CN115455839A - Numerical control system information security function deployment strategy optimization method based on balanced income - Google Patents
Numerical control system information security function deployment strategy optimization method based on balanced income Download PDFInfo
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Abstract
The invention relates to a balanced gain-based numerical control system information security function deployment strategy optimization method, which is used for calculating the overall gain of a numerical control system through a strategy for numerical control system information security function deployment and analyzing the aspects of safety and benefit optimization of the numerical control system from a theoretical level. The method specifically comprises the following steps: establishing an information security function deployment profit model in the numerical control system; defining the total profit of the information security function deployment strategy, and establishing a constraint condition of the information security function deployment strategy; by means of the improved particle swarm optimization, the numerical control system information security deployment strategy with higher profit and safety is realized under the constraint condition of the information security function deployment strategy. The method provided by the invention can be distinguished from the traditional optimization aiming at the safety of the numerical control system, and the information safety function deployment strategy of the numerical control system is researched on the theoretical level by combining with the benefit guarantee.
Description
Technical Field
The invention relates to a numerical control system information security function deployment strategy optimization method based on balanced income, and belongs to the field of industrial control network communication security.
Background
The numerical control machine tool is a high-precision and high-efficiency automatic machine tool, is provided with a multi-position tool turret or a power tool turret, has wide processing technological performance, and plays a good economic effect in the batch production of complex parts. With the rapid development of intelligent chemical control networks, new manufacturing modes and emerging industries are promoted in intelligent manufacturing, the original closed numerical control system is opened, and the network environment has the characteristics of diversification and complexity. The numerical control system is used as the brain of a machine tool and is facing industrial virus and network attack, the information security problem is increasingly prominent, once the numerical control system is attacked, the numerical control system can be paralyzed or information leakage can be caused, and the national security and the enterprise survival are directly influenced. Therefore, it is very important to deploy information security function for the numerical control system.
However, in the actual production of the numerical control system, a plurality of nodes are often matched together to complete the machining of the component. The information security function deployed aiming at the node ensures that the system is not attacked, so that indirect benefits are generated, the more the information security function is deployed, the larger the occupied resources are, the slower the speed of processing a certain procedure is, the production efficiency is reduced, and the benefit of an enterprise is reduced. Therefore, how to balance the income and enterprise benefits brought by the information security function is very important.
At present, some scholars at home and abroad pay attention to the research on the information security function deployment of the numerical control system, but most methods basically focus on the research emphasis on the coverage rate and the production efficiency of the information security function, and are in a blank state at present on an information security function deployment strategy considering the income and the enterprise benefit of the information security function. Therefore, by using an optimization algorithm and designing an information security function deployment strategy, the safety of the numerical control system is improved on the basis of ensuring the efficient production of the nodes of the numerical control system, which is an urgent problem to be solved.
The high speed, high precision, safety and intellectualization are important directions for the development of numerical control machine tools in the future, and the numerical control system is used as the core of the numerical control machine tool, so how to realize the optimal arrangement of the information safety function for balancing the information safety function income and the enterprise benefit, which is beneficial to enhancing the safety and the stability of the numerical control system in China, and further has great strategic significance for promoting the intellectualization of the numerical control system in China.
Disclosure of Invention
Aiming at the technical defects, the invention aims to provide a numerical control system information security function deployment strategy optimization method based on balanced profit, and a numerical control system information security function deployment profit model is established; defining the total income, and establishing constraint conditions of information security function deployment; by means of the improved particle swarm optimization, efficient and safe information security function deployment is achieved under the constraint condition of information security function deployment.
The technical scheme adopted by the invention for realizing the purpose is as follows:
the method for optimizing the information security function deployment strategy of the numerical control system based on the balanced profit comprises the following steps:
establishing a numerical control system information security function deployment profit model;
according to the profit model of the information security function deployment of the numerical control system, determining an optimization target of an information security function deployment strategy, and establishing an information security function deployment constraint condition;
and under the constraint condition of information security function deployment, optimizing an information security function deployment strategy of the numerical control system by using a particle swarm algorithm, and taking the optimized information security function deployment strategy which accords with the optimization target of the information security function deployment strategy as a final information security function deployment strategy.
