CN113077109B - Intelligent scheduling system, method, equipment and medium for machine patrol plan - Google Patents
Intelligent scheduling system, method, equipment and medium for machine patrol plan Download PDFInfo
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Abstract
The invention discloses an intelligent dispatching system, method, equipment and medium for a machine patrol plan, and relates to the technical field of patrol dispatching. The system comprises an information acquisition module, a resource acquisition module and a resource acquisition module, wherein the information acquisition module is used for acquiring patrol information and machine patrol resource information in a preset time period; the cluster analysis module is used for carrying out cluster analysis on the patrolled area according to the patrol information and the machine patrol resource information; the target analysis module is used for constructing a machine patrol scheduling target in a preset time period according to the patrol information and the machine patrol resource information; the scheme generation module is used for generating a machine patrol scheduling scheme set according to the patrol information, the machine patrol resource matching information and the machine patrol scheduling target; and the scheme screening module is used for searching the optimal scheduling scheme from the slave patrol scheduling scheme in a centralized manner through a neural network algorithm. The method and the device can solve the problems of rough machine patrol scheduling, low efficiency, difficult adjustment and the like, improve the scientificity, accuracy and convenience of operation of line operation and machine patrol scheduling decisions, and realize the balance optimization of machine patrol resources and efficiency.
Description
Technical Field
The invention relates to the technical field of routing inspection scheduling, in particular to an intelligent scheduling system, method, equipment and medium for a machine inspection plan.
Background
China has broad breadth, many distribution points and wide distribution range of the power transmission lines, most of the power transmission lines are far away from cities and towns and distributed in open-air areas such as vast regions and sparse people, the landform is complex, and the natural environment is severe. The power line and the pole tower accessories are exposed outdoors for a long time, and can be continuously influenced by mechanical tension, electric flashover and material aging to generate damages such as strand breakage, abrasion, corrosion and the like, so that the faults are obviously higher than those of other equipment. Therefore, the power transmission line is subjected to regular inspection tour, the running condition of the power transmission line, the change conditions of the surrounding environment of the line and the line protection area are mastered and known at any time, and the power transmission line is an important and tedious daily work of a power supply department.
Regular inspection of the transmission line generally comprises a machine inspection operation mode, but the schedule and adjustment of the conventional machine inspection operation plan are manually designed one by one manually, so that the management requirement of the future machine inspection operation can not be met gradually. The existing machine patrol operation has the problems of long design cycle of machine patrol plan scheduling, difficult plan adjustment, low intellectualization degree and automation degree and the like, is difficult to realize the optimization matching between human-machine resources and operation tasks, and is not beneficial to the refined quality management and the operation efficiency improvement of machine patrol operation production.
Therefore, the machine patrol plan scheduling needs to be developed towards the direction of automation and intelligence, a whole set of machine patrol plan intelligent scheduling method needs to be constructed urgently, efficient scheduling of the machine patrol plan is achieved, a plurality of defects of manual operation and maintenance are changed fundamentally, fault points of the power transmission line are identified quickly and accurately, routing inspection efficiency is improved, labor cost is reduced, and a support is provided for routing inspection error rate.
Disclosure of Invention
The invention aims to provide an intelligent scheduling system, method, equipment and medium for a machine patrol plan, which are used for solving the problems of extensive scheduling mode, low efficiency, difficult adjustment and the like of an operation plan, improving the scientificity, accuracy and convenience of operation of line operation and machine patrol scheduling decisions and realizing the balance optimization of machine patrol resources and efficiency.
In order to achieve the above object, the present invention provides an intelligent scheduling system for a machine patrol plan, comprising:
the information acquisition module is used for acquiring patrol information and machine patrol resource information in a preset time period; the patrol information comprises patrol requirements and operation and maintenance strategies;
the cluster analysis module is used for carrying out cluster analysis on the patrolled area by taking the initial position of the patrolled resource as the center according to the patrolling information and the patrolling resource information to obtain patrolled area patrolling resource matching information;
the target analysis module is used for constructing a machine patrol scheduling target in a preset time period according to the patrol information and the machine patrol resource information;
the scheme generation module is used for generating a machine patrol scheduling scheme set according to the patrol information, the machine patrol resource matching information and the machine patrol scheduling target; the machine patrol scheduling scheme set comprises a plurality of machine patrol scheduling schemes;
and the scheme screening module is used for searching the optimal scheduling scheme from the machine patrol scheduling scheme set through a neural network algorithm.
