CN115454042A - Route decision method, device, equipment and storage medium of inspection robot - Google Patents
Route decision method, device, equipment and storage medium of inspection robot Download PDFInfo
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Abstract
The invention provides a route decision method, a route decision device, electronic equipment and a storage medium of an inspection robot, wherein the method comprises the following steps: rasterizing the target inspection area to obtain a plurality of grids, and determining grids where a plurality of candidate inspection points are located from all the grids; determining a grid where the inspection robot is located currently; calculating the difficulty of the inspection robot from the current grid to the grids where the candidate inspection points are located, calculating the danger fluctuation coefficients of the candidate inspection points, and acquiring the first time corresponding to the candidate inspection points; and determining the inspection willingness degrees of a plurality of candidate inspection points according to the difficulty degree, the danger fluctuation coefficient and the first time, and taking the candidate inspection point with the highest inspection willingness degree as a target inspection point. The invention comprehensively considers the polling efficiency, the occurrence possibility of dangerous conditions and the polling state, can improve the response speed of the polling process to the dangerous conditions, and improves the safety of the system.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a route decision method, a route decision device, route decision equipment and a storage medium of an inspection robot.
Background
Compared with manual inspection, the intelligent robot inspection system can realize 24-hour continuous inspection in an informationized, digitalized, high-frequency and leakage-free manner, has strong technical advantages, and is more and more widely applied along with the development of informatization.
The existing inspection robot basically inspects all inspection points by adopting a mode of traversing repeatedly in sequence, thereby realizing inspection of all inspection points within a certain time.
The conventional inspection mode is suitable for conventional scenes, and the conventional scenes are characterized in that objective environmental conditions in the scenes are not changed greatly, and the probability of abnormal conditions of inspection points is basically the same. The existing inspection mode has the problem of low reaction speed to dangerous conditions, and is not suitable for scenes in which the dangerous conditions are easy to occur or the adverse effect of the dangerous conditions is large.
Disclosure of Invention
The invention provides a path decision method and device of an inspection robot, electronic equipment and a storage medium, which are used for solving the defect that the inspection process in the prior art has slow response to dangerous conditions and realizing quick response to the dangerous conditions.
The invention provides a path decision method of an inspection robot, which comprises the following steps:
rasterizing a target inspection area to obtain a plurality of grids, wherein one grid corresponds to one inspection point, and determining grids where a plurality of candidate inspection points are located from all grids;
acquiring the latest inspection time of the inspection point corresponding to each grid, and determining the grid where the inspection robot is located currently based on the latest inspection time;
calculating the difficulty of the inspection robot reaching the grids where the candidate inspection points are located from the grid where the inspection robot is located, calculating the danger fluctuation coefficients of the candidate inspection points, and acquiring first time corresponding to the candidate inspection points, wherein the first time is obtained according to the difference between the current time and the latest inspection time of the candidate inspection points;
and determining the inspection willingness degrees of the candidate inspection points according to the difficulty, the danger fluctuation coefficient and the first time, and taking the candidate inspection point with the highest inspection willingness degree as a target inspection point.
According to the path decision method for the inspection robot provided by the invention, the calculating the difficulty of the inspection robot reaching the grids where the plurality of candidate inspection points are located from the grid where the inspection robot is located at present comprises the following steps:
determining the passing difficulty degree of all grids according to the congestion condition;
determining a path from the grid where the inspection robot is currently located to the grid where each candidate inspection point is located according to the algorithm A and the passing difficulty degree of all grids, wherein the path with the lowest passing difficulty degree is used as an inspection path corresponding to each candidate inspection point;
and taking the passing difficulty degree of the routing inspection path as the difficulty degree of the routing inspection robot reaching the grid where each candidate routing inspection point is located from the grid where the routing inspection robot is located currently.
According to the path decision method for the inspection robot provided by the invention, the calculating of the risk fluctuation coefficients of the candidate inspection points comprises the following steps:
determining the water accumulation amount and the facility distribution condition of a grid where each candidate inspection point is located;
and determining the danger fluctuation coefficient of each candidate patrol point according to the water accumulation amount and the facility distribution condition.
