A method for solving optimal path planning by using operations research and cybernetics methods

Abstract

The present invention discloses a method for solving the optimal path planning by using operations research and cybernetics methods, including constructing an optimal path planning model through a mathematical model, controlling the data search speed in the optimal path planning process through a control method, monitoring the abnormal data information of the optimal path planning through a fault diagnosis method, and realizing the output of the optimal path planning information through an optimization solution method; wherein the optimal path planning model is an ant colony algorithm model; the control method is a PID fuzzy control method; the fault diagnosis method is a diagnosis model with a path comparison function; and the optimization solution method is a path optimization method through a data function model. This method realizes the best path search through the ant colony algorithm model, realizes the control of data information through the PID fuzzy control method, and realizes the optimization and processing of the optimal path planning through the path optimization method, improving the optimal path planning ability.

Description

Technical Field

[0001] The present invention relates to the technical field of operations research and cybernetics, and particularly relates to a method for solving optimal path planning by using operations research and cybernetics methods. Background Art

[0002] Operations research and cybernetics mainly use mathematics and computers as tools. From the perspectives of system and information processing, it studies and solves various problems such as modeling, analysis, planning, design, control, and optimization of systems in society, economy, finance, military, production management, and planning and decision-making. The rapid development of science and technology and the high-speed development of modern computer technology have made mathematics increasingly important in science and technology and the development of human society. "Mathematical science is crucial for economic competitiveness. Mathematical science is a key and universal ability-cultivating technology" and "the essence of high-tech is mathematical technology" have increasingly become people's consensus. Therefore, to rejuvenate the country through science and education, it is necessary to develop mathematics education and revitalize mathematical science. The interaction and mutual promotion between mathematics and other disciplines and the emergence of new application fields have made people fully realize the urgency and importance of developing applied mathematics.

[0003] Although this discipline has applications in multiple disciplines, in the process of data information processing, especially for a large amount of node data information, how to achieve the best path retrieval of a large amount of data information is still a difficult problem, and how to achieve the control and solution of optimal path planning is still an urgent problem to be solved. Summary of the Invention

[0004] In view of the above deficiencies of the technology, the present invention discloses a method for solving optimal path planning by using operations research and cybernetics methods. It realizes the best path search through the ant colony algorithm model, realizes data information control through the PID fuzzy control method, and realizes the optimization and processing of optimal path planning through the path optimization method, improving the optimal path planning ability.

[0005] To achieve the above technical effects, the present invention adopts the following technical solutions:

[0006] A method for solving optimal path planning by using operations research and cybernetics methods, wherein the method includes: constructing an optimal path planning model through a mathematical model, controlling the data search speed in the optimal path planning process through a control method, monitoring abnormal data information in the optimal path planning through a fault diagnosis method, and outputting optimal path planning information through an optimization solution method;

[0007] Wherein the optimal path planning model is an ant colony algorithm model;

[0008] The control method is a PID fuzzy control method;

[0009] The fault diagnosis method is a diagnosis model with a path comparison function;

[0010] The optimization solution method is a path optimization method through a data function model.

[0011] As a further technical solution of the present invention, the ant colony algorithm model includes the following steps:

[0012] Step 1: Initialization; Initialize the obtained optimal path data information. The total population data information is denoted as y(t), and let y(t)=y max , all elements of the ant's optimal path element are initialized to 0, and then randomly select the starting position of the ant's optimal path element; where the search information factor is set where is the minimum value of the search information factor, is the maximum value of the search information factor, and the heuristic factor where β min is the minimum value of the heuristic factor, β max is the maximum value of the heuristic factor, and the pheromone concentration evaporation factor ρ∈[ρ min ,ρ max ; ρ min represents the minimum value of the pheromone concentration evaporation factor, and ρ max is the maximum value of the pheromone concentration evaporation factor;

[0013] Step 2: Randomly place the optimal path elements of m ants at N positions. Let the number of cycles of the ant's optimal path element searching path be N c , and cycle in the order of N c +1; The optimal path update function is denoted as:

[0014]

[0015] In formula (1), i represents the cycle number of the ant's optimal path element, and ρ(iN) represents the optimal path update information when the i-th cycle of the ant's optimal path element is performed under the condition that the cycle number is N; ρ min represents the minimum pheromone concentration evaporation factor; ρ(i(N + 1)) represents the optimal path update information when the i-th cycle of the ant's optimal path element is performed under the condition that the cycle number is N + 1; where the search information factor function in the optimal path element search process is expressed as:

[0016]

[0017] In formula (2), represents the data update output at the time of searching for the information factor of N + 1, and i represents the i-th cycle of the ant's optimal path element, Indicates the best information search factor; among which the heuristic factor representation function is:

[0018]

[0019] In formula (3), β max Indicates the maximum heuristic factor during the cycle of the optimal path elements of the ant, β min Indicates the minimum heuristic factor during the cycle of the optimal path elements of the ant, and β(iN) represents the heuristic factor data information during the i-th cycle of the optimal path elements of the ant when the number of cycles is N;

[0020] Step 3: Control the optimal path of the ant through the PID fuzzy control method, and calculate the probability of the ant choosing the position j of the optimal path according to the state transition probability formula of the following formula; then there is:

[0021]

[0022] In formula (4), δ is the visibility factor of the optimal path of the ant, and δ is Indicates the visibility factor of the optimal path of the ant when the path is s under the i-th cycle. The visibility factor represents the reciprocal of the distance between different positions, and r ij (t) represents the information release concentration when choosing the position j under the i-th cycle, and r is (t) represents the information concentration when the path is s under the i-th cycle. α is the relative importance parameter of the pheromone concentration, β is the relative importance index of the visibility factor, and Node is the set of positions directly connected to the position i and not yet traversed by the optimal path elements of the ant;

[0023] Step 4: Select the position with the maximum state transition probability of the optimal path element of the ant, move the optimal path element of the ant to the position with the maximum state transition probability, and store the optimal position. The number of stored positions is denoted as k;

[0024] Step 5: Judge. If all positions in the set of optimal path elements of the ant are visited, let k < m, where m is the number of positions of the optimal path elements of the ant. Then perform a loop operation through k + 1. If not all positions in the set are visited, update the amount of information on each path;

[0025] Step 6: Check the termination condition. Check whether the termination condition is satisfied. The termination condition is that the probability of the ant choosing the position j of the optimal path is greater than 93%. If the termination condition is satisfied, further operations are performed;

[0026] Step 7: Determine whether a new group is formed. If the termination condition is that the probability of the ant's optimal path selection position j is less than 93%, a new group needs to be formed, and the pheromone matrix is updated again. The update method is to recalculate the minimum data matrix D.

[0027] Step 8: Determine whether the termination genetic condition is satisfied. When the termination genetic condition is satisfied, that is, the probability of the ant's optimal path selection position j is greater than 93%, the calculation result is output.

[0028] As a further technical solution of the present invention, PID fuzzy control is used to calculate the optimal path speed control, and the control method is as follows:

[0029] Calculate the optimal path speed within the calculation period, and the integral equation control function is:

[0030]

[0031] In formula (5), Q d and Q c are both the diagnostic optimal search path data flows. Q d represents the theoretical value of the diagnostic optimal search path data flow, and Q c represents the actual value of the diagnostic optimal search path data flow. I d and I c are both integral equivalent data flows. I d represents the theoretical value of the data flow fluctuation within the diagnostic period, and σ c represents the actual value of the data flow fluctuation within the diagnostic period. d o and σ c are both the data flow fluctuations within the diagnostic period. d o represents the theoretical value of the data flow fluctuation within the diagnostic period, and σ c represents the actual value of the data flow fluctuation within the diagnostic period. T d and T c represent the optimal path diagnostic period. T d represents the theoretical value of the optimal path diagnostic period, and T c represents the actual value of the optimal path diagnostic period. i represents the optimal path control data flow, and t represents time.

[0032] As a further technical solution of the present invention, the path optimization method is as follows:

[0033] The optimization function is:

[0034]

[0035] In formula (6), is the maximum data flow of the optimal path, and λ is the correction coefficient of other fault parameter change constants in the diagnosis and optimization process of the optimal path operation route;

[0036] The operating states of the optimal path are divided into three states: normal data control, abnormal data control, and data control stop. After collecting the actual state data of the optimal search of all paths of the same type, calculate the distribution probabilities of different states P = [P 1 、P 2 、P 3 、P 4 . Then the cumulative distribution probability F = [F 1 、F 2 、F 3 、1] is obtained through the cumulative distribution probability P, and the control output is:

[0037] x = α * [-ln(1 - F)] 1 / β *P (7)