Preferably, the system further comprises a risk analysis module, which is used for acquiring risk information of the patrolled area according to the patrolling information, predicting risks according to the risk information, and analyzing the execution success rate of patrolling tasks in a preset time period; the machine patrol scheduling targets in the preset time period constructed by the target analysis module further comprise the highest execution success rate in the time period of task execution.
Preferably, the risk information includes basic information of maintenance of the power transmission line, information of towers of the power transmission line, the condition of sharing the towers of the power transmission line, crossing of the power transmission line, a special section of the power transmission line, an airspace hazard source, a line hazard source, model applicability of the maintenance section, the condition of an operation team, a machine patrol operation take-off and landing point and historical weather conditions.
Preferably, the operation and maintenance strategy comprises an operation and maintenance period and a patrol type; the patrol type comprises fine patrol and channel patrol.
Preferably, the machine patrol resource information includes patrol efficiency of machine patrol resources, and the number and time of the machine patrol resources that can be utilized.
Preferably, the patrol requirement comprises a patrol area, a patrol object and a patrol purpose; the inspection object comprises a pole tower, an insulator and a line, and the inspection purpose comprises judging whether the inspection object is deformed, inclined, accumulated dust, broken or wound.
Preferably, the machine patrol scheduling target includes that the moving distance of the machine patrol resource is shortest, the machine patrol scheduling conforms to the operation and maintenance strategy, and the workload is balanced between the machine patrol resources.
The invention also provides an intelligent scheduling method of the machine patrol plan, which comprises the following steps:
acquiring patrol information and machine patrol resource information in a preset time period; the patrol information comprises patrol requirements and operation and maintenance strategies;
according to the patrol information and the machine patrol resource information, performing cluster analysis on a patrolled area by taking the initial position of the machine patrol resource as a center to obtain machine patrol resource matching information of the patrolled area;
constructing a machine patrol scheduling target in a preset time period according to the patrol information and the machine patrol resource information;
generating a machine patrol scheduling scheme set according to the patrol information, the machine patrol resource matching information and the machine patrol scheduling target; the machine patrol scheduling scheme set comprises a plurality of machine patrol scheduling schemes;
and searching an optimal scheduling scheme from the machine patrol scheduling scheme set through a neural network algorithm.
The invention also provides a computer terminal device comprising one or more processors and a memory. A memory coupled to the processor for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the intelligent scheduling method for machine tour planning as described in any of the above embodiments.
The present invention also provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for intelligently scheduling a machine patrol plan according to any of the above embodiments.
In the intelligent scheduling method of the machine patrol plan of the embodiment of the invention,
compared with the prior art, the invention has the following beneficial effects:
the invention provides an intelligent scheduling system for a machine patrol plan, which comprises: the information acquisition module is used for acquiring patrol information and machine patrol resource information in a preset time period; the patrol information comprises patrol requirements and operation and maintenance strategies; the cluster analysis module is used for carrying out cluster analysis on the patrolled area by taking the initial position of the patrolling resource as the center according to the patrolling information and the patrolling resource information to obtain patrolling resource matching information of the patrolled area; the target analysis module is used for constructing a machine patrol scheduling target in a preset time period according to the patrol information and the machine patrol resource information; the scheme generation module is used for generating a machine patrol scheduling scheme set according to the patrol information, the machine patrol resource matching information and the machine patrol scheduling target; the machine patrol scheduling scheme set comprises a plurality of machine patrol scheduling schemes; and the scheme screening module is used for searching the optimal scheduling scheme from the machine patrol scheduling scheme set through a neural network algorithm. The method and the system can solve the problems of rough scheduling mode, low efficiency, difficult adjustment and the like of the operation plan, improve the scientificity, accuracy and convenience of operation of line operation and machine patrol scheduling decision, and realize the balance optimization of machine patrol resources and efficiency.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an intelligent scheduling system for a machine patrol plan according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of an intelligent scheduling method for a machine patrol plan according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer terminal device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an intelligent scheduling system for a machine patrol plan according to an embodiment of the present invention. The machine patrols planned intelligent scheduling system that this embodiment provided includes:
the information obtaining module 110 is configured to obtain patrol information and machine patrol resource information within a preset time period. Wherein, the patrol information comprises patrol requirements and operation and maintenance strategies. The operation and maintenance strategy comprises an operation and maintenance period and a patrol type. The tour type includes fine tour and channel tour.