According to the path decision method of the inspection robot provided by the invention, the step of determining the inspection willingness degrees of the candidate inspection points according to the difficulty degree, the danger fluctuation coefficient and the first time comprises the following steps:
calculating the inspection willingness degrees of the candidate inspection points by using a preset formula according to the difficulty degree, the danger fluctuation coefficient and the first time;
wherein, the preset formula is as follows:
will(dif,w,t)＝-c 1 *dif+c 2 *w+c 3 *t
wherein dif is the difficulty, w is the risk fluctuation coefficient, t is the first time, c 1 ，c 2 And c 3 A reference weight for each attribute to be preset, c 1 ，c 2 And c 3 The values of (A) are all more than 0;
or the like, or, alternatively,
wherein dif is the difficulty, w is the risk fluctuation coefficient, t is the first time, c 4 ，c 5 And c 6 A reference weight for each attribute to be preset, c 4 ，c 5 And c 6 The values of (A) are all more than 0;
or the like, or, alternatively,
and dif is the difficulty degree, w is the dangerous fluctuation coefficient, t is the first time, k is a preset reference weight, and the value of k is greater than 0.
The routing decision method of the inspection robot provided by the invention further comprises the following steps:
sending an inspection instruction to an inspection robot, wherein the inspection instruction comprises an inspection signal and a parking instruction;
sending and patrolling and examining the instruction and patrolling and examining the robot and include:
generating a routing inspection signal according to the target routing inspection point and a routing inspection path corresponding to the target routing inspection point;
sending the inspection signal to an inspection robot;
if an inspection completion signal sent by the inspection robot is received, updating the latest inspection time of the target inspection point according to the inspection completion signal;
and generating a stopping instruction, and sending the stopping instruction to the inspection robot.
The invention also provides a path decision device of the inspection robot, which comprises:
the rasterization module is used for rasterizing the target inspection area to obtain a plurality of grids, wherein one grid corresponds to one inspection point, and the grids where a plurality of candidate inspection points are located are determined from all the grids;
the inspection robot position determining module is used for acquiring the latest inspection time of the inspection point corresponding to each grid and determining the grid where the inspection robot is located currently based on the latest inspection time;
the calculation module is used for calculating the difficulty of the inspection robot reaching the grids where the candidate inspection points are located from the grid where the inspection robot is located, calculating the danger fluctuation coefficients of the candidate inspection points, and acquiring first time corresponding to the candidate inspection points, wherein the first time is obtained according to the difference between the current time and the latest inspection time of the candidate inspection points;
and the target inspection point determining module is used for determining the inspection willingness degrees of the plurality of inspection points according to the difficulty, the danger fluctuation coefficient and the first time, and taking the inspection point with the highest inspection willingness degree as the target inspection point.
According to the path decision device of the inspection robot provided by the invention, the calculation module is used for:
determining the passing difficulty degrees of all grids according to the congestion condition;
determining a path with the lowest passing difficulty degree from the grid where the inspection robot is currently located to the grid where each candidate inspection point is located as an inspection path corresponding to the candidate inspection point according to the A-algorithm and the passing difficulty degrees of all the grids;
and taking the passing difficulty degree of the routing inspection path as the difficulty degree of the candidate routing inspection point.
According to the path decision device of the inspection robot provided by the invention, the calculation module is used for
Determining the water accumulation amount and the facility distribution condition of the grid where the candidate inspection point is located;
and determining the dangerous fluctuation coefficient of the candidate inspection point according to the water accumulation amount and the facility distribution condition.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, and is characterized in that the processor realizes the steps of the route decision method of the inspection robot when executing the program.
The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the path decision method of the inspection robot.
According to the route decision method, the route decision device, the electronic equipment and the storage medium of the inspection robot, the inspection willingness degrees of the candidate inspection points are determined according to the difficulty degree, the danger fluctuation coefficient and the current time of the last inspection distance, the inspection point with the highest inspection willingness degree is taken as a target inspection point, a macroscopic inspection point determination mode is adopted, the inspection efficiency, the possibility of dangerous situations and the inspection state are comprehensively considered, the response speed of the inspection process to the dangerous situations is improved, and the safety of the system is improved.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a path decision method of an inspection robot according to the present invention;
fig. 2 is a schematic flow chart of the present invention for calculating the difficulty of the inspection robot reaching the grids of the candidate inspection points from the grid where the inspection robot is currently located;
FIG. 3 is a schematic diagram of a process for calculating the risk fluctuation coefficients of the candidate inspection points according to the present invention;
FIG. 4 is a schematic flow chart of routing inspection instructions to an inspection robot according to the present invention;
fig. 5 is a second flowchart of the route decision method for the inspection robot according to the present invention;
fig. 6 is a schematic structural diagram of a path decision device of the inspection robot provided by the invention;
fig. 7 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a path decision method for an inspection robot according to an embodiment of the present invention, as shown in fig. 1, including the following steps:
Specifically, the entire environment may be divided into a plurality of large areas, each of which is responsible for inspection by one inspection robot. Within the large areas, a target inspection area may be determined. The target inspection area is discretized by using a grid method, and the discretization process divides the target inspection area into a plurality of grids, wherein each grid corresponds to one inspection point. And selecting important patrol from the plurality of patrol points as candidate patrol points, and marking the grids where the candidate patrol points are located.
