A factory monitoring method based on generalization analysis

Abstract

The invention discloses a factory monitoring method based on generalization analysis, which relates to the technical field of factory monitoring, and comprises the following steps: first, dividing a monitoring area into a plurality of interval routes; second, determining the state variables of a patrol robot in each interval route; third, calculating the steering variables of the patrol robot in each interval route; fourth, determining the optimal function corresponding to each stage; and fifth, obtaining the optimized dynamic variables and steering variables of the patrol robot in each interval route; by optimizing the dynamic variables of the patrol robot in each interval route, the robot can be moved in an optimal manner during the inspection process, thereby reducing path redundancy and time waste; and by optimizing the steering variables of the patrol robot in each interval route, the robot can be turned in an optimal manner at a turn, thereby improving the inspection efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of factory monitoring, and in particular to a factory monitoring method based on generalization analysis. Background Art

[0002] With the rapid development of industrial automation, more and more factories have realized automated production. In the automated factory environment, an autonomous, flexible and efficient way of inspection and monitoring is needed. Factory monitoring not only focuses on production efficiency and quality, but also pays more and more attention to environmental protection and sustainable development. Patrol robots can monitor environmental parameters, energy consumption, etc., and provide data support and suggestions so that factories can improve energy efficiency and environmental impact. At the same time, some monitoring tasks in the factory may be dangerous, such as high temperature areas, harmful gas environments, etc. Patrol robots can patrol the factory to ensure the safety of workers. At the same time, robots can accurately record and collect data to avoid human errors and subjectivity, so as to ensure the normal operation of equipment and the continuity of production.

[0003] However, in the process of patrolling factories through patrolling robots, it is impossible to optimize the state variables and steering variables of the patrolling robots in various states in the monitoring patrol area, resulting in path redundancy and time waste, which affects the patrol efficiency and accuracy of the patrolling robots. Based on this, a factory monitoring method based on generalization analysis is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a factory monitoring method based on generalization analysis, which solves the technical problem that the state variables and steering variables of the patrol robot cannot be optimized in each state in the monitoring patrol area, resulting in path redundancy and time waste.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A factory monitoring method based on generalization analysis includes the following steps:

[0007] Step 1: According to the starting point and target point of the patrol robot, the initial monitoring route is obtained, and the monitoring area is divided into multiple interval routes;

[0008] Step 2: Map multiple interval routes to the two-dimensional map of the factory area, and determine the state variables of the patrol robot in each interval route, namely, the position variable and the speed variable;

[0009] Step 3: Calculate the turning variable according to the state variables of the patrol robot in each interval route;

[0010] Step 4: Calculate the index function corresponding to each interval route according to the state variables and steering variables of the patrol robot in each interval route, and determine the optimal function corresponding to each stage by minimizing the value of the index function;

[0011] Step 5: Through the dynamic recursive equation, the optimized state variables and steering variables of the patrol robot in each interval route are obtained.

[0012] As a further solution of the present invention: the specific method of dividing the interval route is:

[0013] According to the starting point and target point of the monitoring route of the patrol robot, a two-dimensional map of the factory area is obtained, and then the initial monitoring route of the patrol robot is obtained according to the starting point and target point of the patrol robot, and the initial monitoring route is divided into multiple interval routes with equal route lengths.

[0014] As a further solution of the present invention: the specific method of obtaining the state variable is:

[0015] The position variable of the patrol robot is defined by (x, y), and the speed variable of the patrol robot is defined by (Vx, Vy), where x is the horizontal coordinate position of the patrol robot in the two-dimensional map of the factory area, and y is the vertical coordinate position of the patrol robot in the two-dimensional map of the factory area;

[0016] By B a (i) = [V a x (i), V a y (i), X a (i), Y a (i)], represents the state variables of the patrol robot in the interval route, where a represents that each interval route includes a position variables and speed variables, that is, a state variables, i represents the number of interval routes, and satisfies a≥1, i>1;

[0017] Among them, V a x (i) and V a y (i) refers to the speed of the patrol robot in the state variable a within the interval route i, X a (i) and Y a (i) refers to the position of the patrol robot under the state variable a within the interval route i.