The patrol requirement acquisition comprises patrol line length, channel length, patrol type, name of the patrol line required in each region, voltage grade, initial pole tower number, termination pole tower number, pole tower cardinal number and line equipment grade in a preset time period. For example, the local city bureau patrol requirement of the preset year is obtained, the local city bureau patrol requirement comprises the name, the voltage level, the initial pole tower number, the termination pole tower number, the pole tower base number and the equipment level of a line to be patrolled in each local city bureau, and the line length and the channel length sum of helicopter fine patrol, helicopter channel patrol, fixed wing unmanned aerial vehicle laser patrol and fixed wing unmanned aerial vehicle oblique photography are required to be adopted respectively. It is understood that the patrol requirement includes patrol areas, patrol objects, and patrol purposes. The inspection object comprises a tower, an insulator and a line, and the inspection purpose comprises judging whether the inspection object is deformed, inclined, accumulated dust, broken or wound.
The acquisition of the operation and maintenance strategy comprises the operation and maintenance period and the operation and maintenance type planning of each level of line. For example, the acquisition machine patrol center presets an annual operation and maintenance strategy, 1-time patrol in 1 year in a refined mode, 1-time patrol in 1 quarter in a channel patrol mode, wherein the channel patrol mode comprises helicopter channel patrol, fixed-wing unmanned aerial vehicle laser patrol and fixed-wing unmanned aerial vehicle oblique photography. The method also comprises the step of obtaining the appointed operation and maintenance strategy of the machine patrol center, wherein the appointed operation and maintenance strategy comprises the time period of the appointed city bureau operation and maintenance of the machine patrol center and the adopted operation and maintenance type. The machine patrol center sets the starting position of helicopter fine operation patrol according to actual operation and maintenance requirements, sets the operation and maintenance time and operation and maintenance type of a specific local bureau, sets 1/3 that the helicopter fine operation patrol operation in 1-3 months of a preset year is ordinary operation, and sets that the machine patrol operation plan in each year is completely completed before 15 days of 12 months. In addition, the operation and maintenance strategy also comprises a time period for forbidding operation and maintenance, for example, a specific local bureau is set to forbid operation and maintenance in a specific time period.
And the cluster analysis module 120 is configured to perform cluster analysis on the patrolled area by taking the initial position of the machine patrol resource as a center according to the patrol information and the machine patrol resource information, so as to obtain machine patrol resource matching information of the patrolled area.
The machine patrol resource information comprises patrol efficiency of the machine patrol resources, and the quantity and time of the machine patrol resources which can be utilized. For example, the fine patrol efficiency of the helicopter is set to be 150 km/day, the fast patrol efficiency of the helicopter is set to be 450 km/day, and the patrol efficiency of the unmanned aerial vehicle is set to be 90 km/day. Determining available flight patrol resources, including the number and time of available helicopters, the number and time of available fleet of fixed wing drones, e.g., 2 available helicopters for a period of time available throughout the year; the number of fixed wing unmanned aerial vehicle fleets assumes sufficient resources, and the use requirements can be completely met.
And the target analysis module 130 is configured to construct a machine patrol scheduling target within a preset time period according to the patrol information and the machine patrol resource information. The machine patrol scheduling target comprises the shortest moving distance of the machine patrol resource, the conformity of the machine patrol scheduling to the operation and maintenance strategy and the workload balance between the machine patrol resource.
In one embodiment, the machine tour schedule objective may be described by the algorithm objective as: under the conditions of considering available machine patrol resources, city task execution time constraints and operation and maintenance strategy constraints, reasonably arranging city local machine patrol operation tasks to ensure that (1) the moving distance of machine patrol between cities is shortest; (2) and (3) satisfying the operation and maintenance strategy constraint: appointing operation and maintenance strategy constraints and forbidding the operation and maintenance strategy constraints; (3) the workload among the helicopter teams is balanced as much as possible; (4) the task expectation success rate is the greatest.