The profit model for information security function deployment of the numerical control system specifically comprises the following steps:
and (2) taking each part as a numerical control system information safety function deployment profit model, wherein i processes are needed for processing each part, each process is taken as a processing node of the model, j information safety functions are deployed by each processing node according to the requirements, each information safety function generates quantitative profit C, and one part is processed to generate quantitative profit L.
The information security function deployment optimization objective is expressed as a total profit within a defined time T:
wherein S is total For the total profit in time T, L represents the quantitative profit generated by processing a single part, alpha represents the product profit weight of the numerical control system, beta represents the safety profit weight of the numerical control system, k represents the delay coefficient brought by the information safety function, N i Number of information security functions, t, representing i-node deployment i Indicating the time required for the inode to process the process, and C the quantitative yield that each information security function can produce.
The method for optimizing the information security function deployment strategy of the numerical control system by using the particle swarm optimization comprises the following steps:
the method comprises the following steps: constructing an information security function deployment matrix W, wherein each element represents the number of information security functions deployed by a certain node;
step two, setting 2 initial values W meeting the deployment constraint conditions of the information security function 01 And W 02 And respectively setting the pbest as the optimal pbest of the history particles in the particle swarm optimization 0 And population optimal gbest 0 Taking each element in the information security function deployment matrix as a particle in the particle swarm algorithm, and taking the information security function deployment matrix as a population;
selecting particles and populations meeting the information safety function deployment constraint conditions for each iteration to update the optimal historical particles and the optimal populations; and when the iteration times reach a set value, stopping the optimization process, and outputting the current optimal population, namely an information security function deployment strategy.
The information security function deployment constraint conditions comprise: the number of information safety functions deployed aiming at a certain node and the number of processing components in a limited time T;
the specific requirements for meeting the deployment constraint conditions of the information security function are as follows:
the method comprises the following steps of meeting the threshold value of the number of information safety functions deployed aiming at a certain node: the number of the information security functions deployed aiming at a certain node is more than or equal to a threshold value;
satisfying the threshold of the number of machined parts: and in the T time, the number of the processed parts is greater than or equal to a threshold value.
The method for selecting the particles and the populations meeting the information security function deployment constraint conditions to update the historical particle optimality and the population optimality comprises the following steps:
wherein the content of the first and second substances,
is the flight velocity, ω, of particle i in the kth iteration k Is used for adjusting the inertia weight of the space search range, c 1 And c 2 Is a learning factor, r 1 And r 2 Is a random number, and is a random number,
representing the ith element of the information security function deployment matrix W after the (k-1) th iteration;
and obtaining a new information security function deployment matrix W after each iteration through the formula, and updating the information security function deployment matrix W.
The process of updating the information security function deployment matrix W specifically includes:
and (4) carrying out information security function deployment constraint condition inspection on the information security function deployment matrix W, continuing the next iteration process on the W meeting the information security function deployment constraint condition, and otherwise, directly entering the next particle swarm optimization iteration process.
The information security function deployment constraint condition inspection specifically comprises the following steps:
calculating the total income corresponding to the information security function deployment matrix W at the moment according to the information security function deployment strategy targetS total Best pbest with the historical particle at the previous time 0 And population optimal gbest 0 Comparing the corresponding total earnings; if the total income at the moment is larger, replacing the W at the moment with the optimal pbest of the historical particles with the maximum total income at the last moment 0 Or population-optimal gbest 0 And carrying out the next iteration process; otherwise, using the best pbest of the historical particles at the last moment 0 And population optimal gbest 0 And directly carrying out the next particle swarm optimization iteration process.
The utility model provides a numerical control system information security function deployment strategy optimization system based on balanced profit, includes:
the information security function deployment model building module is used for building a numerical control system information security function deployment profit model;
the information security function deployment initialization module is used for determining an information security function deployment strategy target according to the information security function deployment profit model and establishing an information security function deployment constraint condition;
the information security function deployment optimization module is used for optimizing an information security function deployment strategy in the numerical control system by using a particle swarm algorithm under the constraint condition of information security function deployment, and taking the optimized information security function deployment strategy which accords with the optimization target of the information security function deployment strategy as the optimized information security function deployment strategy;
and the information security function deployment module is used for realizing the information security function deployment of the numerical control system according to the optimized information security function deployment strategy.
The invention has the following beneficial effects and advantages:
1. the invention establishes the profit model for the information security function deployment of the numerical control system and provides a tool for the application of the information security technology of the numerical control system.