[0018] As a further solution of the present invention: the specific method of obtaining the steering variables corresponding to each state variable of the robot in each interval route is:

[0019] After the state variables of the robot in each interval route are determined, the steering variable β of the patrol robot in state a in the corresponding interval route is obtained and defined as the steering variable β a (i), where β refers to the steering angle of the patrol robot under the corresponding state variable in each interval route.

[0020] As a further solution of the present invention: the specific method of obtaining the optimal function corresponding to each stage is:

[0021] According to the formula, X b (c+1)=X b (c+1)+V b x (c) ×T b (c);

[0022] Y b (c+1)=Y b (c+1)+V b y (c) ×T b (c);

[0023] V b x (c) = V b x cosβ a (c);

[0024] V b y (c) = V b x cosβ a (c);

[0025] V x min ≤V b x (c)≤V x max；

[0026] V y min ≤V b y (c)≤V y max；

[0027] The optimal function corresponding to the patrol robot in each interval route is calculated, where i≥c≥1, a≥b≥1.

[0028] As a further solution of the present invention: the index function of each interval route depends on its corresponding state variable and turning variable, and the optimal value corresponding to the index function of the state variable is used as the optimal value function H of the corresponding interval route.a [Z i (a)], and then obtain the optimal value function corresponding to each interval route;

[0029] The optimal value function can be expressed as: ;

[0030] Where T i (d) represents the navigation time corresponding to each state of the patrol robot in each interval route.

[0031] As a further solution of the present invention: the specific method of obtaining the optimized state variables and steering variables corresponding to the patrol robot in each interval route is:

[0032] Through the dynamic recursive equation: H b+1 [Z c (b+1)]=min {V b [Z c (b) ]+H b [Z c (b)]}, calculate the optimized state variables and steering variables corresponding to each state of the patrol robot in each interval route, and finally obtain the optimized state variables and steering variables corresponding to each state of the patrol robot in the monitoring patrol area, i≥c>1, a≥b≥1.

[0033] As a further solution of the present invention: the position of the patrol robot in each interval route is represented by the position variable of the patrol robot, and the speed corresponding to the position of the patrol robot in each interval route is represented by the speed variable of the patrol robot. Therefore, the patrol robot has multiple position variables and speed variables in each interval route, that is, there are multiple state variables.

[0034] Beneficial effects of the present invention:

[0035] The present invention obtains the optimized state variables and steering variables of the patrol robot in each interval route through a dynamic recursive equation. By optimizing the state variables of the patrol robot in each interval route, the robot can be moved in an optimal manner during the patrol process, reducing path redundancy and time waste. By optimizing the steering variables of the patrol robot in each interval route, the robot can be turned in an optimal manner at a turn, thereby improving patrol efficiency and accuracy, further improving patrol efficiency and path planning accuracy, facilitating the patrol robot to monitor and inspect a factory area, and improving the monitoring efficiency of the patrol robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] Figure 1It is a schematic diagram of the system framework structure of the present invention. DETAILED DESCRIPTION

[0038] 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.

[0039] Embodiment 1

[0040] See also Figure 1 As shown, the present invention is a factory monitoring method based on generalization analysis, comprising the following steps:

[0041] Step 1: According to the starting point and target point of the monitoring route of the patrol robot, the initial monitoring route is obtained, and the monitoring patrol area is divided into multiple interval routes according to preset conditions;

[0042] The specific method of dividing the interval route is as follows:

[0043] First, a two-dimensional map of the factory area is obtained, and then the initial monitoring route of the patrol robot is obtained according to the starting point and target point of the patrol robot, and the initial monitoring route is divided into multiple interval routes with equal route lengths;

[0044] Step 2: Map multiple interval routes to the two-dimensional map of the factory area, and obtain the state variables corresponding to the patrol robot in the multiple interval routes. The state variables are represented by the position variables and speed variables of the patrol robot;