For example, according to the target description of the annual machine patrol plan intelligent scheduling algorithm, the annual plan scheduling optimization objective function is established as follows:
min O＝dis+time_punish1+time_punish2+time_punish3+load_punish-poss
wherein, O is a normalized optimized target value, dis is a normalized machine patrol moving distance, time _ push 1 is a penalty not meeting a specified operation and maintenance time constraint, time _ push 2 is a penalty not meeting an operation and maintenance prohibition time constraint, time _ push 3 is a penalty scheduled to exceed one year, load _ push is a penalty of the imbalance degree of workload between machine patrol, and pos is a task expected success rate estimated by historical weather data.
And the scheme generating module 140 is configured to generate a machine patrol scheduling scheme set according to the patrol information, the machine patrol resource matching information, and the machine patrol scheduling target. The machine patrol scheduling scheme set comprises a plurality of machine patrol scheduling schemes.
The method comprises the following steps of obtaining the number of flexibly available machine patrol resources and continuously available time periods, and calculating a city bureau for flexibly allocating the resources according to an Allot allocation function, wherein the algorithm mainly comprises the following steps: (1) screening out a city list which can be executed by using flexible resources under the condition of considering the specified operation and maintenance time constraint and the forbidden operation and maintenance time constraint; (2) allocating the workload of the flexible resources according to the time proportion of the flexible resources to the fixed resources; (3) by using the clustering idea as a reference, under the constraint of workload, a city set for executing tasks by flexible resources is selected from the selectable city sets, and the moving range of the flexible resources is ensured to be as small as possible. Wherein the input parameters include: fixing the number of the helicopters, the patrol distance of corresponding tasks in each city, the positions of each city, the patrol efficiency, the specified operation and maintenance constraints and the forbidden operation and maintenance constraints; the output parameters include: a list of cities for which tour jobs are performed by flexible resources.
And an improved K-means algorithm is adopted to primarily distribute the machine patrol tasks of the helicopters, ensure the balance of tasks among helicopters of the same type, complete the initialization of a genetic algorithm and obtain the initial solution of the algorithm. The main idea of the improved K-means algorithm is as follows: clustering all cities by taking the initial positions of K helicopters in the space as the center, classifying the cities closest to the K helicopters, and gradually updating the values of all clustering centers through iteration until the best clustering result is obtained, so that the moving ranges of the helicopters can be ensured to be concentrated as much as possible, and the cost is reduced.
And adjusting the K classes according to the requirement of workload balance. The method mainly comprises the following steps: (1) dividing the task helicopters into K clusters according to the number of the task helicopters of the type; (2) taking the position of each helicopter as a first mass center; (3) for each point x in the set of geodetic locations, calculating its distance d (x) from the nearest centroid (referring to the selected cluster center) and attributing it to the nearest cluster; (4) calculating a new center; (5) repeating the steps 3 and 4 until K clusters are selected; (6) and calculating the workload of each cluster, and adjusting to balance the workload among the helicopters as much as possible. Wherein the input parameters include: the number K of the fixed helicopters, the patrol distance of corresponding tasks in each city, the position set of each city and the patrol efficiency. The output parameters include: each stationary helicopter executes a city list of tasks.
And the scheme screening module 150 is used for searching the optimal scheduling scheme from the patrol scheduling scheme set through a neural network algorithm.