2. The method is different from the traditional research method only aiming at the coverage rate and the production efficiency of the information security function, the income and the enterprise benefit of the information security function are considered in a balanced manner, and the efficiency and the safety of the numerical control system are improved.
3. In order to meet the application background of the numerical control system, the invention introduces an improved discrete binary particle swarm algorithm to optimize the information security function deployment strategy.
Drawings
FIG. 1 is a flow chart of a numerical control system information security function deployment optimization method;
FIG. 2 is a schematic diagram of a yield model for information security function deployment of a numerical control system;
FIG. 3 is a flow chart of particle swarm optimization under constraint conditions.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The invention provides a numerical control system information security function deployment strategy optimization method based on balanced income, which comprises the steps of establishing a numerical control system information security function deployment income model; defining the total income, and establishing constraint conditions for information security function deployment; by means of the improved particle swarm optimization, efficient and safe information security function deployment is achieved under the constraint condition of information security function deployment.
Establishing a numerical control system information security function deployment profit model;
defining an information security function deployment strategy target according to the profit model characteristic of the information security function deployment of the numerical control system;
establishing an information security function deployment constraint method from 2 aspects such as an information security function quantity threshold value, a processing component quantity threshold value and the like which are deployed aiming at a certain node;
under the information security function deployment constraint method, optimizing an information security deployment strategy in the numerical control system by using a particle swarm algorithm to obtain an optimized information security function deployment strategy;
according to the background of the information security function deployment of the numerical control system, defining an information security function deployment matrix, particles and a population in a particle swarm algorithm;
deploying constraint conditions based on the information security function, and optimizing by using a particle swarm algorithm;
and (4) repeatedly executing the particle swarm optimization step according to the preset maximum iteration times to complete the information security function deployment of the numerical control system.
The establishing of the information security function deployment profit model in the numerical control system environment specifically comprises the following steps:
as shown in FIG. 2, the model is divided into two parts, the information security function revenue generated by the information security function deployed on the node and the process component revenue generated by the node process output component.
The optimization goal of the information security deployment strategy is to solve the maximum value of the total income, which is specifically expressed as S total . The research aims at increasing the information security function benefits and the processing component benefits, and the system is divided into two parts through optimization modeling, wherein the first part represents the information security function benefits, and the second part represents the processing component benefits.
The information security function deployment constraint method is embodied by considering 2 constraint conditions: the number threshold of information safety functions and the number threshold of processing parts.
In the context of this problem, these 2-dimensional constraints will be applied to the subsequent optimization process.
The optimizing of the information security function deployment strategy by using the particle swarm algorithm specifically comprises the following steps:
step one, constructing an information security function deployment matrix W, wherein each element is represented;
step two, according to the limitation in the information security function deployment constraint method, 2 initial values meeting the conditions can be set, and are respectively set as the optimal historical particles and the optimal population in the particle swarm optimization, and each column in the information security function deployment matrix is set as a particle in the particle swarm optimization;
and step three, carrying out constraint detection in the information security function deployment constraint method on the particles and the population obtained by each iteration, and updating the optimal historical particles and the optimal population only when the constraint in the information security function deployment constraint method is met.
Through the steps, the communication path selection strategy can be optimized.
And circulating the particle swarm optimization process of optimizing the communication path selection strategy by using the particle swarm algorithm, and obtaining the final communication path selection optimization strategy. The particle swarm algorithm circulation process needs to set the maximum iteration number in advance.
Through the steps, the application background of the numerical control system can be met, and meanwhile, more efficient communication path selection can be realized, so that intelligent and safe landing of the numerical control system network is promoted.
The invention provides a numerical control system information security function deployment strategy optimization method based on balanced income, and the method can promote the improvement of the operation efficiency and the safety of a numerical control system.
As shown in fig. 1, the method specifically comprises the following steps:
the method comprises the following steps of firstly, modeling the information security function deployment income of the numerical control system, wherein the model is divided into two parts, namely the information security function income and the processing part income.