[0045] The position of the patrol robot in each interval route is represented as the position variable of the patrol robot, and the speed corresponding to the position of the patrol robot in each interval route is represented as the speed variable of the patrol robot. Therefore, the patrol robot has multiple position variables and speed variables in each interval route, that is, there are multiple state variables;

[0046] The position variable of the patrol robot is defined by (x, y), and the speed variable of the patrol robot is defined by (Vx, Vy), where x is the horizontal coordinate position of the patrol robot in the two-dimensional map of the factory area, and y is the vertical coordinate position of the patrol robot in the two-dimensional map of the factory area;

[0047] By B a (i) = [V a x (i), V a y (i), X a (i), Ya (i)], represents the state variables of the patrol robot in the interval route, where a represents that each interval route includes a position variables and speed variables, that is, a state variables, i represents the number of interval routes, and satisfies a≥1, i>1;

[0048] Among them, V a x (i) and V a y (i) refers to the speed of the patrol robot in the state variable a within the interval route i, X a (i) and Y a (i) refers to the position of the patrol robot corresponding to the state variable a within the interval route i;

[0049] Step 3: According to the state variables corresponding to each state variable of the patrol robot in each interval route, the steering variables corresponding to each state variable of the robot in each interval route are obtained. The specific method is as follows:

[0050] After the state variables of the robot in each interval route are determined, the steering variable β of the patrol robot in state a in the corresponding interval route is obtained and defined as the steering variable β a (i), where β refers to the steering angle of the patrol robot under the corresponding state variable in each interval route;

[0051] Step 4: According to the state variables and steering variables of the patrol robot under each state variable in each interval route, the index function corresponding to the interval route is calculated, and then the optimal value corresponding to the index function of the state variable in each stage is used as the optimal function corresponding to each stage. The specific method of obtaining the optimal function corresponding to each stage is:

[0052] According to the formula, X b (c+1)=X b (c+1)+V b x (c) ×T b (c);

[0053] Y b (c+1)=Y b (c+1)+V b y (c) ×T b (c);

[0054] V b x (c) = V b x cosβ a (c);

[0055] V b y (c) = V b x cosβ a (c);

[0056] V x min ≤V b x (c)≤V x max；

[0057] V y min ≤V b y (c)≤V y max；

[0058] Calculate and obtain the optimal function corresponding to each interval route of the patrol robot, where i≥c>1, a≥b≥1;

[0059] The index function of each interval route depends on its corresponding state variable and turning variable. The optimal value corresponding to the index function of the state variable is taken as the optimal value function H of the corresponding interval route. a [Z i (a)], and then obtain the optimal value function corresponding to each interval route;

[0060] The optimal value function can be expressed as: ;

[0061] Where T i (d) represents the navigation time corresponding to each state of the patrol robot in each interval route, and obtains the minimum value of the index function corresponding to each interval route through the optimal value function corresponding to each interval route, and uses it to represent the optimal function corresponding to the patrol robot in the interval route, and obtains the optimal position of the patrol robot at the end of each interval route through the optimal function;

[0062] Step 5: Obtain the state variables and steering variables of the patrol robot at the end of each interval route, and then use the dynamic recursive equation to optimize the state variables and steering variables of each interval route of the patrol robot during the monitoring process, and then obtain the optimized state variables and steering variables corresponding to the patrol robot in each interval route. The specific method is:

[0063] Through the dynamic recursive equation: H b+1 [Z C (b+1)]=min {V b [Z c(b) ]+H b [Z C (b)]}, i≥c>1, a≥b≥1;

[0064] The optimized state variables and steering variables corresponding to each state of the patrol robot in each interval route are calculated by dynamic recursive equations. Finally, the optimized state variables and steering variables corresponding to each state of the patrol robot in the monitoring patrol area can be obtained, so as to achieve the purpose of optimizing the state variables and steering variables of the patrol robot in each state of the interval route during the monitoring process, which is helpful for the patrol robot to monitor and inspect the factory area and improve the monitoring efficiency of the patrol robot.