Specifically, a time array is established, the latest inspection time of each inspection point is stored, and the inspection robot can stop at an appointed position corresponding to the inspection point after inspection is completed, so that the inspection point closest to the current time can be determined according to the latest inspection time of each inspection point, and the grid where the inspection robot is located currently is determined according to the position of the inspection point.
102, calculating the difficulty of the inspection robot reaching the grids where the candidate inspection points are located from the grid where the inspection robot is located, calculating the risk fluctuation coefficients of the candidate inspection points, and acquiring first time corresponding to the candidate inspection points, wherein the first time is obtained according to the difference between the current time and the latest inspection time of the candidate inspection points.
Specifically, a plurality of paths exist from the grid where the inspection robot determined in step 101 is currently located to the grid where each candidate inspection point determined in step 100 is located, a path with the lowest difficulty level is determined from the plurality of paths, the difficulty level corresponding to the path with the lowest difficulty level is taken as the difficulty level of the candidate inspection point, which represents the difficulty level of the robot reaching the candidate inspection point, and the higher the difficulty level is, the lower the inspection will be.
And determining the dangerous fluctuation coefficient of the grid according to the water accumulation amount and the facility distribution condition of the grid where each candidate patrol point is located. And in the process of determining the inspection point, the danger fluctuation coefficient is used as a reference value, so that the response speed to the dangerous condition in the inspection process can be improved. The larger the risk fluctuation coefficient is, the larger the inspection will be.
And determining the latest polling time of each candidate polling point, and subtracting the latest polling time from the current time to obtain the last polling time of each candidate polling point as the first time. The larger the first time is, the longer the time from the last inspection is, and the inspection will also be larger.
And 103, determining the inspection willingness degrees of the candidate inspection points according to the difficulty, the danger fluctuation coefficient and the first time, and taking the candidate inspection point with the highest inspection willingness degree as a target inspection point.
According to the difficulty, the risk fluctuation coefficient and the first time determined in the step 102, the inspection willingness of each candidate inspection point can be determined. The inspection willingness degree and the difficulty degree are in negative correlation, and the inspection willingness degree and the danger fluctuation coefficient and the first time are in positive correlation, and the calculation mode of the inspection willingness degree is not specifically limited. And selecting the inspection point with the highest inspection will degree as a target inspection point.
According to the embodiment of the invention, the routing inspection willingness degrees of the candidate routing inspection points are determined according to the difficulty, the danger fluctuation coefficient and the current time of the last routing inspection distance, the routing inspection point with the highest routing inspection willingness degree is taken as the target routing inspection point, a macroscopic routing inspection point determining mode is adopted, the routing inspection efficiency, the occurrence possibility of dangerous conditions and the routing inspection state are comprehensively considered, the response speed of the routing inspection process to the dangerous conditions is increased, and the safety of the system is improved.
Fig. 2 is a schematic flow chart of calculating the difficulty of the inspection robot reaching the grids of the candidate inspection points from the grid where the inspection robot is currently located, as shown in fig. 2, in an embodiment, the calculating the difficulty of the inspection robot reaching the grids of the candidate inspection points from the grid where the inspection robot is currently located includes the following sub-steps:
and 200, determining the passing difficulty of all grids according to the congestion condition.
Specifically, all grids can be divided into a passable grid and a non-passable grid, the passable grid can be used for calculating the passable grid difficulty according to the congestion condition, and the passable grid difficulty is marked for the subsequent calculation of the path weight. Wherein the congestion condition is determined by a method of measuring terrain, measuring road induction coils and processing camera images.
Among them, the a-algorithm is an improvement of Dijkstra algorithm, and is widely used in the problem of solving the shortest path. And adding known heuristic information of a state space in the searching process, and evaluating the cost by defining a proper cost function so as to dynamically adjust the searching strategy path to calculate the optimal solution. The cost function of the a-algorithm is:
f * (j)＝g(j)+h * (j)
wherein j represents a target node to be solved; g (j) is the actual cost from the starting point to the target node j along the generated path, the value of the cost is determined according to the selected metric of the road network, and the cost is calibrated based on two indexes of the shortest driving path/time and parking difficulty; h is * (j) Is an estimate of the cost from the current node j to the end point.
Specifically, according to the a-x algorithm and the passing difficulty levels of all the grids determined in step 200, a path from the grid where the inspection robot is currently located to the grid where each candidate inspection point is located, which has the lowest passing difficulty level, is determined as an inspection path corresponding to the candidate inspection point, where the inspection path is an optimal path from the grid where the inspection robot is located to each candidate inspection point, and the inspection path corresponding to each candidate inspection point is stored, where the storage manner is not specifically limited here.