And step two, the information security function deployment strategy model is that the numerical control system is provided with j processing nodes, each processing node is required to deploy the information security function, each information security function can generate quantitative profit C, but the occupied system resources can increase the processing time. I processes are required to process each part, each process being at a different time, and the processing of the part may yield a quantified benefit L. Defining the total profit:
wherein S is total For the total profit in time T, L represents the quantitative profit generated by processing a single part, alpha represents the product profit weight of the numerical control system, beta represents the safety profit weight of the numerical control system, k represents the delay coefficient brought by the information safety function, N i Number of information security functions, t, representing i-node deployment i Indicating the time required for the inode to process the process, and C the quantitative yield that each information security function can produce.
Therefore, the system model is changed into a non-restrictive planning problem, and finally a group of optimal information security function deployment decisions are obtained to determine how to deploy the information security functions.
And step three, establishing an information security function deployment constraint method according to the setting in the step two. In the communication path selection constraint process, 2 aspects of limitation are carried out: 1. the number threshold of the information safety functions, 2, the number threshold of the processing parts.
1. Information security function number threshold: at least one information security function is deployed for each device, namely the number of the information security functions is more than or equal to 1.
2. Threshold of number of machined parts: within a limited time T, if N parts can be originally processed, at least N/2 parts should be produced after the information security function is added.
In the context of this problem, these two aspect information security function deployment constraints will be applied in the subsequent optimization process.
And after the fourth step and the third step, the invention plans to use a particle swarm algorithm to optimize the information security function deployment strategy, and the total income is the fitness function value in the invention. Setting the number of information security functions deployed for each node to W i Then, an information security function deployment matrix is formed:
W＝[W 1 ,W 2 ,W 3 ,...,W i ]
for the information security function deployment mode, if the number of the information security functions deployed for a certain device in the strategy is 1, the value is given as 1, and so on, and the total income can be calculated through the matrix.
According to the limitation of step three, 2 initial values W satisfying the condition can be set 01 And W 02 And respectively setting the pbest as the optimal pbest of the history particles in the particle swarm optimization 0 And population optimal gbest 0 For the information security function deployment matrix W, each column W is set vi Is a particle in a particle swarm algorithm. The optimization process speed of the particle swarm optimization in the invention is updated as follows:
wherein
Is the flight velocity, ω, of particle i in the kth iteration k Is a non-negative number representing inertial weight for adjusting the spatial search range and is set to
c 1 And c 2 Is a learning factor (all set to 2 in the present invention), r 1 And r 2 Is two random numbers and has a value range of [0,1]，
And the ith column of the information security function deployment matrix W after the (k-1) th iteration is represented.
The position of the particle swarm optimization process in the invention is updated as follows:
the specific particle swarm optimization process is shown in fig. 3, and it is worth noting that the particles and the population obtained by each iteration need to be subjected to constraint detection in step three, and the history particle optimal pbest and the population optimal gbest can be updated only when the constraints in step three are satisfied.
And step five, through the step three and the step four, the communication path selection strategy after final optimization can be obtained after initial value setting is carried out. The specific initial value comprises a maximum iteration number K, when the iteration number reaches K, the optimization process is stopped, and the optimal population gbest at the moment is output;
through the steps, the deployment strategy of the information security function of the numerical control system is finally obtained.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to method flow diagrams according to embodiments of the application. It will be understood that each flow in the flow diagrams can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (9)
1. The numerical control system information security function deployment strategy optimization method based on balanced income is characterized by comprising the following steps of:
establishing a numerical control system information security function deployment profit model;
determining an information security function deployment strategy optimization target according to the profit model for the information security function deployment of the numerical control system, and establishing an information security function deployment constraint condition;
and under the constraint condition of information security function deployment, optimizing an information security function deployment strategy of the numerical control system by using a particle swarm algorithm, and taking the optimized information security function deployment strategy which accords with the optimization target of the information security function deployment strategy as a final information security function deployment strategy.
2. The balanced-yield-based optimization method for information security function deployment strategies of the numerical control systems according to claim 1, wherein the yield model for information security function deployment of the numerical control systems is specifically:
and (2) taking each part as a numerical control system information safety function deployment profit model, wherein i processes are needed for processing each part, each process is taken as a processing node of the model, j information safety functions are deployed by each processing node according to the requirements, each information safety function generates quantitative profit C, and one part is processed to generate quantitative profit L.