In a specific embodiment, an improved genetic algorithm and a tabu search algorithm are combined to be used as a solving algorithm of the annual plan, the annual plan is obtained through solving, whether all constraint conditions are met or not is judged, if all constraint conditions are met, the annual plan is output, if not, the requirement of shortening the operation and maintenance interval is prompted, and input parameters are reset to carry out optimization algorithm solving. The improved genetic algorithm idea and steps are as follows:
(1) individuals are composed of two parts: machine patrol operation number and machine number
The numbering rule of the machine patrol operation is as follows: (citynum-1) × V _ task +1, …, (citynum-1) × V _ task + V _ task. 19 city, so citynum ═ 19, two helicopter missions, V _ task ═ 2, 1 for refinement, 2 for fast cruise. Therefore, the number of the machine patrol operation is 1-38;
machine patrol resource numbering: for the fleet performing the corresponding operation, for example, in 2019, 2 helicopter refinement flees and 1 helicopter fast patrol fleet exist, so that the mission numbered 1 in each city can be performed by the fleets numbered 1 and 2, and the mission numbered 3 can be performed by the fleets numbered 3;
(2) fitness calculation
The fitness calculation rule is as follows: firstly, fine tasks are preferentially arranged, and a fleet with short current task time in fine helicopter fleets is searched; then, all task numbers executed by the helicopter are taken out from the machine numbers, and are sequenced according to the positions of the task numbers in the task codes, the tasks at the location of the machine are arranged firstly, and then the tasks are arranged according to the coding sequence; after the fine helicopters are arranged, arranging the fast patrol helicopters; and finally, judging the time relationship between refinement and quick patrol of each city, and arranging the patrol task of the fixed-wing unmanned aerial vehicle.
(3) Cross computation
The machine patrols the operation part: two-point crossing is adopted, two positions are randomly generated, genes of the two genes between the positions are crossed, and duplication and deletion are deleted and completed;
and (3) a machine patrol resource part: and (4) executing machine codes of the interchangeable task genes.
(4) Variance calculation
The machine patrols the operation part: randomly generating two positions, and exchanging task codes of the two positions;
and (3) a machine patrol resource part: randomly generating a position, and randomly generating a new machine code if the task code can be executed by any one of the plurality of fleets of machines.
(5) Tabu search algorithm
For part of optimal individuals, tabu search is adopted to improve the search efficiency of the algorithm, a neighborhood preferred search method is adopted for the tabu search algorithm, and in order to avoid circulation, the algorithm places some recently accepted movements in a tabu table and prohibits the movements in subsequent iterations. I.e. only the better solution in the no-longer-tabu table (possibly worse than the current solution) can be accepted as the initial solution for the next generation iteration. And continuously updating the tabu table along with the iteration, after a certain number of iterations, the movement which enters the tabu table at the earliest is forbidden from the tabu table and exits, and iterating until a feasible solution meeting the conditions is obtained or the end condition is reached.
The method mainly comprises the following steps:
initialization: setting a counter t to be 0; maximum iteration number maxgen is 100; based on the output of the K-means function, M individuals are randomly generated as an initial population Pop (0). Individual evaluation: calculating the fitness of each individual in the population Pop (t). Selecting and operating: the selection operator is applied to the population. And (3) cross operation: the crossover operator is applied to the population. And (3) mutation operation: and (4) applying mutation operators to the population. And (t) selecting the population P (t), and performing cross operation to obtain a next generation population P (t + 1). And (4) judging termination conditions: if T is less than or equal to T, T is T +1, and the individual evaluation step is carried out continuously; if t is greater than maxgen, the individual with the maximum fitness obtained in the iterative process is used as the optimal output, and the calculation is terminated.
Wherein the input parameters include: the method comprises the following steps of refining the number of teams of helicopters, the number of fast patrolling teams of helicopters, the number of flexible helicopters, the available time period of the flexible helicopters, the initial position of the teams, operation and maintenance intervals, the number of cities, the patrolling distance of each task in each city, specified operation and maintenance strategy constraints, forbidden operation and maintenance strategy constraints, the starting time of executing the last task in each city last time, a city list of executing the task by each fixed helicopter and a city list of executing the task by the flexible helicopters. The output parameters include: and (5) resolving the optimal solution.
In order to make the scheduling scheme more scientific, the machine patrol plan intelligent scheduling system further includes a risk analysis module 160, which is used for obtaining risk information of a patrolled area according to the patrol information, predicting risks according to the risk information, and analyzing the execution success rate of the patrol tasks within a preset time period. The machine patrol scheduling target in the preset time period, which is constructed by the target analysis module 130, further includes the highest execution success rate in the time period of task execution.