[0038] The operating states of the optimal path are divided into three states: normal data control, abnormal data control, and data control stop. After collecting the actual state data of the optimal search of all paths of the same type, calculate the distribution probabilities of different states P = [P 1 、P 2 、P 3 、P 4 . Then the cumulative distribution probability F = [F 1 、F 2 、F 3 、1] is obtained through the cumulative distribution probability P, and the control output is:

[0039] x = α * [-ln(1 - F)] 1 / β *P*y (7)

[0040] In formula (7), according to equation (7), calculate the different states of the three controls. y represents the stable percentage of the model during the operation of the optimal path. P 1 、P 2 、P 3 、P 4 are numbers less than 1 respectively, and F 1 、F 2 、F 3 are numbers less than;

[0041] x represents three different data states. When x is greater than 5, it represents the optimal path control output. When x is between 1 and 5, it represents the optimal path control delay. When x is less than 1, it represents the stop of the optimal path operation control.

[0042] As a further technical solution of the present invention, the diagnostic model with a path comparison function includes the following methods:

[0043] Train the normal and faulty sample data of the training path flow data to obtain k + 1 optimal paths, where k represents the number of fault types; 1 is the number of normal path flow data controls. If the data does not belong to the k + 1 optimal paths, it is considered that the switch belongs to the "Else" fault, and the PID control model is started to adjust the control path.

[0044] During the optimal path fault diagnosis process, the fault diagnosis function is:

[0045] f = R 2 -|E′ i -a| 2 (8)

[0046] In formula (8), R represents the set of k + 1 optimal path data; a represents a constant between 0 and 5, and E′ i represents the data value of the control state being in a good state; then the optimal path fault decision value is:

[0047]

[0048] The fault type of the diagnostic optimal path is realized through formula (9).

[0049] The beneficial and positive effects of the present invention are as follows:

[0050] Different from the conventional technology, the present invention adopts a method for solving the optimal path planning by using the methods of operational research and cybernetics, including constructing an optimal path planning model through a mathematical model, controlling the data search speed in the optimal path planning process through a control method, monitoring the abnormal data information of the optimal path planning through a fault diagnosis method, and realizing the output of the optimal path planning information through an optimization solution method; among them, the optimal path planning model is an ant colony algorithm model; the control method is a PID fuzzy control method; the fault diagnosis method is a diagnosis model with a path comparison function; the optimization solution method is a path optimization method through a data function model. This method realizes the best path search through the ant colony algorithm model, realizes the data information control through the PID fuzzy control method, and realizes the optimization and processing of the optimal path planning through the path optimization method, improving the optimal path planning ability. Description of the Drawings

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings, where:

[0052] Figure 1 It is a schematic diagram of the PID fuzzy control method architecture in the present invention. Specific implementation manners

[0053] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0054] As Figure 1 shown, a method for solving the optimal path planning by using the methods of operational research and cybernetics includes: constructing an optimal path planning model through a mathematical model, controlling the data search speed in the optimal path planning process through a control method, monitoring the abnormal data information of the optimal path planning through a fault diagnosis method, and outputting the optimal path planning information through an optimization solution method;

[0055] wherein the optimal path planning model is an ant colony algorithm model;

[0056] The control method is a PID fuzzy control method;

[0057] The fault diagnosis method is a diagnosis model with a path comparison function;

[0058] The optimization solution method is a path optimization method through a data function model.

[0059] In the above embodiment, the ant colony algorithm model includes the following steps:

[0060] Step 1, initialization; initialize the obtained optimal path data information, record the total population data information as y(t), set y(t)=y max , initialize all elements of the ant optimal path element to 0, and then randomly select the starting position of the ant optimal path element; wherein set the search information factor where is the minimum value of the search information factor, is the maximum value of the search information factor, the heuristic factor where β min is the minimum value of the heuristic factor, β max is the maximum value of the heuristic factor, the search pheromone concentration evaporation factor ρ∈[ρ min ,ρ max ; ρ min represents the minimum value of the search pheromone concentration evaporation factor, ρ max is the maximum value of the search pheromone concentration evaporation factor;

[0061] Step 2: Randomly place the optimal path elements of m ants at N positions. Let the number of loops for the ants to find the optimal path elements be N c , and perform loops in the order of N c +1; the optimal path update function is denoted as:

[0062]

