max , the maximum speed of the drone v max . Convergence value θ and maximum number of iterations R max The computational task that the UAV offloads to the kth ground AP in the nth time slot is l k [n], the flight speed of the UAV in the nth time slot is v[n], and the transmission power of the initial UAV unloaded to the kth ground AP in the nth time slot is P k [n], initialize the unmanned trajectory corresponding to the nth time slot to q[n].

[0046] Step S2: establishing a system channel model, an energy consumption model and a system safety rate maximization model according to the UAV flight parameters.

[0047] Furthermore, establishing a system channel model includes:

[0048] Determine the channel power gain between the drone and the ground AP, the channel power gain between the drone and the ground eavesdropper, and the channel power gain between the ground AP and the ground eavesdropper;

[0049] Define the scheduling factors and determine the constraints of the scheduling factors;

[0050] Determine the rate between the drone and the ground AP, the rate between the drone and the ground eavesdropper, and the confidentiality rate achieved by the drone in the nth time slot with the kth ground AP, and build a system channel model.

[0051] The channel power gains between the drone and the ground AP, the channel power gains between the drone and the ground eavesdropper, and the channel power gains between the ground AP and the ground eavesdropper are:

[0052]

[0053] ξ is the Rayleigh fading coefficient following an exponential distribution with unit mean.

[0054] Consider the time division multiple access (TDMA) protocol to realize the computation offloading of the UAV, which means that the UAV is associated with at most one ground AP in each time slot. Define the scheduling factor a k [n], means when a k When [n] = 1, the UAV is connected to the kth ground AP in the nth time slot, otherwise, a k [n] = 0. Therefore, a k [n] The following constraints must be met:

[0055]

[0056] The rate of the UAV in the nth time slot with the kth ground AP is expressed as:

[0057]

[0058] Since there is an eavesdropper on the ground, the rate between the drone and the ground eavesdropper is:

[0059]

[0060] Among them, ε [·] denotes the mathematical expectation of a random variable ξ, and the inequality in Equation (6) is due to Jensen’s inequality and the fact that the function is concave with respect to ξ. Equation (6) shows that R ue [n] is the upper bound. Assume that Eve can reach this upper bound to consider the worst-case confidentiality rate performance. Based on the above discussion, the confidentiality rate that the drone can achieve with the kth ground AP in the nth time slot is given by the following formula:

[0061]

[0062] in

[0063] The establishment of a system energy consumption model includes:

[0064] Determine the mission energy consumption, wherein the mission energy consumption includes computing mission energy consumption, communication mission energy consumption, and flight propulsion energy consumption;

[0065] The energy consumption constraints of each task are determined according to the energy consumption of the task; and the system energy consumption model required by the UAV in the process of transmitting power, calculating task allocation, and trajectory optimization is constructed according to the constraint conditions.

[0066] In the process of computing task energy consumption, in order to reduce the pressure of local computing, the drone can offload part of the computing task to the ground AP equipped with MEC server for computing. k [n] is the computing task that the UAV offloads to the kth ground AP in the nth time slot and satisfies:

[0067]

[0068] The confidentiality rate of the UAV with the kth ground AP in the nth time slot must not be lower than the unloading rate to ensure that the unloaded data bits are secure, that is:

[0069]

[0070] The drone communicates with the ground AP in turn, and the computing energy and communication energy consumed by the drone are:

[0071]

[0072] P k [n] represents the transmission power of the UAV unloaded to the kth ground AP in the nth time slot. Due to the influence of battery life, it is subject to the peak power P max The restrictions are:

[0073]

[0074] The drone calculation time is:

[0075]

[0076] The drone computing time should be less than the drone flight time and should meet the following requirements:

[0077] T u ≤T (14)

[0078] In this system, the drone flies at a constant altitude. In time T, the drone flies from the starting point to the end point. Then the trajectory of the drone will be subject to the following constraints:

[0079] ||q[n+1]-q[n]||=v[n]δ,n∈N (15)

[0080]

[0081] q[1]=q I ,q[N+1]=q F (17)

[0082] The UAV flies at a fixed altitude, and only kinetic energy is considered in the flight energy consumption. For a rotor UAV with a flight speed of v[n], the flight propulsion energy consumption of the UAV at the nth time slot is expressed as:

[0083]

[0084] The total energy of the drone is limited. During the flight time T, the energy consumed by the drone should meet the constraints:

[0085] E ulo +E utr +E fly ≤E u (20)

[0086] Given the above two models, a safe rate maximization model is established, including:

[0087] Define scheduling factors, UAV trajectories, task allocation, and transmission power as block variables;

[0088] The system energy consumption model and the system channel model are combined, and under the constraints of the system energy consumption model, the maximum system safety rate is determined by optimizing the UAV's transmission power, task offloading decision and flight trajectory.