And step 202, taking the passing difficulty degree of the inspection path as the difficulty degree of the inspection robot reaching the grid where each candidate inspection point is located from the grid where the inspection robot is located currently.
Specifically, the passing difficulty level of the inspection path corresponding to the candidate inspection point determined in step 201 is used as the difficulty level of the inspection robot reaching the grid where each candidate inspection point is located from the grid where the inspection robot is currently located, and the difficulty level is the lowest difficulty level from the grid where the inspection robot is located to each candidate inspection point.
According to the embodiment of the invention, the difficulty of grid passing is determined according to the congestion condition, the A-algorithm is adopted to determine the difficulty of each target inspection point, the inspection willingness of a plurality of candidate inspection points is determined according to the difficulty, the danger fluctuation coefficient and the current time of the last inspection distance, the inspection point with the highest inspection willingness is taken as the target inspection point, and the macroscopic inspection point determination mode is adopted, so that the response speed of the inspection process to the dangerous condition is increased, and the safety of the system is improved.
Fig. 3 is a schematic flowchart of a process for calculating risk fluctuation coefficients of the candidate inspection points according to an embodiment of the present application, and as shown in fig. 3, in an embodiment, the calculating the risk fluctuation coefficients of the candidate inspection points includes the following sub-steps:
and step 300, determining the water accumulation amount and the facility distribution condition of the grid where each candidate inspection point is located.
Specifically, the characteristic of low time delay of the 5G technology can be utilized to ensure the real-time performance of the water accumulation amount and the facility distribution condition of the grid where the determined candidate inspection point is located. The water accumulation amount and the facility distribution condition of the grid where the candidate patrol point is located are related to the occurrence possibility of dangerous situations.
And 301, determining a danger fluctuation coefficient of each candidate patrol point according to the water accumulation amount and the facility distribution condition.
Specifically, a danger fluctuation coefficient of each candidate patrol point is determined according to the water accumulation amount and the facility distribution situation, and the danger fluctuation coefficient represents the occurrence possibility of the danger situation of each candidate patrol point.
According to the embodiment of the invention, the danger fluctuation coefficient of the candidate inspection points is determined according to the water accumulation amount and the facility distribution condition, the inspection willingness degrees of the plurality of candidate inspection points are determined according to the difficulty degree, the danger fluctuation coefficient and the current time of the last inspection distance, the inspection point with the highest inspection willingness degree is taken as the target inspection point, and a macroscopic inspection point determination mode is adopted, so that the response speed of the inspection process to the danger condition is improved, and the safety of the system is improved.
In one embodiment, determining the patrol willingness degrees of the candidate patrol points according to the difficulty level, the risk fluctuation coefficient and the first time comprises the following steps:
and calculating the inspection willingness degrees of the candidate inspection points by using a preset formula according to the difficulty degree, the danger fluctuation coefficient and the first time.
Wherein the preset formula is as follows:
will(dif,w,t)＝-c 1 *dif+c 2 *w+c 3 *t
wherein dif is the difficulty, w is the risk fluctuation coefficient, t is the first time, c 1 ，c 2 And c 3 A reference weight for each attribute to be preset, c 1 ，c 2 And c 3 The values of (A) are all more than 0;
or the like, or, alternatively,
wherein dif is the difficulty, w is the risk fluctuation coefficient, t is the first time, c 4 ，c 5 And c 6 A reference weight for each attribute to be preset, c 4 ，c 5 And c 6 The values of (A) are all more than 0;
or the like, or, alternatively,
and dif is the difficulty degree, w is the dangerous fluctuation coefficient, t is the first time, k is a preset reference weight, and the value of k is greater than 0.
According to the method and the device, the routing inspection willingness degrees of a plurality of candidate routing inspection points are calculated by using a preset formula according to the difficulty, the danger fluctuation coefficient and the current time of the last routing inspection distance, the accuracy of routing inspection willingness calculation is improved, the routing inspection point with the highest routing inspection willingness degree is used as a target routing inspection point, a macroscopic routing inspection point determining mode is adopted, routing inspection efficiency, the occurrence possibility of dangerous conditions and routing inspection states are comprehensively considered, the response speed of a routing inspection process to the dangerous conditions is improved, and the safety of the system is improved.
In an embodiment, the method further includes sending an inspection instruction to the inspection robot, where the inspection instruction includes an inspection signal and a stop instruction, fig. 4 is a schematic flowchart of a process for calculating risk fluctuation coefficients of the candidate inspection points according to an embodiment of the present disclosure, and as shown in fig. 4, sending the inspection instruction to the inspection robot includes the following sub-steps:
and 400, generating a routing inspection signal according to the target routing inspection point and a routing inspection path corresponding to the target routing inspection point.