[0063] In formula (1), i represents the loop number of the ant optimal path element, and ρ(iN) represents the optimal path update information during the i-th loop of the ant optimal path element when the loop number is N; ρ min represents the factor for finding the minimum evaporation of pheromone concentration; ρ(i(N + 1)) represents the optimal path update information during the i-th loop of the ant optimal path element when the loop number is N + 1;

[0064] The optimal path update can be achieved through formula (1). The basis of this formula is that during the ant colony algorithm process, by continuously searching for information, the optimal path update information is obtained. This method is based on the basic principle of the ant colony algorithm model during the application of the ant colony algorithm, and the method of PID fuzzy control is added on this basis, greatly improving the application ability of the ant colony algorithm.

[0065] Among them, the function of the information factor for searching during the optimal path element search process is expressed as:

[0066]

[0067] In formula (2), represents the data update output for finding the information factor at time N + 1, i represents the i-th loop of the ant optimal path element, represents the optimal information factor for finding;

[0068] The information factor for finding can be calculated through formula (2). The purpose of calculating the information factor for finding is to improve the search ability of the optimal path element. In this way, the optimal path planning ability can be improved, and the search ability of information during the path finding process is greatly improved.

[0069] Among them, the function of the heuristic factor is expressed as:

[0070]

[0071] In formula (3), β max represents the maximum heuristic factor during the loop of the ant optimal path element, β minIt represents the minimum heuristic factor during the cyclic process of the optimal path elements of ants. β(iN) represents the heuristic factor data information during the i-th cyclic process of the optimal path elements of ants when the number of cycles is N.

[0072] Through formula (2), the excitation of different data elements within the ant colony algorithm can be calculated. By setting the heuristic factor function, the foraging process during the path search can be effectively solved. The principle is a positive feedback mechanism or an enhanced learning system. It reaches the final convergence on the optimal path through [the increase in the number of ants on the optimal path → the increase in the pheromone intensity → the increase in the selection probability of later ants → the even greater increase in the number of ants on the optimal path].

[0073] Step 3: Control the optimal path of ants through the PID fuzzy control method, and calculate the probability of the ant choosing the optimal path position j according to the state transition probability formula of the following formula. Then there is:

[0074]

[0075] In formula (4), δ is the visibility factor of the optimal path of ants, and δ is represents the visibility factor of the optimal path of ants when the path is s under the i-th cycle. The visibility factor represents the reciprocal of the distance between different positions, and r ij (t) represents the information release concentration when choosing position j under the i-th cycle, and r is (t) represents the information concentration when the path is s under the i-th cycle. α is the relative importance parameter of the pheromone concentration, β is the relative importance index of the visibility factor, and Node is the set of positions directly connected to position i and where the optimal path elements of ants have not passed.

[0076] In a specific embodiment, ants leave a volatile secretion (pheromone, hereinafter referred to as pheromone) on the paths they pass through. The pheromone will gradually volatilize and disappear over time. Ants can perceive the existence and intensity of this substance during the foraging process and use it to guide their movement direction, tending to move towards the direction with a high intensity of this substance, that is, the probability of choosing this path is proportional to the intensity of this substance on this path at that time. The higher the intensity of the pheromone on a path, the more ants choose it, and then the intensity of the pheromone left on this path is greater. And the pheromone with a high intensity attracts more ants, thus forming a positive feedback. Through this positive feedback, ants can finally find the best path, resulting in most ants walking on this path.

[0077] In the above method, the PID fuzzy control method can control ants to leave a volatile secretion on the paths they pass through, calculate the optimal path speed within the calculation period, and thus realize the control of different data information.

[0078] Step 4: Select the position with the maximum state transition probability among the ant optimal path elements, move the ant optimal path elements to the position with the maximum state transition probability, and store the optimal positions. The number of stored positions is denoted as k.

[0079] Step 5: Judge. If all positions in the set of ant optimal path elements have been visited, let k < m, where m is the number of positions of the ant optimal path elements, then perform a loop operation through k + 1. If not all positions in the set have been visited, then update the amount of information on each path.

[0080] Step 6: Check the termination condition, check whether the termination condition is satisfied. The termination condition is that the probability of the ant optimal path selecting position j is greater than 93%. If the termination condition is satisfied, then perform further operations.

[0081] Step 7: Judge whether a new group is formed. If the termination condition is that the probability of the ant optimal path selecting position j is less than 93%, then a new group needs to be formed, and the pheromone matrix is updated again. The update method is to recalculate the minimum data matrix D.