The risk information comprises basic information of the maintenance power transmission line, tower information of the power transmission line, the common tower condition of the lines, line cross spanning, special line sections, airspace hazard sources, line hazard sources, model applicability of the maintenance section, the condition of an operation team, the starting and landing points of the mechanical patrol operation and historical weather conditions.
The weather condition is generally acquired by taking a month as a unit, the number of days of weather suitable for the executive machine to patrol the task, such as a sunny day, a cloudy day and the like, in each month in a period of 1-6 months is calculated, and the data is used as a reference for calculating the execution success rate of the expected task.
Referring to fig. 2, fig. 2 is a schematic flow chart of an intelligent scheduling method for a machine patrol plan according to an embodiment of the present invention. The same portions of this embodiment as those of the above embodiments will not be described herein again.
The intelligent scheduling method for the machine patrol plan provided by the embodiment comprises the following steps:
and S210, acquiring patrol information and machine patrol resource information in a preset time period. Wherein, the patrol information comprises patrol requirements and operation and maintenance strategies.
And S220, according to the patrol information and the machine patrol resource information, performing cluster analysis on the patrolled area by taking the initial position of the machine patrol resource as a center to obtain machine patrol resource matching information of the patrolled area.
And S230, constructing a machine patrol scheduling target in a preset time period according to the patrol information and the machine patrol resource information.
And S240, generating a machine patrol scheduling scheme set according to the patrol information, the machine patrol resource matching information and the machine patrol scheduling target. The machine patrol scheduling scheme set comprises a plurality of machine patrol scheduling schemes.
And S250, searching the optimal scheduling scheme from the slave patrol scheduling scheme in a centralized manner through a neural network algorithm.
In a specific embodiment, the performing a business operation according to the intelligent scheduling system for a machine patrol plan includes the following steps:
reporting annual machine patrol operation requirements by a city bureau;
secondly, the machine patrol center examines annual machine patrol operation requirements reported by a city bureau according to the condition of an airspace hazard source, and comprehensively forms annual machine patrol operation requirements of the whole province;
step three, the machine patrol center inputs requirements of annual machine patrol operation of the whole province, annual project planning, power transmission line ledgers, special section inventory, section machine applicability, regional seasonal factors, line operation and maintenance strategies, local city bureau operation and maintenance requirements and operation prohibition dates as input parameters into a system, a system background generates an annual machine patrol operation plan according to an intelligent scheduling algorithm model of the machine patrol plan, and issues the generated plan to each city bureau for opinion collection;
step four, each local city bureau puts forward suggestion suggestions to the preliminary arranged annual plans according to the actual situation of the local city bureau, and puts forward suggestion feedback to the engine patrol center in the system;
step five, the machine patrol center receives the suggestion suggestions returned by each city bureau, makes a decision on whether to adjust, receives the suggestions if the adjustment is determined, modifies the suggestions, generates a new annual machine patrol plan and collects new suggestions; if the adjustment is not determined, generating a final edition of annual plan and issuing the annual plan to each city bureau;
step six, the machine patrol center performs monthly planning arrangement according to the generated annual machine patrol operation plan, annual operation completion condition and special patrol operation plan to form a monthly machine patrol operation plan, and issues the monthly plan to a local city bureau for opinion collection;
step seven, each city bureau puts forward suggestion suggestions to the primarily arranged monthly plans according to the actual situation of the local city bureau, and puts forward suggestion feedback to the locomotive patrol center in the system;
step eight, the machine patrol center receives the suggestion suggestions returned by each city bureau, makes a decision on whether to adjust, receives the suggestions if the adjustment is determined, modifies the suggestions, generates a new monthly machine patrol plan and collects new opinions; if the adjustment is not determined, generating a final version of monthly plan, and issuing the final version of monthly plan to each city bureau;
step nine, each city bureau has special patrol demands, special patrol demand is filled in the system, and a machine patrol operation center checks the special patrol demand and forms a special patrol operation plan after the special patrol demand passes; if the machine patrol center has special patrol requirements, filling special patrol operation requirements in the system, and forming a special patrol operation plan;