Specifically, in step 201, a path with the lowest passing difficulty from the grid where the inspection robot is currently located to the grid where each candidate inspection point is located is determined as an inspection path corresponding to each candidate inspection point, and since the target inspection point is one of the candidate inspection points, the inspection paths corresponding to the candidate inspection points stored in step 201 include the inspection path corresponding to the target inspection point. And generating a patrol instruction according to the target patrol point and the patrol corresponding to the target patrol point.
And step 401, sending the inspection signal to an inspection robot.
Specifically, the patrol inspection instruction generated in step 400 is sent to the patrol inspection robot, and after receiving the patrol inspection instruction, the patrol inspection robot can reach the target patrol inspection point through the patrol inspection path corresponding to the target patrol inspection point according to the instruction to perform patrol inspection.
And 402, if a patrol finishing signal sent by the patrol robot is received, updating the latest patrol time of the target patrol point according to the patrol finishing signal.
Specifically, the inspection robot generates an inspection completion signal after finishing inspection work at the target inspection point, determines the latest inspection time of the target inspection point according to the time of receiving the inspection completion signal or subtracting the preset signal transmission time difference from the time of receiving the inspection completion signal after receiving the inspection completion signal, and updates the latest inspection time of the target inspection point.
And 403, generating a parking instruction, and sending the parking instruction to the inspection robot.
Specifically, a stopping instruction is generated and sent to the inspection robot, and the inspection robot stops to a specified position corresponding to the target inspection point after receiving the stopping instruction.
According to the embodiment of the invention, the inspection willingness degrees of a plurality of candidate inspection points are determined according to the difficulty, the danger fluctuation coefficient and the current time of the previous inspection distance, a macroscopic inspection point determination mode is adopted, the inspection efficiency, the occurrence possibility of dangerous conditions and the inspection state are comprehensively considered, the response speed of the inspection process to the dangerous conditions is increased, and the safety of the system is improved. And sending a polling instruction to the polling robot, and according to a received polling completion signal sent by the polling robot and the latest polling time of the target polling point, ensuring the accuracy of the determination of the next target polling point.
Fig. 5 is a second flowchart of the route decision method for the inspection robot according to the embodiment of the present invention, as shown in fig. 5, including the following steps:
and 500, rasterizing the target inspection area to obtain a plurality of grids, wherein one grid corresponds to one inspection point, and determining grids where a plurality of candidate inspection points are located from all grids.
Specifically, the target inspection area is discretized by using a grid method, and the target inspection area is divided into a plurality of grids in the discretization process, wherein each grid corresponds to one inspection point. And selecting N important patrols from the plurality of patrolling points as candidate patrolling points, and marking N grids in which the N candidate patrolling points are positioned.
And 501, acquiring the latest polling time of the polling point corresponding to each grid, and determining the grid where the polling robot is currently located based on the latest polling time.
Specifically, a time array is established, the latest polling time of each polling point is stored, and the polling robot can stop at the designated position corresponding to the polling point after polling is completed, so that the polling point closest to the current time can be determined according to the latest polling time of each polling point, and the grid where the polling robot is located currently is determined according to the position of the polling point.
Specifically, each grid corresponds to the passing difficulty, and the passing difficulty of all grids is marked to prepare for determining the path. The maximum inspection willingness willMAX, the target inspection point target, the path pathT and the i are all variables, and before the target inspection point is determined, the variables are initialized to prepare for a circulating process.
The inspection willingness degree and the difficulty degree are in negative correlation, and the inspection willingness degree and the danger fluctuation coefficient and the first time are in positive correlation, and the calculation mode of the inspection willingness degree is not specifically limited.
And step 504, judging whether the will (i) is larger than the will (i), if so, setting the value of the will (i) as will (i), setting the target as i, and setting the path as path (i).
Specifically, if the null (i) is greater than the null max, the null (i) is the routing point with the highest option willingness among the currently traversed candidate routing points, so that i, null (i) and path (i) corresponding to the routing point are recorded in a manner that the value of the null max is set to the null (i), the target is set to i, and the path is set to the path (i).
And 505, judging whether the value of i is larger than or equal to N, if the value of i is smaller than N, adding 1 to the value of the variable i, and repeating the step 503 and the step 504.
Specifically, if the value of i is smaller than N, the traversal is not completed, the value of the variable i needs to be incremented by one, and step 503 and step 504 are repeated to calculate the inspection willingness of the next candidate inspection point.