[0082] Step 8: Judge whether the termination genetic condition is satisfied. When the termination genetic condition is satisfied, the termination genetic condition is that the probability of the ant optimal path selecting position j is greater than 93%, then output the calculation result.

[0083] The ant colony algorithm is a probabilistic algorithm used to find the optimal path, inspired by the behavior of ants finding paths during the process of searching for food. This algorithm has the characteristics of distributed computing, positive feedback of information, and heuristic search. Essentially, it is a heuristic global optimization algorithm in evolutionary algorithms. In specific applications, the behavior of a single ant is extremely simple, with the number of behaviors within 10. However, a colony of thousands of ants can possess great wisdom, which is inseparable from their way of information transmission - pheromone. Ants will release a substance called "pheromone" during walking to mark their walking paths. During the process of searching for food, they choose the walking direction according to the concentration of pheromone and finally reach the place where the food is located. Pheromone will gradually volatilize over time. Based on the above principles, this research uses the methods of operations research and cybernetics to realize the retrieval of the positions of ant optimal path elements, and uses the PID fuzzy control method to realize the optimal path control of ant optimal path elements. Through the above methods, the computing power of the ant colony algorithm can be improved.

[0084] Different from the conventional technology, the PID fuzzy control method is added to the ant colony algorithm model in this research, which can achieve the ant colony search speed and greatly improve the speed of solving the optimal path in the application process of the ant colony algorithm model, showing outstanding technical effects. By adjusting the efficiency of the optimal path through the PID fuzzy control method, the control ability of the path search speed is greatly improved.

[0085] In a specific embodiment, the PID fuzzy control is used to calculate the optimal path speed control, and the control method is as follows:

[0086] In the process of optimal path control, the parameters of each optimal path data information collected are relative ratio values, and it is necessary to convert the calibration values of the optimal path data information parameters with different ratios. In actual operation, the optimal path data information parameters are random. To accurately diagnose the operation data of the optimal path, an equivalent transformation is performed on the changes in its operation parameters to form a data likelihood estimation method. For the abnormal data information generated during the diagnosis process of the optimal path search operation, the fuzzy control PID method is used to complete the perturbation calculation. The optimal path speed within the calculation period is controlled by using the integral equation function as follows:

[0087]

[0088] In Equation (5), Q d and Q c are both the diagnostic optimal search path data flows. Q d represents the theoretical value of the diagnostic optimal search path data flow, Q c represents the actual value of the diagnostic optimal search path data flow. I d and I c are both integral equivalent data flows. I d represents the theoretical value of the data flow fluctuation within the diagnostic period, σ c represents the actual value of the data flow fluctuation within the diagnostic period. d o and σ c are both the data flow fluctuations within the diagnostic period. d o represents the theoretical value of the data flow fluctuation within the diagnostic period, σ c represents the actual value of the data flow fluctuation within the diagnostic period. T d and T c represent the optimal path diagnostic period. T d represents the theoretical value of the optimal path diagnostic period, T c represents the actual value of the optimal path diagnostic period. i represents the optimal path control data flow, and t represents time.

[0089] The basis of formula (5) mainly lies in the influence amounts of different data information in the optimal search path, such as various data information like data flow rate, data flow rate fluctuation, etc. These data have an important influence in the ant colony optimal planning algorithm. Designing this formula fully considers the influencing factors in the optimal path search process. In the above embodiment, the path optimization method is as follows:

[0090] According to the analysis result of the optimal path data flow rate characteristics, path diagnosis and optimization are carried out, and its optimization function is:

[0091]

[0092] In formula (6), is the maximum data flow rate of the optimal path, and λ is the correction coefficient of the change constants of other fault parameters in the optimal path operation route diagnosis and optimization process; formula (6) fully considers the optimal path data flow rate characteristics.