[0089] Defining block variables They represent the scheduling factor, the trajectory of the UAV, the task allocation and the transmission power respectively. Therefore, the system safety rate maximization model can be expressed as:

[0090]

[0091] st(4),(8),(9),(12),(14),(16),(17),(20) (21a)

[0092] Step S3: Solve the task allocation, scheduling factor, UAV transmission power and UAV flight trajectory through the alternating iterative optimization algorithm until the safety rate of the UAV-assisted MEC system converges, and output the optimized safety rate maximization result.

[0093] Specifically, the initialized UAV flight parameters are iteratively optimized, the UAV trajectory and transmission power are preset, the first stage problem is solved, and the scheduling factors and task allocation of the first stage are determined;

[0094] According to the scheduling factors and task allocation of the first stage and the initialized UAV trajectory, the second stage problem is solved by the continuous convex approximation method to obtain the UAV transmission power of the second stage;

[0095] Through the scheduling factors and task allocation in the first stage and the UAV transmission power in the second stage, slack variables are introduced into the confidentiality rate and flight propulsion energy consumption achieved by the UAV in the nth time slot and the kth ground AP. The third stage problem is solved by the continuous convex approximation method to obtain the UAV aircraft trajectory and slack variables in the third stage.

[0096] The UAV flight parameters in step S1 are used to iteratively search for the optimal result, that is, the result of maximizing the safety rate. By jointly optimizing the scheduling factor, computing task allocation, UAV transmission power and flight trajectory, the system safety rate is maximized under the constraints of UAV energy consumption and system confidentiality rate. This alternating iterative optimization algorithm divides the original problem into three sub-problems. Suboptimal solutions to the sub-problems can be obtained in each stage. Finally, it returns to the initial stage and continuously iterates and optimizes until the convergence conditions are met to obtain the optimal solution, effectively improving the performance of the communication system and enhancing the security of the communication system.

[0097] In the first stage, when the UAV trajectory Q and the transmission power P are preset, the original problem (P1) has been transformed into a convex problem, and the constraint (21a) has been transformed into a convex constraint. The scheduling factor A and the task allocation L can be directly solved using the CVX tool in Matlab. The local optimal solution of the convex optimization problem will also be the global optimal solution.

[0098] In the second stage, we first give the optimized scheduling factor A, task allocation L and initialized UAV trajectory Q, and then combine (21) with the non-convex constraint (9) in (21a). The expression is converted into a convex expression by the SCA (Continuous Convex Approximation) method and solved using the CVX tool in Matlab.

[0099] The SCA method (Continuous Convex Approximation) includes:

[0100] If f(x) is a concave function, first determine an initial point x0, and perform a first-order Taylor expansion at the initial point x0 to obtain the global upper bound of f(x), expressed as:

[0101]

[0102] After the first-order Taylor expansion, the function is a linear function of x, so the function It is a convex function, and can be used in subsequent processing To approximate the original function f(x).

[0103] In the third stage, given the optimized scheduling factor A in the first stage, the task allocation L and the UAV transmission power P in the second stage, (21) and (21a) are then combined to form the non-convex expression in (9): By introducing slack variables And meet

[0104]

[0105] The SCA method is used to convert it into a convex expression. Similarly, the CVX tool in Matlab is used for calculation.

[0106] The task allocation, scheduling factor, UAV transmission power and UAV flight trajectory are solved by alternating iterative optimization algorithm until the safety rate of the UAV-assisted MEC system converges, that is, the difference between the maximum safety rate of the current iteration and the maximum safety rate of the previous iteration is less than the convergence value or the maximum number of iterations is reached. And calculate η (r+1) The optimal value of and reinitialize the variable When |η (r+1) -η (r) |<θorr>R max Then exit the loop iteration.

[0107] If the output system safety rate converges, the optimized safety rate maximization result is output. Otherwise, iterative optimization is continued until the system safety rate is maximized after convergence.

[0108] The implementation method of the safety rate maximization method based on the drone-assisted MEC system of the present invention is as shown in the attached figure. Figure 2 The software environment configuration used in the present invention includes Matlab and CVX toolbox. The following examples give numerical results to illustrate the performance of the proposed algorithm. 2 K = 4 APs are distributed in the geographical area, and the flight altitude of the drone is H = 50m.

[0109] Table 1 Parameter settings

[0110]

[0111] The method for maximizing the safety rate of a drone-assisted MEC system provided in this application meets the requirements of computationally intensive or delay-sensitive tasks for low latency and computing, enhances the security of the drone-to-ground communication system, and effectively improves the performance of the communication system.

[0112] Based on the same inventive concept, the present application provides a drone control system, including

[0113] Rotary wing drones;

[0114] At least one ground access point (AP);

[0115] Mobile Edge Computing (MEC) servers;

[0116] A control unit is used to implement any of the safety rate maximization methods described above.

[0117] Based on the same inventive concept, the present invention further provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the aforementioned method are implemented.