And 506, if the value of i is larger than or equal to N, generating a patrol signal according to the pathT and the target, sending the patrol signal to the patrol robot, updating the latest patrol time of the target patrol point, and sending a patrol stopping signal to the patrol robot.
Specifically, if the value of i is greater than or equal to N, it is proved that the candidate patrol point is traversed and ended, at this time, the cycle is ended, the target patrol point is stored in the variable target, the patrol path corresponding to the target patrol point is stored in the variable pathT, and a patrol signal is generated according to the pathT and the target and is sent to the patrol robot. And the inspection robot performs inspection operation according to the received inspection signal.
According to the embodiment of the invention, the routing inspection willingness degrees of the candidate routing inspection points are determined according to the difficulty degree, the danger fluctuation coefficient and the current time of the last routing inspection distance, the routing inspection point with the highest routing inspection willingness degree is taken as the target routing inspection point, a macroscopic routing inspection point determining mode is adopted, the routing inspection efficiency, the occurrence possibility of dangerous situations and the routing inspection state are comprehensively considered, the response speed of the routing inspection process to the dangerous situations is increased, and the safety of the system is improved.
The following describes a path decision apparatus of an inspection robot according to the present invention, and the path decision apparatus of an inspection robot described below and the path decision method of an inspection robot described above may be referred to in correspondence to each other.
Another embodiment of the present invention provides a path deciding apparatus for an inspection robot, as shown in fig. 6, including: a rasterizing module 610, a patrol robot position determination module 620, a calculation module 630, and a target patrol point determination module 640, wherein,
the rasterizing module 610 is configured to rasterize the target inspection area to obtain multiple grids, where one grid corresponds to one inspection point, and determine grids where multiple candidate inspection points are located from all the grids;
the inspection robot position determining module 620 is configured to obtain the latest inspection time of the inspection point corresponding to each grid, and determine the grid where the inspection robot is currently located based on the latest inspection time;
a calculating module 630, configured to calculate a difficulty level of the inspection robot reaching the grid where the plurality of candidate inspection points are located from the grid where the inspection robot is currently located, calculate a risk fluctuation coefficient of the plurality of candidate inspection points, and obtain first time corresponding to the plurality of candidate inspection points, where the first time is obtained according to a difference between the current time and a latest inspection time of the plurality of candidate inspection points;
and the target inspection point determining module 640 is used for determining the inspection willingness degrees of the plurality of inspection points according to the difficulty degree, the danger fluctuation coefficient and the first time, and taking the inspection point with the highest inspection willingness degree as the target inspection point.
Optionally, the calculation module includes a difficulty degree calculation sub-module, a risk fluctuation coefficient calculation sub-module, and a first time calculation sub-module, and the difficulty degree determination sub-module is specifically configured to:
determining the passing difficulty degrees of all grids according to the congestion condition;
determining a path with the lowest passing difficulty degree from the grid where the inspection robot is currently located to the grid where each candidate inspection point is located as an inspection path corresponding to the candidate inspection point according to the A-algorithm and the passing difficulty degrees of all the grids;
and taking the passing difficulty degree of the routing inspection path as the difficulty degree of the candidate routing inspection point.
Optionally, the risk fluctuation coefficient calculation sub-module 720 is specifically configured to:
determining the water accumulation amount and the facility distribution condition of a grid where each candidate inspection point is located;
and determining the danger fluctuation coefficient of each candidate patrol point according to the water accumulation amount and the facility distribution condition.
Optionally, the target patrol point determining module 640 is configured to:
calculating the inspection willingness degrees of the candidate inspection points by using a preset formula according to the difficulty degree, the danger fluctuation coefficient and the first time;
wherein, the preset formula is as follows:
will(dif,w,t)＝-c 1 *dif+c 2 *w+c 3 *t
wherein dif is the difficulty, w is the risk fluctuation coefficient, t is the first time, c 1 ，c 2 And c 3 A reference weight for each attribute to be preset, c 1 ，c 2 And c 3 The values of (A) are all more than 0;
or the like, or a combination thereof,
wherein dif is the difficulty, w is the risk fluctuation coefficient, t is the first time, c 4 ，c 5 And c 6 A reference weight for each attribute to be preset, c 4 ，c 5 And c 6 The values of (A) are all more than 0;
or the like, or a combination thereof,
and dif is the difficulty degree, w is the dangerous fluctuation coefficient, t is the first time, k is a preset reference weight, and the value of k is greater than 0.