[0065] According to the starting point and target point of the patrol robot, the initial monitoring route is obtained, and the monitoring area is divided into multiple interval routes. The multiple interval routes are mapped to the two-dimensional map of the factory area, and the state variables of the patrol robot in each interval route are determined. According to the state variables of the patrol robot in each interval route, the steering variables of the patrol robot in each interval route are calculated. According to the state variables and steering variables of the patrol robot in each interval route, the indicator function corresponding to the interval route is calculated, and the optimal function corresponding to each stage is determined based on it. The optimized state variables and steering variables of the patrol robot in each interval route are obtained through dynamic recursive equations, so as to achieve the purpose of optimizing the state variables and steering variables of the patrol robot in each state in the interval route during the monitoring process, which is helpful for the patrol robot to monitor and inspect the factory area and improve the monitoring efficiency of the patrol robot.

[0066] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0067] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A factory monitoring method based on generalization analysis, characterized in that: The following steps are involved: Step 1: According to the starting point and target point of the patrol robot, the initial monitoring route is obtained, and the monitoring area is divided into multiple interval routes; Step 2: Map multiple interval routes to the two-dimensional map of the factory area, and determine the state variables of the patrol robot in each interval route, namely, the position variable and the speed variable; Step 3: Calculate the turning variable according to the state variables of the patrol robot in each interval route; Step 4: Calculate the index function corresponding to each interval route according to the state variables and steering variables of the patrol robot in each interval route, and determine the optimal function corresponding to each stage by minimizing the value of the index function; Step 5: Obtain the optimized state variables and steering variables of the patrol robot in each interval route through the dynamic recursive equation; The specific method of dividing the interval route is as follows: According to the starting point and the target point of the monitoring route of the patrol robot, a two-dimensional map of the factory area is obtained, and then the initial monitoring route of the patrol robot is obtained according to the starting point and the target point of the patrol robot, and the initial monitoring route is divided into a plurality of interval routes with equal route lengths; The specific way to obtain the status variable is: The position variable of the patrol robot is defined by x and y, and the speed variable of the patrol robot is defined by Vx and Vy, where x is the horizontal coordinate position of the patrol robot in the two-dimensional map of the factory area, and y is the vertical coordinate position of the patrol robot in the two-dimensional map of the factory area; By B a (i) = [V a x (i), V a y (i), X a (i), Y a (i)], represents the state variables of the patrol robot in the interval route, where a represents that each interval route includes a position variables and speed variables, that is, a state variables, i represents the number of interval routes, and satisfies a≥1, i>1; Among them, V a x (i) and V a y (i) refers to the speed of the patrol robot in the state variable a within the interval route i, X a (i) and Y a (i) refers to the position of the patrol robot corresponding to the state variable a within the interval route i; The specific method of obtaining the steering variables corresponding to each state variable of the robot in each interval route is: After the state variables of the robot in each interval route are determined, the steering variable β of the patrol robot in state a in the corresponding interval route is obtained and defined as the steering variable β a (i), where β refers to the steering angle of the patrol robot under the corresponding state variable in each interval route; The index function of each interval route depends on its corresponding state variable and turning variable. The optimal value corresponding to the index function of the state variable is taken as the optimal value function H of the corresponding interval route. a [Z i (a)], and then obtain the optimal value function corresponding to each interval route; The optimal value function can be expressed as: ; Where T i (d) represents the navigation time of the patrol robot in each state a within each interval route; The specific method of obtaining the optimized state variables and steering variables corresponding to the patrol robot in each interval route is: Through the dynamic recursive equation: H b+1 [Z c (b+1)]=min {V b [Z c (b) ]+H b [Z c (b)]}, calculate the optimized state variables and steering variables corresponding to each state of the patrol robot in each interval route, and finally obtain the optimized state variables and steering variables corresponding to each state of the patrol robot in the monitoring patrol area, i≥c>1, a≥b≥1.

2. A factory monitoring method based on generalization analysis according to claim 1, characterized in that: The position of the patrol robot in each interval route is represented by the position variable of the patrol robot, and the speed corresponding to the position of the patrol robot in each interval route is represented by the speed variable of the patrol robot. Therefore, the patrol robot has multiple position variables and speed variables in each interval route, that is, there are multiple state variables.

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