[0118] In this application, the security rate maximization method emphasizes the optimization of communication confidentiality performance under the threat of eavesdropping. When mobile energy storage products and outdoor energy storage devices, especially when connected to the Internet of Things or require remote management, this method can be used to improve the security of data transmission, prevent sensitive data from being eavesdropped or tampered with in wireless networks, and ensure the reliability and privacy of device communications. Outdoor energy storage equipment can introduce edge computing to process and optimize power distribution tasks in real time to meet complex power demand scenarios. The task offloading and scheduling factors in this method can be applied to energy storage systems to achieve more flexible power management and further optimize the distribution and utilization efficiency of electric energy.

[0119] Among them, the computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form, etc. Computer readable media may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in computer readable media can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer readable media do not include electric carrier signals and telecommunication signals.

[0120] The present invention can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0121] The present application also provides an electronic device, comprising a memory, multiple processors, and a program stored in the memory, wherein the program is configured to be executed by the processors, and the steps of the above method are implemented when the multiple processors execute the program.

[0122] In addition, the present invention also provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the aforementioned method are implemented. The present invention can be used in many general or special computing system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0123] The device in this embodiment and the method in the aforementioned embodiment are two aspects based on the same inventive concept. The method implementation process has been described in detail above, so those skilled in the art can clearly understand the structure and implementation process of the system in this embodiment based on the aforementioned description. For the sake of brevity of the specification, they will not be repeated here.

[0124] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for maximizing safety rate, characterized in that: The following steps are involved: Initialize the UAV flight parameters; Establishing a system channel model, an energy consumption model and a system safety rate maximization model according to the UAV flight parameters; The task allocation, scheduling factor, UAV transmission power and UAV flight trajectory are solved through an alternating iterative optimization algorithm until the safety rate of the UAV-assisted MEC system converges, and the optimized safety rate maximization result is output.

2. The method for maximizing the safety rate according to claim 1, characterized in that: Establishing the system channel model includes: Determine the channel power gain between the drone and the ground AP, the channel power gain between the drone and the ground eavesdropper, and the channel power gain between the ground AP and the ground eavesdropper; Define the scheduling factors and determine the constraints of the scheduling factors; Determine the rate between the drone and the ground AP, the rate between the drone and the ground eavesdropper, and the confidentiality rate achieved by the drone in the nth time slot with the kth ground AP, and build a system channel model.

3. The method for maximizing the safety rate according to claim 1, characterized in that: Establishing a system energy consumption model includes: Determine the mission energy consumption, wherein the mission energy consumption includes computing mission energy consumption, communication mission energy consumption, and flight propulsion energy consumption; The energy consumption constraints of each task are determined according to the energy consumption of the task; and the system energy consumption model required by the UAV in the process of transmitting power, calculating task allocation, and trajectory optimization is constructed according to the constraint conditions.

4. The method for maximizing the safety rate according to claim 1, characterized in that: Building a safe rate maximization model includes: Define scheduling factors, task allocation, transmission power, and UAV trajectory as block variables; The system energy consumption model and the system channel model are combined, and under the constraints of the system energy consumption model, the maximum system safety rate is determined by optimizing the UAV's transmission power, task offloading decision and flight trajectory.

5. The method for maximizing safety rate according to claim 1, characterized in that: The task allocation and scheduling factors, UAV launch power, and UAV flight trajectory are solved through an alternating iterative optimization algorithm, including: Iteratively optimize the initialized UAV flight parameters, preset the UAV trajectory and transmission power, solve the first stage problem, and determine the scheduling factors and task allocation of the first stage.

6. The method for maximizing the safety rate according to claim 5, characterized in that: The task allocation, scheduling factor, UAV transmission power and UAV flight trajectory are solved through an alternating iterative optimization algorithm, including: According to the scheduling factors, task allocation and initialized UAV trajectory in the first stage, the second stage problem is solved by continuous convex approximation method to obtain the UAV transmission power in the second stage.

7. The method for maximizing the safety rate according to claim 6, characterized in that: The task allocation, scheduling factor, UAV transmission power and UAV flight trajectory are solved through an alternating iterative optimization algorithm, including: Through the scheduling factors and task allocation in the first stage and the UAV transmission power in the second stage, slack variables are introduced into the confidentiality rate and flight propulsion energy consumption achieved by the UAV in the nth time slot and the kth ground AP. The third stage problem is solved by the continuous convex approximation method to obtain the UAV aircraft trajectory and slack variables in the third stage.

8. The method for maximizing safety rate according to claim 1, characterized in that: Based on drone-assisted MEC system safety rate convergence, including: The difference between the maximum system safety rate of the current iteration and the maximum system safety rate of the previous iteration is less than the convergence value or the maximum number of iterations is reached.

9. A drone control system, characterized in that: include Rotary wing drones; at least one ground access point; Mobile edge computing server; A control unit, used to implement the safety rate maximization method as described in any one of claims 1 to 8.

10. A storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the safety rate maximization method according to any one of claims 1 to 8 is implemented.

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