Optionally, the path decision device of the inspection robot further includes an instruction sending module 650, and the instruction sending module 650 is configured to:
sending an inspection instruction to an inspection robot, wherein the inspection instruction comprises an inspection signal and a parking instruction;
sending and patrolling and examining the instruction and patrolling and examining the robot and include:
generating a routing inspection signal according to the target routing inspection point and a routing inspection path corresponding to the target routing inspection point;
sending the inspection signal to an inspection robot;
if an inspection completion signal sent by the inspection robot is received, updating the latest inspection time of the target inspection point according to the inspection completion signal;
and generating a stopping instruction, and sending the stopping instruction to the inspection robot.
The path decision device of the inspection robot provided by the invention can realize each process realized by the method embodiments of fig. 1 to 5, and achieve the same technical effect, and is not repeated here for avoiding repetition.
Fig. 7 illustrates a physical structure diagram of an electronic device, and as shown in fig. 7, the electronic device may include: a processor (processor) 710, a communication Interface (Communications Interface) 720, a memory (memory) 730, and a communication bus 740, wherein the processor 710, the communication Interface 720, and the memory 730 communicate with each other via the communication bus 740. The processor 710 may invoke logic instructions in the memory 730 to perform a path decision method of the inspection robot, the method comprising: rasterizing the target inspection area to obtain a plurality of grids, wherein one grid corresponds to one inspection point, and determining grids where a plurality of candidate inspection points are located from all grids;
acquiring the latest polling time of the polling point corresponding to each grid, and determining the grid where the polling robot is currently located based on the latest polling time;
calculating the difficulty of the inspection robot reaching the grids where the candidate inspection points are located from the grid where the inspection robot is located, calculating the danger fluctuation coefficients of the candidate inspection points, and acquiring first time corresponding to the candidate inspection points, wherein the first time is obtained according to the difference between the current time and the latest inspection time of the candidate inspection points;
and determining the inspection willingness degrees of the candidate inspection points according to the difficulty, the danger fluctuation coefficient and the first time, and taking the candidate inspection point with the highest inspection willingness degree as a target inspection point.
In addition, the logic instructions in the memory 730 can be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions which, when executed by a computer, enable the computer to perform the route decision method for an inspection robot provided by the above methods, the method including: rasterizing the target inspection area to obtain a plurality of grids, wherein one grid corresponds to one inspection point, and determining grids where a plurality of candidate inspection points are located from all grids;
acquiring the latest inspection time of the inspection point corresponding to each grid, and determining the grid where the inspection robot is located currently based on the latest inspection time;
calculating the difficulty of the inspection robot reaching the grids of the candidate inspection points from the grid where the inspection robot is located, calculating the danger fluctuation coefficients of the candidate inspection points, and acquiring first time corresponding to the candidate inspection points, wherein the first time is obtained according to the difference between the current time and the latest inspection time of the candidate inspection points;
and determining the polling willingness degrees of the candidate polling points according to the difficulty, the danger fluctuation coefficient and the first time, and taking the candidate polling point with the highest polling willingness degree as a target polling point.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor, is implemented to perform the path decision method of an inspection robot provided above, the method including: rasterizing the target inspection area to obtain a plurality of grids, wherein one grid corresponds to one inspection point, and determining grids where a plurality of candidate inspection points are located from all grids;
acquiring the latest polling time of the polling point corresponding to each grid, and determining the grid where the polling robot is currently located based on the latest polling time;
calculating the difficulty of the inspection robot reaching the grids where the candidate inspection points are located from the grid where the inspection robot is located, calculating the danger fluctuation coefficients of the candidate inspection points, and acquiring first time corresponding to the candidate inspection points, wherein the first time is obtained according to the difference between the current time and the latest inspection time of the candidate inspection points;
and determining the inspection willingness degrees of the candidate inspection points according to the difficulty, the danger fluctuation coefficient and the first time, and taking the candidate inspection point with the highest inspection willingness degree as a target inspection point.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A path decision method of an inspection robot is characterized by comprising the following steps:
rasterizing the target inspection area to obtain a plurality of grids, wherein one grid corresponds to one inspection point, and determining grids where a plurality of candidate inspection points are located from all grids;
acquiring the latest polling time of the polling point corresponding to each grid, and determining the grid where the polling robot is currently located based on the latest polling time;
calculating the difficulty of the inspection robot reaching the grids where the candidate inspection points are located from the grid where the inspection robot is located, calculating the danger fluctuation coefficients of the candidate inspection points, and acquiring first time corresponding to the candidate inspection points, wherein the first time is obtained according to the difference between the current time and the latest inspection time of the candidate inspection points;
and determining the inspection willingness degrees of the candidate inspection points according to the difficulty, the danger fluctuation coefficient and the first time, and taking the candidate inspection point with the highest inspection willingness degree as a target inspection point.