[0093] The operating states of the optimal path are divided into three states: normal data control, abnormal data control, and data control stop. After collecting the actual state data of the optimal search of all paths of the same type, calculate the distribution probabilities P = [P 1 、P 2 、P 3 、P 4 , then the cumulative distribution probability F = [F 1 、F 2 、F 3 、1] is obtained through the cumulative distribution probability P, and the control output is:

[0094] x = α * [-ln(1 - F)] 1 / β * P * y (7)

[0095] In formula (7), according to equation (7), calculate the different states of the three controls. y represents the stable percentage of the model during the operation of the optimal path operating state. P 1 、P 2 、P 3 、P 4 are respectively numbers less than 1, and F 1 、F 2 、F 3 are respectively numbers less than;

[0096] x represents three different data states. When x is greater than 5, it represents the optimal path control output. When x is between 1 and 5, it represents the optimal path control delay. When x is less than 1, it represents the stop of the operation control of the optimal path. y represents the stable percentage of the model during the operation state of the optimal path. When applying the formula, the key data information and main factors affecting these factors are considered. During the optimal path search process, it is divided into normal data control, abnormal data control, and data control stop. Among these states, the data information affecting is the maximum data flow, the probability of the path in different states, and many other data information as described above.

[0097] In the above embodiments, the diagnostic model with the path comparison function includes the following methods:

[0098] Train the normal path flow data control and fault sample data to obtain k + 1 optimal paths, where k represents the number of fault types; 1 is the number of normal path flow data controls. If the data does not belong to the k + 1 optimal paths, it is considered that the switch belongs to the "Else" fault, and the PID control model is thus started to adjust the control path;

[0099] During the optimal path fault diagnosis process, the fault diagnosis function is:

[0100] f = R 2 -|E′ i -α| 2 (8)

[0101] In formula (8), R represents the data set of k + 1 optimal paths; α represents a constant between 0 - 5, and E′ i represents the data value of the control state being a good state; then the optimal path fault decision value is:

[0102]

[0103] The fault type of the diagnostic optimal path is realized through formula (9).

[0104] Through the above calculations, the method of this research is discussed through specific embodiments below

[0105] In a specific embodiment, by using mathematics and computer as the main tools to solve and calculate the optimal path, the data calculation ability can be improved. This application mainly studies the deterministic model of operations research, and realizes a new algorithm through the PID control method in this model, thereby improving the convergence ability and solution ability of the optimal path. Assuming that the optimal solution path is affected by various factors, it is affected by different data information such as PID control data parameters, path congestion, human error, model parameter influence, data transmission failure, model influence from the outside world, ant element failure, or control parameter error. Through the embodiment, the diagnostic rules can be obtained as shown in Table 1:

[0106] Table 1 Optimal Path Fault Diagnosis Rule Table

[0107]

[0108] During the optimal path fault diagnosis process, affected by various data information, when this research uses mathematics and computer as the main tools, it converts the optimal path planning data information into microscopic data thinking, which can improve the application ability of data information. The above research method realizes path optimization and processing through the ant colony algorithm model, PID fuzzy control method, diagnostic model with path comparison function, and data function model, greatly improving the fault diagnosis ability.

[0109] The present invention provides a system for solving the optimal path planning by using the methods of operations research and cybernetics. The system includes:

[0110] The first module is used to construct an optimal path planning model through a mathematical model;

[0111] The second module is used to control the data search speed during the optimal path planning process through a control method;

[0112] The third module is used to monitor the abnormal data information of the optimal path planning through a fault diagnosis method;

[0113] The fourth module is used to output the optimal path planning information through an optimization solution method.

[0114] For the specific limitations of the system for solving the optimal path planning by using the methods of operations research and cybernetics, reference can be made to the limitations of the method for solving the optimal path planning by using the methods of operations research and cybernetics in the above text, which will not be elaborated here. Each module in the above system for solving the optimal path planning by using the methods of operations research and cybernetics can be implemented in whole or in part by software, hardware, and their combinations. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0115] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0116] Construct an optimal path planning model through a mathematical model, control the data search speed during the optimal path planning process through a control method, monitor the abnormal data information of the optimal path planning through a fault diagnosis method, and output the optimal path planning information through an optimization solution method;

[0117] The optimal path planning model is an ant colony algorithm model;

[0118] The control method is a PID fuzzy control method;

[0119] The fault diagnosis method is a diagnosis model with a path comparison function;

[0120] The optimization solution method is a path optimization method through a data function model.

[0121] For the specific limitations of each step, reference can be made to the limitations on the method for solving the optimal path planning method using operations research and control theory in the above text, which will not be elaborated here.