step ten, the machine patrol center makes a week operation plan according to the monthly machine patrol operation plan and the special patrol operation plan, and issues the formed week machine patrol operation plan to a local city bureau for opinion collection;
step eleven, each local city bureau puts forward suggestion suggestions to the preliminarily arranged weekly plans according to the actual situation of the local city bureau, and puts forward suggestion feedback to the engine patrol center in the system;
step twelve, the machine patrol center receives the suggestion suggestions returned by each city bureau, makes a decision on whether to adjust, receives the suggestions if the adjustment is determined, modifies the suggestions, generates a new week machine patrol plan and collects a new round of opinions; if the adjustment is not determined, generating a final edition of week plan and releasing the week plan to each city bureau;
step thirteen, each city bureau carries out weekly plan dispatching, and the weekly plan is dispatched to a machine patrol operation team responsible for executing operation flight;
fourteen, the machine patrol operating team responsible for executing the operation flight receives the distributed weekly operation tasks, and submits a daily machine patrol operation plan application to a machine patrol center by combining the specific operation flight take-off and landing point information;
and fifthly, the machine patrol center receives a daily machine patrol operation plan application submitted by a machine patrol operation team, performs daily operation plan pre-deduction under the visual environment provided by the system by combining an airspace hazard source, a line hazard source, line cross-over, weather forecast information and typhoon forecast information, performs daily operation plan audit according to deduction conditions, and if the deduction result meets the operation safety requirement, the audit is passed, otherwise, the audit is not passed. After the audit, the machine patrol center replies the audit result to a machine patrol operation team and gives an operation risk prompt;
sixthly, the machine patrol center issues the finally determined operation plan of the whole province day to each city bureau;
seventhly, each machine patrol team executes operation flight according to the daily operation plan reply result and the risk prompt of the machine patrol center, and the machine patrol center performs operation plan completion process monitoring and result statistics.
Maintaining basic information of the power transmission line, tower sharing condition of the power transmission line, line cross spanning, special sections of the line, airspace hazard sources, line hazard sources, model applicability of the maintenance sections, condition of operation teams, machine patrol operation take-off and landing points, line operation and maintenance strategies and machine patrol plan intelligent scheduling rules. The management of the requirement of the machine patrol operation provides the functions of maintenance, query, report, approval/refund and derivation of the requirement of the annual machine patrol operation of local and city bureaus, and comprises the management of the requirement of the annual machine patrol operation and the management of the requirement of special patrol operation.
The machine patrol planning support environment is used for assisting a user to conveniently carry out the work of planning a machine patrol operation route and planning a machine patrol plan through the association of the machine patrol plan and the operation environment, the auxiliary decision support of the adjustment of the machine patrol plan, the derivation based on a template and the like.
The functions comprise planning visual arrangement environment, intelligent arrangement of annual operation plans, intelligent arrangement of monthly operation plans, intelligent arrangement of weekly operation plans, intelligent arrangement of special patrol operation plans, adjustment of machine patrol operation plans and monitoring of machine patrol operation plans. The machine patrol operation plan management provides functions of generating, maintaining, inquiring, deriving, checking and the like of the machine patrol operation plan, and comprises annual operation plan management, monthly operation plan management, weekly operation plan management and special patrol operation plan management.
Referring to fig. 3, an embodiment of the invention provides a computer terminal device, which includes one or more processors and a memory. The memory is coupled to the processor and configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the smart scheduling method of maneuver planning as in any of the above embodiments.
The processor is used for controlling the overall operation of the computer terminal equipment so as to complete all or part of the steps of the intelligent scheduling method of the machine patrol plan. The memory is used to store various types of data to support the operation at the computer terminal device, which data may include, for example, instructions for any application or method operating on the computer terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In an exemplary embodiment, the computer terminal Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is configured to perform the above-mentioned machine planning intelligent scheduling method and achieve technical effects consistent with the above-mentioned methods.