2. The routing decision method for the inspection robot according to claim 1, wherein the calculating the difficulty of the inspection robot reaching the grid where the plurality of candidate inspection points are located from the grid where the inspection robot is currently located comprises:
determining the passing difficulty degrees of all grids according to the congestion condition;
determining a path with the lowest passing difficulty degree from the grid where the inspection robot is currently located to the grid where each candidate inspection point is located as an inspection path corresponding to each candidate inspection point according to the A-algorithm and the passing difficulty degrees of all the grids;
and taking the passing difficulty degree of the routing inspection path as the difficulty degree of the routing inspection robot reaching the grid where each candidate routing inspection point is located from the grid where the routing inspection robot is located currently.
3. The routing decision method for an inspection robot according to claim 1, wherein the calculating the risk fluctuation coefficients for the plurality of candidate inspection points includes:
determining the water accumulation amount and the facility distribution condition of a grid where each candidate inspection point is located;
and determining the danger fluctuation coefficient of each candidate patrol point according to the water accumulation amount and the facility distribution condition.
4. The route decision method for the inspection robot according to claim 1, wherein the determining inspection willingness degrees of the plurality of candidate inspection points according to the difficulty, the risk fluctuation coefficient and the first time comprises:
calculating the inspection willingness degrees of the candidate inspection points by using a preset formula according to the difficulty degree, the danger fluctuation coefficient and the first time;
wherein, the preset formula is as follows:
will(dif,w,t)＝-c 1 *dif+c 2 *w+c 3 *t
wherein dif is the difficulty, w is the risk fluctuation coefficient, t is the first time, c 1 ，c 2 And c 3 A reference weight for each attribute to be preset, c 1 ，c 2 And c 3 The values of (A) are all more than 0;
or the like, or a combination thereof,
wherein dif is the difficulty and w isA coefficient of risk fluctuation, t being the first time, c 4 ，c 5 And c 6 A reference weight for each attribute preset, c 4 ，c 5 And c 6 The values of (A) are all more than 0;
or the like, or, alternatively,
and dif is the difficulty degree, w is the dangerous fluctuation coefficient, t is the first time, k is a preset reference weight, and the value of k is greater than 0.
5. The routing decision method for the inspection robot according to claim 2, further comprising:
sending an inspection instruction to an inspection robot, wherein the inspection instruction comprises an inspection signal and a parking instruction;
sending and patrolling and examining the instruction and patrolling and examining the robot and include:
generating a routing inspection signal according to the target routing inspection point and a routing inspection path corresponding to the target routing inspection point;
sending the inspection signal to an inspection robot;
if an inspection completion signal sent by the inspection robot is received, updating the latest inspection time of the target inspection point according to the inspection completion signal;
and generating a stopping instruction, and sending the stopping instruction to the inspection robot.
6. A path decision device of a patrol robot is characterized by comprising:
the rasterization module is used for rasterizing the target inspection area to obtain a plurality of grids, wherein one grid corresponds to one inspection point, and the grids where a plurality of candidate inspection points are located are determined from all the grids;
the inspection robot position determining module is used for acquiring the latest inspection time of the inspection point corresponding to each grid and determining the grid where the inspection robot is located currently based on the latest inspection time;
the calculation module is used for calculating the difficulty of the inspection robot reaching the grids where the candidate inspection points are located from the grid where the inspection robot is located, calculating the danger fluctuation coefficients of the candidate inspection points, and acquiring first time corresponding to the candidate inspection points, wherein the first time is obtained according to the difference between the current time and the latest inspection time of the candidate inspection points;
and the target inspection point determining module is used for determining the inspection willingness degrees of the plurality of inspection points according to the difficulty, the danger fluctuation coefficient and the first time, and taking the inspection point with the highest inspection willingness degree as the target inspection point.
7. The routing decision device of the inspection robot according to claim 6, wherein the calculation module is configured to:
determining the passing difficulty degrees of all grids according to the congestion condition;
determining a path with the lowest passing difficulty degree from the grid where the inspection robot is currently located to the grid where each candidate inspection point is located as an inspection path corresponding to the candidate inspection point according to the A-algorithm and the passing difficulty degrees of all the grids;
and taking the passing difficulty degree of the routing inspection path as the difficulty degree of the candidate routing inspection point.
8. The routing decision device of the inspection robot according to claim 6, wherein the calculation module is configured to:
determining the water accumulation amount and the facility distribution condition of the grid where the candidate patrol point is located;
and determining the dangerous fluctuation coefficient of the candidate inspection point according to the water accumulation amount and the facility distribution condition.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the path decision method for an inspection robot according to any one of claims 1 to 5.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the path decision method for an inspection robot according to any one of claims 1 to 5.
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