[0122] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0123] Construct an optimal path planning model through a mathematical model, control the data search speed during the optimal path planning process through a control method, monitor the abnormal data information of the optimal path planning through a fault diagnosis method, and output the optimal path planning information through an optimization solution method;

[0124] The optimal path planning model is an ant colony algorithm model;

[0125] The control method is a PID fuzzy control method;

[0126] The fault diagnosis method is a diagnosis model with a path comparison function;

[0127] The optimization solution method is a path optimization method through a data function model.

[0128] For the specific limitations of each step, reference can be made to the limitations on the method for solving the optimal path planning method using operations research and control theory in the above text, which will not be elaborated here.

[0129] Although the specific embodiments of the present invention have been described above, those skilled in the art should understand that these specific embodiments are only illustrative. Without departing from the principles and essence of the present invention, those skilled in the art can make various omissions, substitutions, and changes to the details of the above methods and systems. For example, combining the above method steps so as to perform substantially the same function in a substantially the same manner to achieve substantially the same result falls within the scope of the present invention. Therefore, the scope of the present invention is only defined by the appended claims.

Claims

1. A method for solving the optimal path planning by using operations research and cybernetics methods, characterized in that: The method includes: constructing an optimal path planning model through a mathematical model, controlling the data search speed during the optimal path planning process through a control method, monitoring abnormal data information of the optimal path planning through a fault diagnosis method, and outputting the optimal path planning information through an optimization solution method; wherein the optimal path planning model is an ant colony algorithm model; the control method is a PID fuzzy control method; the fault diagnosis method is a diagnosis model with a path comparison function; the optimization solution method is a path optimization method through a data function model; Calculating the optimal path speed with PID fuzzy control, the control method is: Calculating the optimal path speed within the calculation period, and the integral equation control function is: In formula (1), Q d , Q c are both the data traffic of the optimal search path for diagnosis. Q d represents the theoretical value of the data traffic of the optimal search path for diagnosis, and Q c represents the actual value of the data traffic of the optimal search path for diagnosis. I d , I c are both the integral equivalent data traffic. I d represents the theoretical value of the data traffic fluctuation within the diagnosis period, and σ c represents the actual value of the data traffic fluctuation within the diagnosis period. d o , σ c are both the data traffic fluctuations within the diagnosis period. d o represents the theoretical value of the data traffic fluctuation within the diagnosis period, and σ c represents the actual value of the data traffic fluctuation within the diagnosis period. T d , T c represent the diagnosis period of the optimal path. T d represents the theoretical value of the diagnosis period of the optimal path, and T c represents the actual value of the diagnosis period of the optimal path. i represents the control data traffic of the optimal path, and t represents time; The path optimization method is: The optimization function is: In formula (2), is the maximum data flow of the optimal path, and λ is the correction coefficient of the change constants of other fault parameters in the diagnosis and optimization process of the optimal path operation route; The operating states of the optimal path are divided into three states: normal data control, abnormal data control, and data control stop. After collecting the actual state data of the optimal search for all paths of the same type, calculate the distribution probabilities of different states P = [P 1 、P 2 、P 3 、P 4 . Then the cumulative distribution probability F = [F 1 、F 2 、F 3 、1] is obtained through the cumulative distribution probability P, and the control output is as follows: x = α * [-ln(1 - F)] 1 / β *P*y (3) In Equation (3), different states of the three controls are calculated according to Equation (3), where y represents the stable percentage of the model during the operation of the optimal path, P 1 , P 2 , P 3 , P 4 are numbers less than 1 respectively, and F 1 , F 2 , F 3 are numbers less than; x represents three different data states. When x is greater than 5, it represents the optimal path control output. When x is between 1 and 5, it represents the optimal path control delay. When x is less than 1, it represents the stop of the operation control of the optimal path; The diagnosis model with a path comparison function includes the following methods: Training the normal and fault sample data of the path flow data control to obtain k + 1 optimal paths, where k represents the number of fault types; 1 is the number of normal path flow data controls. If the data does not belong to the k + 1 optimal paths, the switch will be considered to belong to the "Else" fault, and the PID control model will be started to adjust the control path; During the optimal path fault diagnosis process, the fault diagnosis function is: f = R 2 -|E′ i -a| 2 (4) In formula (4), R represents a set of k + 1 optimal path data; a is a constant between 0 and 5, and E i ′ represents the data value of the control state being the optimal state; then the optimal path fault decision value is: The fault type of the diagnostic optimal path is realized through formula (5).