3. The balanced-yield-based numerical control system information security function deployment strategy optimization method according to claim 1, wherein the information security function deployment optimization objective is expressed as a total yield within a defined time T:
wherein S is total For the total profit in time T, L represents the quantified profit from processing the individual parts, α represents the product profit weight of the numerical control system, β represents the numerical control systemThe total security gain weight, k represents the delay factor brought by the information security function, N i Number of information security functions, t, representing i-node deployment i Indicating the time required for the inode to process the process, and C the quantitative yield that each information security function can produce.
4. The method for optimizing information security function deployment strategy of numerical control system based on balanced yield according to claim 1, wherein the optimizing the information security function deployment strategy of numerical control system by using particle swarm optimization comprises the following steps:
the method comprises the following steps: constructing an information security function deployment matrix W, wherein each element represents the number of information security functions deployed by a certain node;
step two, setting 2 initial values W meeting the deployment constraint conditions of the information security function 01 And W 02 And respectively setting the pbest as the optimal pbest of the history particles in the particle swarm optimization 0 And population optimal gbest 0 Taking each element in the information security function deployment matrix as a particle in the particle swarm algorithm, and taking the information security function deployment matrix as a population;
selecting particles and populations meeting the information safety function deployment constraint conditions for each iteration to update the optimal historical particles and the optimal populations; and when the iteration times reach a set value, stopping the optimization process, and outputting the optimal population at the moment, namely an information security function deployment strategy.
5. The method for optimizing information security function deployment strategy of numerical control system based on balanced revenue according to claim 1 or 4, wherein the constraint conditions for information security function deployment include: the number of information safety functions deployed aiming at a certain node and the number of processing parts in a limited time T;
the specific requirements for meeting the deployment constraint conditions of the information security function are as follows:
the method comprises the following steps of meeting the threshold value of the number of information safety functions deployed aiming at a certain node: the number of the information security functions deployed aiming at a certain node is more than or equal to a threshold value;
satisfying the threshold of the number of machined parts: and in the T time, the number of the processed parts is greater than or equal to a threshold value.
6. The balanced-yield-based optimization method for information security function deployment strategies of the numerical control system according to claim 4, wherein the selection of the particles and the population which meet the deployment constraint conditions of the information security functions updates the historical particle optimality and the population optimality, and comprises the following steps:
wherein the content of the first and second substances,
is the flight velocity, ω, of particle i in the kth iteration k Is used for adjusting the inertial weight of the space search range, c 1 And c 2 Is a learning factor, r 1 And r 2 Is a random number, and is a random number,
representing the ith element of the information security function deployment matrix W after the (k-1) th iteration;
and obtaining a new information security function deployment matrix W after each iteration through the formula, and updating the information security function deployment matrix W.
7. The balanced yield-based optimization method for information security function deployment strategies of the numerical control system according to claim 6, wherein the updating process of the information security function deployment matrix W is specifically as follows:
and (4) carrying out information safety function deployment constraint condition inspection on the information safety function deployment matrix W, continuing the next iteration process on the W meeting the information safety function deployment constraint condition, and otherwise, directly entering the next particle swarm optimization iteration process.
8. The balanced yield-based optimization method for information security function deployment strategy of numerical control system according to claim 7, wherein the information security function deployment constraint condition test specifically comprises:
calculating the total income S corresponding to the information security function deployment matrix W at the moment according to the information security function deployment strategy target total Best pbest with the historical particle at the previous time 0 And population optimal gbest 0 Comparing the corresponding total earnings; if the total income at the moment is larger, replacing the W at the moment with the optimal pbest of the historical particles with the maximum total income at the last moment 0 Or population optimal gbest 0 And carrying out the next iteration process; otherwise, using the optimal pbest of the historical particles at the last moment 0 And population optimal gbest 0 And directly carrying out the next particle swarm optimization iteration process.
9. A strategy optimization system for information security function deployment of a numerical control system based on balanced income is characterized by comprising the following steps:
the information security function deployment model building module is used for building a numerical control system information security function deployment profit model;
the information security function deployment initialization module is used for determining an information security function deployment strategy target according to the information security function deployment profit model and establishing an information security function deployment constraint condition;
the information security function deployment optimization module is used for optimizing an information security function deployment strategy in the numerical control system by using a particle swarm algorithm under the constraint condition of information security function deployment, and taking the optimized information security function deployment strategy which accords with the optimization target of the information security function deployment strategy as the optimized information security function deployment strategy;
and the information security function deployment module is used for realizing the information security function deployment of the numerical control system according to the optimized information security function deployment strategy.
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