In another exemplary embodiment, a computer readable storage medium including program instructions is further provided, which when executed by a processor, implement the steps of the machine tour intelligent scheduling method in any one of the above embodiments. For example, the computer readable storage medium may be the above-mentioned memory including program instructions, which are executable by the processor of the computer terminal device to implement the above-mentioned smart scheduling method for a machine tour plan, and achieve the technical effects consistent with the above-mentioned method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (7)
1. The utility model provides a machine patrols plan intelligent scheduling system which characterized in that includes:
the information acquisition module is used for acquiring patrol information and machine patrol resource information in a preset time period; the patrol information comprises patrol requirements and operation and maintenance strategies;
the cluster analysis module is used for carrying out cluster analysis on the patrolled area by taking the initial position of the patrolled resource as the center according to the patrolling information and the patrolling resource information to obtain patrolled area patrolling resource matching information;
the target analysis module is used for constructing a machine patrol scheduling target in a preset time period according to the patrol information and the machine patrol resource information;
the scheme generation module is used for generating a machine patrol scheduling scheme set according to the patrol information, the machine patrol resource matching information and the machine patrol scheduling target; the machine patrol scheduling scheme set comprises a plurality of machine patrol scheduling schemes;
the scheme screening module is used for searching an optimal scheduling scheme from the machine patrol scheduling scheme set through a neural network algorithm;
the risk analysis module is used for acquiring risk information of the patrolled area according to the patrolling information, predicting risks according to the risk information and analyzing the execution success rate of patrolling tasks in a preset time period; the machine patrol scheduling target in the preset time period constructed by the target analysis module also comprises the highest execution success rate in the task execution time period; the risk information comprises basic information of the maintenance of the power transmission line, tower information of the power transmission line, the common-tower condition of the lines, cross spanning of the lines, special sections of the lines, airspace hazard sources, line hazard sources, model applicability of maintenance sections, the condition of operation teams, machine patrol operation take-off and landing points and historical weather conditions;
the machine patrol scheduling target comprises the shortest moving distance of the machine patrol resource, the conformity of the machine patrol scheduling to the operation and maintenance strategy and the workload balance between the machine patrol resource.
2. The intelligent scheduling system of the machine patrol plan according to claim 1, wherein the operation and maintenance strategy comprises an operation and maintenance period and a patrol type; the patrol type comprises fine patrol and channel patrol.
3. The system according to claim 1, wherein the machine patrol resource information includes patrol efficiency of machine patrol resources, and the amount and time that the machine patrol resources can be utilized.
4. The machine patrol plan intelligent scheduling system according to claim 1, wherein the patrol requirement includes a patrol area, a patrol object, and a patrol purpose; the inspection object comprises a pole tower, an insulator and a line, and the inspection purpose comprises judging whether the inspection object is deformed, inclined, accumulated dust, broken or wound.
5. The intelligent scheduling method for the machine patrol plan is characterized by comprising the following steps of:
acquiring patrol information and machine patrol resource information in a preset time period; the patrol information comprises patrol requirements and operation and maintenance strategies;
according to the patrol information and the machine patrol resource information, performing cluster analysis on a patrolled area by taking the initial position of the machine patrol resource as a center to obtain machine patrol resource matching information of the patrolled area;
acquiring risk information of an inspected area according to the inspection information, predicting risks according to the risk information, and analyzing the execution success rate of inspection tasks in a preset time period;
constructing a machine patrol scheduling target in a preset time period according to the patrol information and the machine patrol resource information; wherein, the machine patrol scheduling target in the preset time period is constructed, and the method comprises the following steps: the execution success rate is highest within the time period of task execution;
generating a machine patrol scheduling scheme set according to the patrol information, the machine patrol resource matching information and the machine patrol scheduling target; the machine patrol scheduling scheme set comprises a plurality of machine patrol scheduling schemes;
searching an optimal scheduling scheme from the machine patrol scheduling scheme set through a neural network algorithm;
the risk information comprises basic information of the maintenance power transmission line, tower information of the power transmission line, the common tower condition of the lines, cross spanning of the lines, special line sections, airspace hazard sources, line hazard sources, model applicability of the maintenance section, the condition of an operation team, the take-off and landing points of the machine patrol operation and historical weather conditions;
the machine patrol scheduling target comprises the shortest moving distance of the machine patrol resource, the conformity of the machine patrol scheduling to the operation and maintenance strategy and the workload balance between the machine patrol resource.
6. A computer terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the smart scheduling method of machine tour planning of claim 5.
7. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method for intelligent scheduling of a machine tour plan according to claim 5.
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