2. The method for solving the optimal path planning by using operations research and cybernetics methods according to claim 1, characterized in that: The ant colony algorithm model of the ant colony algorithm model includes the following steps: Step 1. Initialization; Initialize the obtained optimal path data information. Denote the total population data information as y(t), and set y(t) = y max , and initialize all elements of the ant optimal path element to 0, then randomly select the starting position of the ant optimal path element; Among them, set the search information factor where is the minimum value of the search information factor, is the maximum value of the search information factor, and the heuristic factor β ∈ [β min , β max . Among them, β min is the minimum value of the heuristic factor, β max is the maximum value of the heuristic factor, and the pheromone concentration evaporation factor ρ ∈ [ρ min , ρ max ; ρ min represents the minimum value of the pheromone concentration evaporation factor, and ρ max is the maximum value of the pheromone concentration evaporation factor; Step 2: Randomly place the optimal path elements of m ants at N positions. Let the number of cycles for the ants to find the optimal path be N c , and perform the loop in the order of N c +1; the optimal path update function is denoted as: In formula (6), i represents the loop count of the optimal path elements of the ant, and ρ(iN) represents the optimal path update information when the i-th loop of the optimal path elements of the ant is performed under the condition that the loop count is N; ρ min represents the factor for finding the minimum evaporation of pheromone concentration; ρ(i(N + 1)) represents the optimal path update information when the i-th loop of the optimal path elements of the ant is performed under the condition that the loop count is N + 1; where the function for finding the information factor in the search process of the optimal path elements is expressed as: In formula (7), represents the data update output when searching for the information factor for N + 1 times, and i represents the i-th loop of the elements of the optimal path of the ant. represents the optimal information factor to be found; among them, the heuristic factor representation function is: In formula (8), β max represents the maximum heuristic factor in the cycle of the optimal path elements of the ant, and β min represents the minimum heuristic factor in the cycle of the optimal path elements of the ant. β(iN) represents the heuristic factor data information when performing the i-th cycle of the optimal path elements of the ant under the condition that the number of cycles is N; Step 3: Control the optimal path of the ant through the PID fuzzy control method, and calculate the probability of the ant to select the position j of the optimal path according to the state transition probability formula of the following formula; then there is: In formula (9), δ is the visibility factor of the optimal path of the ant, and δ is represents the visibility factor of the optimal path of the ant when the path is s in the i-th iteration. The visibility factor represents the reciprocal of the distance between different positions, and r ij (t) represents the information release concentration when position j is selected in the i-th iteration, and r is (t) represents the information concentration when the path is s in the i-th iteration. α is the relative importance parameter of the pheromone concentration, β is the relative importance index of the visibility factor, and Node is the set of positions directly connected to position i and not yet traversed by the elements of the optimal path of the ant; Step 4: Select the position with the largest state transition probability of the ant optimal path element, move the ant optimal path element to the position with the largest state transition probability, and store the optimal position. The number of stored positions is denoted as k; Step 5: Judge. If all positions in the ant optimal path element set are visited, let k < m, where m is the number of ant optimal path element positions. Then perform a loop operation through k + 1. If not all positions in the set are visited, update the amount of information on each path; Step 6: Check the termination condition, check whether the termination condition is satisfied. The termination condition is that the probability of the ant to select the position j of the optimal path is greater than 93%. If the termination condition is satisfied, further operations are performed; Step 7: Judge whether a new group is formed. If the termination condition is that the probability of the ant to select the position j of the optimal path is less than 93%, then a new group needs to be formed, and the pheromone matrix needs to be updated again. The update method is to recalculate the minimum data matrix D; Step 8: Determine whether the termination inheritance condition is satisfied. When the termination inheritance condition is satisfied, and the termination inheritance condition is that the probability of the ant's optimal path selection position j is greater than 93%, the calculation result is output.

3. A system for solving optimal path planning by using operations research and cybernetics methods, based on the method according to any one of claims 1 to 2, Characterized in that the system includes: A first module for constructing an optimal path planning model through a mathematical model; A second module for controlling the data search speed during the optimal path planning process through a control method; A third module for monitoring abnormal data information in the optimal path planning through a fault diagnosis method; A fourth module for outputting optimal path planning information through an optimization solution method.

4. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, Characterized in that when the processor executes the computer program, the steps of the method according to any one of claims 1 to 2 are implemented.

5. A computer-readable storage medium, on which a computer program is stored, Characterized in that when the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 2 are implemented.

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